Optimal strategies for monitoring lipid levels in patients at risk or with cardiovascular disease: a systematic review with statistical and cost-effectiveness modelling

Abstract

BackgroundVarious lipid measurements in monitoring/screening programmes can be used, alone or in cardiovascular risk scores, to guide treatment for prevention of cardiovascular disease (CVD). Because some changes in lipids are due to variability rather than true change, the value of lipid-monitoring strategies needs evaluation.ObjectiveTo determine clinical value and cost-effectiveness of different monitoring intervals and different lipid measures for primary and secondary prevention of CVD.Data sourcesWe searched databases and clinical trials registers from 2007 (including 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) to update and extend previous systematic reviews. Patient-level data from the Clinical Practice Research Datalink and St Luke’s Hospital, Japan, were used in statistical modelling. Utilities and health-care costs were drawn from the literature.MethodsIn two meta-analyses, we used prospective studies to examine associations of lipids with CVD and mortality, and randomised controlled trials to estimate lipid-lowering effects of atorvastatin doses. Patient-level data were used to estimate progression and variability of lipid measurements over time, and hence to model lipid-monitoring strategies. Results are expressed as rates of true-/false-positive and true-/false-negative tests for high lipid or high CVD risk. We estimated incremental costs per quality-adjusted life-year.ResultsA total of 115 publications reported strength of association between different lipid measures and CVD events in 138 data sets. The summary adjusted hazard ratio per standard deviation of total cholesterol (TC) to high-density lipoprotein (HDL) cholesterol ratio was 1.25 (95% confidence interval 1.15 to 1.35) for CVD in a primary prevention population but heterogeneity was high (I2 = 98%); similar results were observed for non-HDL cholesterol, apolipoprotein B and other ratio measures. Associations were smaller for other single lipid measures. Across 10 trials, low-dose atorvastatin (10 and 20 mg) effects ranged from a TC reduction of 0.92 mmol/l to 2.07 mmol/l, and low-density lipoprotein reduction of between 0.88 mmol/l and 1.86 mmol/l. Effects of 40 mg and 80 mg were reported by one trial each. For primary prevention, over a 3-year period, we estimate annual monitoring would unnecessarily treat 9 per 1000 more men (28 vs. 19 per 1000) and 5 per 1000 more women (17 vs. 12 per 1000) than monitoring every 3 years. However, annual monitoring would also undertreat 9 per 1000 fewer men (7 vs. 16 per 1000) and 4 per 1000 fewer women (7 vs. 11 per 1000) than monitoring at 3-year intervals. For secondary prevention, over a 3-year period, annual monitoring would increase unnecessary treatment changes by 66 per 1000 men and 31 per 1000 women, and decrease undertreatment by 29 per 1000 men and 28 per 1000 men, compared with monitoring every 3 years. In cost-effectiveness, strategies with increased screening/monitoring dominate. Exploratory analyses found that any unknown harms of statins would need utility decrements as large as 0.08 (men) to 0.11 (women) per statin user to reverse this finding in primary prevention.LimitationHeterogeneity in meta-analyses.ConclusionsWhile acknowledging known and potential unknown harms of statins, we find that more frequent monitoring strategies are cost-effective compared with others. Regular lipid monitoring in those with and without CVD is likely to be beneficial to patients and to the health service. Future research should include trials of the benefits and harms of atorvastatin 40 and 80 mg, large-scale surveillance of statin safety, and investigation of the effect of monitoring on medication adherence.Study registrationThis study is registered as PROSPERO CRD42013003727.FundingThe National Institute for Health Research Health Technology Assessment programme.

HbA1c monitoring interval in patients on treatment for stable type 2 diabetes. A ten-year retrospective, open cohort study

“…To distinguish true change in HbA1c progression, we adapted a statistical method for distinguishing the properties of a test from the variability of its measurements; the methodology has been described in detail elsewhere [8][9][10]. Briefly, linear random effect models with random intercept and random slope, adjusted for gender and age at first measurement of HbA1c (continuous value), were used to derive parameters describing HbA1c progression.…”

Evaluation of clarity of presentation and applicability of monitoring instructions for patients using lithium in clinical practice guidelines for treatment of bipolar disorder

“…Information is needed on when to start monitoring, when to stop monitoring, how frequently to monitor, what critical values are and how to respond to these values . CPGs should preferably include monitoring parameters that are specific, sensitive, accessible, affordable and applicable, and that provide early results to enable intervention . Previous studies by our group revealed that instructions for monitoring in Summary of Product Characteristics of (psychotropic) drugs in general were often found to be too ambiguous to be applicable in clinical practice .…”

“…10,11 CPGs should preferably include monitoring parameters that are specific, sensitive, accessible, affordable and applicable, and that provide early results to enable intervention. 12 Previous studies by our group revealed that instructions for monitoring in Summary of Product Characteristics of (psychotropic) drugs in general were often found to be too ambiguous to be applicable in clinical practice. 13,14 The applicability of monitoring instructions for patients using lithium in CPGs for treatment of BD is still unknown.…”

Diabetes screening intervals based on risk stratification

“…To distinguish true change in HbA1c progression, we adapted a statistical method for distinguishing the properties of tests from the variability of measurements; the methodology has been described in detail elsewhere [25, 26]. Briefly, linear random effect models with random intercept and random slope, adjusted for gender, age and BMI at first measurement of HbA1c as continuous value, were used to derive parameters describing HbA1c progression.…”

“…We use the signal-to-noise ratio (SNR) as a quantitative marker to distinguish individuals with true HbA1c change from those with apparent change due to noise. Based on previous reports, we defined the minimal informative screening interval as the time at which the signal to noise ratio exceeds 1 [26, 27]. We calculated confidence intervals for these ratios through non-parametric bootstrapping (15,000 times).…”