Procalcitonin testing to guide antibiotic therapy for the treatment of sepsis in intensive care settings and for suspected bacterial infection in emergency department settings: a systematic review and cost-effectiveness analysis

Diagnosis and monitoring

There is currently no NICE clinical guideline covering the diagnosis and management of sepsis in general; NICE clinical guideline CG151 addresses the specific issue of prevention and management of neutropenic sepsis in cancer patients;22 neutropenic sepsis is outside the scope of this assessment. A new NICE guideline, ‘Sepsis: The recognition, diagnosis and management of severe sepsis’, is currently under development and publication is expected in July 2016. 23 There is also an ongoing study by the National Confidential Enquiry into Patient Outcome and Death (NCEPOD), commissioned by the Health Quality Improvement Partnership (HQIP), which aims to ‘identify and explore avoidable and remediable factors in the process of care for patients with known or suspected sepsis’. 24 This study will examine organisational issues, systems and processes, recognition or early signs of sepsis, appropriate management of established severe infection, communication with families and carers, and use of the ‘acute’ end-of-life pathway and ceilings of treatment; publication is expected in November 2015.

Comprehensive guidance on the diagnosis and management of sepsis is provided by the Surviving Sepsis Campaign (SSC), a joint collaboration of the Society of Critical Care Medicine (SCCM) and the European Society of Intensive Care Medicine (ESICM). 2 This guideline was last updated in 2012 and is currently undergoing revision. The guideline was developed following the principles of the Grading of Recommendations Assessment, Development and Evaluation (GRADE) system: the quality of evidence was rated as high (A) to very low (D), and recommendations were classified as strong (1) or weak (2). 25

The SSC guideline specifies the presence of some the following criteria, alongside the presence of proven or suspected infection, for the diagnosis of sepsis:2,3

• Clinical criteria Fever or hypothermia, elevated heart, tachypnoea, altered mental status, significant oedema or positive fluid balance, hyperglycaemia in the absence of diabetes.

• Inflammatory markers Abnormal white blood cell count, elevated plasma C-reactive protein (CRP) or PCT levels.

• Haemodynamic status Arterial hypotension, or decrease in systolic blood pressure of > 40 mmHg in adults or < 2 SDs (standard deviations) below the age-specific normal range.

• Organ dysfunction signs Arterial hypoxaemia, acute oliguria or elevated creatinine level, coagulation abnormalities, ileus (absent bowel sounds), thrombocytopenia, hyperbilirubinemia.

• Tissue perfusion status Hyperlactatemia, decreased capillary refill or mottling.

Definitions of sepsis in children are similar to adult definitions but depend on age-specific heart rate, respiratory rate and white blood cell count cut-off values. Special considerations for managing sepsis in paediatric patients are described in the SSC guidelines. 2

The SSC guidelines include the specific recommendation (GRADE 1C – strong recommendation, low or very low quality evidence) that blood (and urine, cerebrospinal fluid, wounds, respiratory secretions, or other body fluids, as appropriate) cultures should be taken before initiating antimicrobial therapy, provided that this does not significantly delay (> 45 minutes) the start of antimicrobial therapy. 2 It should be noted that, although the guideline includes elevated PCT level in the list of criteria indicative of sepsis (see above), no specific recommendation is made for its use in the diagnosis of sepsis.

Treatment

The SSC guidelines provide the following recommendations on antimicrobial therapy:2

• ‘The administration of effective intravenous antimicrobials within the first hour of recognition of septic shock (GRADE 1B – strong recommendation, moderate quality evidence) and severe sepsis without septic shock (GRADE 1C – strong recommendation, low or very low quality evidence) should be a goal of therapy.’

• ‘Initial empiric anti-infective therapy should include one or more drugs that have activity against all likely pathogens (bacterial and/or fungal or viral) and that penetrate in adequate concentrations into the tissues presumed to be the source of sepsis.’ (GRADE 1B – strong recommendation, moderate quality evidence).

• ‘Combination empirical therapy for neutropenic patients with severe sepsis’ (GRADE 2B – weak recommendation, moderate quality evidence) ‘and for patients with difficult-to-treat, multidrug resistant bacterial pathogens such as Acinetobacter and Pseudomonas spp.’ (GRADE 2B – weak recommendation, moderate quality evidence). ‘For patients with severe infections associated with respiratory failure and septic shock, combination therapy with an extended spectrum beta-lactam and either an aminoglycoside or a fluoroquinolone is for P. aeruginosa bacteraemia’ (GRADE 2B – weak recommendation, moderate quality evidence). ‘A combination of beta-lactam and macrolide for patients with septic shock from bacteraemic Streptococcus pneumoniae infections’ (GRADE 2B – weak recommendation, moderate quality evidence).

• ‘Empiric combination therapy should not be administered for > 3–5 days. De-escalation to the most appropriate single therapy should be performed as soon as the susceptibility profile is known’ (GRADE 2B – weak recommendation, moderate quality evidence).

• ‘Duration of therapy typically 7–10 days; longer courses may be appropriate in patients who have a slow clinical response, undrainable foci of infection, bacteraemia with S. aureus; some fungal and viral infections or immunologic deficiencies, including neutropenia’ (GRADE 2B – weak recommendation, low or very low quality evidence).

• ‘Antiviral therapy initiated as early as possible in patients with severe sepsis or septic shock of viral origin’ (GRADE 2B – weak recommendation, low or very low quality evidence).

• ‘Antimicrobial agents should not be used in patients with severe inflammatory states determined to be of non-infectious cause’ (ungraded recommendation).

The SSC guidelines also include a recommendation (GRADE 2C – weak recommendation, low or very low quality evidence) for the use of PCT or similar biomarkers to aid the clinician in discontinuation of empiric antibiotics, when there is no subsequent evidence of infection. 2

Diagnosis and monitoring

The NICE guideline on the diagnosis and management of community- and hospital-acquired pneumonia in adults9 includes elements that are relevant to the work-up of suspected bacterial infection in the ED.

These guidelines recommend the following:

• Assess people with a clinical diagnosis of CAP at presentation to hospital to determine whether they are at low, intermediate or high risk of death using their CURB65 score26

• Put in place processes to allow diagnosis and treatment of CAP within 4 hours of presentation to hospital.

The NICE clinical guideline CG160, on the assessment and management of feverish illness in children aged < 5 years,27 included a research recommendation for a UK study on the performance characteristics and cost-effectiveness of PCT versus CRP in identifying serious bacterial infection in children with fever of unknown origin. However, it should be noted that, although the guideline included a systematic review of studies assessing the diagnostic accuracy of these biomarkers, this review did not appear to have considered RCTs comparing the effectiveness of diagnostic strategies with and without PCT testing. Although the guideline cites later studies by the same authors, it does not include the RCT described above (p. 4, Index test section). 20

Treatment

The NICE guideline on pneumonia9 makes the following recommendations regarding antibiotic treatment:

• Offer antibiotic therapy as soon as possible after diagnosis, and certainly within 4 hours, to all patients with CAP admitted to hospital.

Low-severity community-acquired pneumonia:

• Offer a 5-day course of a single antibiotic to patients with low-severity CAP.

• Consider amoxicillin in preference to a macrolide or tetracycline for patients with low-severity CAP; consider a macrolide or tetracycline for patients who are allergic to penicillin.

• Consider extending the course of the antibiotic for > 5 days as a possible management strategy for patients with low-severity CAP, whose symptoms do not improve as expected after 3 days.

• Explain to patients with low-severity CAP who are treated in the community, and, when appropriate, their families or carers, that they should seek further medical advice if their symptoms do not begin to improve within 3 days of starting the antibiotic, or earlier if their symptoms are worsening.

• Do not routinely offer patients with low-severity CAP:

  • a fluoroquinolone

  • dual antibiotic therapy.

Moderate- and high-severity CAP:

• Consider dual antibiotic therapy with amoxicillin and a macrolide (such as clarithromycin) for patients with moderate-severity CAP.

• Consider dual antibiotic therapy with a beta-lactamase stable beta-lactam (such as co-amoxiclav) and a macrolide (such as clarithromycin) for patients with high-severity CAP.

• Consider a 7- to 10-day course of antibiotic therapy for patients with moderate- or high-severity CAP.

Monitoring:

• Consider measuring a baseline CRP concentration in patients with CAP on admission to hospital, and repeat the test if clinical progress is uncertain after 48–72 hours.

The guideline also includes the following research recommendation:

• In patients hospitalised with moderate- to high-severity CAP, does using CRP monitoring in addition to clinical observation to guide antibiotic duration safely reduce the total duration of antibiotic therapy compared with a fixed empirical antibiotic course?

This assessment summarises the evidence on the use of PCT testing to determine whether or not to initiate antibiotics, and to guide the duration of therapy in patients who have been appropriately treated with antibiotics.

Rapid appraisal searches

To assess the scope and scale of the literature, and to identify candidate search terms, a rapid appraisal of the literature was conducted.

The following databases were searched for relevant studies from database inception date to June 2014:

• The Cochrane Library:

  • Cochrane Database of Systematic Reviews (CDSR): up to Issue 4 of 12, April 2014

  • Database of Abstracts of Reviews of Effects (DARE): up to Issue 1 of 4, January 2014

  • Health Technology Assessment (HTA) database: up to Issue 1 of 4, January 2014

  • NHS Economic Evaluation Database (NHS EED): up to Issue 1 of 4, January 2014.

• PROSPERO (internet): up to 9.4.14 (www.crd.york.ac.uk/prospero/).

• National Institute for Health and Care Excellence (NICE) Guidance (internet): up to 8 April 2014 (www.nice.org.uk/).

• National Institute for Health Research (NIHR) Health Technology Assessment (HTA) programme (internet): up to 8 April 2014 (www.hta.ac.uk/).

• US Food and Drug Administration (FDA) (internet): up to 8 April 2014 (www.fda.gov/).

• Guidelines International Network (G-I-N) (internet): up to 9 April 2014 (www.g-i-n.net/).

• National Guideline Clearinghouse (NGCH) (internet): up to 9 April 2014 (www.guideline.gov/index.aspx).

• Medicines and Healthcare Products Regulatory Agency (MHRA) (internet): up to 9 April 2014 (www.mhra.gov.uk/index.htm).

• The Medion database up to 2014/5/4 (internet): up to 9 April 2014 (www.mediondatabase.nl/).

Randomised controlled trial searches

The following databases were searched for relevant studies from 1995 to June 2014:

• EMBASE (OvidSP): 1995 – 27 June 2014.

• MEDLINE (OvidSP): 1995 – June Week 3 2014.

• MEDLINE In-Process & Other Non-Indexed Citations and Daily Update (OvidSP): 1995 – 27 June 2014.

• PubMed (www.ncbi.nlm.nih.gov/pubmed): 1995 – 14 July 2014.

• CINAHL (Cumulative Index to Nursing and Allied Health Literature) (EBSCOhost): 1995 – 25 June 2014.

• Cochrane Central Register of Controlled Trials (CENTRAL) (Wiley): 1995 – Issue 5 of 12, May 2014.

• Science Citation Index (SCI) (Web of Science): 1995 – 27 June 2014.

• Latin American and Caribbean Health Sciences Literature (LILACS) (internet): 1995 – 1 July 2014 (http://regional.bvsalud.org/php/index.php?lang = en).

• NIHR Health Technology Assessment Programme (internet): up to 1 July 2014 (www.nets.nihr.ac.uk/programmes/hta).

Completed and ongoing trials were identified by searches of the following resources (1995-present):

• National Institutes of Health ClinicalTrials.gov: up to 14 July 2014 (www.clinicaltrials.gov/).

• Current Controlled Trials (CCT): up to 14 July 2014 (www.controlled-trials.com/).

• World Health Organization International Clinical Trials Registry Platform (ICTRP): up to 14 July 2014 (www.who.int/ictrp/en/).

Paediatric population searches

The following databases were searched for relevant studies from 1995 to August/September 2014:

• EMBASE (OvidSP): 1995 – 29 August 2014.

• MEDLINE (OvidSP): 1995 – August Week 3 2014.

• MEDLINE In-Process & Other Non-Indexed Citations and Daily Update (OvidSP): 1995 – 29 August 2014.

• PubMed (www.ncbi.nlm.nih.gov/pubmed): 1995 – 2 September 2014.

• CINAHL (EBSCOhost): 1995 – 27 August 2014.

• SCI (Web of Science): 1995 – 29 August 2014.

• LILACS (internet) (http://regional.bvsalud.org/php/index.php?lang = en): 1995 – 2 September 2014.

Electronic searches were undertaken for abstracts and poster presentations of studies of PCT from the following conferences:

• Royal College of Paediatrics and Child Health (RCPCH) meetings: 2009–14 (www.escmid.org/research_projects/eccmid/past_eccmids/).

• European Congress of Clinical Microbiology and Infectious Diseases (ECCMID): 2009–14 (www.escmid.org/research_projects/eccmid/past_eccmids/.

• International Symposium on Intensive Care and Emergency Medicine: 2009–14 (http://ccforum.com/supplements/).

Population

1. Adults and children with confirmed or highly suspected sepsis, in whom antibiotic therapy is indicated, who are being treated in ICUs.

2. Adults and children presenting to the ED with suspected bacterial infection.

Studies of neonates or immunosuppressed neutropenic patients on chemotherapy, immunosuppressant drugs or transplant programmes were excluded.

