Karel G. M. Moons

Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modelling Studies: The CHARMS Checklist

Carl Moons and colleagues provide a checklist and background explanation for critically appraising and extracting data from systematic reviews of prognostic and diagnostic prediction modelling studies. Please see later in the article for the Editors Summary

Clinical decision rules for excluding pulmonary embolism: a meta-analysis.

BACKGROUNDnClinical probability assessment is combined with d-dimer testing to exclude pulmonary embolism (PE).nnnPURPOSEnTo compare the test characteristics of gestalt (a physicians unstructured estimate) and clinical decision rules for evaluating adults with suspected PE and assess the failure rate of gestalt and rules when used in combination with d-dimer testing.nnnDATA SOURCESnArticles in MEDLINE and EMBASE in English, French, German, Italian, Spanish, or Dutch that were published between 1966 and June 2011.nnnSTUDY SELECTIONn3 reviewers, working in pairs, selected prospective studies in consecutive patients suspected of having PE. Studies had to estimate the probability of PE by using gestalt or a decision rule and verify the diagnosis by using an appropriate reference standard.nnnDATA EXTRACTIONnData on study characteristics, test performance, and prevalence were extracted. Reviewers constructed 2 × 2 tables and assessed the methodological quality of the studies.nnnDATA SYNTHESISn52 studies, comprising 55 268 patients, were selected. Meta-analysis was performed on studies that used gestalt (15 studies; sensitivity, 0.85; specificity, 0.51), the Wells rule with a cutoff value less than 2 (19 studies; sensitivity, 0.84; specificity, 0.58) or 4 or less (11 studies; sensitivity, 0.60; specificity, 0.80), the Geneva rule (5 studies; sensitivity, 0.84; specificity, 0.50), and the revised Geneva rule (4 studies; sensitivity, 0.91; specificity, 0.37). An increased prevalence of PE was associated with higher sensitivity and lower specificity. Combining a decision rule or gestalt with d-dimer testing seemed safe for all strategies, except when the less-sensitive Wells rule (cutoff value ≤4) was combined with less-sensitive qualitative d-dimer testing.nnnLIMITATIONSnStudies had substantial heterogeneity due to prevalence of PE and differences in threshold. Many studies (63%) had potential bias due to differential disease verification.nnnCONCLUSIONnClinical decision rules and gestalt can safely exclude PE when combined with sensitive d-dimer testing. The authors recommend standardized rules because gestalt has lower specificity, but the choice of a particular rule and d-dimer test depend on both prevalence and setting.

Diagnostic accuracy of conventional or age adjusted D-dimer cut-off values in older patients with suspected venous thromboembolism: systematic review and meta-analysis

Objective To review the diagnostic accuracy of D-dimer testing in older patients (>50 years) with suspected venous thromboembolism, using conventional or age adjusted D-dimer cut-off values. Design Systematic review and bivariate random effects meta-analysis. Data sources We searched Medline and Embase for studies published before 21 June 2012 and we contacted the authors of primary studies. Study selection Primary studies that enrolled older patients with suspected venous thromboembolism in whom D-dimer testing, using both conventional (500 µg/L) and age adjusted (age×10 µg/L) cut-off values, and reference testing were performed. For patients with a non-high clinical probability, 2×2 tables were reconstructed and stratified by age category and applied D-dimer cut-off level. Results 13 cohorts including 12u2009497 patients with a non-high clinical probability were included in the meta-analysis. The specificity of the conventional cut-off value decreased with increasing age, from 57.6% (95% confidence interval 51.4% to 63.6%) in patients aged 51-60 years to 39.4% (33.5% to 45.6%) in those aged 61-70, 24.5% (20.0% to 29.7% in those aged 71-80, and 14.7% (11.3% to 18.6%) in those aged >80. Age adjusted cut-off values revealed higher specificities over all age categories: 62.3% (56.2% to 68.0%), 49.5% (43.2% to 55.8%), 44.2% (38.0% to 50.5%), and 35.2% (29.4% to 41.5%), respectively. Sensitivities of the age adjusted cut-off remained above 97% in all age categories. Conclusions The application of age adjusted cut-off values for D-dimer tests substantially increases specificity without modifying sensitivity, thereby improving the clinical utility of D-dimer testing in patients aged 50 or more with a non-high clinical probability.

Prediction models for risk of developing type 2 diabetes: systematic literature search and independent external validation study

Objective To identify existing prediction models for the risk of development of type 2 diabetes and to externally validate them in a large independent cohort. Data sources Systematic search of English, German, and Dutch literature in PubMed until February 2011 to identify prediction models for diabetes. Design Performance of the models was assessed in terms of discrimination (C statistic) and calibration (calibration plots and Hosmer-Lemeshow test).The validation study was a prospective cohort study, with a case cohort study in a random subcohort. Setting Models were applied to the Dutch cohort of the European Prospective Investigation into Cancer and Nutrition cohort study (EPIC-NL). Participants 38u2009379 people aged 20-70 with no diabetes at baseline, 2506 of whom made up the random subcohort. Outcome measure Incident type 2 diabetes. Results The review identified 16 studies containing 25 prediction models. We considered 12 models as basic because they were based on variables that can be assessed non-invasively and 13 models as extended because they additionally included conventional biomarkers such as glucose concentration. During a median follow-up of 10.2 years there were 924 cases in the full EPIC-NL cohort and 79 in the random subcohort. The C statistic for the basic models ranged from 0.74 (95% confidence interval 0.73 to 0.75) to 0.84 (0.82 to 0.85) for risk at 7.5 years. For prediction models including biomarkers the C statistic ranged from 0.81 (0.80 to 0.83) to 0.93 (0.92 to 0.94). Most prediction models overestimated the observed risk of diabetes, particularly at higher observed risks. After adjustment for differences in incidence of diabetes, calibration improved considerably. Conclusions Most basic prediction models can identify people at high risk of developing diabetes in a time frame of five to 10 years. Models including biomarkers classified cases slightly better than basic ones. Most models overestimated the actual risk of diabetes. Existing prediction models therefore perform well to identify those at high risk, but cannot sufficiently quantify actual risk of future diabetes.

