Performance of point-of-care HbA1c test devices: implications for use in clinical practice – a systematic review and meta-analysis

Regular monitoring of glycated hemoglobin subfraction A1c (HbA1c) in people with diabetes and treatment with glucose-lowering medications to improve glycaemic control can reduce the risk of developing complications [1]. In 2011, a World Health Organization consultation concluded that HbA1cat a threshold of 6.5% (48 mmol/mol) can be used as a diagnostic test for diabetes [2]. HbA1c monitoring often requires the patient to attend the health center twice: once to have blood taken and then returning to get test results and receive adjustments to medication. Point-of-care (POC) analysers are bench-top instruments that use a finger-prick blood sample and are designed for use in a treatment room or at the bed-side. They provide a test result within a few minutes allowing clinical decisions and medication changes to take place immediately. The suitability of many of these devices for the accurate measurement of HbA1c has been questioned, with some POC HbA1c test devices reported not to meet accepted accuracy and precision criteria [3]. Ideal imprecision goals for HbA1c should be coefficient of variation (CV) of <2% for HbA1c reported in % units (or <3% in SI units, mmol/mol) [4], [5], [6]. Most evaluations of POC HbA1c devices have taken place in laboratory settings [7], [8]; fewer studies have assessed device performance in a POC setting or with clinicians performing the tests [9], [10]. The only published review that has attempted to combine data from accuracy studies identified five studies covering three devices and compared correlation coefficients [11]. Systematically reporting and pooling data estimates of bias and precision between POC HbA1c devices and laboratory measurements would enable end users to assess which analysers best meet their analytical performance needs. This may be of particular importance for clinicians in primary care settings where much of the management of diabetes patients takes place. The comparison of accuracy between devices over the entire therapeutic range would need to be carried out by combining data on measurement error (bias) between POC and laboratory tests [12]. The aim of this study was to compare accuracy and precision of POC HbA1c devices with the local laboratory method based on data from published studies and discuss the clinical implications of the findings

Comparing Self-Reported Running Distance and Pace With a Commercial Fitness Watch Data: Reliability Study

BackgroundThere is substantial evidence exploring the reliability of running distance self-reporting and GPS wearable technology, but there are currently no studies investigating the reliability of participant self-reporting in comparison to GPS wearable technology. There is also a critical sports science and medical research gap due to a paucity of reliability studies assessing self-reported running pace. ObjectiveThe purpose of this study was to assess the reliability of weekly self-reported running distance and pace compared to a commercial GPS fitness watch, stratified by sex and age. These data will give clinicians and sports researchers insights into the reliability of runners’ self-reported pace, which may improve training designs and rehabilitation prescriptions. MethodsA prospective study of recreational runners was performed. Weekly running distance and average running pace were captured through self-report and a fitness watch. Baseline characteristics collected included age and sex. Intraclass correlational coefficients were calculated for weekly running distance and running pace for self-report and watch data. Bland-Altman plots assessed any systemic measurement error. Analyses were then stratified by sex and age. ResultsYounger runners reported improved weekly distance reliability (median 0.93, IQR 0.92-0.94). All ages demonstrated similar running pace reliability. Results exhibited no discernable systematic bias. ConclusionsWeekly self-report demonstrated good reliability for running distance and moderate reliability for running pace in comparison to the watch data. Similar reliability was observed for male and female participants. Younger runners demonstrated improved running distance reliability, but all age groups exhibited similar pace reliability. Running pace potentially should be monitored through technological means to increase precision

Trends in kidney function testing in UK primary care since the introduction of the Quality and Outcomes Framework:a retrospective cohort study using CPRD