Intervention/index test

Treatment decisions based on laboratory-based PCT testing, using any of the tests currently available to the NHS as described in Chapter 2 (see Intervention technologies and comparator), in addition to standard practice (as reported in individual studies).

Point-of-care tests, which do not provide a quantitative estimate of PCT levels, were excluded.

Comparator

Treatment decisions based on standard practice (as reported in individual studies), without PCT testing.

Outcomes

Antibiotic exposure (initiation/duration of antibiotic therapy), resource use (number of hospital admissions, length of hospital/ICU stay, costs), adverse clinical outcomes (e.g. SOFA scores, in-hospital mortality, condition-specific outcomes), antibiotic-related adverse events.

Study design

Randomised controlled trials, or controlled clinical trials (CCTs) when no RCTs were available. Where no controlled trials (RCTs or CCTs) were available for a specified population, studies assessing the change in diagnostic accuracy associated with the addition of PCT testing to standard diagnostic work-up were sought. On the advice of clinical specialist members of the Assessment Subgroup, such studies were required to use adjudication of infection by independent panel as the reference standard; microbiological testing alone was not considered adequate. Studies that assessed the diagnostic accuracy of PCT testing alone, or that used culture alone as the reference standard, were excluded.

Study details

Eight RCTs,33,37,41,45,50,52,54,61 reported in 12 publications,33,37,38,41,45,46,50–54,61 provided data on the effectiveness of adding PCT testing to the information used to guide antibiotic therapy in ICU settings. All studies33,37,41,45,50,52,54,61 were conducted in adult populations. Four studies33,37,45,52 fully matched the participant inclusion criteria for this review (adults with confirmed or highly suspected sepsis, in whom antibiotic therapy is indicated, who are being treated in ICUs). A further study41 included adults who were being treated in an ICU for suspected bacterial infection, or who developed sepsis during their ICU stay. Two additional studies54,61 that included adults being treated in ICU settings, who were considered to be at increased risk of developing sepsis, were also included: one study included adults with acute pancreatitis54 and the other included adults with ventilator-associated pneumonia (VAP). 61 The final study50 included adults who were being treated for suspected bacterial infections in ICU settings. This was the only study, conducted in an ICU setting, to assess the effectiveness of adding PCT testing to the information used to guide the initiation of antibiotic treatment, reflecting the lower level of symptom severity in the included population;50 all of the other studies33,37,41,45,52,54,61 conducted in ICU settings assessed the effectiveness of adding PCT testing to the information used to decide when to discontinue antibiotic treatment.

All studies33,37,41,45,50,52,54,61 used PCT algorithms with multiple decision thresholds to guide antibiotic treatment in the intervention arm, with final treatment decisions always remaining at the discretion of the treating clinician. The details of the PCT algorithm varied between studies; however, all discontinuation algorithms included a component that strongly encouraged/encouraged discontinuation of antibiotics when the PCT level was < 0.25 ng/ml,33,37,41,52,61 and/or encouraged discontinuation of antibiotics when the PCT level was < 0.5 ng/ml. 37,41,45,50,54,61 Discontinuation studies reported measuring PCT at baseline and daily33,41,52,54,61 or every 2 days37,45 until discontinuation, discharge or death. The study50 that assessed the effectiveness of adding PCT testing to the information used to guide the initiation of antibiotic treatment used similar thresholds; initiation of antibiotic treatment was strongly discouraged when PCT levels were < 0.25 ng/ml, less strongly discouraged when PCT levels were between 0.25 ng/ml and 0.5 ng/ml, less strongly recommended when PCT levels were between 0.5 and 1.0 ng/ml, and strongly recommended when PCT levels were > 1.0 ng/ml. This study50 stated that PCT levels were measured when infection was suspected. Full details of all PCT algorithms are reported in Appendix 3.2. All studies compared the intervention, a PCT algorithm combined with clinical decision-making, to decisions about antibiotic treatment based on standard clinical decision-making without PCT levels; full details of the standard clinical decision-making comparator are reported in Appendix 3.2.

Four of the studies37,41,52,61 conducted in ICU settings used the BRAHMS PCT Sensitive Kryptor assay to measure PCT levels: two45,50 used the VIDAS BRAHMS PCT assay and two33,54 used an unspecified quantitative PCT assay.

Antibiotic exposure

The only study,50 conducted in an ICU setting, to assess the effectiveness of adding PCT testing to the information used to guide the initiation of antibiotic treatment found no significant difference in the proportion of participants who were prescribed antibiotics (RR 1.24, 95% CI 0.89 to 1.71).

Four33,41,52,54 of the seven33,37,41,45,52,54,61 studies that assessed the effectiveness of adding PCT testing to the information used to decide when to discontinue antibiotic treatment reported data to allow the calculation of mean difference in the duration of antibiotic therapy between study arms. Three of these studies33,41,54 found that the inclusion of a PCT algorithm in the clinical decision-making process resulted in a statistically significant reduction in the mean duration of antibiotic therapy; the fourth study52 found that the PCT algorithm was associated a trend towards reduction in the duration of antibiotic therapy, which was not statistically significant (Table 2). The summary effect estimate, derived from these four studies,33,41,52,54 indicated that the addition of a PCT algorithm to the clinical decision-making process was associated with a statistically significant reduction in the duration of antibiotic therapy (WMD –3.19 days, 95% CI –5.44 to –0.95 days); however, between-study heterogeneity was high (I² = 95.2%) (Figure 3). The study with the largest effect size was conducted in adults with severe acute pancreatitis (mean difference –5.17 days, 95% CI –6.41 to –3.93 days; see Table 2 and Figure 3). 54 Of the remaining three studies33,41,52 included in the meta-analysis two33,52 conducted in populations with suspected or confirmed sepsis and one41 included both people with suspected bacterial infection and those who developed sepsis whilst in the ICU. When the meta-analysis was restricted to the two studies33,52 conducted in populations with suspected or confirmed sepsis, the summary effect estimate still indicated that the addition of a PCT algorithm to the clinical decision-making process was associated with a statistically significant reduction in the duration of antibiotic therapy (WMD –1.20 days, 95% CI –1.33 to –1.07 days) (Figure 4). One of these studies used the BRAHMS PCT Sensitive Kryptor assay52 and the other used the VIDAS BRAHMS PCT assay;33 there was no clear difference in effect between the two studies. Three further studies37,45,61 assessed the effectiveness of adding PCT testing to the information used to decide when to discontinue antibiotic treatment, but reported the outcome as median [interquartile range (IQR)] duration of antibiotic therapy, with p-values for the between-group comparison. Two of these studies37,45 were conducted in people with suspected or confirmed sepsis and reported results indicating that adding a PCT algorithm to the clinical decision-making process had no statistically significant effect on the duration of antibiotic treatment (see Table 2). The remaining study61 was conducted in adults with VAP and found that, in these patients, inclusion of a PCT algorithm in the clinical decision-making process was associated with a statistically significant reduction in the median duration of antibiotic therapy from 15 to 10 days (see Table 2). 61

FIGURE 3.

Duration of antibiotic therapy.

FIGURE 4.

Duration of antibiotic therapy (studies that included only people with suspected or confirmed sepsis).

The study by Bouadma et al.,41 which included both people with suspected bacterial infection and those who developed sepsis whilst in the ICU, was the only ICU study to report duration of antibiotic therapy stratified by clinical diagnosis (UTI, CAP, VAP, infection with positive blood culture, and intra-abdominal infection). The inclusion of a PCT algorithm in the clinical decision-making process was associated with a statistically significant reduction in the duration of antibiotic therapy for people with UTI (mean difference –7.1 days, 95% CI –12.1 to –2.1 days), CAP (mean difference –5.0 days, 95% CI –6.5 to –3.5 days) or VAP (mean difference –2.1 days, 95% CI –3.9 to –0.3 days), but not for people with infection and positive blood cultures (mean difference –3.0 days, 95% CI –6.0 to 0.0 days) or intra-abdominal infections (mean difference –2.7 days, 95% CI –7.7 to 2.3 days). 41 Full results, including all clinical subgroup data are presented in Appendix 3.3 and 3.4.

Resource use and costs

Resource use and costs are illustrated in Table 3 and Figures 5–8. Seven of the studies33,37,41,45,52,54,61 conducted in ICU settings reported data on resource use and costs outcomes. All of these studies assessed the effectiveness of adding PCT testing to the information used to decide when to discontinue antibiotic treatment. All seven studies33,37,41,45,52,54,61 reported information on both the duration of hospital stay and six studies33,37,41,45,52,54 reported data on the duration of ICU stay.

Four studies33,41,52,54 reported data to allow the calculation of mean difference in the duration of hospital stay between study arms. Two of these studies33,54 found that the inclusion of a PCT algorithm in the clinical decision-making process resulted in a statistically significant reduction in the mean duration of hospital stay, and one study52 found that the PCT algorithm was associated a trend towards reduction in the duration of hospital stay, which was not statistically significant (Table 3). The results of study by Bouadma et al.,41 which included both people with suspected bacterial infection and those who developed sepsis whilst in the ICU, indicated that the inclusion of a PCT algorithm in the clinical decision-making process did not reduce the duration of hospital stay for these patients (mean difference 0.3 days, 95% CI –3.26 to 2.66 days); this may be related to the less clinically severe spectrum of clinical presentations represented. The summary effect estimate, derived from these four studies,33,41,52,54 indicated that the PCT algorithm was associated with a statistically significant reduction in the duration of hospital stay (WMD –3.85 days, 95% CI –6.78 to –0.92 days); however, between-study heterogeneity was high (I² = 75.2%) (Figure 5). As with duration of antibiotic therapy, the largest effect size was derived from the study54 conducted in adults with severe acute pancreatitis (mean difference –7.15 days, 95% CI –9.16 to –4.34 days) (see Table 3 and Figure 5). Two33,52 of the remaining three studies included in the meta-analysis were conducted in populations with suspected or confirmed sepsis, and one study41 included both people with suspected bacterial infection and those who developed sepsis whilst in the ICU. When the meta-analysis was restricted to studies conducted in people with suspected or confirmed sepsis,33,52 the PCT algorithm appeared to be associated with a greater reduction in duration of hospital stay (WMD –4.32 days, 95% CI –6.50 to –2.14 days) (Figure 6). One of these studies52 used the BRAHMS PCT Sensitive Kryptor assay and the other study33 used the VIDAS BRAHMS PCT assay; there was no clear difference in effect between the two studies. Three further studies37,45,61 assessed the effectiveness of adding PCT testing to the information used to decide when to discontinue antibiotic treatment, but reported duration of hospital stay as median (IQR), with p-values for the between-group comparison. Two37,45 of these studies were conducted in people with suspected or confirmed sepsis and one study61 was conducted in people with VAP; all reported results indicating that the PCT algorithm had no statistically significant effect on the duration of hospital stay (see Table 3).

FIGURE 5.

Duration of hospital stay.

FIGURE 6.

Duration of hospital stay (studies that included only people with suspected or confirmed sepsis).

Four studies33,41,52,54 reported data to allow the calculation of mean difference in the duration of ICU stay between study arms. Two of these studies33,54 found that the inclusion of a PCT algorithm in the decision to discontinue antibiotics resulted in a statistically significant reduction in the mean duration of ICU stay, and one study52 found that the PCT algorithm was associated a trend towards reduction in the duration of hospital stay, which was not statistically significant (see Table 3). As with duration of hospital stay, the results of the study by Bouadma et al. 41 indicated that the inclusion of a PCT algorithm in the decision to discontinue antibiotics did not reduce the duration of ICU stay for these patients with a less severe spectrum of disease (mean difference 1.5 days, 95% CI –0.88 to 3.88 days). 41 The summary effect estimate, derived from these four studies,33,41,52,54 indicated that the inclusion of a PCT algorithm in the decision to discontinue antibiotics was associated with a trend towards decreased duration of ICU stay, which did not reach statistical significance (WMD –2.03 days, 95% CI –4.19 to 0.13 days); however, between-study heterogeneity was high (I² = 81.0%) (Figure 7). The largest effect size was again derived from the study conducted in adults with severe acute pancreatitis (mean difference –3.72 days, 95% CI –4.99 to –2.45 days) (see Table 3 and Figure 6). 54 Two33,52 of the remaining three studies33,41,52 included in the meta-analysis were conducted in populations with suspected or confirmed sepsis, and one study41 included both people with suspected bacterial infection and those who developed sepsis whilst in the ICU. When the meta-analysis was restricted to studies conducted in people with suspected or confirmed sepsis,33,52 the summary effect estimate indicated that the inclusion of a PCT algorithm in the decision to discontinue antibiotics was associated with a statistically significant reduction in the duration of ICU stay (WMD –2.31 days, 95% CI –3.97 to –0.65 days) (Figure 8). One52 of these studies used the BRAHMS PCT Sensitive Kryptor assay and the other used the VIDAS BRAHMS PCT assay;33 there was no clear difference in effect between the two studies. Two further studies37,45 assessed the effectiveness of adding PCT testing to the information used to decide when to discontinue antibiotic treatment, but reported duration of ICU stay as median (IQR), with p-values for the between-group comparison. Both of these studies37,45 were conducted in people with suspected or confirmed sepsis and both reported results indicating that adding the PCT algorithm had no statistically significant effect on the duration of ICU stay (see Table 3).

FIGURE 7.

