Optimal strategies for monitoring lipid levels in patients at risk or with cardiovascular disease: best marker for monitoring and cost-effectiveness of different monitoring frequencies

Primary prevention

The seminal statin trials in the 1990s, such as 4S (Scandinavian simvastatin survival study),18 LIPID (The long-term intervention with pravastatin in ischaemic disease study),33 CARE (Cholesterol and current events trial)34 and WOSCOPS (West of Scotland coronary prevention study),19 focused on TC as the main lipid measurement for screening and monitoring. As a result, initial guidelines also focused on TC for screening, targets, and monitoring. 35,36 Although an important CVD risk factor, serum TC on its own is actually a relatively poor predictor of who will go on to have an event. 22,36 In practice, CVD can rarely be attributed to a single underlying risk factor; more commonly it is the additive and synergistic effect of several risk factors that lead to the atherosclerotic progression underlying most CVD. Thus, over time, there has been a shift towards assessment of absolute CVD risk, using CVD risk equations that include a combination of important risk factors. 37 For initial risk measurement, there is evidence from cohort studies38–40 and a meta-analysis41 to suggest that lipid ratios (TC/HDL cholesterol and LDL/HDL cholesterol) have greater independent predictive values for CHD than individual lipid levels. The better predictive ability, and ease of measuring TC compared with LDL, has led to the inclusion of TC/HDL cholesterol in many CVD risk equations,2,42 which, in turn, has meant a move to measuring and using a combination of TC and HDL for screening in primary prevention patients. Although TC and HDL, based upon cardiovascular risk, are the most commonly recommended lipids for screening, it is often suggested that LDL be either calculated using the Friedewald equation43 (requiring a fasting sample) or measured directly, even when guidelines do not specify what to do with the information. Additionally, most guidelines also recommend measuring TGs, although TGs play only a minor role in treatment choices.

Secondary prevention

For people with established CVD, statins reduce total mortality, cardiovascular mortality and morbidity, and are cost-effective, particularly for those with CHD. 16,44 However, substantial variation in the use of statins for secondary prevention combined with poor adherence to treatment, even in those who have experienced a CVD event,45–47 mean that in many instances serum cholesterol often remains at unacceptably high levels,48 and can be further improved with advice, support and treatment. Management of statins for the secondary prevention of CVD is usually based on controlling cholesterol levels to a target dose,2,49–51 but there is a lack of published evidence on the cost-effectiveness of treating to target compared with fixed doses of statins,2 and recommendations are based upon either economic simulation models or expert opinion.

Primary and secondary prevention

Information provided on the frequency of lipid monitoring in current CVD prevention guidelines differs greatly: some guidelines give vague references to testing lipids or assessing CVD risk at ‘regular intervals’, whereas others give specific intervals for specific groups of people, often with more frequent screening and measurement for those at higher risk or in secondary prevention groups. Most guidelines do not reference the evidence or level of evidence on which the recommendation was based, and, of those that do, most use only weak levels of evidence, consensus or expert opinion. 12

Search methods for identification of studies

We reviewed all published systematic reviews since 2007 using the Montori systematic review filter74 for MEDLINE and the Wilczynki filter75 for EMBASE (both 75% sensitivity). Based on this overview, we opted to stratify results by statin use: populations taking statins and populations not taking statins. Consequently, two separate searches were carried out and two sets of results are presented, although the general methods remained the same for both groups. Dates for the searches were staggered, as the review was broken down into manageable tasks based on statin usage and existing reviews.

Searches were conducted in the Cochrane Central Register of Controlled Trials, MEDLINE, EMBASE, the Clinical Trials Register, the Current Controlled Trials register, and the Cumulative Index to Nursing and Allied Health Literature (CINAHL) to identify relevant studies.

All trials included in identified reviews were considered for inclusion, along with any trials identified by the work of the Cholesterol Treatment Trialists’ Collaboration. 66

Populations not taking statins A search was conducted (unrestricted for study design) from 1 September 2007 to 18 June 2013. This updated/extended the search carried out by the Emerging Risk Factors Collaboration (ERFC). 76 The search was restricted to English language only and did not include conference abstracts (see Appendix 3).

Populations taking statins Two strategies were used, based on study design type. For non-randomised controlled trials (non-RCTs) a search was completed for all publications up to 4 December 2012, with no restriction on language or publication type. For RCTs, all studies were included from previous reviews,72,73 plus a search was conducted to extend these review’s searches up to 9 April 2013. In line with these reviews, the strategy was for English language-only publications (both strategies are presented in Appendix 4). An additional search was conducted within the international Clinical Trials Registry and ClinicalTrials.gov for any studies in progress from 2010 to April 2013.

The title and abstract of each paper identified in the searches was reviewed by two reviewers to identify potentially relevant references. The full text of potentially relevant studies was then obtained, and two reviewers independently selected studies to be included in the review using predetermined inclusion criteria. In all cases, disagreements about study inclusion were resolved by consensus and a third reviewer was consulted if disagreements persisted. Flow charts were constructed to show the selection process.

Study selection criteria

We included all prognostic cohort studies of any design (univariable or multivariable) that measured lipids in humans. Studies from any setting regardless of statin treatment were included. RCTs with adequate follow-up were treated as cohorts providing evidence to a specific study group [e.g. a statin vs. placebo RCT would provide evidence for both populations taking statins (statin arm) and not taking statins (placebo arm) separately].

Consistent with previous reviews in this area, we included studies where at least 1000 relevant participants were recruited per cohort/intervention arm and a minimum 2-year average follow-up duration. 66,72 There was no restriction on gender. We included studies with only two or more lipid measures assessed at baseline/relevant time point, as the focus of the review was to compare predictive performance across different lipid measures.

Target group in PECO (Population, Exposure, Comparator, Outcome) format:

• P Population with or without existing CVD.

• E Measurement of lipid measures at baseline/relevant time point. Ten lipids measures were studied including TC, HDL cholesterol, LDL cholesterol, non-HDL cholesterol, TGs, Apo B, Apo A-I, plus combinations of these measures as ratios TC/HDL cholesterol, LDL/HDL cholesterol and Apo B/Apo A-I.

• C Control or comparator may or may not be present.

• O Outcomes included cardiovascular events, cardiovascular mortality and all-cause mortality. Strokes were excluded if they were the sole outcome (included if treated as part of composite outcome with other CVD events).

In addition, we considered all studies from previous reviews for inclusion irrespective of whether or not they met the 1000-participant, minimum two lipid measures at baseline or 2-year follow-up thresholds to maximise comparability of our results with those published in previous reviews.

Included studies provided quantitative results for one or more of the following outcomes:

• cardiovascular events [CHD, non-fatal myocardial infarction (MI), stroke, angina attack, other CVD, coronary bypass]

• cardiovascular mortality (fatal MI, sudden cardiac death, stroke, congestive heart failure, other fatal CVD)

• all-cause mortality.

Outcome definition was based on that reported in the included studies.