Safe exclusion of pulmonary embolism using the Wells rule and qualitative D-dimer testing in primary care: prospective cohort study

Objective To validate the use of the Wells clinical decision rule combined with a point of care D-dimer test to safely exclude pulmonary embolism in primary care. Design Prospective cohort study. Setting Primary care across three different regions of the Netherlands (Amsterdam, Maastricht, and Utrecht). Participants 598 adults with suspected pulmonary embolism in primary care. Interventions Doctors scored patients according to the seven variables of the Wells rule and carried out a qualitative point of care D-dimer test. All patients were referred to secondary care and diagnosed according to local protocols. Pulmonary embolism was confirmed or refuted on the basis of a composite reference standard, including spiral computed tomography and three months’ follow-up. Main outcome measures Diagnostic accuracy (sensitivity and specificity), proportion of patients at low risk (efficiency), number of missed patients with pulmonary embolism in low risk category (false negative rate), and the presence of symptomatic venous thromboembolism, based on the composite reference standard, including events during the follow-up period of three months. Results Pulmonary embolism was present in 73 patients (prevalence 12.2%). On the basis of a threshold Wells score of ≤4 and a negative qualitative D-dimer test result, 272 of 598 patients were classified as low risk (efficiency 45.5%). Four cases of pulmonary embolism were observed in these 272 patients (false negative rate 1.5%, 95% confidence interval 0.4% to 3.7%). The sensitivity and specificity of this combined diagnostic approach was 94.5% (86.6% to 98.5%) and 51.0% (46.7% to 55.4%), respectively. Conclusion A Wells score of ≤4 combined with a negative qualitative D-dimer test result can safely and efficiently exclude pulmonary embolism in primary care.

Comparing risk prediction models.

Should be routine when deriving a new model for the same purpose

Validation of two age dependent D-dimer cut-off values for exclusion of deep vein thrombosis in suspected elderly patients in primary care: retrospective, cross sectional, diagnostic analysis

Objective To determine whether the use of age adapted D-dimer cut-off values can be translated to primary care patients who are suspected of deep vein thrombosis. Design Retrospective, cross sectional diagnostic study. Setting 110 primary care doctors affiliated with three hospitals in the Netherlands. Participants 1374 consecutive patients (936 (68.1%) aged >50 years) with clinically suspected deep vein thrombosis. Main outcome measures Proportion of patients with D-dimer values below two proposed age adapted cut-off levels (age in years×10 μg/L in patients aged >50 years, or 750 μg/L in patients aged ≥60 years), in whom deep vein thrombosis could be excluded; and the number of false negative results. Results Using the Wells score, 647 patients had an unlikely clinical probability of deep vein thrombosis. In these patients (at all ages), deep vein thrombosis could be excluded in 309 (47.8%) using the age dependent cut-off value compared with 272 (42.0%) using the conventional cut-off value of 500 μg/L (increase 5.7%, 95% confidence interval 4.1% to 7.8%). This exclusion rate resulted in 0.5% and 0.3% false negative cases, respectively (increase 0.2%, 0.004% to 8.6%).The increase in exclusion rate by using the age dependent cut-off value was highest in the oldest patients. In patients older than 80 years, deep vein thrombosis could be safely excluded in 22 (35.5%) patients using the age dependent cut-off value compared with 13 (21.0%) using the conventional cut-off value (increase 14.5%, 6.8% to 25.8%). Compared with the age dependent cut-off value, the cut-off value of 750 μg/L had a similar exclusion rate (307 (47.4%) patients) and false negative rate (0.3%). Conclusions Combined with a low clinical probability of deep vein thrombosis, use of the age dependent D-dimer cut-off value for patients older than 50 years or the cut-off value of 750 μg/L for patients aged 60 years and older resulted in a considerable increase in the proportion of patients in primary care in whom deep vein thrombosis could be safely excluded, compared with the conventional cut-off value of 500 μg/L.

Improving the Transparency of Prognosis Research: The Role of Reporting, Data Sharing, Registration, and Protocols

George Peat and colleagues review and discuss current approaches to transparency and published debates and concerns about efforts to standardize prognosis research practice, and make five recommendations. Please see later in the article for the Editors Summary

Using evidence to combat overdiagnosis and overtreatment: evaluating treatments, tests, and disease definitions in the time of too much.

Ray Moynihan and colleagues outline suggestions for improving the way that medical evidence is produced, analysed, and interpreted to avoid problems of overdiagnosis and overtreatment. Please see later in the article for the Editors Summary

Get Real in Individual Participant Data (IPD) Meta-Analysis: A Review of the Methodology.

Individual participant data (IPD) meta‐analysis is an increasingly used approach for synthesizing and investigating treatment effect estimates. Over the past few years, numerous methods for conducting an IPD meta‐analysis (IPD‐MA) have been proposed, often making different assumptions and modeling choices while addressing a similar research question. We conducted a literature review to provide an overview of methods for performing an IPD‐MA using evidence from clinical trials or non‐randomized studies when investigating treatment efficacy. With this review, we aim to assist researchers in choosing the appropriate methods and provide recommendations on their implementation when planning and conducting an IPD‐MA.