Duration of ICU stay.

FIGURE 8.

Duration of ICU stay (studies that included only people with suspected or confirmed sepsis).

The study by Qu et al. 54 conducted in people with severe acute pancreatitis reported that the inclusion of a PCT algorithm in the decision to discontinue antibiotics was associated with a statistically significant reduction in the mean total cost of hospitalisation (mean difference –US$3412, 95% CI – US$4613 to –US$2211).

No study reported clinical subgroup data for resource use and costs outcomes.

Adverse clinical outcomes

Adverse clinical outcomes are illustrated in Table 4 and Figures 9–12. All eight studies33,37,41,45,50,52,54,61 conducted in ICU settings reported some data on adverse clinical outcomes. Three of these studies41,52,61 explicitly stated that they aimed to investigate whether the use of PCT in decision-making can reduce antibiotic exposure, without adversely affecting clinical outcomes, one41 of which specified a non-inferiority design for mortality and reported a Kaplan–Meyer survival curve.

Five studies33,41,52,54,61 reported 28-day all-cause mortality, and all reported no statistically significant difference in mortality rates between participants in the intervention group (decision to discontinue antibiotics based on PCT algorithm plus clinical judgement) and those in the control group (decision to discontinue antibiotics based on clinical judgement alone) (Table 4). The summary RR derived from these five studies33,41,52,54,61 was 0.98 (95% CI 0.76 to 1.27) (Figure 9). This finding was consistent when the meta-analysis was restricted to studies conducted in people with suspected or confirmed sepsis33,52 (RR 1.07, 95% CI 0.54 to 2.12). One study41 also reported mortality at 60 days and found no statistically significant difference between the intervention and control groups (RR 1.15, 95% CI 0.89 to 1.48). One further study,37 conducted in people with apparent septic shock, assessed mortality at 5 days and found no statistically significant difference between the intervention and control groups (RR 1.0, 95% CI 0.25 to 4.04).

FIGURE 9.

All-cause mortality (28 day).

Four studies37,45,52,61 reported in-hospital mortality and, as with all-cause mortality, all reported no statistically significant difference in mortality rates between participants in the intervention and control groups (see Table 4). The summary RR derived from these four studies37,45,52,61 was 0.75 (95% CI 0.49 to 1.16) (Figure 10). This finding was consistent when the meta-analysis was restricted to studies conducted in people with suspected or confirmed sepsis37,45,52 (RR 0.78, 95% CI 0.45 to 1.35).

FIGURE 10.

In-hospital mortality.

Three studies reported ICU mortality. 37,45,50 Two of these studies37,45 assessed the effects of the addition of a PCT algorithm to the information used to guide discontinuation of antibiotics, and were conducted in people with confirmed or suspected sepsis; both reported no statistically significant difference in the ICU-mortality rate between the intervention and control groups (see Table 4). The remaining study50 assessed the effects of adding a PCT algorithm to the information used to decide whether or not to initiate antibiotic treatment and was conducted in people with suspected bacterial infection; this study also found no statistically significant difference in the ICU-mortality rate between the intervention and control groups (see Table 4). The summary RR derived from all three studies37,45,50 was 0.87 (95% CI 0.55 to 1.37) (Figure 11). This finding was consistent when the meta-analysis was restricted to studies conducted in people with suspected or confirmed sepsis37,45 (RR 0.59, 95% CI 0.27 to 1.28).

FIGURE 11.

Intensive care unit mortality.

Four studies33,41,45,52 reported rates of infection relapse/recurrence, and all found no statistically significant difference in mortality rates between participants in the intervention group (decision to discontinue antibiotics based on PCT algorithm plus clinical judgement) and those in the control group (decision to discontinue antibiotics based on clinical judgement alone) (see Table 4). The summary RR derived from these four studies33,41,45,52 was 1.37 (95% CI 0.77 to 2.44) (Figure 12). This finding was consistent when the study by Bouadma et al.,41 which included both people with suspected bacterial infection and those who developed sepsis whilst in the ICU, was excluded from the meta-analysis (RR 1.89, 95% CI 0.47 to 7.59).

FIGURE 12.

Infection relapse/recurrence (ICU population). NR, not reported.

A variety of other general and disease-specific adverse clinical outcomes were reported by one or more studies (see Table 4). These included multidrug-resistant infection,41 sepsis-related mortality,52 MODS,54 VAP-related clinical deterioration,61 duration of mechanical ventilation,37,50 and SOFA score at various time points. 37,41,50 No study reported a statistically significant difference between the intervention and comparator groups for any adverse clinical outcome assessed. None of the included studies reported antibiotic-related adverse events.

No study reported clinical subgroup data for adverse clinical outcomes.

Study details

Ten RCTs,39,42,44,49,55–57,60,62,69 reported in 16 publications,39,40,42–44,47–49,55–59,62,69,70 provided data on the effectiveness of adding PCT testing to the information used to guide antibiotic therapy in ED settings. Two studies39,49 were conducted in children, and the remainder42,44,55–57,60,62,69 were conducted in all adult populations. The presenting characteristics of participants varied between studies; however, all but one study69 were conducted in people with respiratory presentations. Two of the adult studies44,57 were conducted in people with a primary diagnosis of LRTI, three studies42,55,56 were conducted in people with CAP, one study60 included people with COPD exacerbations, one study62 included people with suspected asthma exacerbations, and the final study71 was conducted in people with UTI. Of the two studies39,49 conducted in children, one study39 included children with LRTI (including CAP and non-CAP LRTI)39 and the other study49 included children with CAP. All but one of the studies39,42,44,49,55–57,60,62 conducted in ED settings assessed the effectiveness of adding PCT testing to the information used to guide the initiation of antibiotic treatment, and six of these studies39,42,49,55–57 also assessed the effectiveness of adding PCT testing to the information used to guide the discontinuation of antibiotic treatment. The study69 conducted in adults with UTI only considered the discontinuation application. This study69 divided participants into outpatients and those admitted to hospital; for the outpatient population the PCT algorithm informed an initial decision on the fixed length of antibiotic prescription, whereas for hospitalised participants the PCT algorithm informed the decision on when to discontinue antibiotics in a manner similar to other studies included in this assessment. Data reported in this section are unpublished subgroup data for the hospitalised participants and were supplied by a study author (Dr Werner Albrich, Division of Infectious Diseases and Hospital Epidemiology, Kantonsspital St Gallen, Switzerland, 22 October 2014, personal communication); results for the full study population are reported in Appendix 3.3 and 3.4. 69

With the exception of two studies published as abstracts,55,56 all studies used PCT algorithms with multiple decision thresholds to guide antibiotic treatment in the intervention arm, with final treatment decisions always remaining at the discretion of the treating clinician. The details of the PCT algorithm varied between studies; however, all algorithms (both initiation and discontinuation) discouraged antibiotic use when the PCT level was < 0.25 ng/ml; this decision threshold was also used by the two studies published as abstracts;55,56 these two studies did not report the timing of PCT measurements. Four studies39,42,44,57 used the same initiation algorithm: PCT < 0.1 ng/ml, antibiotics strongly discouraged; PCT 0.1–0.25 ng/ml, antibiotics discouraged; PCT 0.25–0.5 ng/ml, antibiotics encouraged; PCT > 0.5 ng/ml, antibiotics strongly encouraged, and three of these studies42,44,57 used the same thresholds to guide discontinuation decisions. Two further studies used a similar initiation algorithm,60,62 without the upper threshold (PCT > 0.5 ng/ml, antibiotics strongly encouraged). Reported timings for the measurement of PCT were similar; all studies39,42,44,57,60,62 that reported timings included a baseline measurement, three studies39,57,69 reported that repeat measurements were taken at days 3 and 539 or days 3, 5 and 7,57,69 and three studies42,44,49 reported that repeat measurements were taken at days 4, 6 and 842,44 or every 2 days until discontinuation. 49 Four studies42,44,57,62 noted that PCT measurements were repeated at between 6 and 24 hours if antibiotic treatment was initially withheld. Full details of all PCT algorithms are reported in Appendix 3.2. All studies compared the intervention, a PCT algorithm combined with clinical decision-making, to decisions about antibiotic treatment based on standard clinical decision-making without PCT levels; full details of the standard clinical decision-making comparator are reported in Appendix 3.2.

Eight of the studies39,42,44,49,57,60,62,69 conducted in ED settings used the BRAHMS PCT Sensitive Kryptor assay to measure PCT levels, and two studies55,56 used an unspecified quantitative PCT assay.

Antibiotic exposure

Seven studies,42,44,55–57,60,62 conducted in adults presenting to the ED with suspected bacterial infections, assessed the effectiveness of adding PCT testing to the information used to guide the initiation of antibiotic treatment; all of these studies reported the proportion of patients, in the intervention and control groups, who received antibiotic treatment, and all found that adding a PCT algorithm to the information used to decide whether or not to initiate antibiotic treatment was associated with a reduction in antibiotic use (Table 5 and Figure 13). The summary RR, derived from these seven studies42,44,55–57,60,62 was 0.77 (95% CI 0.68 to 0.87) (see Figure 13). When studies reported data for clinical subgroups, a reduction in antibiotic use associated with the PCT algorithm was observed for all groups: severe acute exacerbations of COPD;44 COPD exacerbations; CAP and acute bronchitis;57 and differing severities of asthma62 (mild, moderate, severe and critical). One study62 reported data indicating that the reduction in antibiotic use associated with the PCT algorithm increased with decreasing severity of asthma (critical asthma RR 0.90, 95% CI 0.74 to 1.1; mild asthma RR 0.47, 95% CI 0.31 to 0.71) (see Appendix 3.3). Clinical subgroup data are reported in full in Appendix 3.3.

FIGURE 13.

Initiation of antibiotics in adults.

Both of the two studies39,49 conducted in children presenting to the ED also reported the proportion of patients, in the intervention and control groups, who received antibiotic treatment. However, these two studies39,49 reported contradictory results. The study by Esposito et al.,49 conducted in children with CAP, found that adding a PCT algorithm to the information used to decide whether or not to initiate antibiotic treatment was associated with a statistically significant reduction in antibiotic use (RR 0.85, 95% CI 0.79 to 0.91). Subgroup analyses, by severity of CAP, indicated that the PCT algorithm was associated with a greater reduction in antibiotic use for children with mild CAP (RR 0.69, 95% CI 0.59 to 0.80) than was the case for children with severe CAP (RR 0.96, 95% CI 0.92 to 1.01) (see Appendix 3.3). 49 In contrast, the study by Baer et al.,39 conducted in children with LRTI (including CAP and non-CAP LRTI), reported a trend towards increased antibiotic use when PCT levels were included in decision-making (RR 1.12, 95% CI 0.94 to 1.35) (see Table 5). The Baer et al. study39 also reported data on antibiotic initiation stratified by clinical subgroup (CAP and non-CAP LRTI). These data indicated that, for children presenting with non-CAP LRTI, adding a PCT algorithm to the information used to decide whether or not to initiate antibiotic treatment was associated with a statistically significant increase in antibiotic use (RR 2.71, 95% CI 1.46 to 5.01), whereas for children presenting with CAP the PCT algorithm was associated with a trend towards reduction in antibiotic use (RR 0.92, 95% CI 0.79 to 1.08) (see Table 5). 39 When data from the Esposito et al. study49 were combined with data from the CAP subgroup of the Baer et al. study39 the summary RR was 0.86 (95% CI 0.80 to 0.93) [Figure 14; pooling data for the whole population of both studies resulted in a summary RR of 0.97 (95% CI 0.67 to 1.40)].

FIGURE 14.

Initiation of antibiotics in children with CAP.

Six studies,42,44,55–57,69 conducted in adults presenting to the ED, assessed the effectiveness of adding PCT testing to the information used to decide when to discontinue antibiotic treatment. However, only two studies42,44 reported data to allow the calculation of mean difference in the duration of antibiotic therapy between study arms. Both of these studies42,44 found that the inclusion of a PCT algorithm in the clinical decision-making process resulted in a statistically significant reduction in the mean duration of antibiotic therapy (see Table 5). The summary effect estimate, derived from these two studies,42,44 indicated that the addition of a PCT algorithm to the clinical decision-making process was associated with reduction in the duration of antibiotic therapy, which did not reach statistical significance (WMD –4.49 days, 95% CI –9.59 to 0.61 days) (Figure 15). Four studies,55–57,69 conducted in adults, assessed the effectiveness of adding PCT testing to the information used to decide when to discontinue antibiotic treatment, but reported the outcome as median (IQR) duration of antibiotic therapy, with p-values for the between-group comparison57,69 or mean no estimate of variance. 55,56 The results of these studies55–57,69 were consistent with the two studies42,44 included in the meta-analysis – indicating that adding a PCT algorithm to the clinical decision making process was associated with a reduction in the duration of antibiotic therapy in all populations considered (see Table 5). When studies reported data for clinical subgroups, the observed reduction in duration of antibiotic use associated with use of a PCT algorithm was generally consistent across groups (severe acute exacerbations of COPD,44 and COPD exacerbations, CAP and acute bronchitis);57 however, effects were less clear-cut owing to smaller numbers of patients (see Appendix 3.4). All studies that reported data on the duration of antibiotic treatment included patients with a zero duration (i.e. those who did not receive antibiotics) in their estimates of mean/median duration and hence are not strictly applicable to assessing the effectiveness of using PCT algorithms to inform the decision on when to discontinue antibiotics. We therefore conducted an additional meta-analysis, excluding participants who did not receive antibiotic treatment (see Appendix 8). The summary effect estimate for patients who received antibiotic treatment (i.e. WMD conditional upon receipt of antibiotics) was 1.48 days (95% CI –13.64 to 16.59 days), based on data from two studies. 42,44 The conditional data from one of these studies44 was consistent with PCT testing being associated with a decrease in the duration of antibiotic therapy (mean difference –6.23 days, 95% CI –7.54 to –4.92 days),44 whereas analysis of conditional data from the second study42 resulted in a reversal of the observed effect and indicated that PCT testing was associated with an increase in the duration of antibiotic therapy (mean difference 9.18 days, 95% CI 7.75 to 10.61 days).

