Establishing an evidence base for frequency of monitoring glycated haemoglobin levels in patients with Type 2 diabetes: projections of effectiveness from a regression model

Oke, J; Stevens, R J; Gaitskell, Kezia; Farmer, A J

doi:10.1111/j.1464-5491.2011.03412.x

Cited by 17 publications

(15 citation statements)

References 16 publications

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“…30,32,206,207 These analyses are consistent with such previous research, showing that frequent measurement of lipid levels is more likely to identify change owing to biological variability and inherent measurement error than true changes in an individual's health status. As clinical decisions on treatment are based upon these measurements, this may be detrimental to a patient's health.…”

Section: Discussionsupporting

confidence: 80%

“…The high variability in each lipid measurement resulted in recommendations for unnecessary initiation or up-titration (false-positive ) and undertreatment (false-negative) in all patient groups and under all scenarios considered. Consistent with previous research on long-term chronic conditions such as hypertension, renal function and hypercholesterolaemia, 30,204,206,207 we found that more frequent monitoring resulted in a higher proportion of false-positive tests, which, in these analyses, corresponds to a higher proportion of patients incorrectly recommended for a statin prescription or an up-titration. This is attributable to the low true rate of change over time.…”

Section: Discussionsupporting

confidence: 79%

“…The key concern of false-negative tests is that patients are receiving lower doses than those recommended, potentially leading to a raised risk of CVD. We applied established methods 206,207 to data from the CPRD to estimate the rates of true and false test results, and examine the optimal interval for monitoring dyslipidaemia in primary and secondary prevention patients.…”

Section: Discussionmentioning

confidence: 99%

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

Perera

McFadden

McLellan

et al. 2015

Health Technol Assess

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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.

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Section: Discussionsupporting

confidence: 80%

Section: Discussionsupporting

confidence: 79%

Section: Discussionmentioning

confidence: 99%

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

Perera

McFadden

McLellan

et al. 2015

Health Technol Assess

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“…The model has been subsequently applied to blood pressure monitoring 36 and it has been developed further for monitoring HbA 1c 37 and microalbuminuria in diabetes. 38 In general the model needs to allow for variation between people, for average rate of change and variation in rate of change between people, and for the error rate in any individual test; we have recently published full details of the methodology.…”

Section: Introductionmentioning

confidence: 99%

Development of a cost-effectiveness model for optimisation of the screening interval in diabetic retinopathy screening

Scanlon

Aldington

Leal

et al. 2015

Health Technol Assess

110

167

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BackgroundThe English NHS Diabetic Eye Screening Programme was established in 2003. Eligible people are invited annually for digital retinal photography screening. Those found to have potentially sight-threatening diabetic retinopathy (STDR) are referred to surveillance clinics or to Hospital Eye Services.ObjectivesTo determine whether personalised screening intervals are cost-effective.DesignRisk factors were identified in Gloucestershire, UK using survival modelling. A probabilistic decision hidden (unobserved) Markov model with a misgrading matrix was developed. This informed estimation of lifetime costs and quality-adjusted life-years (QALYs) in patients without STDR. Two personalised risk stratification models were employed: two screening episodes (SEs) (low, medium or high risk) or one SE with clinical information (low, medium–low, medium–high or high risk). The risk factor models were validated in other populations.SettingGloucestershire, Nottinghamshire, South London and East Anglia (all UK).ParticipantsPeople with diabetes in Gloucestershire with risk stratification model validation using data from Nottinghamshire, South London and East Anglia.Main outcome measuresPersonalised risk-based algorithm for screening interval; cost-effectiveness of different screening intervals.ResultsData were obtained in Gloucestershire from 12,790 people with diabetes with known risk factors to derive the risk estimation models, from 15,877 people to inform the uptake of screening and from 17,043 people to inform the health-care resource-usage costs. Two stratification models were developed: one using only results from previous screening events and one using previous screening and some commonly available GP data. Both models were capable of differentiating groups at low and high risk of development of STDR. The rate of progression to STDR was 5 per 1000 person-years (PYs) in the lowest decile of risk and 75 per 1000 PYs in the highest decile. In the absence of personalised risk stratification, the most cost-effective screening interval was to screen all patients every 3 years, with a 46% probability of this being cost-effective at a £30,000 per QALY threshold. Using either risk stratification models, screening patients at low risk every 5 years was the most cost-effective option, with a probability of 99-100% at a £30,000 per QALY threshold. For the medium-risk groups screening every 3 years had a probability of 43 –48% while screening high-risk groups every 2 years was cost-effective with a probability of 55–59%.ConclusionsThe study found that annual screening of all patients for STDR was not cost-effective. Screening this entire cohort every 3 years was most likely to be cost-effective. When personalised intervals are applied, screening those in our low-risk groups every 5 years was found to be cost-effective. Screening high-risk groups every 2 years further improved the cost-effectiveness of the programme. There was considerable uncertainty in the estimated incremental costs and in the incremental QALYs, particularly with regard to implications of an increasing proportion of maculopathy cases receiving intravitreal injection rather than laser treatment. Future work should focus on improving the understanding of risk, validating in further populations and investigating quality issues in imaging and assessment including the potential for automated image grading.Study registrationIntegrated Research Application System project number 118959.Funding detailsThe National Institute for Health Research Health Technology Assessment programme.

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“…136,137 Using these methods, annual monitoring of cholesterol and blood pressure has been shown to lead to more false-positive tests than true-positive tests: that is, a positive test for high cholesterol (or high blood pressure) is more likely to arise from measurement error than from 'true change'. 134 For many clinical monitoring tests, the variability within each measurement is greater than the likely change over 1 year, and regular cycles of testing create repeated opportunities for the occurrence of false-positive tests.…”

mentioning

confidence: 99%

Optimal strategies for identifying kidney disease in diabetes: properties of screening tests, progression of renal dysfunction and impact of treatment – systematic review and modelling of progression and cost-effectiveness

Farmer

Stevens

Hirst

et al. 2014

Health Technology Assessment

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Establishing an evidence base for frequency of monitoring glycated haemoglobin levels in patients with Type 2 diabetes: projections of effectiveness from a regression model

Cited by 17 publications

References 16 publications

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

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

Development of a cost-effectiveness model for optimisation of the screening interval in diabetic retinopathy screening

Optimal strategies for identifying kidney disease in diabetes: properties of screening tests, progression of renal dysfunction and impact of treatment – systematic review and modelling of progression and cost-effectiveness

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