Monitoring cholesterol levels: measurement error or true change?

Centre for Evidence-Based Medicine, Department of Primary Health Care, University of Oxford, Oxford, United Kingdom.
Annals of internal medicine (Impact Factor: 16.1). 05/2008; 148(9):656-61.
Source: PubMed

ABSTRACT Cholesterol level monitoring is a common clinical activity, but the optimal monitoring interval is unknown and practice varies.
To estimate, in patients receiving cholesterol-lowering medication, the variation in initial response to treatment, the long-term drift from initial response, and the detectability of long-term changes in on-treatment cholesterol level ("signal") given short-term, within-person variation ("noise").
Analysis of cholesterol measurement data in the LIPID (Long-Term Intervention with Pravastatin in Ischaemic Disease) study.
Randomized, placebo-controlled trial in Australia and New Zealand (June 1990 to May 1997).
9014 patients with past coronary heart disease who were randomly assigned to receive pravastatin or placebo.
Serial cholesterol concentrations at randomization, 6 months, and 12 months, and then annually to 5 years.
Both the placebo and pravastatin groups showed small increases in within-person variability over time. The estimated within-person SD increased from 0.40 mmol/L (15 mg/dL) (coefficient of variation, 7%) to 0.60 mmol/L (23 mg/dL) (coefficient of variation, 11%), but it took almost 4 years for the long-term variation to exceed the short-term variation. This slow increase in variation and the modest increase in mean cholesterol level, about 2% per year, suggest that most of the variation in the study is due to short-term biological and analytic variability. Our calculations suggest that, for patients with levels that are 0.5 mmol/L or more (> or =19 mg/dL) under target, monitoring is likely to detect many more false-positive results than true-positive results for at least the first 3 years after treatment has commenced.
Patients may respond differently to agents other than pravastatin. Future values for nonadherent patients were imputed.
The signal-noise ratio in cholesterol level monitoring is weak. The signal of a small increase in cholesterol level is difficult to detect against the background of a short-term variability of 7%. In annual rechecks in adherent patients, many apparent increases in cholesterol level may be false positive. Independent of the office visit schedule, the interval for monitoring patients who are receiving stable cholesterol-lowering treatment could be lengthened.

  • Kidney International 02/2014; 85(6):1303-1309. DOI:10.1038/ki.2014.31 · 8.52 Impact Factor
  • [Show abstract] [Hide abstract]
    ABSTRACT: The National Kidney Foundation-Kidney Disease Outcomes Quality Initiative (NKF-KDOQI) guideline for management of dyslipidemia in chronic kidney disease (CKD) was published in 2003. Since then, considerable evidence, including randomized controlled trials of statin therapy in adults with CKD, has helped better define medical treatments for dyslipidemia. In light of the new evidence, KDIGO (Kidney Disease: Improving Global Outcomes) formed a work group for the management of dyslipidemia in patients with CKD. This work group developed a new guideline that contains substantial changes from the prior KDOQI guideline. KDIGO recommends treatment of dyslipidemia in patients with CKD primarily based on risk for coronary heart disease, which is driven in large part by age. The KDIGO guideline does not recommend using low-density lipoprotein cholesterol level as a guide for identifying individuals with CKD to be treated or as treatment targets. Initiation of statin treatment is no longer recommended in dialysis patients. To assist US practitioners in interpreting and applying the KDIGO guideline, NKF-KDOQI convened a work group to write a commentary on this guideline. For the most part, our work group agreed with the recommendations of the KDIGO guideline, although we describe several areas in which we believe the guideline statements are either too strong or need to be more nuanced, areas of uncertainty and inconsistency, as well as additional research recommendations. The target audience for the KDIGO guideline includes nephrologists, primary care practitioners, and non-nephrology specialists such as cardiologists and endocrinologists. As such, we also put the current recommendations into the context of other clinical practice recommendations for cholesterol treatment. Copyright © 2014 National Kidney Foundation, Inc. Published by Elsevier Inc. All rights reserved.
    American Journal of Kidney Diseases 11/2014; 65(3). DOI:10.1053/j.ajkd.2014.10.005 · 5.76 Impact Factor
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: To determine whether a cardiovascular disease (CVD) health screening program is associated with CVD-related health conditions, incidence of cardiovascular events, mortality, healthcare utilization, and costs. Cohort study of a 3% random sample of all Korea National Health Insurance members 40years of age or older and free of CVD or CVD-related health conditions was conducted. A total 443,337 study participants were followed-up from January 1, 2005 through December 31, 2010. In primary analysis, the hazard ratios for CVD mortality, all-cause mortality, incident composite CVD events, myocardial infarction, cerebral infarction, and cerebral hemorrhage comparing participants who attended a screening exam during 2003-2004 compared to those who did not were 0.58 (95% CI: 0.53-0.63), 0.62 (95% CI: 0.60-0.64), 0.82 (95% CI: 0.78-0.85), 0.84 (95% CI: 0.75-0.93), 0.84 (95% CI: 0.79-0.89), and 0.73 (95% CI: 0.67-0.80), respectively. Screening attenders had higher rates of newly diagnosed hypertension, diabetes mellitus, and dyslipidemia, lower inpatient days of stay and cost, and lower outpatient cost compared to non-attenders. Participation in CVD health screening was associated with lower rates of CVD, all-cause mortality, and CVD events, higher detection of CVD-related health conditions, and lower healthcare utilization and costs. Copyright © 2014. Published by Elsevier Inc.
    Preventive Medicine 11/2014; 70C:19-25. DOI:10.1016/j.ypmed.2014.11.007 · 2.93 Impact Factor