FIGURE 15.

Duration of antibiotics in adults.

Only one of the studies39 conducted in children presenting to the ED reported data on duration of antibiotic therapy; this study found that adding a PCT algorithm to the clinical decision-making process was associated with a statistically significant reduction in the duration of antibiotic therapy (mean difference –1.8 days, 95% CI–3.1 to –0.5 days). 39 Subgroup analyses from this study39 indicated that this reduction was apparent only for children with CAP (mean difference –3.4 days, 95% CI –4.9 to –1.7 days); for children with non-CAP LRTI, there was no apparent difference in the duration of antibiotic therapy when a PCT algorithm was used (mean difference was 0.8 days, 95% CI –0.5 to 2.0 days) (see Appendix 3.4).

Resource use and costs

All of the studies conducted in ED settings39,42,44,49,55–57,60,62,69 reported data on one or more resource use or costs outcome.

Six studies,42,44,55–57,60 conducted in adults presenting to the ED with various respiratory conditions, reported data on the effect on duration of hospital stay of adding a PCT algorithm to information used to guide antibiotic treatment (Table 6). The intervention arms of five of these studies42,44,55–57 used PCT algorithms in both the decision on whether or not to initiate antibiotic treatment and the decision on when to discontinue antibiotic treatment, and in the remaining study60 only the decision on whether or not to initiate antibiotic therapy was considered. Only two studies42,44 reported data to allow the calculation of mean difference in the duration of hospital stay between study arms and neither found a statistically significant between-group difference. The summary effect estimate, derived from these two studies,42,44 indicated that the PCT algorithm was associated with a trend towards reduction in the duration of hospital stay (WMD –0.80 days, 95% CI –2.37 to 0.78 days) (Figure 16). Four further studies assessed the effectiveness of adding PCT testing to the information used to guide antibiotic treatment,55–57,60 but reported duration of hospital stay as mean number of days with no estimate of variance55,56 or median (IQR) with p-values for the between-group comparison. 57,60 Two of these studies,55,56 both conducted in people with CAP, reported results indicating that the PCT algorithm was associated with a reduction in the duration of hospital stay (mean duration 9.2 days in the PCT group and 14.6 days in the control group,55 and mean duration 14.6 days in the PCT group and 16 days in the control group56) (see Table 6). The remaining two studies,57,60 one conducted in people with LRTI57 and one conducted in people with COPD exacerbations,60 found that use of a PCT algorithm did not affect the median duration of hospital stay (see Table 6). This finding was consistent for all three clinical subgroups (COPD exacerbations, CAP and acute bronchitis) of the LRTI study (see Appendix 3.4). 57

FIGURE 16.

Duration of hospital stay for adults presenting to the ED.

Both of the studies39,49 conducted in children presenting to the ED with respiratory conditions assessed the effectiveness of including a PCT algorithm in both the decision on whether or not to initiate antibiotic treatment and the decision on when to discontinue antibiotic treatment, and both reported data to allow the calculation of mean difference in the duration of hospital stay between study arms (see Table 6). When data from the subgroup of children with CAP from the Baer study39 were combined with the Esposito study49 the summary effect estimate indicated that the use of a PCT algorithm was associated with a small reduction in the duration of hospital stay (WMD –0.74 days, 95% CI –1.17 to –0.31 days) (Figure 17; this effect was reduced when a summary estimate was calculated using the whole population of both studies: WMD –0.62 days, 95% CI –1.18 to –0.07 days).

FIGURE 17.

Duration of hospital stay for children with CAP.

One ED study60 reported data on duration of ICU stay. 60 This study was conducted in adults with COPD exacerbations and assessed the effectiveness of adding a PCT algorithm to the information used to decide whether or not to initiate antibiotic treatment; there was no statistically significant difference in the mean duration of ICU stay between the study groups (mean difference –0.40, 95% CI –1.06 to 0.26).

Two studies,62,69 one assessing the effectiveness of adding a PCT algorithm to the information used to decide whether or not to initiate antibiotic treatment in adults with acute asthma exacerbations,62 and the other assessing the effectiveness of adding a PCT algorithm to the information used to decide when to discontinue antibiotic treatment in adults with UTI,69 reported hospital re-admission rates. Both studies62,69 found no statistically significant between-group difference in re-admission rates (see Table 6). Similarly, two studies,60,62 both assessing the effectiveness of adding a PCT algorithm to the information used to decide whether or not to initiate antibiotic treatment, in adults with acute asthma exacerbations62 and adults with COPD exacerbations,60 found no statistically significant between-group difference in the rate of secondary ED visits (see Table 6).

Two studies by Christ-Crain et al. ,42,44 both assessing the effectiveness including a PCT algorithm in both the decision on whether or not to initiate antibiotic treatment and the decision on when to discontinue antibiotic treatment, reported that use of the PCT algorithm was associated with reductions in antibiotic costs (see Table 6). These findings are consistent with the reduced rate of antibiotic prescribing and mean duration of antibiotic therapy reported by these two studies, described above (see Antibiotic exposure).

Adverse clinical outcomes

All 10 studies conducted in ED settings39,42,44,49,55–57,60,62,69 reported data on at least one adverse clinical outcome. Five of these studies explicitly stated that they aimed to investigate whether the use of PCT in decision-making can reduce antibiotic exposure,39,42,57,60,69 and three studies further specified that they aimed to investigate whether a reduction in antibiotic exposures can be achieved without adversely affecting clinical outcomes. 42,57,60

Six studies reported all-cause mortality at various time points,42,44,55–57,60 ranging from 14 days to 6 months. The intervention arms of five of these studies42,44,55–57 used PCT algorithms in both the decision on whether or not to initiate antibiotic treatment and the decision on when to discontinue antibiotic treatment, and in the remaining study60 only the decision on whether or not to initiate antibiotic therapy was considered. All studies42,44,55–57,60 reported no statistically significant difference in mortality rates between participants in the intervention group (antibiotic treatment decisions based on PCT algorithm plus clinical judgement) and those in the control group (antibiotic treatment decisions based on clinical judgement alone) (Table 7). When studies reported data for clinical subgroups (acute COPD exacerbations,44 and COPD exacerbations, CAP and acute bronchitis57), this finding was consistent across all subgroups (see Appendix 3.3). The summary RR derived from all six studies reporting mortality data42,44,55–57,60 was 0.95 (95% CI 0.71 to 1.27), I² = 0% (Figure 18). When data from the two studies reporting follow-up (6 months) mortality56,60 were pooled the summary RR was 0.85 (95% CI 0.46 to 1.59).

FIGURE 18.

All-cause mortality in adults presenting to the ED (any time point).

Neither of the two ED studies conducted in children39,49 reported mortality data.

Four studies42,44,57,60 reported data on rates of admission to the ICU. The intervention arms of three of these studies42,44,57 used PCT algorithms in both the decision on whether or not to initiate antibiotic treatment and the decision on when to discontinue antibiotic treatment, and in the remaining study,60 only the decision on whether or not to initiate antibiotic therapy was considered. As was the case for all-cause mortality, all studies42,44,57,60 found no statistically significant between-group differences in ICU admissions (see Table 7) and this finding was consistent for clinical subgroups, when reported (see Appendix 3.3). 44 The summary RR derived from these four studies42,44,57,60 was 0.79 (95% CI 0.59 to 1.05) (Figure 19).

FIGURE 19.

Intensive care unit admission for adults presenting to the ED.

Neither of the two ED studies conducted in children39,49 reported any information on ICU admissions.

Two ED studies,57,69 conducted in adults, reported inconsistent results with respect to rates of infection relapse/recurrence. One study,69 conducted in adults hospitalised with UTI found no statistically significant difference in relapse/recurrence rates between participants in the intervention group (decision to discontinue antibiotics based on PCT algorithm plus clinical judgement) and those in the control group (decision to discontinue antibiotics based on clinical judgement alone) (see Table 7). The second study,57 conducted in adults with LRTI, found that inclusion of a PCT algorithm in both the information used to guide initiation and discontinuation of antibiotics was associated with a statistically significant reduction in infection relapse/recurrence rates (RR 0.57, 95% CI 0.36 to 0.92) (see Table 7).

One ED study,49 conducted in children with CAP, reported very low rates of infection relapse/recurrence and a trend towards lower rates in the PCT group (RR 0.23, 95% CI 0.04 to 1.34) (see Table 7).

One ED study,57 conducted in adults with LRTI, reported numbers of participants experiencing antibiotic-related adverse events. This study57 found that including a PCT algorithm in both the decision on whether or not to initiate antibiotic treatment and the decision on when to discontinue antibiotic treatment was associated with a reduction in antibiotic-related adverse events (RR 0.71, 95% CI 0.58 to 0.86). This finding is consistent with the reduced rate of antibiotic prescribing and mean duration of antibiotic therapy reported by this study, described above (see Antibiotic exposure). 57

Both of the ED studies conducted in children reported numbers of participants experiencing antibiotic-related adverse events. 39,49 Results from the study by Esposito et al.,49 conducted in children with CAP, and from the subgroup of children with CAP from the study by Baer et al.,39 indicated that including a PCT algorithm in both the decision on whether or not to initiate antibiotic treatment and the decision on when to discontinue antibiotic treatment was associated with a reduction in antibiotic-related adverse events (see Table 7). The summary RR derived from these two data sets was 0.37 (95% CI 0.04 to 3.49) (Figure 20; when data for all participants in both studies were included in the meta-analysis, the summary RR was 0.40 (95% CI 0.06 to 2.78).

FIGURE 20.

Antibiotic-related adverse events in children with CAP presenting to the ED.

A variety of other general and disease-specific adverse clinical outcomes were reported by one or more studies (see Table 7). These included composite adverse outcome measures,42,57 need for steroids,57,62 need for mechanical ventilation,62 and complications from pneumonia. 39 No study reported a statistically significant difference between the intervention and comparator groups for any adverse clinical outcome assessed.

Economic evaluations

The following databases were searched for relevant studies from 2005 to August 2014:

• NHS Economic Evaluation Database (NHS EED) (Wiley): 2005 – Issue 3 of 4, July 2014.

• Health Economic Evaluation Database (HEED) (Wiley): 2005 – 20 August 2014 (http://onlinelibrary.wiley.com/book/10.1002/9780470510933).

• IDEAS via Research Papers in Economics (REPEC) (internet): 2005 – 20 August 2014 (http://repec.org/).

• EconLIT (EBSCOhost): 2005 – 20 August 2014.

Michaelidis et al. 2013

The study by Michaelidis et al. 72,73 used a decision tree to analyse the cost-effectiveness of PCT-guided antibiotic therapy versus usual care for outpatient management of ARTIs in adults. Two separate analyses were performed using data from two European RCTs75,76 separately. The first analysis is based on a study published by Briel et al. ,75 which considered all adults presenting to an outpatient clinic with an ARTI and judged by their physicians to require an antibiotic prescription. The second analysis was based on a study published by Bukhardt et al. ,76 which included all adults presenting to an outpatient clinic with an ARTI prior to any decision to initiate antibiotic therapy. PCT-guided antibiotic therapy was both more costly and more effective than care as usual without PCT-guided treatment (see Table 8) leading to incremental cost-effectiveness ratios (ICERs) of US$118,828 and US$575,249 per QALY gained for the first and second analyses, respectively.

Michaelidis et al. 73 also estimated the costs of antibiotic resistant infections attributable to an antibiotic prescription. It was estimated that these costs per antibiotic prescription (in the outpatient setting for management of ARTIs in adults) would range between US$0 and US$333 with a base-case value of US$43. These estimated costs of antibiotic resistance are not used in the economic evaluation. It is argued by the authors that these costs can be used as the willingness-to-pay per antibiotic prescription safely avoided and hence that PCT-guided antibiotic therapy would be cost-effective for adults presenting to an outpatient clinic with an ARTI and judged by their physicians to require an antibiotic prescription (probability of being cost-effective: 58%). Using this threshold PCT-guided antibiotic therapy would not be considered cost-effective for all adults presenting to an outpatient clinic with an ARTI prior to any decision to initiate antibiotic therapy (probability of being cost-effective: 3%).

