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Objective:  To perform an external validation and direct comparison of published prognostic models for early prediction of the risk of gestational diabetes mellitus, including predictors applicable in the first trimester of pregnancy., Design:  External validation of all published prognostic models in large scale, prospective, multicentre cohort study., Setting:  31 independent midwifery practices and six hospitals in the Netherlands., Participants:  Women recruited in their first trimester (<14 weeks) of pregnancy between December 2012 and January 2014, at their initial prenatal visit. Women with pre-existing diabetes mellitus of any type were excluded., Main Outcome Measures:  Discrimination of the prognostic models was assessed by the C statistic, and calibration assessed by calibration plots., Results:  3723 women were included for analysis, of whom 181 (4.9%) developed gestational diabetes mellitus in pregnancy. 12 prognostic models for the disorder could be validated in the cohort. C statistics ranged from 0.67 to 0.78. Calibration plots showed that eight of the 12 models were well calibrated. The four models with the highest C statistics included almost all of the following predictors: maternal age, maternal body mass index, history of gestational diabetes mellitus, ethnicity, and family history of diabetes. Prognostic models had a similar performance in a subgroup of nulliparous women only. Decision curve analysis showed that the use of these four models always had a positive net benefit., Conclusions:  In this external validation study, most of the published prognostic models for gestational diabetes mellitus show acceptable discrimination and calibration. The four models with the highest discriminative abilities in this study cohort, which also perform well in a subgroup of nulliparous women, are easy models to apply in clinical practice and therefore deserve further evaluation regarding their clinical impact., (Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://group.bmj.com/group/rights-licensing/permissions.)

Objective:  To provide an overview of prediction models for risk of cardiovascular disease (CVD) in the general population., Design:  Systematic review., Data Sources:  Medline and Embase until June 2013., Eligibility Criteria for Study Selection:  Studies describing the development or external validation of a multivariable model for predicting CVD risk in the general population., Results:  9965 references were screened, of which 212 articles were included in the review, describing the development of 363 prediction models and 473 external validations. Most models were developed in Europe (n=167, 46%), predicted risk of fatal or non-fatal coronary heart disease (n=118, 33%) over a 10 year period (n=209, 58%). The most common predictors were smoking (n=325, 90%) and age (n=321, 88%), and most models were sex specific (n=250, 69%). Substantial heterogeneity in predictor and outcome definitions was observed between models, and important clinical and methodological information were often missing. The prediction horizon was not specified for 49 models (13%), and for 92 (25%) crucial information was missing to enable the model to be used for individual risk prediction. Only 132 developed models (36%) were externally validated and only 70 (19%) by independent investigators. Model performance was heterogeneous and measures such as discrimination and calibration were reported for only 65% and 58% of the external validations, respectively., Conclusions:  There is an excess of models predicting incident CVD in the general population. The usefulness of most of the models remains unclear owing to methodological shortcomings, incomplete presentation, and lack of external validation and model impact studies. Rather than developing yet another similar CVD risk prediction model, in this era of large datasets, future research should focus on externally validating and comparing head-to-head promising CVD risk models that already exist, on tailoring or even combining these models to local settings, and investigating whether these models can be extended by addition of new predictors., (Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://group.bmj.com/group/rights-licensing/permissions.)

Objective: To validate all diagnostic prediction models for ruling out pulmonary embolism that are easily applicable in primary care., Design: Systematic review followed by independent external validation study to assess transportability of retrieved models to primary care medicine., Setting: 300 general practices in the Netherlands., Participants: Individual patient dataset of 598 patients with suspected acute pulmonary embolism in primary care., Main Outcome Measures: Discriminative ability of all models retrieved by systematic literature search, assessed by calculation and comparison of C statistics. After stratification into groups with high and low probability of pulmonary embolism according to pre-specified model cut-offs combined with qualitative D-dimer test, sensitivity, specificity, efficiency (overall proportion of patients with low probability of pulmonary embolism), and failure rate (proportion of pulmonary embolism cases in group of patients with low probability) were calculated for all models., Results: Ten published prediction models for the diagnosis of pulmonary embolism were found. Five of these models could be validated in the primary care dataset: the original Wells, modified Wells, simplified Wells, revised Geneva, and simplified revised Geneva models. Discriminative ability was comparable for all models (range of C statistic 0.75-0.80). Sensitivity ranged from 88% (simplified revised Geneva) to 96% (simplified Wells) and specificity from 48% (revised Geneva) to 53% (simplified revised Geneva). Efficiency of all models was between 43% and 48%. Differences were observed between failure rates, especially between the simplified Wells and the simplified revised Geneva models (failure rates 1.2% (95% confidence interval 0.2% to 3.3%) and 3.1% (1.4% to 5.9%), respectively; absolute difference -1.98% (-3.33% to -0.74%)). Irrespective of the diagnostic prediction model used, three patients were incorrectly classified as having low probability of pulmonary embolism; pulmonary embolism was diagnosed only after referral to secondary care., Conclusions: Five diagnostic pulmonary embolism prediction models that are easily applicable in primary care were validated in this setting. Whereas efficiency was comparable for all rules, the Wells rules gave the best performance in terms of lower failure rates., (© Hendriksen et al 2015.)