Data extraction and management

For all included studies, two reviewers independently carried out data extraction. Details extracted included study name and year, cohort numbers, baseline age, gender, prevention group, statin usage and dose if applicable, follow-up period, blood sample details and lipid data. When several follow-up periods were reported then data for the longest follow-up were extracted. Disagreements were resolved by consensus with a third reviewer being consulted when required.

The methodological quality of included studies was assessed using the Quality in Prognostic Studies (QUIPS) assessment tool. 77 Quality of studies with populations taking statins was independently assessed by two reviewers and the level of agreement tested by the kappa statistic (κ). For studies of populations not taking statins, quality was assessed by one reviewer. Quality was assessed in six bias domains, with emphasis placed on prognostic factors and attrition:

• Study participants No quality bias was anticipated in this area, as all populations were included in the review. Studies were categorised by prevention group, which provided inception cohorts.

• Study attrition There is no clear guidance for the assessment of attrition rates in prognostic studies. Previous prognostic reviews have considered attrition rates of 10–20% as low risk of quality bias, but these have been based on small populations with relatively rare conditions and short follow-up periods. 78,79 As the new cohorts in this review have > 1000 participants with long follow-ups, we anticipated that dropout could be higher. Risk of study attrition bias was considered to be low – if it was documented – and < 25%, moderate if 25–40% or not reported, and high risk if > 40%.

• Prognostic factor Lipid measures were considered to be at low risk of quality bias if all tests were processed by a central accredited laboratory and procedures for different measures were described. Risk was assessed as moderate if a central laboratory was used but no further information given, and high risk of bias when no information was provided.

• Outcome measure This dimension was not considered to be significant, as it was a necessary requirement in the study entry criteria. However, outcome bias could have been introduced owing to clarity of outcome definitions; this was explicitly addressed in sensitivity analyses.

• Study confounding This too was not considered to be a significant concern, as study confounders were specially accounted for in the data analysis of each study: studies were divided into unadjusted and adjusted data.

• Statistical analysis As most of the included studies were not designed to report the outcome data required for this review, we did not assess the quality of the study in relation to how the study data were analysed; instead we addressed this issue separately by carrying out a sensitivity analysis considering the type of analysis used in the original study.

Data analysis

Data were sought from the included studies for predictive outcomes as follows:

1. Direct estimates for one standard deviation (SD) incremental change either as HRs, odds ratios (OR) or risk ratios (RRs) and their variances. These ratios were assumed to approximate the same measure of risk. This assumption was tested in a sample of studies with the highest event rates and the ratios showed little variation.

2. Data in an alternative format to incremental change by one SD were converted where possible.

3. Univariable and multivariable models (unadjusted and adjusted). Multivariable analyses included any studies with adjustment for one or more of the following covariates: age, gender, body mass index (BMI), smoking, etc. Where a study produced more than one multivariable model then the most advanced model was selected for data inclusion. However, we specifically excluded any model that adjusted for any other lipid measure.

4. IPD were not actively sought, but used when available.

5. When no direct estimates were reported, alternative indirect methods for estimation were used. 80,81 Additionally, two further alternative indirect methods for estimation were used based on (1) generalised least squares for trend82,83 and (2) simulation methods. A brief description of these can be found in Appendix 5.

The primary analysis estimated the predictive strength (HR) for each lipid measure by each of the three outcomes. Outcomes are reported separately and stratified by unadjusted/adjusted estimates. Analyses were subgrouped by prevention group (primary or secondary prevention). Studies were considered to be only primary or secondary prevention if > 95% of the participants fell in that category; otherwise studies were considered as mixed. Numbers of studies, participants and study characteristics are presented in tables.

For each lipid measure, random effect meta-analyses were carried out to obtain summary HRs (by one SD) using Review Manager (RevMan) 5.2 (The Cochrane Collaboration, The Nordic Cochrane Centre, Copenhagen, Denmark) and results were displayed as forest plots. A random-effects model was selected because high heterogeneity was anticipated in both populations. Any risk of small study bias should be reduced by the majority of included studies having > 1000 participants. Overall summary plots presenting pooled estimates for all lipid measures (HDL and Apo A-I as inverse associations to allow clear comparisons with other measures) by outcome and by unadjusted/adjusted models were produced. Although pooling of data for lipid measures with only three or fewer studies could be potentially misleading, the figures were included in the summary plots as an indication of potential trends had there been more study data.

Heterogeneity was summarised using the I² and chi-squared statistics. Potential sources of any heterogeneity (clinical or statistical) were explored using subgroup and sensitivity analyses.

Subgroup analyses, where possible, were conducted by:

• pre-existing condition (e.g. diabetes)

• study type [prospective (including RCTs) or retrospective cohorts]

• age (threshold of median 40 years at inception).

Sensitivity analyses were conducted to determine the impact of study quality and data extraction on outcome. Focus was placed on consistency of overall results with those obtained in better-quality studies. When sufficient studies were available, sensitivity analyses were carried out by omitting:

• studies in which the predictive outcomes were defined by CHD, a surrogate measure of CVD

• studies assessed to have a higher/moderate risk of quality bias (based on prognostic factors and attrition quality domains)

• studies in which the majority of the lipid tests were not completed in a central laboratory (yes/no)

• studies in which summary data were extracted indirectly

• within the populations not taking statins, studies that confirmed the use of lipid modification therapy.

The search aimed to be as broad as possible to include as many studies as possible. However, it is acknowledged that there is considerable publication bias in prognostic studies that is difficult to characterise;84 therefore we did not carry an analysis for potential publication bias.

A post hoc analysis was conducted as recommended by the external peer reviewer with the aim of exploring sources of potential heterogeneity. A meta-regression was completed to examine the associations between variables considered in the subgroup and sensitivity analyses. Meta-regression was completed, where possible, for those outcomes and variables with sufficient study data.

Studies based on populations not taking statins

Figure 3 summarises the selection process; 3776 publications were screened from the search, with a further 165 potential studies identified from existing reviews and hand searching. Of these, 90 studies met the inclusion criteria and provided results (see Appendix 8), with 16 studies providing two or more sets of data; this resulted in a total of 110 sets of data. Twelve studies divided the data by gender [TLGS,88 Yao City,89 AMORIS,90 CB project,91 Copenhagen City,92 DUBBO,93 Framingham Offspring,40 IKNS (Yao),94 MONICA,95 Northwick Park I,96 Reykjavik,97 Rotterdam98]. Two studies divided the data by gender and then further by ethnicity (Charleston99) or presence of metabolic syndrome [European Prospective Investigation of Cancer (EPIC) – Norfolk study (EPIC-Norfolk)100] so providing four data sets each. Puerto Rico101 divided the data by rural and urban populations, and MRFIT102 by black and white men.

FIGURE 3.

Flow chart of study inclusion for populations not taking statins.