Smith et al. 2013

The cost-effectiveness analysis by Smith et al. 74 (see Table 8) used a decision tree to estimate the cost-effectiveness of PCT-guided antibiotic therapy versus usual care in CAP. The analysis considered low-risk CAP patients [Pneumonia Severity Index (PSI) risk class of ≤ 3, or a CURB-65 score of ≤ 2 (CURB-65 is an acronym for five risk factors: confusion of new onset; blood urea nitrogen; respiratory rate; blood pressure; and aged ≥ 65 years)] and high-risk CAP patients (PSI risk classes 4 or 5, or CURB-65 scores of ≥ 3). The base-case analysis assumed no differences in clinical outcomes or hospital length of stay between the treatment strategies. This assumption was relaxed in the probabilistic sensitivity analysis (PSA) (using a disutility of 0.2 for hospitalisation). This analysis indicated that PCT-guided antibiotic therapy is both more costly and effective than care without PCT-guided treatment and is likely to be cost-effective for willingness-to-pay values of > US$90,000 per QALY for low-risk patients using PCT for initiating antibiotic only, US$40,000 per QALY when PCT is also used for monitoring antibiotic use for low-risk patients, and for high-risk patients this is US$170,000 per QALY (using PCT for both initiating antibiotic and monitoring antibiotic use).

Utility values

The following databases were searched for relevant studies from database inception date to September 2014:

• MEDLINE (OvidSP): 1946 – August Week 3 2014.

• MEDLINE In-Process Citations & Other Non-Indexed Citations and Daily Update (OvidSP): up to 2 September 2014.

• EMBASE (OvidSP): 1974 to 2 September 2014.

• CENTRAL (Wiley): up to Issue 8 of 12, August 2014.

• HTA database (Wiley): up to Issue 3 of 4, July 2014.

• PubMed (www.ncbi.nlm.nih.gov/pubmed): up to 3 September 2014.

• PROQOLID (internet) (www.proqolid.org/): up to 3 September 2014.

Adult intensive care unit

All seven studies86–93 that considered adult patients with sepsis, who were being treated in the ICU, used the European Quality of Life-5 Dimensions (EQ-5D) to elicit utility scores. Only one93 of these studies (abstract only) reported short-term utility scores for a sepsis patient group (n = 93) that stayed in hospital (56% of the patients were in the hospital at day 30). This study93 reported utility values for 30, 60, 90 and 180 days after admission of 0.53, 0.62, 0.68 and 0.69, respectively. Long-term follow-up utility values found in the literature were 0.8489 and 0.6792 at 6 months, 0.7590 at 1.4 years, 0.7291 at 2 years, 0.6488 at 3.5 years and 0.6888 at 5 years. One study reported a utility value of 0.6886,87 for patients 1 year or later after discharge. The long-term utility values varied substantially between studies. These differences between the studies may be caused by context related factors (e.g. patient mix, countries and valuation functions). Studies with longitudinal data tended to show an increasing utility score over time (i.e. positive correlation between utility score and time since ICU admission). The Scottish study by Cuthbertson et al. 88 probably provides the most representative long-term utilities for the UK population (0.64 at 3.5 years and 0.68 at 5 years).

With regard to the long-term impact of sepsis ICU admission on HRQoL, the Finnish study by Karlsson et al. 90 concluded [based on the intention-to-treat (ITT) population] that there is a long-term utility decrement attributable to sepsis ICU admission, as the utility value at 17 months was lower than utility values measured before sepsis. It should be noted, however, that in most cases the first questionnaire (at the ICU considering HRQoL before acute critical illness) was filled out by a next of kin.

Paediatric intensive care unit

A Dutch study85 measured long-term HRQoL (median follow-up interval: 10 years) using the Health Utilities Index (HUI) in patients who experienced meningococcal septic shock and were admitted to the paediatric ICU (median age at admission: 3 years). The utility values reported by the respondents (n = 120) were 0.82 [Health Utilities Index Mark 3 (HUI3)] and 0.88 [Health Utilities Index Mark 2 (HUI2)], and were considered to be lower than those of a representative sample of 1435 Dutch school children aged between 5 and 13 years (HUI2 0.93 and HUI3 0.94).

Paediatric emergency department

In a study conducted in the USA,84 a total of 94 parents who presented at the paediatric ED with their children (aged between 3 and 36 months) were asked to elicit utility values to eight health state descriptions for their children using the standard gamble method. These health states and their valuations were death (0.02), meningitis with severe brain damage (0.39), meningitis with minor brain damage (0.74), meningitis with deafness (0.86), meningitis with recovery (0.98), hospitalisation for antibiotic (0.99), local infection (0.99), and blood drawn (1.00). It was concluded that extremely high utility values were found for health states without permanent sequelae (blood drawn, local infection, hospitalisation for antibiotic, and meningitis with recovery).

All-cause mortality

The assessment of clinical effectiveness (see Results of the assessment of clinical effectiveness assessment, above) was the primary input for the baseline probabilities and RRs used for the economic evaluation. Whenever a meta-analysis over the results of the identified studies was not possible, the most plausible source was chosen. This was based on two criteria: (1) compatibility with the population in the given scenario (low risk vs. high risk) and (2) availability of data for relevant outcomes.

Table 11 gives an overview of the selected sources used for the baseline mortality probabilities for each of the populations and the justifications for each of the choices. Table 12 gives an overview of the baseline mortality probabilities and mortality RRs used.

Adverse events

Antibiotic-related adverse events were incorporated through the time on antibiotic treatment (using a disutility for being on antibiotic treatment), as antibiotic-related adverse events were mostly reported as a compound end point instead of the individual adverse events. No differences in disease-specific complications were found between the intervention and comparator groups for any adverse clinical outcome assessed (see Effectiveness of adding procalcitonin testing to the information used to guide antibiotic therapy for the treatment of confirmed or highly suspected sepsis in intensive care unit settings, above). Moreover, disease-specific complications were also reported as a compound end point, making it difficult to incorporate these complications using complication-specific disutilities. Therefore, the disease-specific complications were not included and thus assumed to be equal for the comparators.

Health-state utilities

The systematic review of HRQoL studies (see Chapter 4, Review of health-related quality of life studies) was used as input for utility values for the economic evaluation (Table 13). For adults being treated in the ICU, a utility of score of 0.53 was used for the decision tree period, whereas a utility of 0.68 was used for the period thereafter (both retrieved from Drabinksi et al.,93 the only study with short-term utility values). In a scenario analysis the utility value of 0.68 was replaced with the 3.5-year utility value of 0.64 from Cuthbertson et al. ,88 which was judged to provide the most representative long-term utilities for the UK population.

No utility values for adults presenting to the ED with suspected infection were identified in the systematic review of HRQoL studies. Therefore, this utility value was retrieved from Oppong et al. ,77 which estimated a utility value (EQ-5D) for adults presenting to their primary care clinician with LRTI. The baseline and 4-week utility values reported in this study were used to calculate a weighted average (based on the number of patients per utility estimation) for England and Wales for the initial 28 days’ decision tree period (0.70) and thereafter (0.86).

For children presenting to the ED, a constant base utility of 0.99 was assumed (utility for local infection) from Bennett et al. 84 (only study available).

To incorporate antibiotic-related adverse events in adults being treated in the ICU, a disutility of 0.046 for being on antibiotic treatment was taken from Oppong et al. 77 (weighted average for England and Wales). Although this disutility might be higher for people being treated in the ICU, due to the intravenous route of administration, it was conservatively assumed that this disutility is equal for all settings and populations. Moreover, it was conservatively assumed that there is no disutility for staying in hospital.

Resource use and costs

Resource use consisted of duration of hospital stay (days), ICU stay (days) and antibiotic treatment duration (days). The estimates were retrieved from studies identified in the systematic review. The same criteria, as described above for the probabilities and RRs, are used to choose a study for a specific input parameter.

For the ED, antibiotic duration was calculated based on the probability of initiation of antibiotic treatment and the duration of antibiotic treatment conditional on that antibiotic treatment having been initiated, i.e. the mean from the studies excluding those patients with zero use. For the ICU, it was assumed that antibiotics were initiated for all patients and thus the antibiotic treatment duration mean for the whole sample from the studies was used.

The studies chosen for baseline resource use and the accompanying justification are given in Table 14. The resource-use parameters are given in Table 15.

Data for the cost analyses were drawn from routine NHS sources (e.g. NHS reference costs and British National Formulary (BNF)96 and discussions with manufacturers of the PCT tests. Table 16 gives an overview of the unit prices and their sources as used in the health economic analysis.

Antibiotic treatment costs were calculated using average unit prices per day. These average prices were calculated separately for the ED setting (children and adults) and for the ICU setting (adults). Antibiotic prices were retrieved from the BNF. 96 The price per day for antibiotic treatment were calculated based on the dosage recommended in the treatment guidelines. LRTI treatment guidelines were used for the hospitalised non-ICU setting,98 and treatment guidelines for suspected or confirmed sepsis were used for the ICU settings. 99 The prices of different antibiotic treatment strategies (recommended by the guideline for a specific setting) were averaged. It was assumed that there was no wastage with regards to the antibiotic use (i.e. antibiotics were provided in perfectly dividable packages that correspond to the duration of the treatment, as in the treatment strategy). This assumption would be plausible especially for the ICU setting given the ‘return for re-issue’ approach used for handling partially used packs in UK hospitals. On the other hand, given the low unit costs of antibiotics the effects of the drug wastage on total costs are expected to be very small for both settings. It should be noted that the costs of antibiotic-related adverse events are conservatively not incorporated.

Costs of hospital stay, ED stay and ICU stay were retrieved from the UK’s National Schedule of Reference Costs. 97 The costs were calculated as weighted averages of the specific services taking into account the national average unit cost and the total number of attendances for each of the cost categories. The reference codes were: XB01Z–XB09Z for paediatric ICU stay, XC01Z–XC07Z for adults ICU stay, VB01Z–VB09Z for children/adults ED stay, and DZ22D–DZ22J for children/adults hospital stay for unspecified acute lower respiratory infection.

The unit price for the PCT test was calculated based on the information provided by assay manufacturers in response to the request for information made by NICE at the beginning of the assessment and forwarded by NICE (F Nixon, Health Technology Analyst, Diagnostics Assessment Programme, NICE, July 2014, personal communication). The average price was based on the listed prices of the test (excluding the VAT) and with no discounts assumed (see the upper part of Table 17). Moreover, overhead costs including capital, service/maintenance and calibration costs (see Table 17) were included. Overhead costs were calculated incorporating the initial capital costs (wherever these were provided by the manufacturer(s), the lifetime of the assay (assumed to be 5 years) and the average number of tests/day (an average of 272 tests/day). A similar estimation was performed taking into account the frequency of the maintenance and calibration costs whenever they were provided by the manufacturers. The inclusion of capital costs and other costs was considered as a conservative approach and therefore used in the base-case analysis. A separate scenario analysis considered the exclusion of overhead costs.

The number of PCT tests used was considered different for the ED and the ICU setting as in Table 18.

The Cochrane Library

Searched: 7 April 2014.

Records found:

• CDSR Issue 4 of 12, April 2014 = 14.

• DARE Issue 1 of 4, January 2014 = 13.

• HTA Issue 1 of 4, January 2014 = 0.

• NHS EED Issue 1 of 4, January 2014 = 5.

#1 MeSH descriptor: [Systemic Inflammatory Response Syndrome] explode all trees (3265)

#2 “systemic inflammatory response syndrome” or SIRS (1130)

#3 sepsis* or septic* or sepses (6770)

#4 bacill*emia* or bacter*emia* or endotox*emia* or pyoh*emia* or py*emia* (2020)

#5 fusobacterium near/2 necrophorum (6)

#6 Lemierre* near/2 (disease* or syndrome*) (1)

#7 necrobacillosis or necrobacilloses or meningococc*emia or urosepsis or fung*emia or candid*emia (265)

#8 Neisseria near/2 meningitidis near/2 bacter*emia (0)

#9 staphylococc* near/2 bacter*emia (74)

#10 (bacter*emic or bacterial or endotoxin* or toxi*) near/3 shock* (47)

#11 toxic near/2 forward near/2 failure (0)

#12 blood near/2 poison* (136)

#13 #1 or #2 or #3 or #4 or #5 or #6 or #7 or #8 or #9 or #10 or #11 or #12 (9831)

#14 MeSH descriptor: [Protein Precursors] explode all trees (2483)

#15 MeSH descriptor: [Calcitonin] this term only (553)

#16 #14 and #15 (141)

#17 PCT (369)

#18 procalcitonin or “pro-calcitonin” or “56645-65-9” or (calcitonin near/2 precursor*) (270)

#19 brahms or KRYPTOR or “b r a h m s” (21)

#20 #16 or #17 or #18 or #19 (543)

#21 #13 and #20 (131)

(CDSR = 14; DARE = 13; HTA = 0; NHS EED = 5)

PROSPERO

www.crd.york.ac.uk/prospero/ up to 9 April 2014.

Searched: 7 April 2014.

National Institute for Health and Care Excellence guidance

URL: www.nice.org.uk/ up to 8 April 2014.

Searched: 8 April 2014.

Limited to information type “Guidance”

National Institute for Health Research Health Technology Assessment programme

URL: www.hta.ac.uk/ up to 8 April 2014.

Searched: 8 April 2014.

US Food and Drug Administration

URL: www.fda.gov/ up to 8 April 2014.

Searched: 8 April 2014.

Searched whole site

Guidelines International Network (G-I-N)

URL: www.g-i-n.net/ up to 9 April 2014.

Searched: 9 April 2014.

National Guidelines Clearinghouse (NGCH)

URL: www.guideline.gov/index.aspx up to 9 April 2014.

Searched: 9 April 2014.