Objectives: To characterise serum creatinine and urinary protein testing in UK general practices from 2005 to 2013 and to examine how the frequency of testing varies across demographic factors, with the presence of chronic conditions and with the prescribing of drugs for which kidney function monitoring is recommended. Design: Retrospective open cohort study. Setting: Routinely collected data from 630 UK general practices contributing to the Clinical Practice Research Datalink. Participants: 4 573 275 patients aged over 18 years registered at up-to-standard practices between 1 April 2005 and 31 March 2013. At study entry, no patients were kidney transplant donors or recipients, pregnant or on dialysis. Primary outcome measures: The rate of serum creatinine and urinary protein testing per year and the percentage of patients with isolated and repeated testing per year. Results: The rate of serum creatinine testing increased linearly across all age groups. The rate of proteinuria testing increased sharply in the 2009–2010 financial year but only for patients aged 60 years or over. For patients with established chronic kidney disease (CKD), creatinine testing increased rapidly in 2006–2007 and 2007–2008, and proteinuria testing in 2009–2010, reflecting the introduction of Quality and Outcomes Framework indicators. In adjusted analyses, CKD Read codes were associated with up to a twofold increase in the rate of serum creatinine testing, while other chronic conditions and potentially nephrotoxic drugs were associated with up to a sixfold increase. Regional variation in serum creatinine testing reflected country boundaries. Conclusions: Over a nine-year period, there have been increases in the numbers of patients having kidney function tests annually and in the frequency of testing. Changes in the recommended management of CKD in primary care were the primary determinant, and increases persist even after controlling for demographic and patient-level factors. Future studies should address whether increased testing has led to better outcomes.</p

Competing risks methodology in the evaluation of cardiovascular and cancer mortality as a consequence of albuminuria in type 2 diabetes

Background: 'Competing risks' are events that either preclude or alter the probability of experiencing the primary study outcome(s). Many standard survival models fail to account for competing risks, introducing an unknown level of bias in their measures of absolute and relative risk. Individuals with type 2 diabetes mellitus (T2DM) and albuminuria are at increased risk of multiple competing causes of mortality, including cardiovascular disease (CVD), cancer and renal disease, yet studies to date have not implemented competing risks methodology. Aim: Using albuminuria in T2DM as a case study, this Thesis set out to quantify differences between standard- and competing-risks-adjusted survival analysis estimates of absolute and relative risk for the outcomes of cardiovascular and cancer mortality. Methods: 86,962 patients aged ≥35 years with T2DM present on or before 2005 were identified in the Clinical Practice Research Datalink. To quantify differences in measures of absolute risk, cumulative risk estimates for cardiovascular and cancer mortality from standard survival analysis methods (Kaplan-Meier estimator) were compared to those from competing-risks-adjusted methods (cumulative incidence competing risk estimator). Cumulative risk estimates were stratified by patient albuminuria level (normoalbuminuria vs albuminuria). To quantify differences in measures of relative risk, estimates for the effect of albuminuria on the relative hazards of cardiovascular and cancer mortality were compared between standard cause-specific hazard (CSH) models (Cox-proportional-hazards regression), competing risk CSH models (unstratified Lunn-McNeil model), and competing risk subdistribution hazard (SDH) models (Fine-Gray model). Results: Patients with albuminuria, compared to those with normoalbuminuria, were older (p&amp;LT;0.001), had higher systolic blood pressure (p&amp;LT;0.001), had worse glycaemic control (p&amp;LT;0.001), and were more likely to be current or ex-smokers (p&amp;LT;0.001). Over the course of nine years of follow-up 22,512 patients died; 8,800 from CVD, 5,239 from cancer, and 8,473 from other causes. Median follow-up was 7.7 years. In patients with normoalbuminuria, nine-year standard and competing-risks-adjusted cumulative risk estimates for cardiovascular mortality were 11.1% (95% confidence interval (CI): 10.8-11.5%) and 10.2% (95% CI: 9.9-10.5%), respectively. For cancer mortality, these figures were 8.0% (95% CI: 7.7-8.3%) and 7.2% (95% CI: 6.9-7.5%). In patients with albuminuria, standard and competing-risks-adjusted estimates for cardiovascular mortality were 21.8% (95% CI: 20.9-22.7%) and 18.5% (95% CI: 17.8-19.3%), respectively. For cancer mortality, these figures were 10.7% (95% CI: 10.0-11.5%) and 8.6% (8.1-9.2%). For the effect of albuminuria on cardiovascular mortality, hazard ratios from multivariable standard CSH, competing risks CSH, and subdistribution hazard ratios from competing risks SDH models were 1.75 (95% CI: 1.63-1.87), 1.75 (95% CI: 1.64-1.87), and 1.58 (95% CI: 1.48-1.69), respectively. For the effect of albuminuria on cancer mortality, these values were 1.27 (95% CI: 1.16-1.39), 1.28 (95% CI: 1.17-1.40), and 1.11 (95% CI: 1.01-1.21). Conclusions: When evaluating measures of absolute risk, differences between standard and competing-risks-adjusted methods were small in absolute terms, but large in relative terms. For the investigation of epidemiological relationships using relative hazards models, standard survival analysis methods produced near-identical risk estimates to the CSH competing risks methods for the clinical associations evaluated in this Thesis. For the evaluation of risk prediction using relative hazards models, CSH models produced consistently higher risk estimates than SDH models, and their use may lead to over-estimation of the predictive effect of albuminuria on either outcome. Where outcomes are less common (like cancer) CSH models provide poor estimates of risk prediction, and SDH models should be used. This research demonstrates that differences can be present between risk estimates derived using CSH and SDH methods, and that the two are not necessarily interchangeable. Moreover, such differences may be present in other clinical areas.</p