Full details of the characteristics of the 90 included studies are shown in Table 8. Of the included studies 73% were prospective cohorts and 17% were placebo arms of RCTs. The remaining nine studies were two retrospective cohorts (Swedish NDR;164 Juntendo, Japan134), three RCTs (of non-lipid modifying interventions) for which both arms were included in this review (SHEP,159 TREAT,167 Women’s Health172) and four nested case–control studies (BUPA,113 GLOSTRUP Population,124 Nurses’ Health,147 Physicians’ Health150). Studies provided data for between 1 and 10 lipid measures. Two studies provided data for all of the 10 lipid measures (FIELD,121 Women’s Health172). Data details for all studies are shown in Appendix 8. Table 9 shows the number of studies and participants by the 10 lipid measures. Numbers are also shown for three additional measures (Apo B/HDL cholesterol, TG/HDL cholesterol/HDL cholesterol/TC) that were revealed by the review; however, data were available from only one study for one outcome for each measure and therefore no pooled estimates were obtained.

Outcome data were available for CVD events, CVD mortality and all-cause mortality. Data were further divided into unadjusted (CVD events, CVD mortality, all-cause mortality) and adjusted data [CVD events (adjusted), CVD mortality (adjusted), all-cause mortality (adjusted)] and then by lipid measure. Meta-analyses were not possible for all combinations of outcomes and lipid measures. Outcome data for all lipid measures were pooled for CVD events (except LDL/HDL cholesterol) and CVD events (adjusted). For CVD mortality, outcome data were available only for nine lipid measures (no studies examined the ratio Apo B/Apo A-I) and only HDL cholesterol, TGs and TC had sufficient (more than three studies) to pool data. Outcome data were available for all 10 lipid measures for CVD mortality (adjusted), but Apo A-I, LDL/HDL cholesterol, Apo B and Apo B/Apo A-I data could not be pooled due to an insufficient number of studies. Data were available for four lipid measures for all-cause mortality (LDL, HDL, TC, TGs), all of which had sufficient studies to pool data. Finally, for all lipid measures bar non-HDL cholesterol, outcome data for all-cause mortality (adjusted) were provided, although only HDL cholesterol, LDL cholesterol, TC and TGs could be pooled as a result of insufficient studies.

Heterogeneity

Heterogeneity was generally high across all prevention groups, outcome data and lipid measures with few exceptions. For CVD events, both adjusted and unadjusted, the overall heterogeneity was considerable (I² > 73%) for all lipid measures except Apo A-I in the unadjusted data (56%) and LDL/HDL cholesterol (55%) in the adjusted data where it was moderate. When further examined, the unadjusted CVD event data for primary prevention groups is considerably heterogeneous (77–97%) for all lipid measures except Apo A-I (5% based on five studies) and Apo B/Apo A-I (59% based on three studies). For adjusted CVD events outcome data in the primary prevention populations the heterogeneity is large (92–98%) for all lipid measures except LDL/HDL cholesterol were it is low (27%); though this is based on eight data sets, these were obtained from only three studies. There were insufficient data to examine heterogeneity in any of the lipid measures in the secondary prevention populations for CVD events adjusted outcome data. However, for these populations, in the unadjusted CVD events outcome data the heterogeneity was measurable in five out of the nine lipid measures and varied: LDL cholesterol was considerable (76%), TC, HDL cholesterol and TGs were moderate (34%, 52% and 63%) and for Apo B and Apo A-I there was no heterogeneity (0%). This was for the same two studies each (LIPID,138 Thrombo165). Heterogeneity for CVD mortality outcome data (unadjusted and adjusted) was substantial for all pooled lipid measures (67–91%). There was no heterogeneity statistic for secondary prevention populations owing to insufficient data. For the primary prevention groups in these data the heterogeneity ranged from 40% to 89%. Few lipid measures provided sufficient data for all-cause mortality and therefore assessment of heterogeneity was limited. For those that did, LDL cholesterol, TC, HDL cholesterol, TGs, heterogeneity for the summary estimate was moderate to considerable (52–95%), there were insufficient data to assess secondary prevention groups. All-cause mortality unadjusted data for primary prevention groups varied between two measures (LDL cholesterol, TC) showing considerable heterogeneity (> 95%) and two measures (HDL cholesterol and TGs) showing no heterogeneity (0%). Data from only two studies were available for each of these last two measures: of the two studies reporting HDL data, one study had 44,457 participants (AMORIS90) and the other 1013 (InCHIANTI131); for TGs, the participant numbers were 1013 (InCHIANTI131) and 2086 (Lan 2007136). Forest plots of all of these results appear in Appendix 9.

Figures 4–9 show the summaries of the overall results for all lipid measures for CVD events, CVD events (adjusted), CVD mortality, CVD mortality (adjusted), all-cause mortality and all-cause mortality (adjusted), respectively.

FIGURE 4.

Summary results (pooled estimates) by all lipid measures as prognostic markers for cardiovascular events in populations not taking statins. All data sets include primary prevention data sets, secondary prevention data sets, and data sets with mixed or unreported prevention groups. Heterogeneity: For all measures, for all data sets, I² > 87% except for Apo A-I (I² = 56%). For all measures for primary prevention groups, I² = 77–97% except Apo A-I (5%) and Apo B/Apo A-I (59%). For secondary prevention groups, I² for limited measures as follows: LDL (76%), TC (34%), TGs (63%), HDL (52%), Apo B (0%), Apo A-I (0%). Full details are available in Appendix 9. IV, inverse variance.

FIGURE 5.

Summary results (pooled estimates) by all lipid measures as prognostic markers for cardiovascular events (after adjustment) in populations not taking statins. All data sets include primary prevention data sets, secondary prevention data sets, and data sets with mixed or unreported prevention groups. Heterogeneity: For all measures, for all data sets, I² = 73–98% except for LDL/HDL cholesterol (55%). For all measures for primary prevention groups, I² = 92–98% except LDL/HDL cholesterol (27%). Unable to calculate heterogeneity for secondary prevention groups owing to insufficient data. Full details are available in Appendix 9. IV, inverse variance.

FIGURE 6.

Summary results (pooled estimates) by all lipid measures as prognostic markers for cardiovascular mortality in populations not taking statins. All data sets include primary prevention data sets, secondary prevention data sets, and data sets with mixed or unreported prevention groups. Heterogeneity: For limited measures, for all data sets, I² as follows: TC (86%), HDL (67%), TGs (68%). For limited measures for primary prevention groups, I² as follows: TC (85%), HDL (54%), TGs (40%). Unable to calculate heterogeneity for secondary prevention groups owing to lack of data. Full details are available in Appendix 9. IV, inverse variance.

FIGURE 7.

Summary results (pooled estimates) by all lipid measures, as prognostic markers for cardiovascular mortality (adjusted estimates) in populations not taking statins. All data sets include primary prevention data sets, secondary prevention data sets, and data sets with mixed or unreported prevention groups. Heterogeneity: For limited measures, for all data sets, I² as follows: LDL (91%), TC (84%), HDL (71%), TGs (79%), non-HDL cholesterol (84%), TC/HDL (88%). For limited measures for primary prevention groups I² as follows: LDL (84%), TC (70%), HDL (65%), TGs (84%), non-HDL cholesterol (89%), TC/HDL (52%). Unable to calculate heterogeneity for secondary prevention groups owing to insufficient data. Full details are available in Appendix 9. IV, inverse variance.