Medicines and Healthcare Products Regulatory Agency (MHRA)

URL: www.mhra.gov.uk/index.htm up to 9 April 2014.

Searched: 9 April 2014.

The Medion database

URL: www.mediondatabase.nl/ up to 9 April 2014.

Searched: 23 September 2014.

EMBASE (OvidSP)

1974 to 27 June 2014.

Searched: 30 June 2014.

Records found: 1210.

1. exp systemic inflammatory response syndrome/ (172,787)

2. exp bacterial infection/ (745,043)

3. (systemic inflammatory response syndrome$ or SIRS).ti,ab,ot,hw. (10,736)

4. (sepsis$ or septic$ or sepses).ti,ab,ot,hw. (191,638)

5. (bacill?emia$ or bacter?emia$ or endotox?emia$ or pyoh?emia$ or py?emia$).ti,ab,ot,hw. (48,133)

6. (fusobacterium adj2 necrophorum).ti,ab,ot,hw. (1164)

7. (Lemierre$ adj2 (disease$ or syndrome$)).ti,ab,ot,hw. (798)

8. (necrobacillosis or necrobacilloses or meningococc?emia or urosepsis).ti,ab,ot,hw. (3762)

9. (Neisseria adj2 meningitidis adj2 bacter?emia).ti,ab,ot,hw. (19)

10. tetanus.ti,ab,ot,hw. (34,768)

11. ((bacter?emic or bacterial or endotoxi$ or toxi$) adj3 shock$).ti,ab,ot,hw. (11,163)

12. (toxic adj2 forward adj2 failure).ti,ab,ot,hw. (0)

13. (blood adj2 poison$).ti,ab,ot,hw. (257)

14. infect$.ti,ab,ot. (1,461,612)

15. (bacterial adj2 (meningitis or pneumonia or peritonitis or endocarditis or superinfection or disease$)).ti,ab,ot,hw. (60,674)

16. (bartonellosis or bordetellosis or Bordetella or pertussis or botryomycosis or brucellosis or campylobacter$ or legionnaire$ disease or listeriosis or mycoplasmosis or pyomyositis or pyonephrosis or Staphylococc$ or Streptococc$ or “e coli”).ti,ab,ot,hw. (475,125)

17. or/1-16 (2,327,414)

18. Procalcitonin/ (4820)

19. PCT.ti,ab,ot. (6593)

20. (procalcitonin or pro-calcitonin or 56645-65-9 or (calcitonin adj2 precursor$)).ti,ab,ot,hw,rn,tn. (5087)

21. brahms.af. (915)

22. KRYPTOR.af. (221)

23. b r a h m s.af. (11)

24. or/18-23 (10,280)

25. 17 and 24 (4786)

26. Random$.tw. or clinical trial$.mp. or exp health care quality/ (3,261,790)

27. 25 and 26 (1231)

28. animal/ (1,569,119)

29. animal experiment/ (1,782,343)

30. (rat or rats or mouse or mice or murine or rodent or rodents or hamster or hamsters or pig or pigs or porcine or rabbit or rabbits or animal or animals or dogs or dog or cats or cow or bovine or sheep or ovine or monkey or monkeys).ti,ab,ot,hw. (5,658,580)

31. or/28-30 (5,658,580)

32. exp human/ (14,900,947)

33. human experiment/ (326,401)

34. or/32-33 (14,902,376)

35. 31 not (31 and 34) (4,528,206)

36. 27 not 35 (1218)

37. limit 36 to yr=“1995 -Current” (1210)

MEDLINE (OvidSP)

1946 to June Week 3 2014.

Searched: 30 June 2014.

Records found: 739.

1. exp Systemic Inflammatory Response Syndrome/ (94,981)

2. exp bacterial infections/ (719,780)

3. (systemic inflammatory response syndrome$ or SIRS).ti,ab,ot,hw. (6774)

4. (sepsis$ or septic$ or sepses).ti,ab,ot,hw. (123,216)

5. (bacill?emia$ or bacter?emia$ or endotox?emia$ or pyoh?emia$ or py?emia$).ti,ab,ot,hw. (36,734)

6. (fusobacterium adj2 necrophorum).ti,ab,ot,hw. (896)

7. (Lemierre$ adj2 (disease$ or syndrome$)).ti,ab,ot,hw. (483)

8. (necrobacillosis or necrobacilloses or meningococc?emia or urosepsis).ti,ab,ot,hw. (1520)

9. (Neisseria adj2 meningitidis adj2 bacter?emia).ti,ab,ot,hw. (17)

10. tetanus.ti,ab,ot,hw. (24,082)

11. ((bacter?emic or bacterial or endotoxi$ or toxi$) adj3 shock$).ti,ab,ot,hw. (8473)

12. (toxic adj2 forward adj2 failure).ti,ab,ot,hw. (0)

13. (blood adj2 poison$).ti,ab,ot,hw. (139)

14. infect$.ti,ab,ot. (1,157,024)

15. (bacterial adj2 (meningitis or pneumonia or peritonitis or endocarditis or superinfection$ or disease$)).ti,ab,ot,hw. (46,704)

16. (bartonellosis or bordetellosis or Bordetella or pertussis or botryomycosis or brucellosis or campylobacter$ or legionnaire$ disease or listeriosis or mycoplasmosis or pyomyositis or pyonephrosis or Staphylococc$ or Streptococc$ or “e coli”).ti,ab,ot,hw. (367,834)

17. or/1-16 (1,896,545)

18. exp Protein Precursors/ and Calcitonin/ (2200)

19. PCT.ti,ab,ot. (3921)

20. (procalcitonin or pro-calcitonin or 56645-65-9 or (calcitonin adj2 precursor$)).ti,ab,ot,hw,rn. (2468)

21. brahms.af. (318)

22. KRYPTOR.af. (68)

23. b r a h m s.af. (18)

24. or/18-23 (5718)

25. 17 and 24 (2279)

26. randomized controlled trial.pt. (376,175)

27. controlled clinical trial.pt. (88,531)

28. randomized.ab. (274,544)

29. placebo.ab. (146,796)

30. drug therapy.fs. (1,708,719)

31. randomly.ab. (194,627)

32. trial.ab. (284,610)

33. groups.ab. (1,250,317)

34. or/26-33 (3,208,598)

35. exp animals/ not (exp animals/ and humans/) (3,954,108)

36. 34 not 35 (2,730,725)

37. 25 and 36 (752)

38. limit 37 to yr=“1995 -Current” (739)

MEDLINE In-Process & Other Non-Indexed Citations, MEDLINE Daily Update (OvidSP)

27 June 2014.

Searched: 30 June 2014.

Records found: 67.

1. exp Systemic Inflammatory Response Syndrome/ (100)

2. exp bacterial infections/ (628)

3. (systemic inflammatory response syndrome$ or SIRS).ti,ab,ot,hw. (361)

4. (sepsis$ or septic$ or sepses).ti,ab,ot,hw. (6694)

5. (bacill?emia$ or bacter?emia$ or endotox?emia$ or pyoh?emia$ or py?emia$).ti,ab,ot,hw. (1503)

6. (fusobacterium adj2 necrophorum).ti,ab,ot,hw. (52)

7. (Lemierre$ adj2 (disease$ or syndrome$)).ti,ab,ot,hw. (49)

8. (necrobacillosis or necrobacilloses or meningococc?emia or urosepsis).ti,ab,ot,hw. (121)

9. (Neisseria adj2 meningitidis adj2 bacter?emia).ti,ab,ot,hw. (2)

10. tetanus.ti,ab,ot,hw. (793)

11. ((bacter?emic or bacterial or endotoxi$ or toxi$) adj3 shock$).ti,ab,ot,hw. (229)

12. (toxic adj2 forward adj2 failure).ti,ab,ot,hw. (0)

13. (blood adj2 poison$).ti,ab,ot,hw. (15)

14. infect$.ti,ab,ot. (77,602)

15. (bacterial adj2 (meningitis or pneumonia or peritonitis or endocarditis or superinfection$ or disease$)).ti,ab,ot,hw. (1024)

16. (bartonellosis or bordetellosis or Bordetella or pertussis or botryomycosis or brucellosis or campylobacter$ or legionnaire$ disease or listeriosis or mycoplasmosis or pyomyositis or pyonephrosis or Staphylococc$ or Streptococc$ or “e coli”).ti,ab,ot,hw. (15,810)

17. or/1-16 (93,626)

18. exp Protein Precursors/ and Calcitonin/ (4)

19. PCT.ti,ab,ot. (358)

20. (procalcitonin or pro-calcitonin or 56645-65-9 or (calcitonin adj2 precursor$)).ti,ab,ot,hw,rn. (291)

21. brahms.af. (26)

22. KRYPTOR.af. (7)

23. b r a h m s.af. (0)

24. or/18-23 (525)

25. 17 and 24 (255)

26. randomized controlled trial.pt. (957)

27. controlled clinical trial.pt. (84)

28. randomized.ab. (23,138)

29. placebo.ab. (8510)

30. drug therapy.fs. (1798)

31. randomly.ab. (20,509)

32. trial.ab. (24,490)

33. groups.ab. (117,569)

34. or/26-33 (157,619)

35. exp animals/ not (exp animals/ and humans/) (2712)

36. 34 not 35 (157,125)

37. 25 and 36 (67)

38. limit 37 to yr=“1995 -Current” (67)

PubMed

www.ncbi.nlm.nih.gov/pubmed/

1995 to 14 July 2014.

Searched: 14 July 2014.

Records found: 86.

• This strategy aims to identify records that are on PubMed, but not included in MEDLINE or MEDLINE In-Process (OvidSP). Line #7 limits the search results in this way.

• The sepsis/bacterial infection facet was excluded to keep search as broad as possible.

#10 “Search ((((((((((protein precursors[MeSH Terms]) AND calcitonin[MeSH Terms])) OR PCT[Title/Abstract]) OR (((procalcitonin[Title/Abstract]) OR ““pro-calcitonin””[Title/Abstract]) OR ““calcitonin precursor*””[Title/Abstract])) OR (((brahms) OR kryptor) OR ““b r a h m s””))) AND (((((““randomized controlled trial””[Publication Type]) OR ““controlled clinical trial””[Publication Type])) OR (((((randomized[Title/Abstract]) OR placebo[Title/Abstract]) OR randomly[Title/Abstract]) OR trial[Title/Abstract]) OR groups[Title/Abstract])) OR ““drug therapy””[MeSH Subheading])) AND ((pubstatusaheadofprint OR publisher[sb] OR pubmednotmedline[sb])))) NOT (animals [mh] NOT humans [mh])” (86)

#9 “Search animals [mh] NOT humans [mh]” (3,904,987)

#8 “Search ((((((((protein precursors[MeSH Terms]) AND calcitonin[MeSH Terms])) OR PCT[Title/Abstract]) OR (((procalcitonin[Title/Abstract]) OR ““pro-calcitonin””[Title/Abstract]) OR ““calcitonin precursor*””[Title/Abstract])) OR (((brahms) OR kryptor) OR ““b r a h m s””))) AND (((((““randomized controlled trial””[Publication Type]) OR ““controlled clinical trial””[Publication Type])) OR (((((randomized[Title/Abstract]) OR placebo[Title/Abstract]) OR randomly[Title/Abstract]) OR trial[Title/Abstract]) OR groups[Title/Abstract])) OR ““drug therapy””[MeSH Subheading])) AND ((pubstatusaheadofprint OR publisher[sb] OR pubmednotmedline[sb]))” (86)

#7 “Search (pubstatusaheadofprint OR publisher[sb] OR pubmednotmedline[sb])” (1,792,621)

#6 “Search ((((““randomized controlled trial””[Publication Type]) OR ““controlled clinical trial””[Publication Type])) OR (((((randomized[Title/Abstract]) OR placebo[Title/Abstract]) OR randomly[Title/Abstract]) OR trial[Title/Abstract]) OR groups[Title/Abstract])) OR ““drug therapy””[MeSH Subheading]” (3,394,868)

#5 “Search (((((protein precursors[MeSH Terms]) AND calcitonin[MeSH Terms])) OR PCT[Title/Abstract]) OR (((procalcitonin[Title/Abstract]) OR ““pro-calcitonin””[Title/Abstract]) OR ““calcitonin precursor*””[Title/Abstract])) OR (((brahms) OR kryptor) OR ““b r a h m s””)” (6314)

#4 “Search ((brahms) OR kryptor) OR ““b r a h m s””” (394)

#3 “Search ((procalcitonin[Title/Abstract]) OR ““pro-calcitonin””[Title/Abstract]) OR ““calcitonin precursor*””[Title/Abstract]” (2617)

#2 “Search PCT[Title/Abstract]” (4327)

#1 “Search (protein precursors[MeSH Terms]) AND calcitonin[MeSH Terms]” (2189)

Cumulative Index to Nursing and Allied Health Literature (CINAHL) (EBSCOhost)

1995 to 25 June 2014.

Searched: 30 June 2014.

Records found: 205.