Standard and competing risk analysis of the effect of albuminuria on cardiovascular and cancer mortality in patients with type 2 diabetes mellitus

Abstract Background Competing risks occur when populations may experience outcomes that either preclude or alter the probability of experiencing the main study outcome(s). Many standard survival analysis methods do not account for competing risks. We used mortality risk in people with diabetes with and without albuminuria as a case study to investigate the impact of competing risks on measures of absolute and relative risk. Methods A population with type 2 diabetes was identified in Clinical Practice Research Datalink as part of a historical cohort study. Patients were followed for up to 9 years. To quantify differences in absolute risk estimates of cardiovascular and cancer, mortality standard (Kaplan-Meier) estimates were compared to competing-risks-adjusted (cumulative incidence competing risk) estimates. To quantify differences in measures of association, regression coefficients for the effect of albuminuria on the relative hazard of each outcome were compared between standard cause-specific hazard (CSH) models (Cox proportional hazards regression) and two competing risk models: the unstratified Lunn-McNeil model, which estimates CSH, and the Fine-Gray model, which estimates subdistribution hazard (SDH). Results In patients with normoalbuminuria, standard and competing-risks-adjusted estimates for cardiovascular mortality were 11.1% (95% confidence interval (CI) 10.8–11.5%) and 10.2% (95% CI 9.9–10.5%), respectively. For cancer mortality, these figures were 8.0% (95% CI 7.7–8.3%) and 7.2% (95% CI 6.9–7.5%). In patients with albuminuria, standard and competing-risks-adjusted estimates for cardiovascular mortality were 21.8% (95% CI 20.9–22.7%) and 18.5% (95% CI 17.8–19.3%), respectively. For cancer mortality, these figures were 10.7% (95% CI 10.0–11.5%) and 8.6% (8.1–9.2%). For the effect of albuminuria on cardiovascular mortality, regression coefficient values from multivariable standard CSH, competing risks CSH, and competing risks SDH models were 0.557 (95% CI 0.491–0.623), 0.561 (95% CI 0.494–0.628), and 0.456 (95% CI 0.389–0.523), respectively. For the effect of albuminuria on cancer mortality, these values were 0.237 (95% CI 0.148–0.326), 0.244 (95% CI 0.154–0.333), and 0.102 (95% CI 0.012–0.192), respectively. Conclusions Studies of absolute risk should use methods that adjust for competing risks to avoid over-stating risk, such as the CICR estimator. Studies of relative risk should consider carefully which measure of association is most appropriate for the research question

Additional file 6: of Standard and competing risk analysis of the effect of albuminuria on cardiovascular and cancer mortality in patients with type 2 diabetes mellitus

Figure S4. Proportional subdistribution hazards assessment for cardiovascular mortality. (PDF 1100 kb

Additional file 5: of Standard and competing risk analysis of the effect of albuminuria on cardiovascular and cancer mortality in patients with type 2 diabetes mellitus

Figure S3. Proportional cause-specific hazards assessment for other mortality. (PDF 1695 kb

Additional file 1: of Standard and competing risk analysis of the effect of albuminuria on cardiovascular and cancer mortality in patients with type 2 diabetes mellitus

Table S1. Adjusted estimates for the effect of baseline risk factors on cardiovascular mortality from Cox-PH, Lunn-McNeil and Fine-Gray Models. (DOCX 15 kb

Additional file 2: of Standard and competing risk analysis of the effect of albuminuria on cardiovascular and cancer mortality in patients with type 2 diabetes mellitus

Table S2. Adjusted estimates for the effect of baseline risk factors on cancer mortality from Cox-PH, Lunn-McNeil and Fine-Gray Models. (DOCX 15 kb

Additional file 7: of Standard and competing risk analysis of the effect of albuminuria on cardiovascular and cancer mortality in patients with type 2 diabetes mellitus

Figure S5. Proportional subdistribution hazards assessment for cancer mortality. (PDF 865 kb