FIGURE 8.

Summary results (pooled estimates) by all lipid measures as prognostic markers for all-cause mortality in populations not taking statins. All data sets include primary prevention data sets, secondary prevention data sets, and data sets with mixed or unreported prevention groups. Heterogeneity: For limited measures, for all data sets, I² as follows: LDL (92%), TC (95%), HDL (94%), TGs (52%). For limited measures for primary prevention groups I² as follows: LDL (96%), TC (95%), HDL (0%), TGs (0%). Unable to calculate heterogeneity for secondary prevention groups owing to lack of data. Full details are available in Appendix 9. IV, inverse variance.

FIGURE 9.

Summary results (pooled estimates) by all lipid measures as prognostic markers for all-cause mortality (adjusted estimates) in populations not taking statins. All data sets include primary prevention data sets, secondary prevention data sets, and data sets with mixed or unreported prevention groups. Heterogeneity: For limited measures, for all data sets, I² as follows: LDL (56%), TC (85%), HDL (53%), TGs (52%). For limited measures for primary prevention groups I² as follows: TC (68%), TGs (56%). Unable to calculate heterogeneity for secondary prevention groups owing to lack of data. Full details are available in Appendix 9. IV, inverse variance.

For unadjusted estimates – CVD events:

• When all of the data were combined for all of the lipid measures with more than three data sets, Apo B/Apo A-I (four studies: HR 1.62, 95% CI 1.07 to 2.45), followed by Apo B (10 studies: HR 1.54, 95% CI 1.31 to 1.81), non-HDL cholesterol (six studies: HR 1.49, 95% CI 1.25 to 1.77) and TC/HDL cholesterol (seven studies: HR 1.48, 95% CI 1.18 to 1.85) offer the highest predictive value.

• For primary prevention populations, of the single measures Apo B (five studies: HR 1.70, 95% CI 1.48 to 1.96) and non-HDL cholesterol (four studies: HR 1.57, 95% CI 1.33 to 1.85), offer the strongest predictive value, whereas for the ratios, TC/HDL cholesterol (five studies: HR 1.57, 95% CI 1.18 to 2.08) offered the strongest value.

• In the secondary prevention populations, only TC (five studies: HR 1.08, 95% CI 1.03 to 1.13) offers a significant association with CVD events.

For adjusted estimates – CVD events:

• All lipid measures have data for this outcome. When all data are combined, Apo B/Apo A-I offers the strongest predictive value based on 16 sets of data (HR 1.35, 95% CI 1.22 to 1.50). The other two lipid ratios (TC/HDL cholesterol and LDL/HDL cholesterol), TC, Apo B and non-HDL cholesterol all have similar predictive values based on similar or more studies (HR range 1.22–1.28).

• For primary prevention groups, the data follows the same pattern with Apo B/Apo A-I as the strongest predictor (HR 1.36, 95% CI 1.21 to 1.53). Of note in comparison with the unadjusted data, now Apo B/Apo A-I (14 studies) is the strongest predictor, followed by LDL/HDL cholesterol (eight studies: HR 1.29, 95% CI 1.27 to 1.32).

• For secondary prevention there were insufficient study data to provide results.

For unadjusted estimates – CVD mortality:

• The amount of evidence for CVD mortality is considerably smaller than for CVD events particularly in relation to lipid ratios.

• Only three lipid measures had sufficient data to meta-analyse (HDL, TGs, TC).

• HDL cholesterol offered the strongest predictive value for combined data (six studies: HR 1.28, 95% CI 1.11 to 1.48) with TC and TGs providing estimates of similar magnitude (combined: TC HR 1.14, 95% CI 1.14 to 1.42; five studies: TGs HR 1.24, 95% CI 1.08 to 1.42).

• In the primary prevention group, TGs offered the strongest predictive value (four studies: HR 1.30, 95% CI 1.14 to 1.49), but, as with the combined data, TC and HDL cholesterol have estimates of a similar magnitude (six studies: TC HR 1.27, 95% CI 1.01 to 1.60; four studies: HDL cholesterol HR 1.27, 95% CI 1.06 to 1.51).

• There were no data for secondary prevention populations.

For adjusted estimates – CVD mortality:

• Similar to the unadjusted data, the amount of evidence for CVD mortality (adjusted) is considerably smaller than for CVD events particularly in relation to lipid ratios.

• For all studies combined HDL (10 studies: HR 1.18, 95% CI 1.08 to 1.28), TC (19 studies: HR 1.17, 95% CI 1.10 to 1.24) and TGs (five studies: HR 1.17, 95% CI 1.04 to 1.31) offered the strongest predictive values.

• For the primary prevention group only HDL (five studies: HR 1.16, 95% CI 1.01 to 1.34) and TGs (four studies: HR 1.16, 95% CI 1.01 to 1.34) showed an association with adjusted CVD mortality data.

• Data were available for secondary prevention groups but with insufficient study numbers to obtain pooled results.

For unadjusted estimates – all-cause mortality:

• There were data for only four measures (HDL cholesterol, TC, LDL cholesterol, TGs).

• The associations followed a different pattern to that observed for CVD events and mortality (except for HDL cholesterol and TG).

• For all studies combined, only HDL cholesterol (nine studies: HR 1.25, 95% CI 1.10 to 1.43) and TGs (eight studies: HR 1.09, 95% CI 1.04 to 1.15) showed an association with all-cause mortality.

• For primary prevention populations there were insufficient data to provide results. There were no data for secondary prevention populations.

For adjusted estimates – all-cause mortality:

• Lipid measure data were available for all measures, except for non-HDL cholesterol, but in a small number of studies.

• TC (19 studies) was the only lipid measure reported in more than six studies.

• For all studies combined, only TGs (five studies: HR 1.09, 95% CI 1.06 to 1.13) and TC (19 studies, HR 1.05, 95% CI 1.02 to 1.09) showed an association with all-cause mortality (adjusted).

• For primary prevention groups there was no evidence of association or insufficient data.

• There were no data for secondary prevention populations.

Studies based on populations taking statins

The study selection process is summarised in Figure 10. In total, 2260 publications from the non-RCT search and 667 publications from the RCT search were screened, reducing to 25 included studies, with 28 sets of data meeting the inclusion criteria and providing results. Three studies (J-LIT,174,175 Phase Z176 and SEARCH58) offered two sets of data; two studies had two intervention arms with different statin doses (Phase Z176 and SEARCH58). In addition, one study stratified results into separate primary and secondary prevention groups (J-LIT174,175).

FIGURE 10.

Flow chart of study inclusion for populations taking statins. CTT, Cholesterol Treatment Trialists’ Collaboration.