S1 (MH “Systemic Inflammatory Response Syndrome+”) (6393)

S2 (MH “Bacterial Infections+”) (50,338)

S3 “systemic inflammatory response syndrome” or SIRS (965)

S4 sepsis* or septic* or sepses (11,696)

S5 bacill#emia* or bacter#emia* or endotox#emia* or pyoh#emia* or py#emia* (14,336)

S6 fusobacterium N2 necrophorum (26)

S7 Lemierre* N2 (disease* or syndrome*) (93)

S8 necrobacillosis or necrobacilloses or meningococc#emia or urosepsis (116)

S9 Neisseria N2 meningitidis N2 bacter#emia (1)

S10 tetanus (1899)

S11 (bacter#emic or bacterial or endotoxin* or toxi*) N3 shock* (368)

S12 toxic N2 forward N2 failure (0)

S13 blood N2 poison* (133)

S14 infect* (159,239)

S15 bacterial N2 (meningitis or pneumonia or peritonitis or endocarditis or superinfection* or disease*) (4245)

S16 bartonellosis or bordetellosis or Bordetella or pertussis or botryomycosis or brucellosis or campylobacter* or “legionnaire* disease” or listeriosis or mycoplasmosis or pyomyositis or pyonephrosis or Staphylococc* or Streptococc* or “e coli” (18,464)

S17 S1 OR S2 OR S3 OR S4 OR S5 OR S6 OR S7 OR S8 OR S9 OR S10 OR S11 OR S12 OR S13 OR S14 OR S15 OR S16 (193,148)

S18 (MH “Protein Precursors+”) (2085)

S19 (MH “Calcitonin”) (816)

S20 S18 AND S19 (199)

S21 PCT (725)

S22 procalcitonin or “pro-calcitonin” or “56645-65-9” or (calcitonin N2 precursor*) (376)

S23 brahms or KRYPTOR or “b r a h m s” (12)

S24 S20 OR S21 OR S22 OR S23 (1011)

S25 S17 AND S24 (393)

S26 (MH “Prognosis+”) (146,028)

S27 (MH “Study Design+”) (521,326)

S28 random* (144,824)

S29 S26 OR S27 OR S28 (629,916)

S30 S25 AND S29 (205)

S31 (ZR “1995”) or (ZR “1996”) or (ZR “1997”) or (ZR “1998”) or (ZR “1999”) or (ZR “2000”) or (ZR “2001”) or (ZR “2002”) or (ZR “2003”) or (ZR “2004”) or (ZR “2005”) or (ZR “2006”) or (ZR “2007”) or (ZR “2008”) or (ZR “2009”) or (ZR “2010”) or (ZR “2011”) or (ZR “2012”) or (ZR “2013”) or (ZR “2014”) (2,807,096)

S32 S30 AND S31 (205)

Cochrane Central Register of Controlled Trials (The Cochrane Library – Wiley)

Issue 5 of 12, May 2014.

Searched: 30 June 2014.

Records found: 203.

#1MeSH descriptor: [Systemic Inflammatory Response Syndrome] explode all trees (3289)

#2 [mh “bacterial infections”] (14,301)

#3 “systemic inflammatory response syndrome” or SIRS (1164)

#4 sepsis* or septic* or sepses (6903)

#5 bacill*emia* or bacter*emia* or endotox*emia* or pyoh*emia* or py*emia* (2052)

#6 fusobacterium near/2 necrophorum (6)

#7 Lemierre* near/2 (disease* or syndrome*) (1)

#8 necrobacillosis or necrobacilloses or meningococc*emia or urosepsis (82)

#9 Neisseria near/2 meningitidis near/2 bacter*emia (0)

#10 tetanus (1529)

#11 (bacter*emic or bacterial or endotoxin* or toxi*) near/3 shock* (47)

#12 toxic near/2 forward near/2 failure (0)

#13 blood near/2 poison* (136)

#14 infect* (69,614)

#15 bacterial near/2 (meningitis or pneumonia or peritonitis or endocarditis or superinfection* or disease*) (1954)

#16 bartonellosis or bordetellosis or Bordetella or pertussis or botryomycosis or brucellosis or campylobacter* or “legionnaire* disease” or listeriosis or mycoplasmosis or pyomyositis or pyonephrosis or Staphylococc* or Streptococc* or “e coli” (8400)

#17 #1 or #2 or #3 or #4 or #5 or #6 or #7 or #8 or #9 or #10 or #11 or #12 or #13 or #14 or #15 or #16 (79,707)

#18 MeSH descriptor: [Protein Precursors] explode all trees (2487)

#19 MeSH descriptor: [Calcitonin] this term only (557)

#20 #18 and #19 (142)

#21 PCT (374)

#22 procalcitonin or “pro-calcitonin” or “56645-65-9” or (calcitonin near/2 precursor*) (288)

#23 brahms or KRYPTOR or “b r a h m s” (22)

#24 #20 or #21 or #22 or #23 (561)

#25 #17 and #24 Publication Year from 1995 to 2014, in Trials (203)

Science Citation Index (Web of Science)

1995 to 27 June 2014.

Searched: 30 June 2014.

Records found: 1292.

#27 (1292) #25 not #26

#26 (1,748,209) TOPIC: (cat or cats or dog or dogs or animal or animals or rat or rats or hamster or hamster or feline or ovine or canine or bovine or sheep)

#25 (1341) #24 AND #20

#24 (4,218,379) #23 OR #22 OR #21

#23 (779,600) TOPIC: ((study OR studies) SAME design)

#22 (3,726,032) TOPIC: ((clinic* SAME trial*) OR (placebo* OR random* OR control* OR prospectiv*))

#21 (173,285) TOPIC: ((singl* or doubl* or trebl* or tripl*) SAME (blind* or mask*))

#20 (3010) #19 AND #15

#19 (9574) #18 OR #17 OR #16

#18 (363) TOPIC: (brahms or KRYPTOR or “b r a h m s”)

#17 (3384) TOPIC: (procalcitonin or “pro-calcitonin” or “56645-65-9” or (calcitonin near/2 precursor*))

#16 (7037) TOPIC: (PCT)

#15 (1,203,220) #14 OR #13 OR #12 OR #11 OR #10 OR #9 OR #8 OR #7 OR #6 OR #5 OR #4 OR #3 OR #2 OR #1

#14 (239,288) TOPIC: (bartonellosis or bordetellosis or Bordetella or pertussis or botryomycosis or brucellosis or campylobacter* or “legionnaire* disease” or listeriosis or mycoplasmosis or pyomyositis or pyonephrosis or Staphylococc* or Streptococc* or “e coli”)

#13 (17,350) TOPIC: (bacterial near/2 (meningitis or pneumonia or peritonitis or endocarditis or superinfection* or disease*))

#12 (973,337) TOPIC: (infect*)

#11 (108) TOPIC: (blood near/2 poison*)

#10 (0) TOPIC: (toxic near/2 forward near/2 failure)

#9 (6314) TOPIC: ((bacter$emic or bacterial or endotoxin* or toxi*) near/3 shock*)

#8 (9233) TOPIC: (tetanus)

#7 (7) TOPIC: (Neisseria near/2 meningitidis near/2 bacter$emia)

#6 (1124) TOPIC: (necrobacillosis or necrobacilloses or meningococc$emia or urosepsis)

#5 (488) TOPIC: (Lemierre* near/2 (disease* or syndrome*))

#4 (525) TOPIC: (fusobacterium near/2 necrophorum)

#3 (4297) TOPIC: (bacill$emia* or bacter$emia* or endotox$emia* or pyoh$emia* or py$emia*)

#2 (87,338) TOPIC: (sepsis* or septic* or sepses)

#1 (14,020) TOPIC: (“systemic inflammatory response syndrome” or SIRS)

Latin American and Caribbean Health Sciences Literature (LILACS)

URL: http://regional.bvsalud.org/php/index.php?lang=en

1995 to date.

Searched: 1 July 2014.

Records found: 5.

procalcitonin OR pct OR brahms OR kryptor AND (instance:”regional”) AND ( db:(“LILACS”) AND type_of_study:(“clinical_trials”)

National Institute of Health Research Health Technology Assessment programme

URL: www.nets.nihr.ac.uk/programmes/hta.

1995 to date.

Searched: 1 July 2014.

Records found: 0.

procalcitonin OR pct OR brahms OR kryptor

ClinicalTrials.gov

URL: http://clinicaltrials.gov/.

Searched: 14 July 2014.

Records found: 136.

procalcitonin OR “pro-calcitonin” OR “calcitonin precursor”

Current Controlled Trials

URL: www.controlled-trials.com.

Searched: 14 July 2014.

Records found: 59.

Procalcitonin* OR pro-calcitonin OR calcitonin precursor*

WHO International Clinical Trials Registry Platform (ICTRP)

URL: www.who.int/ictrp/en/.

Searched: 14 July 2014.

Records found: 118.

Procalcitonin* OR “pro-calcitonin” OR “calcitonin precursor*”

Royal College of Paediatrics and Child Health (RCPCH) meetings

URL: http://adc.bmj.com/content/supplemental.

Searched: 16 September 2014.

Records found: 0.

Limits: 2009–14.

Title field only

European Congress of Clinical Microbiology and Infectious Diseases (ECCMID)

URL: www.escmid.org/research_projects/eccmid/past_eccmids/.

Searched: 16 September 2014.

Records found: 31.

Limits: 2009–14.

Title field only

International Symposium on Intensive Care and Emergency Medicine

URL: http://ccforum.com/supplements/.

Searched: 16 September 2014.

Records found: 25.

Limits: 2009–14.

Title field only

EMBASE (OvidSP)

1974 to 2014 August 29.

Searched: 2 September 2014.

Records found: 297.

1. exp systemic inflammatory response syndrome/ (175,423)

2. exp bacterial infection/ (750,468)

3. (systemic inflammatory response syndrome$ or SIRS).ti,ab,ot,hw. (10,939)

4. (sepsis$ or septic$ or sepses).ti,ab,ot,hw. (194,269)

5. (bacill?emia$ or bacter?emia$ or endotox?emia$ or pyoh?emia$ or py?emia$).ti,ab,ot,hw. (48,625)

6. (fusobacterium adj2 necrophorum).ti,ab,ot,hw. (1168)

7. (Lemierre$ adj2 (disease$ or syndrome$)).ti,ab,ot,hw. (804)

8. (necrobacillosis or necrobacilloses or meningococc?emia or urosepsis).ti,ab,ot,hw. (3815)

9. (Neisseria adj2 meningitidis adj2 bacter?emia).ti,ab,ot,hw. (19)

10. tetanus.ti,ab,ot,hw. (34,954)

11. ((bacter?emic or bacterial or endotoxi$ or toxi$) adj3 shock$).ti,ab,ot,hw. (11,223)

12. (toxic adj2 forward adj2 failure).ti,ab,ot,hw. (0)

13. (blood adj2 poison$).ti,ab,ot,hw. (259)

14. infect$.ti,ab,ot. (1,477,857)

15. (bacterial adj2 (meningitis or pneumonia or peritonitis or endocarditis or superinfection$ or disease$)).ti,ab,ot,hw. (61,241)

16. (bartonellosis or bordetellosis or Bordetella or pertussis or botryomycosis or brucellosis or campylobacter$ or legionnaire$ disease or listeriosis or mycoplasmosis or pyomyositis or pyonephrosis or Staphylococc$ or Streptococc$ or “e coli”).ti,ab,ot,hw. (478,890)

17. or/1-16 (2,349,530)

18. Procalcitonin/ (5000)

19. PCT.ti,ab,ot. (6741)

20. (procalcitonin or pro-calcitonin or 56645-65-9 or (calcitonin adj2 precursor$)).ti,ab,ot,hw,rn,tn. (5268)

21. brahms.af. (929)

22. KRYPTOR.af. (225)

23. b r a h m s.af. (11)

24. or/18-23 (10,524)

25. Emergency Treatment/ (14,191)

26. Evidence Based Emergency Medicine/ (197)

27. Pediatric Advanced Life Support/ (421)

28. exp Emergency Care/ (22,685)

29. Emergency/ (37,050)

30. Emergency Medicine/ (27,958)

31. Emergency Health Service/ (67,376)

32. Emergency Patient/ (1522)

33. Emergency Ward/ (64,201)

34. Intensive Care/ (88,402)

35. Intensive Care Unit/ (86,833)

36. (intensive care or high dependency unit$ or intensive therapy unit$).ti,ab,ot,hw. (213,198)

37. (ICU or ICUs or PICU or PICUs or HDU or HDUs or CCU or CCUs or ITU or ITUs or ER or ERs or ED or EDs or AAU or AAUs).ti,ab,ot. (224,054)

38. ((accident adj2 emergency) or “A&E” or “A & E”).ti,ab,ot,hw. (31,162)

39. ((emergency or emergencies) adj3 (treat$ or admit$ or admission$ or episode$ or case$ or patient$ or department$ or room or rooms or ward$ or care or medic$ or interven$ or therap$ or hospital$ or service$ or patient$ or unit$ or centre$ or center$ or facility or facilities)).ti,ab,ot,hw. (235,516)

40. ((acute or critical) adj3 (admit$ or admission$ or care or medic$ or service$ or patient$)).ti,ab,ot,hw. (227,520)

41. acute assessment unit$.ti,ab,ot,hw. (33)

42. (casualty adj2 (department$ or admit$ or admission$ or patient$)).ti,ab,ot,hw. (906)

43. or/25-42 (795,109)

44. child/ or boy/ or girl/ or hospitalized child/ or preschool child/ or school child/ or toddler/ (1,576,924)

45. exp adolescent/ (1,228,544)

46. exp puberty/ (31,712)

47. pediatrics/ or child urology/ (60,377)

48. (paediatr$ or pediatr$).ti,ab,ot. (327,061)

49. (Child$ or preschool$ or pre-school$ or toddler$ or juvenile$ or kid or kids).ti,ab,ot. (1,300,129)

50. (teen or teens or teenage$ or teen-age$ or adolescen$ or postpubescen$ or pubescen$ or minors or youth$ or puberty).ti,ab,ot. (358,040)

51. or/44-50 (2,777,254)

52. 17 and 24 and 43 and 51 (299)

53. animal/ (1,574,790)

54. animal experiment/ (1,795,561)

55. (rat or rats or mouse or mice or murine or rodent or rodents or hamster or hamsters or pig or pigs or porcine or rabbit or rabbits or animal or animals or dogs or dog or cats or cow or bovine or sheep or ovine or monkey or monkeys).ti,ab,ot,hw. (5,695,924)

56. or/53-55 (5,695,924)

57. exp human/ (15058258)

58. human experiment/ (328,401)

59. or/57-58 (15059687)

60. 56 not (56 and 59) (4,553,031)

61. 52 not 60 (299)

62. limit 61 to yr=“1995 -Current” (297)

MEDLINE (OvidSP)

1946 to August Week 3 2014.

