Excluding venous thromboembolism using point of care D-dimer tests in outpatients: a diagnostic meta-analysis

Objective To review the evidence on the diagnostic accuracy of the currently available point of care D-dimer tests for excluding venous thromboembolism

Beyond Diagnostic Accuracy: The Clinical Utility of Diagnostic Tests

Like any other medical technology or intervention, diagnostic tests should be thoroughly evaluated before their introduction into daily practice. Increasingly, decision makers, physicians, and other users of diagnostic tests request more than simple measures of a test's analytical or technical performance and diagnostic accuracy; they would also like to see testing lead to health benefits. In this last article of our series, we introduce the notion of clinical utility, which expresses-preferably in a quantitative form-to what extent diagnostic testing improves health outcomes relative to the current best alternative, which could be some other form of testing or no testing at all. In most cases, diagnostic tests improve patient outcomes by providing information that can be used to identify patients who will benefit from helpful downstream management actions, such as effective treatment in individuals with positive test results and no treatment for those with negative results. We describe how comparative randomized clinical trials can be used to estimate clinical utility. We contrast the definition of clinical utility with that of the personal utility of tests and markers. We show how diagnostic accuracy can be linked to clinical utility through an appropriate definition of the target condition in diagnostic-accuracy studies. (C) 2012 American Association for Clinical Chemistr

Sensitivity analysis for the effects of multiple unmeasured confounders

PURPOSE: Observational studies are prone to (unmeasured) confounding. Sensitivity analysis of unmeasured confounding typically focuses on a single unmeasured confounder. The purpose of this study was to assess the impact of multiple (possibly weak) unmeasured confounders. METHODS: Simulation studies were performed based on parameters estimated from the British Women's Heart and Health Study, including 28 measured confounders and assuming no effect of ascorbic acid intake on mortality. In addition, 25, 50, or 100 unmeasured confounders were simulated, with various mutual correlations and correlations with measured confounders. RESULTS: The correlated unmeasured confounders did not need to be strongly associated with exposure and outcome to substantially bias the exposure-outcome association at interest, provided that there are sufficiently many unmeasured confounders. Correlations between unmeasured confounders, in addition to the strength of their relationship with exposure and outcome, are key drivers of the magnitude of unmeasured confounding and should be considered in sensitivity analyses. However, if the unmeasured confounders are correlated with measured confounders, the bias yielded by unmeasured confounders is partly removed through adjustment for the measured confounders. CONCLUSIONS: Discussions of the potential impact of unmeasured confounding in observational studies, and sensitivity analyses to examine this, should focus on the potential for the joint effect of multiple unmeasured confounders to bias results

Reevaluation of the role of VEGF-B suggests a restricted role in the revascularization of the ischemic myocardium .

peer reviewedOBJECTIVE: The endogenous role of the VEGF family member vascular endothelial growth factor-B (VEGF-B) in pathological angiogenesis remains unclear. METHODS AND RESULTS: We studied the role of VEGF-B in various models of pathological angiogenesis using mice lacking VEGF-B (VEGF-B(-/-)) or overexpressing VEGF-B(167). After occlusion of the left coronary artery, VEGF-B deficiency impaired vessel growth in the ischemic myocardium whereas, in wild-type mice, VEGF-B(167) overexpression enhanced revascularization of the infarct and ischemic border zone. By contrast, VEGF-B deficiency did not affect vessel growth in the wounded skin, hypoxic lung, ischemic retina, or ischemic limb. Moreover, VEGF-B(167) overexpression failed to enhance vascular growth in the skin or ischemic limb. CONCLUSIONS: VEGF-B appears to have a relatively restricted angiogenic activity in the ischemic heart. These insights might offer novel therapeutic opportunitie

Performance of prediction models for nephropathy in people with type 2 diabetes:systematic review and external validation study