The characteristics of the included studies are shown in Table 10. Of the 25 studies, only three were non-RCTs (CLIP,177 LIVES Ex,182 J-LIT174,175), one was a nested case control from a RCT (TNT185) and the rest were RCTs. Studies provided data for between one and seven lipid measures, and no single study examined all markers. No data were available for LDL/HDL cholesterol. No results were available for CVD mortality or all-cause mortality. Full details of the data can be seen in Appendix 11. Table 11 shows the number of studies and participants by lipid measure. Outcome data per lipid measure were separated into unadjusted CVD events (CVD events) and adjusted CVD events [CVD events (adjusted)]. Only half of the reported combinations of lipid measures and outcomes could be pooled using meta-analysis. Individual forest plots for these measures are presented in Appendix 12. The remaining data had fewer studies than our threshold for meta-analysis; however, a pooled estimate was obtained for all lipid measure summaries, but meta-analyses have not been presented.

Heterogeneity

Across both prevention groups, heterogeneity was substantial for all but one lipid measure (non-HDL cholesterol). Only LDL cholesterol (CVD events) showed moderate heterogeneity for primary prevention populations and non-HDL cholesterol (CVD events) for secondary prevention populations.

Figure 11 shows a summary of the overall results for all lipid measures for CVD events, whereas Figure 12 shows the same, for adjusted estimates.

FIGURE 11.

Summary results (pooled estimates) by all lipid measures as prognostic markers for cardiovascular events in populations taking statins. All data sets include primary prevention data sets, secondary prevention data sets, and data sets with mixed or unreported prevention groups. Heterogeneity: For limited measures for all data sets I² as follows: TC (74%), LDL (84%), HDL (88%), TGs (82%), non-HDL cholesterol (34%), Apo B (83%). For limited measures for primary prevention groups I² as follows: TC (64%), LDL (33%), HDL (88%), TGs (60%). For limited measures for secondary prevention groups I² as follows: TC (81%), LDL (84%), HDL (80%), TGs (85%), non-HDL cholesterol (49%), Apo B (86%). Full details are available in Appendix 12. IV, inverse variance.

FIGURE 12.

Summary results (pooled estimates) by all lipid measures as prognostic markers for cardiovascular events (adjusted estimates) in populations taking statins. All data sets include primary prevention data sets, secondary prevention data sets, and data sets with mixed or unreported prevention groups. Heterogeneity: For limited measures for all data sets I² as follows: TC (78%), LDL (94%), HDL (89%). For limited measures for primary prevention groups I² as follows: TC (92%), LDL (95%), HDL (85%). For limited measures for secondary prevention groups I² as follows: TC (0%), LDL (81%), HDL (84%). Full details are available in Appendix 12. IV, inverse variance.

For unadjusted estimates – CVD events:

• Overall HDL (19 studies: HR 1.12, CI 1.06 to 1.19) and TC (11 studies: HR 1.12, CI 1.05 to 1.20) offered the strongest association with CVD events.

• For primary prevention populations, LDL cholesterol (seven studies: HR 1.14, CI 1.06 to 1.23) offered the strongest predictive value. All other measures have a similar point estimate but are not significant.

• For secondary prevention groups LDL, Apo B, non-HDL cholesterol and HDL offered a similar predictive value (HR range 1.09–1.10).

For adjusted estimates – CVD events:

• Overall HDL (five studies: HR 1.61, CI 1.19 to 2.18) offered the strongest association with CVD events.

• For both primary and secondary prevention groups there were insufficient data to form conclusions.

Key finding for populations not taking statins

Results from the included studies in this population support Apo B/Apo A-I as the strongest predictor of CVD events (adjusted and unadjusted). Apo B, non-HDL cholesterol, LDL/HDL cholesterol and TC/HDL cholesterol offer similar predictive values and only slightly lower point estimates than Apo B/Apo A-I. However, all of the lipid measures offer positive associations with CVD events and there is considerable overlap in the CIs. This pattern is repeated when considering only primary prevention populations. There were insufficient data sets to pool data for Apo B/Apo A-I and LDL/HDL cholesterol for unadjusted data, but the limited data available do suggest these to be the strongest predictors, and this is reflected in the adjusted data. Although the number of studies were insufficient to pool data for Apo B/Apo A-I, the numbers of participants were comparable to those in the data for TC/HDL cholesterol and non-HDL cholesterol. Data for the secondary prevention group were very limited, but did appear to follow a similar trend, except for TGs, which appeared to have no association with CVD events.

The results from this review are in line with the latest research by the ERFC70 and the 2011 review by Sniderman et al. 71 The ERFC70 examined the association among non-HDL cholesterol, Apo B, Apo A-I, TC/HDL cholesterol and Apo B/Apo A-I and CVD (adjusted outcome data) in 26 studies (primary and secondary prevention groups). The results in this review are almost identical for non-HDL cholesterol, Apo B, and Apo A-I, but the CIs are slightly narrower. Both reviews have found that the ratios TC/HDL cholesterol and Apo B/Apo A-I were more strongly associated with CVD events than other lipid measures. The ERFC’s final conclusion was that TC/HDL cholesterol offers a stronger association with CVD events than the individual measures Apo B and Apo A-I; our review agrees with these findings. However, the ERFC70 did not include LDL/HDL cholesterol in their assessment and our review suggests that this is almost as predictive as Apo B/Apo A-I. Sniderman et al. 71 only considered three lipid measures and their association with CHD. Although our outcome definition differs, results were similar, suggesting that Apo B is superior to non-HDL cholesterol and non-HDL cholesterol is superior to LDL in predicting events. Examination of diabetic populations not taking statins was limited by the lack of data. However, there is some indication that for CVD events (unadjusted) the lipid measures examined provide lower predictive values or no association for this population. Within the adjusted data, more lipid measures were represented, but trends were not clear: most, but not all, lipids were less predictive of CVD events in the diabetic populations than non-diabetic populations.

For CVD mortality (unadjusted and adjusted), HDL offered the strongest association with CVD mortality, but the CIs overlapped with those for TC and TGs. In the unadjusted primary prevention group the same three lipid measures showed an association, with overlapping CIs, but here TGs was the strongest, whereas in the adjusted primary prevention group TC no longer had an association, and HDL and TGs had exactly the same results. However, in the adjusted data, five other lipid measures (TC/HDL cholesterol, LDL/HDL cholesterol, LDL cholesterol, Apo A-I, Apo B) offered an indication that they could provide a similar, or stronger, predictive value in primary prevention groups and for all participants, if more study data were available.

The Asia Pacific Studies Collaboration (APSC)67 assessed 17 studies, and examined the association between seven lipid measures (TC, TGs, HDL cholesterol, non-HDL cholesterol, TC/HDL cholesterol, TG/HDL cholesterol) and CVD mortality, and the association between LDL cholesterol and CHD. For the lipid measures for which we were able to pool data in our review (TC, TG, HDL), the HRs were only slightly greater than the APSC results,67 and, similar to our review, the CIs in their data all overlapped. So, although the strength of our associations was slightly larger, there was similarity in the pattern of results. Results for TG, TC/HDL cholesterol and TG/HDL cholesterol were presented as log results, and therefore were not comparable, and we had no results for non-HDL cholesterol and CVD mortality (unadjusted data) in our review.