Prediction models are developed to aid health care providers in estimating the probability or risk that a specific disease or condition is present (diagnostic models) or that a specific event will occur in the future (prognostic models), to inform their decision making. However, the overwhelming evidence shows that the quality of reporting of prediction model studies is poor. Only with full and clear reporting of information on all aspects of a prediction model can risk of bias and potential usefulness of prediction models be adequately assessed. The Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD) Initiative developed a set of recommendations for the reporting of studies developing, validating, or updating a prediction model, whether for diagnostic or prognostic purposes. This article describes how the TRIPOD Statement was developed. An extensive list of items based on a review of the literature was created, which was reduced after a Web based survey and revised during a three day meeting in June 2011 with methodologists, health care professionals, and journal editors. The list was refined during several meetings of the steering group and in e-mail discussions with the wider group of TRIPOD contributors. The resulting TRIPOD Statement is a checklist of 22 items, deemed essential for transparent reporting of a prediction model study. The TRIPOD Statement aims to improve the transparency of the reporting of a prediction model study regardless of the study methods used. The TRIPOD Statement is best used in conjunction with the TRIPOD explanation and elaboration document. To aid the editorial process and readers of prediction model studies, it is recommended that authors include a completed checklist in their submission (also available at www.tripod-statement.org).To encourage dissemination of the TRIPOD Statement, this article is freely accessible on the Annals of Internal Medicine Web site (www.annals.org) and will be also published in BJOG, British Journal of Cancer, British Journal of Surgery, BMC Medicine, The BMJ, Circulation, Diabetic Medicine, European Journal of Clinical Investigation, European Urology, and Journal of Clinical Epidemiology. The authors jointly hold the copyright of this article. An accompanying explanation and elaboration article is freely available only on www.annals.org; Annals of Internal Medicine holds copyright for that article., (© BMJ Publishing Group Ltd 2014.)

Objective: To assess the accuracy of the Wells rule for excluding deep vein thrombosis and whether this accuracy applies to different subgroups of patients., Design: Meta-analysis of individual patient data., Data Sources: Authors of 13 studies (n = 10,002) provided their datasets, and these individual patient data were merged into one dataset., Eligibility Criteria: Studies were eligible if they enrolled consecutive outpatients with suspected deep vein thrombosis, scored all variables of the Wells rule, and performed an appropriate reference standard., Main Outcome Measures: Multilevel logistic regression models, including an interaction term for each subgroup, were used to estimate differences in predicted probabilities of deep vein thrombosis by the Wells rule. In addition, D-dimer testing was added to assess differences in the ability to exclude deep vein thrombosis using an unlikely score on the Wells rule combined with a negative D-dimer test result., Results: Overall, increasing scores on the Wells rule were associated with an increasing probability of having deep vein thrombosis. Estimated probabilities were almost twofold higher in patients with cancer, in patients with suspected recurrent events, and (to a lesser extent) in males. An unlikely score on the Wells rule (≤ 1) combined with a negative D-dimer test result was associated with an extremely low probability of deep vein thrombosis (1.2%, 95% confidence interval 0.7% to 1.8%). This combination occurred in 29% (95% confidence interval 20% to 40%) of patients. These findings were consistent in subgroups defined by type of D-dimer assay (quantitative or qualitative), sex, and care setting (primary or hospital care). For patients with cancer, the combination of an unlikely score on the Wells rule and a negative D-dimer test result occurred in only 9% of patients and was associated with a 2.2% probability of deep vein thrombosis being present. In patients with suspected recurrent events, only the modified Wells rule (adding one point for the previous event) is safe., Conclusion: Combined with a negative D-dimer test result (both quantitative and qualitative), deep vein thrombosis can be excluded in patients with an unlikely score on the Wells rule. This finding is true for both sexes, as well as for patients presenting in primary and hospital care. In patients with cancer, the combination is neither safe nor efficient. For patients with suspected recurrent disease, one extra point should be added to the rule to enable a safe exclusion.

Objectives: To assess the consequences of applying different mortality timeframes on standardised mortality ratios of individual hospitals and, secondarily, to evaluate the association between in-hospital standardised mortality ratios and early post-discharge mortality rate, length of hospital stay, and transfer rate., Design: Retrospective analysis of routinely collected hospital data to compare observed deaths in 50 diagnostic categories with deaths predicted by a case mix adjustment method., Setting: 60 Dutch hospitals., Participants: 1 228 815 patients discharged in the period 2008 to 2010., Main Outcome Measures: In-hospital standardised mortality ratio, 30 days post-admission standardised mortality ratio, and 30 days post-discharge standardised mortality ratio., Results: Compared with the in-hospital standardised mortality ratio, 33% of the hospitals were categorised differently with the 30 days post-admission standardised mortality ratio and 22% were categorised differently with the 30 days post-discharge standardised mortality ratio. A positive association was found between in-hospital standardised mortality ratio and length of hospital stay (Pearson correlation coefficient 0.33; P=0.01), and an inverse association was found between in-hospital standardised mortality ratio and early post-discharge mortality (Pearson correlation coefficient -0.37; P=0.004)., Conclusions: Applying different mortality timeframes resulted in differences in standardised mortality ratios and differences in judgment regarding the performance of individual hospitals. Furthermore, associations between in-hospital standardised mortality rates, length of stay, and early post-discharge mortality rates were found. Combining these findings suggests that standardised mortality ratios based on in-hospital mortality are subject to so-called "discharge bias." Hence, early post-discharge mortality should be included in the calculation of standardised mortality ratios.