OBJECTIVES To identify and assess the quality and accuracy of prognostic models for nephropathy and to validate these models in external cohorts of people with type 2 diabetes. DESIGN Systematic review and external validation. DATA SOURCES PubMed and Embase. ELIGIBILITY CRITERIA Studies describing the development of a model to predict the risk of nephropathy, applicable to people with type 2 diabetes. METHODS Screening, data extraction, and risk of bias assessment were done in duplicate. Eligible models were externally validated in the Hoorn Diabetes Care System (DCS) cohort (n=11 450) for the same outcomes for which they were developed. Risks of nephropathy were calculated and compared with observed risk over 2, 5, and 10 years of follow-up. Model performance was assessed based on intercept adjusted calibration and discrimination (Harrell's C statistic). RESULTS 41 studies included in the systematic review reported 64 models, 46 of which were developed in a population with diabetes and 18 in the general population including diabetes as a predictor. The predicted outcomes included albuminuria, diabetic kidney disease, chronic kidney disease (general population), and end stage renal disease. The reported apparent discrimination of the 46 models varied considerably across the different predicted outcomes, from 0.60 (95% confidence interval 0.56 to 0.64) to 0.99 (not available) for the models developed in a diabetes population and from 0.59 (not available) to 0.96 (0.95 to 0.97) for the models developed in the general population. Calibration was reported in 31 of the 41 studies, and the models were generally well calibrated. 21 of the 64 retrieved models were externally validated in the Hoorn DCS cohort for predicting risk of albuminuria, diabetic kidney disease, and chronic kidney disease, with considerable variation in performance across prediction horizons and models. For all three outcomes, however, at least two models had C statistics >0.8, indicating excellent discrimination. In a secondary external validation in GoDARTS (Genetics of Diabetes Audit and Research in Tayside Scotland), models developed for diabetic kidney disease outperformed those for chronic kidney disease. Models were generally well calibrated across all three prediction horizons. CONCLUSIONS This study identified multiple prediction models to predict albuminuria, diabetic kidney disease, chronic kidney disease, and end stage renal disease. In the external validation, discrimination and calibration for albuminuria, diabetic kidney disease, and chronic kidney disease varied considerably across prediction horizons and models. For each outcome, however, specific models showed good discrimination and calibration across the three prediction horizons, with clinically accessible predictors, making them applicable in a clinical setting. SYSTEMATIC REVIEW REGISTRATION PROSPERO CRD42020192831.Molecular Epidemiolog

External validation of the UK Prospective Diabetes Study (UKPDS) risk engine in patients with type 2 diabetes

Treatment guidelines recommend the UK Prospective Diabetes Study (UKPDS) risk engine for predicting cardiovascular risk in patients with type 2 diabetes, although validation studies showed moderate performance. The methods used in these validation studies were diverse, however, and sometimes insufficient. Hence, we assessed the discrimination and calibration of the UKPDS risk engine to predict 4, 5, 6 and 8 year cardiovascular risk in patients with type 2 diabetes. The cohort included 1,622 patients with type 2 diabetes. During a mean follow-up of 8 years, patients were followed for incidence of CHD and cardiovascular disease (CVD). Discrimination and calibration were assessed for 4, 5, 6 and 8 year risk. Discrimination was examined using the c-statistic and calibration by visually inspecting calibration plots and calculating the Hosmer-Lemeshow χ(2) statistic. The UKPDS risk engine showed moderate to poor discrimination for both CHD and CVD (c-statistic of 0.66 for both 5 year CHD and CVD risks), and an overestimation of the risk (224% and 112%). The calibration of the UKPDS risk engine was slightly better for patients with type 2 diabetes who had been diagnosed with diabetes more than 10 years ago compared with patients diagnosed more recently, particularly for 4 and 5 year predicted CVD and CHD risks. Discrimination for these periods was still moderate to poor. We observed that the UKPDS risk engine overestimates CHD and CVD risk. The discriminative ability of this model is moderate, irrespective of various subgroup analyses. To enhance the prediction of CVD in patients with type 2 diabetes, this model should be update

A systematic comparison of software dedicated to meta-analysis of causal studies

<p>Abstract</p> <p>Background</p> <p>Our objective was to systematically assess the differences in features, results, and usability of currently available meta-analysis programs.</p> <p>Methods</p> <p>Systematic review of software. We did an extensive search on the internet (Google, Yahoo, Altavista, and MSN) for specialized meta-analysis software. We included six programs in our review: Comprehensive Meta-analysis (CMA), MetAnalysis, MetaWin, MIX, RevMan, and WEasyMA. Two investigators compared the features of the software and their results. Thirty independent researchers evaluated the programs on their usability while analyzing one data set.</p> <p>Results</p> <p>The programs differed substantially in features, ease-of-use, and price. Although most results from the programs were identical, we did find some minor numerical inconsistencies. CMA and MIX scored highest on usability and these programs also have the most complete set of analytical features.</p> <p>Conclusion</p> <p>In consideration of differences in numerical results, we believe the user community would benefit from openly available and systematically updated information about the procedures and results of each program's validation. The most suitable program for a meta-analysis will depend on the user's needs and preferences and this report provides an overview that should be helpful in making a substantiated choice.</p

The PEARL score predicts 90-day readmission or death after hospitalisation for acute exacerbation of COPD.