Post hoc meta-regression showed an association only between CVD events (adjusted) and using a surrogate measure for CVD events for four lipid measures (TC, non-HDL cholesterol, Apo B/Apo A-I, TC/HDL cholesterol). The analysis suggests that including the surrogate measure of CHD could potentially lead to an overestimation of the HR for CVD events (adjusted) by approximately 20%.

From the limited outcome data available for all-cause mortality (unadjusted and adjusted), only two lipid measures had an association. For unadjusted data, TGs had a stronger association, whereas for adjusted data, HDL cholesterol was more strongly associated. Although there were insufficient studies to analyse Apo B, Apo A-I and their ratio, these could potentially offer a stronger predictive value. Unexpectedly, higher levels of some lipid measures (LDL cholesterol, TC, LDL/HDL cholesterol) were associated with a lower risk of all-cause mortality. The level of comorbidities in these populations might account for this result. The IKNS study in Yao City94 concluded that the decreased association with mortality was stronger in heavy drinkers and more significant in follow-up over 5 years. There is no existing review to compare results for all-cause mortality.

Key finding for populations taking statins

For all included studies, HDL and TC offered the strongest association with CVD events (unadjusted) and HDL cholesterol with adjusted data. For unadjusted data, only LDL cholesterol offered an association for primary preventions groups, whereas for secondary preventions groups Apo B, TC, LDL cholesterol, non-HDL cholesterol and HDL cholesterol all had a similar association. For adjusted data, although there were insufficient studies to produce combined results, there was some indication that HDL cholesterol for primary prevention groups and TC for secondary prevention groups could offer strong associations with CVD events.

Boekholdt et al. 72 completed an IPD systematic review using adjusted data from eight studies to assess the association between LDL cholesterol, non-HDL cholesterol, and Apo B and CVD events. In comparison, although 25 studies contributed data to this review, the maximum numbers of studies contributing data to any single measure in our comparable analysis was five. Comparison with the work of Boekholdt et al. 72 was only partially possible. This review’s LDL cholesterol point estimate was higher than the estimate of Boekholdt et al. 72 and not significant; for both Apo B and HDL cholesterol our point estimates were higher, with large CIs and based on only one included study. Boekholdt et al. 72 concluded that there was a stronger association for non-HDL cholesterol with CVD events than LDL and Apo B; this review suggests agreement with this finding but does not have sufficient evidence to confirm it. However, Boekholdt et al. 72 did not assess HDL cholesterol in his research, and this review would suggest that HDL cholesterol has a higher predictive value than non-HDL cholesterol, LDL cholesterol or Apo B for CVD events in populations taking statins.

No outcome data for CVD mortality or all-cause mortality were available for this group.

The lipid measures with the higher associations with CVD events and CVD mortality are consistent with current NICE recommendations for monitoring. 2,62 Our results suggest that alternative measures such as Apo B or the ratios LDL/HDL cholesterol and Apo B/Apo A-I, could be given further consideration in the future based on their clinical validity; however, in 2014 these were unlikely to be affordable or practical.

Strengths and limitations

The main strength of this review is its extensive coverage of all study designs, populations taking and not taking statins, and the wide panel of 10 lipid measures. The review has subgrouped populations into primary and prevention groups to seek to understand whether or not the predictive ability of various lipids varies according to these populations. Previous reviews in this area often look at only a subset of lipid measures, which complicates comparisons and the development of clinical recommendations. There is a tendency for reviews to exclude studies if they do not have data for all of the lipids being assessed in the review. In this review we have included study data even if they contributed to only some of the lipid measures or outcomes under consideration. We have extracted data from publications using indirect methods, as well as direct and converted data.

To complete a review of the scale and complexity of this one, it is required to make decisions about the scope and focus that impact on study identification and selection. Although other lipid-lowering drug groups could have been included in this review (such as fibrates), together with the advisory board we decided to focus only on statins. It is possible that by searching from the dates of previous reviews and modifying the search strategy, studies may have been missed; however, as we have aimed to include all studies from previous reviews and larger trials of > 1000 participants, we believe it is unlikely that large relevant studies have been overlooked. From the existing reviews we were unable to trace 15 potential studies, accounting for 10% of our final sample. This could have been because studies were mentioned as joining collaborations, but the studies never completed or the findings were not published. Despite the large number of included studies in this review, the number of studies reporting data for each pooled estimate is much smaller. This is typical of prognostic reviews in the area; for example, the ERFC used only 68 out of a potential 112 studies. 69

Many included studies did not disclose details such as whether or not the participants had pre-existing CVD or whether or not the participants were taking statins. Assumptions have been made in the data analysis to accommodate these studies, and sensitivity analyses were conducted to account for these assumptions.

No outcome data for CVD mortality or for all-cause mortality were available in populations taking statins, and data for these outcomes were provided only in a smaller proportion of studies of populations not taking statins, compared with CVD events. As expected, heterogeneity was high for most analyses, and contributed to our decision to use a random-effects model for the pooled estimates. The populations included in the studies were hugely varied, with no specific inclusion criteria relating to baseline characteristics. Furthermore, it was expected that there would be significant variation in the way the lipid measures were tested. The strength of predictive value of the lipid measures was largely based on the point estimates, but it should be noted that the CIs were often overlapping and the degree of difference between lipid measures was on the whole small. In meta-regression we did not find any measures that considerably reduced heterogeneity.

All estimates were obtained independently for each lipid measure or ratio. Therefore, comparisons between these measures are all indirect comparisons. We did not explore the alternative approach based around network meta-analysis (NMA) of a subset of studies to make direct comparisons, because this approach would use fewer studies and the methods for NMA are better developed for treatment comparisons than for prognostic reviews.

Assessment of methodological quality of the individual studies was difficult because included studies were not designed to address the same objective as this review and, therefore, the information required for this review to assess quality might not have been a priority for the study authors. Statistical analysis was rarely in a form that was appropriate for our outcomes. Assessment of attrition quality bias had no precedent in previous reviews, and setting boundaries for levels of low, moderate and high attrition bias were proven, subsequently, to be too generous, as no studies had high attrition.

There were a number of changes from the original protocol. The search methods were modified to accommodate the scale of the review and recognise the publication of three reviews in 2012. Similarly, by introducing the minimum number of participants, a 2-year follow-up and a minimum two lipid measures at baseline it was anticipated this would reduce the inclusions of smaller, more-prone-to-bias studies. This also removed the need for sensitivity analysis for smaller studies. In the analysis, all outcome data for each lipid measure was pooled. Although acknowledging that conclusions drawn from three or fewer sets of data could be misleading, it was important to enable an overall comparison between lipid measures and their trends, and view potentially useful lipid measures for future research. The indirect methods for data extraction were changed once included studies were examined; the best methods were used to extract the maximum amount of data. Quality assessment of included studies was instead based on a more recent tool77 that was specifically designed for prognostic reviews: QUIPS.