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 12,497 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.

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.

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: 38,379 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.

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.

Objective: To review the evidence on the diagnostic accuracy of the currently available point of care D-dimer tests for excluding venous thromboembolism., Design: Systematic review of research on the accuracy of point of care D-dimer tests, using bivariate regression to examine sources of variation and to estimate sensitivity and specificity., Data Sources: Studies on the diagnostic accuracy of point of care D-dimer tests published between January 1995 and September 2008 and available in either Medline or Embase. Review methods The analysis included studies that compared point of care D-dimer tests with predefined reference criteria for venous thromboembolism, enrolled consecutive outpatients, and allowed for construction of a 2x2 table., Results: 23 studies (total number of patients 13 959, range in mean age 38-65 years, range of venous thromboembolism prevalence 4-51%) were included in the meta-analysis. The studies reported two qualitative point of care D-dimer tests (SimpliRED D-dimer (n=12) and Clearview Simplify D-dimer (n=7)) and two quantitative point of care D-dimer tests (Cardiac D-dimer (n=4) and Triage D-dimer (n=2)). Overall sensitivity ranged from 0.85 (95% confidence interval 0.78 to 0.90) to 0.96 (0.91 to 0.98) and overall specificity from 0.48 (0.33 to 0.62) to 0.74 (0.69 to 0.78). The two quantitative tests Cardiac D-dimer and Triage D-dimer scored most favourably., Conclusions: In outpatients suspected of venous thromboembolism, point of care D-dimer tests can contribute important information and guide patient management, notably in low risk patients (that is, those patients with a low score on a clinical decision rule).

Objective: To determine which clinical variables provide diagnostic information in recognising heart failure in primary care patients with stable chronic obstructive pulmonary disease (COPD) and whether easily available tests provide added diagnostic information., Design: Cross sectional diagnostic study., Setting: 51 primary care practices., Participants: 1186 patients aged > or = 65 years with COPD diagnosed by their general practitioner who did not have a diagnosis of heart failure confirmed by a cardiologist., Main Outcome Measures: Independent diagnostic variables for concomitant heart failure in primary care patients with stable COPD., Results: 405 patients (34% of eligible patients) underwent a systematic diagnostic investigation, which resulted in 83 (20.5%) receiving a new diagnosis of concomitant heart failure. Independent clinical variables for concomitant heart failure were a history of ischaemic heart disease, high body mass index, laterally displaced apex beat, and raised heart rate (area under the receiver operating characteristic curve (ROC area) 0.70, 95% confidence interval 0.64 to 0.76). Addition of measurement of N-terminal pro-brain natriuretic peptide (NT-proBNP) to the reduced "clinical model" had the largest added diagnostic value, with a significant increase of the ROC area to 0.77 (0.71 to 0.83), followed by electrocardiography (0.75, 0.69 to 0.81). C reactive protein and chest radiography had limited added value. A simplified diagnostic model consisting of the four independent clinical variables plus NT-proBNP and electrocardiography was developed., Conclusions: A limited number of items easily available from history and physical examination, with addition of NT-proBNP and electrocardiography, can help general practitioners to identify concomitant heart failure in individual patients with stable COPD.

Objectives: To develop an easily applicable diagnostic scoring method to determine the presence of peptic ulcers in dyspeptic patients in a primary care setting; to evaluate whether Helicobacter pylori testing adds value to history taking., Design: Cross sectional study., Setting: General practitioners' offices in the Utrecht area of the Netherlands., Participants: 565 patients consulting a general practitioner about dyspeptic symptoms of at least two weeks' duration., Main Outcome Measures: The presence or absence of peptic ulcer; independent predictors of the presence of peptic ulcer as obtained from history taking and the added value of H pylori testing were quantified by using multivariate logistic regression analyses., Results: A history of peptic ulcer, pain on an empty stomach, and smoking were strong and independent diagnostic determinants of peptic ulcer disease, with odds ratios of 5.5 (95% confidence interval 2.6 to 11.8), 2.8 (1.0 to 4.0), and 2.0 (1.4 to 6.0) respectively. The area under the receiver operating characteristic curve (ROC area) of these determinants together was 0.71. Adding the H pylori test increased the ROC area only to 0.75. However, in a group of patients at high risk, identified by means of a simple scoring rule based on history taking, the predictive value for the presence of peptic ulcer increased from 16% to 26% after a positive H pylori test., Conclusions: In the total group of dyspeptic patients in primary care, H pylori testing has no value in addition to history taking for diagnosing peptic ulcer disease. In a subgroup of patients at high risk of having peptic ulcer disease, however, it might be useful to test for and treat H pylori infections.