Searched: 2 September 2014.

Records found: 202.

1. exp Systemic Inflammatory Response Syndrome/ (96,440)

2. exp bacterial infections/ (728,567)

3. (systemic inflammatory response syndrome$ or SIRS).ti,ab,ot,hw. (6898)

4. (sepsis$ or septic$ or sepses).ti,ab,ot,hw. (125,025)

5. (bacill?emia$ or bacter?emia$ or endotox?emia$ or pyoh?emia$ or py?emia$).ti,ab,ot,hw. (37,237)

6. (fusobacterium adj2 necrophorum).ti,ab,ot,hw. (902)

7. (Lemierre$ adj2 (disease$ or syndrome$)).ti,ab,ot,hw. (488)

8. (necrobacillosis or necrobacilloses or meningococc?emia or urosepsis).ti,ab,ot,hw. (1539)

9. (Neisseria adj2 meningitidis adj2 bacter?emia).ti,ab,ot,hw. (17)

10. tetanus.ti,ab,ot,hw. (24,411)

11. ((bacter?emic or bacterial or endotoxi$ or toxi$) adj3 shock$).ti,ab,ot,hw. (8538)

12. (toxic adj2 forward adj2 failure).ti,ab,ot,hw. (0)

13. (blood adj2 poison$).ti,ab,ot,hw. (140)

14. infect$.ti,ab,ot. (1,174,805)

15. (bacterial adj2 (meningitis or pneumonia or peritonitis or endocarditis or superinfection$ or disease$)).ti,ab,ot,hw. (47,254)

16. (bartonellosis or bordetellosis or Bordetella or pertussis or botryomycosis or brucellosis or campylobacter$ or legionnaire$ disease or listeriosis or mycoplasmosis or pyomyositis or pyonephrosis or Staphylococc$ or Streptococc$ or “e coli”).ti,ab,ot,hw. (371,949)

17. or/1-16 (1,922,388)

18. exp Protein Precursors/ and Calcitonin/ (2245)

19. PCT.ti,ab,ot. (4007)

20. (procalcitonin or pro-calcitonin or 56645-65-9 or (calcitonin adj2 precursor$)).ti,ab,ot,hw,rn. (2522)

21. brahms.af. (323)

22. KRYPTOR.af. (73)

23. b r a h m s.af. (18)

24. or/18-23 (5836)

25. Emergency Treatment/ (8299)

26. Evidence-Based Emergency Medicine/ (216)

27. Life Support Care/ (7323)

28. emergency medical services/ or emergency service, hospital/ (74,803)

29. Emergencies/ (34,784)

30. Emergency Medicine/ (9931)

31. intensive care units/ or intensive care units, pediatric/ or respiratory care units/ (41,628)

32. critical care/ or intensive care/ (40,217)

33. (intensive care or high dependency unit$ or intensive therapy unit$).ti,ab,ot,hw. (111,650)

34. (ICU or ICUs or PICU or PICUs or HDU or HDUs or CCU or CCUs or ITU or ITUs or ER or ERs or ED or EDs or AAU or AAUs).ti,ab,ot. (138,941)

35. ((accident adj2 emergency) or “A&E” or “A & E”).ti,ab,ot,hw. (18,920)

36. ((emergency or emergencies) adj3 (treat$ or admit$ or admission$ or episode$ or case$ or patient$ or department$ or room or rooms or ward$ or care or medic$ or interven$ or therap$ or hospital$ or service$ or patient$ or unit$ or centre$ or center$ or facility or facilities)).ti,ab,ot,hw. (148,389)

37. ((acute or critical) adj3 (admit$ or admission$ or care or medic$ or service$ or patient$)).ti,ab,ot,hw. (171,915)

38. acute assessment unit$.ti,ab,ot,hw. (14)

39. (casualty adj2 (department$ or admit$ or admission$ or patient$)).ti,ab,ot,hw. (688)

40. or/25-39 (534,145)

41. adolescent/ or exp child/ (2,449,325)

42. Minors/ (2323)

43. Puberty/ (11,355)

44. Pediatrics/ (40,916)

45. (paediatr$ or pediatr$).ti,ab,ot. (213,041)

46. (Child$ or preschool$ or pre-school$ or toddler$ or juvenile$ or kid or kids).ti,ab,ot. (1,007,516)

47. (teen or teens or teenage$ or teen-age$ or adolescen$ or postpubescen$ or pubescen$ or minors or youth$ or puberty).ti,ab,ot. (271,680)

48. or/41-47 (2,772,896)

49. 17 and 24 and 40 and 48 (204)

50. exp animals/ not (exp animals/ and humans/) (3,998,545)

51. 49 not 50 (204)

52. limit 51 to yr=“1995 -Current” (202)

MEDLINE In-Process & Other Non-Indexed Citations (OvidSP)

29 August 2014.

Searched: 2 September 2014.

Records found: 12.

1. exp Systemic Inflammatory Response Syndrome/ (60)

2. exp bacterial infections/ (328)

3. (systemic inflammatory response syndrome$ or SIRS).ti,ab,ot,hw. (399)

4. (sepsis$ or septic$ or sepses).ti,ab,ot,hw. (7204)

5. (bacill?emia$ or bacter?emia$ or endotox?emia$ or pyoh?emia$ or py?emia$).ti,ab,ot,hw. (1555)

6. (fusobacterium adj2 necrophorum).ti,ab,ot,hw. (53)

7. (Lemierre$ adj2 (disease$ or syndrome$)).ti,ab,ot,hw. (49)

8. (necrobacillosis or necrobacilloses or meningococc?emia or urosepsis).ti,ab,ot,hw. (123)

9. (Neisseria adj2 meningitidis adj2 bacter?emia).ti,ab,ot,hw. (2)

10. tetanus.ti,ab,ot,hw. (794)

11. ((bacter?emic or bacterial or endotoxi$ or toxi$) adj3 shock$).ti,ab,ot,hw. (251)

12. (toxic adj2 forward adj2 failure).ti,ab,ot,hw. (0)

13. (blood adj2 poison$).ti,ab,ot,hw. (17)

14. infect$.ti,ab,ot. (80,478)

15. (bacterial adj2 (meningitis or pneumonia or peritonitis or endocarditis or superinfection$ or disease$)).ti,ab,ot,hw. (1052)

16. (bartonellosis or bordetellosis or Bordetella or pertussis or botryomycosis or brucellosis or campylobacter$ or legionnaire$ disease or listeriosis or mycoplasmosis or pyomyositis or pyonephrosis or Staphylococc$ or Streptococc$ or “e coli”).ti,ab,ot,hw. (16,515)

17. or/1-16 (97,168)

18. exp Protein Precursors/ and Calcitonin/ (5)

19. PCT.ti,ab,ot. (412)

20. (procalcitonin or pro-calcitonin or 56645-65-9 or (calcitonin adj2 precursor$)).ti,ab,ot,hw,rn. (355)

21. brahms.af. (27)

22. KRYPTOR.af. (5)

23. b r a h m s.af. (0)

24. or/18-23 (602)

25. Emergency Treatment/ (7)

26. Evidence-Based Emergency Medicine/ (7)

27. Life Support Care/ (2)

28. emergency medical services/ or emergency service, hospital/ (84)

29. Emergencies/ (7)

30. Emergency Medicine/ (12)

31. intensive care units/ or intensive care units, pediatric/ or respiratory care units/ (34)

32. critical care/ or intensive care/ (28)

33. (intensive care or high dependency unit$ or intensive therapy unit$).ti,ab,ot,hw. (7281)

34. (ICU or ICUs or PICU or PICUs or HDU or HDUs or CCU or CCUs or ITU or ITUs or ER or ERs or ED or EDs or AAU or AAUs).ti,ab,ot. (14,791)

35. ((accident adj2 emergency) or “A&E” or “A & E”).ti,ab,ot,hw. (1794)

36. ((emergency or emergencies) adj3 (treat$ or admit$ or admission$ or episode$ or case$ or patient$ or department$ or room or rooms or ward$ or care or medic$ or interven$ or therap$ or hospital$ or service$ or patient$ or unit$ or centre$ or center$ or facility or facilities)).ti,ab,ot,hw. (10,372)

37. ((acute or critical) adj3 (admit$ or admission$ or care or medic$ or service$ or patient$)).ti,ab,ot,hw. (11,272)

38. acute assessment unit$.ti,ab,ot,hw. (3)

39. (casualty adj2 (department$ or admit$ or admission$ or patient$)).ti,ab,ot,hw. (34)

40. or/25-39 (38,271)

41. adolescent/ or exp child/ (1534)

42. Minors/ (1)

43. Puberty/ (3)

44. Pediatrics/ (38)

45. (paediatr$ or pediatr$).ti,ab,ot. (18,203)

46. (Child$ or preschool$ or pre-school$ or toddler$ or juvenile$ or kid or kids).ti,ab,ot. (65,057)

47. (teen or teens or teenage$ or teen-age$ or adolescen$ or postpubescen$ or pubescen$ or minors or youth$ or puberty).ti,ab,ot. (23,132)

48. or/41-47 (84,088)

49. 17 and 24 and 40 and 48 (12)

50. exp animals/ not (exp animals/ and humans/) (2110)

51. 49 not 50 (12)

52. limit 51 to yr=“1995 -Current” (12)

PubMed

URL: www.ncbi.nlm.nih.gov/pubmed/.

1995 to 2 September 2014.

Searched: 2 September 2014.

Records found: 26.

• This strategy aims to identify records that are on PubMed, but not included in MEDLINE or MEDLINE In-Process (OvidSP). Line #9 limits the search results in this way.

#10 Search ((((((((((protein precursors[MeSH Terms]) AND calcitonin[MeSH Terms])) OR PCT[Title/Abstract]) OR (procalcitonin[Title/Abstract] OR “pro-calcitonin”[Title/Abstract] OR “calcitonin precursor*”[Title/Abstract])) OR (brahms OR kryptor OR “b r a h m s”))) AND (emergency OR emergencies OR intensive OR acute OR critical OR casualty)) AND (child OR children OR adolescence OR adolescents OR paediatric OR pediatric))) AND (pubstatusaheadofprint OR publisher[sb] OR pubmednotmedline[sb]) (26)

#9 Search pubstatusaheadofprint OR publisher[sb] OR pubmednotmedline[sb] (1,816,157)

#8 Search ((((((((protein precursors[MeSH Terms]) AND calcitonin[MeSH Terms])) OR PCT[Title/Abstract]) OR (procalcitonin[Title/Abstract] OR “pro-calcitonin”[Title/Abstract] OR “calcitonin precursor*”[Title/Abstract])) OR (brahms OR kryptor OR “b r a h m s”))) AND (emergency OR emergencies OR intensive OR acute OR critical OR casualty)) AND (child OR children OR adolescence OR adolescents OR paediatric OR pediatric) (480)

#7 Search child OR children OR adolescence OR adolescents OR paediatric OR pediatric (2,952,782)

#6 Search emergency OR emergencies OR intensive OR acute OR critical OR casualty (1,788,387)

#5 Search (((((protein precursors[MeSH Terms]) AND calcitonin[MeSH Terms])) OR PCT[Title/Abstract]) OR (procalcitonin[Title/Abstract] OR “pro-calcitonin”[Title/Abstract] OR “calcitonin precursor*”[Title/Abstract])) OR (brahms OR kryptor OR “b r a h m s”) (6404)

#4 Search brahms OR kryptor OR “b r a h m s” (398)

#3 Search procalcitonin[Title/Abstract] OR “pro-calcitonin”[Title/Abstract] OR “calcitonin precursor*”[Title/Abstract] (2673)

#2 Search PCT[Title/Abstract] (4389)

#1 Search (protein precursors[MeSH Terms]) AND calcitonin[MeSH Terms] (2205)

Cumulative Index to Nursing and Allied Health Literature (CINAHL) (EBSCOhost)

1995 to 27 August 2014.

Searched: 2 September 2014.

Records found: 54.

S1 (MH “Systemic Inflammatory Response Syndrome+”) ((6506)

S2 (MH “Bacterial Infections+”) (50,875)

S3 “systemic inflammatory response syndrome” or SIRS (986)

S4 sepsis* or septic* or sepses (11,903)