This review did not use IPD. It is well acknowledged that this is the ideal data source for these analyses and this means that a number of the limitations above would have been reduced or removed. Future research should look to utilise this approach if possible.

Prognostic systematic reviews are still in the early stages of methodological development. Search strategies, data extraction techniques, quality assessment tools and analysis software are not well established compared with other types of systematic reviews. This made each stage of the review challenging; as this area of research develops, it will provide a clearer path for undertaking this type of review.

Selection of studies

The 181 RCTs reviewed by Naci et al. 188 were considered for inclusion, including trials of both primary and secondary prevention populations. Two authors independently identified relevant publications and extracted data. Disagreements were resolved by consensus with a third author. Included trials met the following criteria: (1) at least 1000 individuals were randomised in total; (2) the average follow-up was at least 1 year (52 weeks); and (3) at least one of the groups was randomised to receive atorvastatin (‘Atorva’), in any dose (fixed or titrated). Risk of bias was assessed independently by two authors using the risk of bias tool provided in the Cochrane Handbook,189 which ascribes subjective ‘low’, ‘high’ or ‘unclear’ risk of bias to seven domains (two domains relating to blinding were combined into a single category).

Outcome measures and data extraction

Seven lipid measures and ratios (LDL cholesterol, HDL cholesterol, TC, TGs, non-HDL cholesterol, TC/HDL cholesterol and LDL/HDL cholesterol) were included as outcomes. Results from studies that reported data for longer periods of follow-up suggest that the most marked change in lipid measures occurs within the first 12 months, remaining relatively constant or with a moderate drift towards baseline values thereafter. 188 For this reason, lipid measure data were extracted at baseline and at the time point closest to 12 months (52 weeks) for which data were reported.

Data analysis

For each study, effect sizes [differences between the means in the atorvastatin treatment group and the comparator group, with standard errors (SEs)] were estimated based on the means, SDs and sample sizes reported at follow-up. As differences between groups at baseline were small, no adjustment was made for baseline values in the estimation of the effect size at follow-up. Units for all lipid measures were converted to millimoles per litre. For studies that did not report numerical data in the form required to make comparisons between the studies, or to directly estimate the effect size, the following steps were used, as required:

• use the group-specific baseline SD and sample size for the equivalent quantities at follow-up, in the absence of additional information

• use the median as an estimate of the mean

• estimate within-group SD as SE multiplied by the square root of the sample size

• use 0.75 times the interquartile range as an estimate of the SD.

Network meta-analysis was then used to investigate whether or not it was possible to combine the estimates of the effect size of atorvastatin in lipid measures across all included studies, taking into account differences in treatments and doses used in different studies. 190 For studies that used dose titration, the average dose across the study period was recorded as an estimate of the dose used in the study. Analyses were carried out using Stata 12.1 (StataCorp LP, College Station, TX, USA) and RevMan 5.2.

Data sources

Parameters for progression and variability of cholesterol were estimated using repeated measures data from two cohorts of patients: the St Luke’s cohort from Tokyo, Japan, described previously,31 and the UK general practice-based Clinical Practice Research Datalink (CPRD; formerly General Practice Research Database). 203 The derivation of the analysis cohorts and relevant variables is described below. The rationale for using the Japanese data set was to take advantage of the rigorous data collection at St Luke’s, with almost 100% complete data collection at regular intervals. Although some values (e.g. population mean of TC, HDL cholesterol and LDL cholesterol) would be expected to vary between Japanese and UK populations, it was hoped that some parameters, in particular, the within-person short-term variability of each lipid might be unaffected by setting and ethnic group. If so, we could take advantage of the St Luke’s cohort data to obtain a highly precise estimate of short-term variability. Noting that this variability is the sum of laboratory or assay variability, and day-to-day variability within individuals, it seemed likely that at least the first component would be transferable to a UK setting.

Statistical methods

We study four components of variability and change in lipid measurements:

1. the tendency for lipid levels to differ systematically between patients, described as the between-subject variation in baseline lipid levels

2. the average rate of change over time in the lipid measure for the whole group

3. the variation in the rate of change between individuals

4. the short-term variability in any single measurement, which is a combination of the analytic variability in the assay and the short-term biological variability.

From a monitoring perspective, the short-term variability equates to the measurement error or ‘noise’ (component 4 above), whereas the true rate of change in individuals is the ‘signal’.

As both the average baseline lipid levels and trends over time may vary, separate models were fitted for each lipid measure, for men and for women, and for each prevention group as defined above. Thus in CPRD data, six separate models (two gender subgroups in each of three prevention groups) were fitted for each lipid measure, whereas the St Luke’s cohort’s two models (two gender subgroups in a single prevention group, primary prevention not taking statins) were fitted per lipid measure.

We use the framework introduced by Glasziou et al. 30 to represent the components of variability and change listed above. Age-adjusted linear random-effects models were fitted to each gender subgroup of each prevention group as follows:

[Note: α_i ∼ N(α, σ_a²), β_i ∼ N(β, σ_b²), with covariance (α_i, β_i) = σ_ab; ε_ij ∼ N(0, σ_w²)]

where Y_ij is the jth observed lipid measurement in the ith person in the cohort and A_i is the age of the ith person at baseline. The rationale, interpretation and properties of this model have been discussed in detail previously:32,204 briefly, α is the average initial value (component 1 above), β is the average annual rate of change in the cohort (component 2) and σ_b, the SD of the individual rates of change, represents the variation of the rate of change between individuals (component 3). The SD σ_w represents the short-term variability of a single measurement (component 4 above). Model assumptions used here include independent normal distributions (denoted N) for α, β and ε. Normal distribution assumptions and model fit were checked graphically through comparison of the empirical and estimated distributions of the residuals. Model assumptions were not met for TGs without transformation and, because of the subsequent difficulties in interpretation, are not presented.

Parameter estimates are presented as mean and SD for each lipid, with 95% CIs. We also report SN ratios for each lipid, calculated as follows:

St Luke’s cohort

Baseline characteristics of participants in the St Luke’s closed and open cohorts are shown in Table 17. From a total of 42,557 subjects of the correct age range in the St Luke’s data, 7694 and 38,388 were eligible for inclusion in the closed and open cohort analyses, respectively, with some subjects included in both analyses. The inclusion criteria (at least 40 years of age in men or 55 years in women) are reflected in a high male–female ratio, and higher average age in women. The mean number of visits and length of follow-up were similar in men and women in each cohort.