BACKGROUND: One in three patients hospitalised due to acute exacerbation of COPD (AECOPD) is readmitted within 90 days. No tool has been developed specifically in this population to predict readmission or death. Clinicians are unable to identify patients at particular risk, yet resources to prevent readmission are allocated based on clinical judgement. METHODS: In participating hospitals, consecutive admissions of patients with AECOPD were identified by screening wards and reviewing coding records. A tool to predict 90-day readmission or death without readmission was developed in two hospitals (the derivation cohort) and validated in: (a) the same hospitals at a later timeframe (internal validation cohort) and (b) four further UK hospitals (external validation cohort). Performance was compared with ADO, BODEX, CODEX, DOSE and LACE scores. RESULTS: Of 2417 patients, 936 were readmitted or died within 90 days of discharge. The five independent variables in the final model were: Previous admissions, eMRCD score, Age, Right-sided heart failure and Left-sided heart failure (PEARL). The PEARL score was consistently discriminative and accurate with a c-statistic of 0.73, 0.68 and 0.70 in the derivation, internal validation and external validation cohorts. Higher PEARL scores were associated with a shorter time to readmission. CONCLUSIONS: The PEARL score is a simple tool that can effectively stratify patients' risk of 90-day readmission or death, which could help guide readmission avoidance strategies within the clinical and research setting. It is superior to other scores that have been used in this population. TRIAL REGISTRATION NUMBER: UKCRN ID 14214

Multinational development and validation of an early prediction model for delirium in ICU patients

Rationale Delirium incidence in intensive care unit (ICU) patients is high and associated with poor outcome. Identification of high-risk patients may facilitate its prevention. Purpose To develop and validate a model based on data available at ICU admission to predict delirium development during a patient’s complete ICU stay and to determine the predictive value of this model in relation to the time of delirium development. Methods Prospective cohort study in 13 ICUs from seven countries. Multiple logistic regression analysis was used to develop the early prediction (E-PRE-DELIRIC) model on data of the first two-thirds and validated on data of the last one-third of the patients from every participating ICU. Results In total, 2914 patients were included. Delirium incidence was 23.6 %. The E-PRE-DELIRIC model consists of nine predictors assessed at ICU admission: age, history of cognitive impairment, history of alcohol abuse, blood urea nitrogen, admission category, urgent admission, mean arterial blood pressure, use of corticosteroids, and respiratory failure. The area under the receiver operating characteristic curve (AUROC) was 0.76 [95 % confidence interval (CI) 0.73–0.77] in the development dataset and 0.75 (95 % CI 0.71–0.79) in the validation dataset. The model was well calibrated. AUROC increased from 0.70 (95 % CI 0.67–0.74), for delirium that developed 6 days. Conclusion Patients’ delirium risk for the complete ICU length of stay can be predicted at admission using the E-PRE-DELIRIC model, allowing early preventive interventions aimed to reduce incidence and severity of ICU delirium

Health, Health-Related Quality of Life, and Quality of Life: What is the Difference?

The terms health, health-related quality of life (HRQoL), and quality of life (QoL) are used interchangeably. Given that these are three key terms in the literature, their appropriate and clear use is important. This paper reviews the history and definitions of the terms and considers how they have been used. It is argued that the definitions of HRQoL in the literature are problematic because some definitions fail to distinguish between HRQoL and health or between HRQoL and QoL. Many so-called HRQoL questionnaires actually measure self-perceived health status and the use of the phrase QoL is unjustified. It is concluded that the concept of HRQoL as used now is confusing. A potential solution is to define HRQoL as the way health is empirically estimated to affect QoL or use the term to only signify the utility associated with a health state