Table 18 shows estimated variability and change parameters in the closed St Luke’s cohort for each lipid measurement. For TC, the average value at baseline, adjusted to age zero, was 5.22 mmol/l (95% CI 5.12 to 5.33 mmol/l) in men and 5.99 mmol/l (95% CI 5.68 to 6.29 mmol/l) in women. Average change per year was 0.00 mmol/l (95% CI –0.01 to 0.00 mmol/l) in both men and women. However, the variation around this average was comparatively large, with SD of 0.09 mmol/l per year in both men and women: this corresponds to an estimate that for 95% of men and women, the rate of change lies between a decrease of 0.18 mmol/l and an increase of 0.18 mmol/l. The estimated SD of the within-measurement variability was 0.35 (95% CI 0.35 to 0.36) in men and 0.37 (95% CI 0.37 to 0.38) in women. The estimated SN ratio at 1 year is therefore 0.09²/0.35² = 0.06 in men and 0.09²/0.37² = 0.06 in women. The estimated SN ratios over longer time periods are shown in Table 19. The SN ratio does not appear to exceed one until 4–5 years. The SN ratios for other lipids showed similar trends, although a stronger SN ratio was seen for the two lipid ratios (TC/HDL cholesterol and LDL/HDL cholesterol) and for LDL cholesterol.

Parameter estimates and SN ratios from the open St Luke’s cohort are shown in Tables 20 and 21. Mean age-adjusted lipid levels at first measurement were similar in both the closed and open Japanese cohorts. The within-measurement variation (noise) for each lipid was greater in the open cohort, and the variance in the annual change in lipid (signal) was smaller, so that the SN ratio did not exceed one until 6 years (e.g. lipid ratios) or 8 years (e.g. TC).

Model structure

We characterised a population of 100,000 men and 100,000 women for each prevention group (primary, primary on statins, and secondary on statins) through sampling with replacement from the CPRD. The populations were generated with the same distribution of observed lipid measures and baseline cardiovascular risk factors as patients in that prevention group in our CPRD data. Risk factors included those used to calculate 10-year CVD risk using QRisk252 and Framingham 1991,53 and were based on individuals without a history of familial hypercholesterolaemia who had any one lipid measurement (TC, HDL cholesterol, LDL cholesterol or TGs) recorded in 2006, the most recent complete year of data in our sample, for which the minimum age was 40 years for men and 55 years for women. This patient group was selected to represent those attending lipid monitoring. Prevention groups were defined as described in Chapter 4. CVD risk factors were defined in reference to the baseline date, which was the date of the first lipid measurement in 2006. Lipid measurements recorded on this date were used as the baseline observed readings. Age at baseline was defined from year of birth and the date of first lipid measurement. Each condition from diabetes, atrial fibrillation, chronic kidney disease, rheumatoid arthritis, use of BP medication, a family history of angina or heart attack in a first-degree relative of < 60 years of age or left-ventricular hypertrophy, was assumed to be present if there was a medical code relating to the condition in patient medical records up to 1 year after the baseline date. The first measurement recorded up to 1 year after baseline was used for baseline smoking status, BMI and systolic BP. Missing data for TC, HDL, LDL, TGs, BMI, systolic BP and smoking status were imputed using the multivariate chained equations method; lipid values and BMI were log transformed prior to imputing. 208 Models assumed that non-lipid CVD risk factors remain constant over time. Separate simulation analyses were run for each lipid, prevention group and gender.

Estimates of lipid variability and the between- and within person trends, derived in UK data in Chapter 4, were used to simulate lipid progression over time (see Tables 23 and 24). The ‘true’ lipid reading for each patient at baseline adjusted for age at measurement, assumed to be unobservable to the patient or clinician, was simulated conditional on the initial observed value, and then an individual annual rate of change was simulated (see Appendix 19). The simulated true lipid reading for each patient at each year of follow-up is then calculated using Equation 1 (see Chapter 4). The observed lipid value in any given sample is simulated from their true value at that time and the model estimate of the within-measurement variability of the lipid (see Equation 2, Chapter 4). For primary prevention patients, the true and observed 10-year CVD risk scores at each time point were also calculated.

The proportion of recommended treatment changes at each time point that are attributable to dyslipidaemia (true-positive results) can be estimated through calculating the proportion of changes for which both the simulated true and observed lipid values in secondary prevention patients, or both true and observed 10-year CVD risk scores in primary prevention patients, were above threshold; likewise the proportion attributable to within-person variability (false-positive results) can be estimated from the proportion for which the simulated observed lipid value/10-year CVD risk on which the decision was based was above threshold, while the true lipid level/10-year CVD risk was below threshold. Similarly, we can estimate the proportions of patients who correctly (true-negative) or incorrectly (false-negative) tested negative. The calculation of these proportions is illustrated in Table 29. For CIs, we repeated the simulations changing the model parameters to plausible values given the data, obtained through non-parametric bootstrapping. Random samples of 100,000 patients were drawn with replacement, 100 times from the simulated data set described above; for each, the simulation was then run 50 times and the average proportions of true-positive, false-positive, true-negative and false-negative treatment decisions were calculated. The SD across the 100 averages is used to estimate the SE and calculate CIs.

For each patient group, each lipid and both genders, we carried out simulation modelling for annual monitoring, biennial monitoring (every 2 years), triennial monitoring (every 3 years) and quinquennial monitoring (every 5 years). We first consider the proportions of false-positive and false-negative treatment decisions for strategies based on guidelines current, at the time of analysis,2 for which primary prevention treatment guidelines use CVD risk thresholds calculated using QRisk252 and secondary prevention uses lipid thresholds; we then consider how rates might be affected by monitoring using different lipids, different CVD risk scores and thresholds, and different patterns of measurement frequency.

Scenarios based on current guidelines

Scenarios based on alternative strategies for monitoring lipids

Primary prevention not taking statins

Results of modelling the rate of eligibility for statin prescriptions for males and females in this group are shown in Figure 21 and Tables 32 and 33. Figure 21 shows the percentage of tests that are false-positives (left) and false-negatives (right) in men and women, for annual, biennial, triennial and quinquennial monitoring strategies over 15 years of follow-up. More frequent monitoring resulted in a higher proportion of patients incorrectly recommended for a statin prescription (left). The proportion of patients incorrectly recommended for a statin prescription decreased over time as patients moved from being classed as false-positive to true-positive (as their underlying CVD risk increased). The proportion of patients incorrectly not recommended for a statin was higher when monitoring occurred at longer intervals. Over follow-up, there was a gradual increase in the proportion of false-negative tests for each monitoring interval. As all non-lipid risk factors other than age were held constant and, on average, the change in lipid levels was approximately zero (see Tables 23 and 24), over time, an increasing proportion of the population is classed as at high CVD risk as a result of population ageing, and therefore an increasing proportion of patients who should be eligible for treatment are missed.

FIGURE 21.

Men and women, primary prevention not taking statins, QRisk252 (TC/HDL cholesterol) using ≥ 20% as threshold. Model estimates for the percentage of changes in treatment that are attributable to the within-measurement variability (false-positive) rather than true changes in lipid value; and the percentage of those not prescribed a change in treatment who are incorrectly classified (false-negative), reported at intervals following a first test using screening intervals of 1, 2, 3 and 5 years.