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Background: Guidelines recommend detection of early chronic obstructive pulmonary disease (COPD), but evidence on the diagnostic work-up for COPD only concerns advanced and established COPD.Aim: To quantify the accuracy of symptoms and signs for early COPD, and the added value of C-reactive protein (CRP), in primary care patients presenting with cough.Design and Setting: Cross-sectional diagnostic study of 73 primary care practices in the Netherlands.Method: Four hundred primary care patients (182 males, mean age 63 years) older than 50 years, presenting with persistent cough (>14 days) without established COPD participated, of whom 382 completed the study. They underwent a systematic diagnostic work-up of symptoms, signs, conventional laboratory CRP level, and hospital lung functions tests, including body plethysmography, and an expert panel decided whether COPD was present (reference test). The independent value of all items was estimated by multivariable logistic regression analysis.Results: According to the expert panel, 118 patients had COPD (30%). Symptoms and signs with independent diagnostic value were age, sex, current smoking, smoking more than 20 pack-years, cardiovascular comorbidity, wheezing complaints, diminished breath sounds, and wheezing on auscultation. Combining these items resulted in an area under the receiver operating characteristic curve (ROC area) of 0.79 (95% confidence interval = 0.74 to 0.83) after internal validation. The proportion of subjects with elevated CRP was higher in those with early COPD, but CRP added no relevant diagnostic information above symptoms and signs.Conclusion: In subjects presenting with persistent cough, the CRP level has no added value for detection of early COPD. [ABSTRACT FROM AUTHOR]

Clinical prediction models are increasingly used to complement clinical reasoning and decision-making in modern medicine, in general, and in the cardiovascular domain, in particular. To these ends, developed models first and foremost need to provide accurate and (internally and externally) validated estimates of probabilities of specific health conditions or outcomes in the targeted individuals. Subsequently, the adoption of such models by professionals must guide their decision-making, and improve patient outcomes and the cost-effectiveness of care. In the first paper of this series of two companion papers, issues relating to prediction model development, their internal validation, and estimating the added value of a new (bio)marker to existing predictors were discussed. In this second paper, an overview is provided of the consecutive steps for the assessment of the model's predictive performance in new individuals (external validation studies), how to adjust or update existing models to local circumstances or with new predictors, and how to investigate the impact of the uptake of prediction models on clinical decision-making and patient outcomes (impact studies). Each step is illustrated with empirical examples from the cardiovascular field. [ABSTRACT FROM AUTHOR]

Prediction models are increasingly used to complement clinical reasoning and decision making in modern medicine in general, and in the cardiovascular domain in particular. Developed models first and foremost need to provide accurate and (internally and externally) validated estimates of probabilities of specific health conditions or outcomes in targeted patients. The adoption of such models must guide physician's decision making and an individual's behaviour, and consequently improve individual outcomes and the cost-effectiveness of care. In a series of two articles we review the consecutive steps generally advocated for risk prediction model research. This first article focuses on the different aspects of model development studies, from design to reporting, how to estimate a model's predictive performance and the potential optimism in these estimates using internal validation techniques, and how to quantify the added or incremental value of new predictors or biomarkers (of whatever type) to existing predictors. Each step is illustrated with empirical examples from the cardiovascular field. [ABSTRACT FROM AUTHOR]

Background: Today quantitative information about the type of complications and their incidence during long-term pacemaker (PM) follow-up is scarce.Objective: To assess the incidence and determinants of short- and long-term complications after first pacemaker implantation for bradycardia.Methods: A prospective multicenter cohort study (the FOLLOWPACE study) was conducted among 1517 patients receiving a PM between January 2003 and November 2007. The independent association of patient and implantation-procedure characteristics with the incidence of PM complications was analyzed using multivariable Cox regression analysis.Results: A total of 1517 patients in 23 Dutch PM centers were followed for a mean of 5.8 years (SD 1.1), resulting in 8797 patient-years. Within 2 months, 188 (12.4%) patients developed PM complications. Male gender, age at implantation, body mass index, a history of cerebrovascular accident, congestive heart failure, use of anticoagulant drugs, and passive atrial lead fixation were independent predictors for complications within 2 months, yielding a C-index of 0.62 (95% confidence interval 0.57-0.66). Annual hospital implanting volume did not additionally contribute to the prediction of short-term complications. Thereafter, 140 (9.2%) patients experienced complications, mostly lead-related complications (n = 84). Independent predictors for long-term complications were age, body mass index, hypertension, and a dual-chamber device, yielding a C-index of 0.62 (95% confidence interval 0.57-0.67). The occurrence of a short-term PM complication was not predictive of future PM complications.Conclusions: Complication incidence in modern pacing therapy is still substantial. Most complications occur early after PM implantation. Although various patient- and procedure-related characteristics are independent predictors for early and late complications, their ability to identify the patient at high risk is rather poor. This relatively high incidence of PM complications and their poor prediction underscores the usefulness of current guidelines for regular follow-up of patients with PM. [ABSTRACT FROM AUTHOR]

Context A recent overview of all CVD models applicable to diabetes patients is not available. Objective To review the primary prevention studies that focused on the development, validation and impact assessment of a cardiovascular risk model, scores or rules that can be applied to patients with type 2 diabetes. Design Systematic review. Data sources Medline was searched from 1966 to 1 April 2011. Study selection A study was eligible when it described the development, validation or impact assessment of a model that was constructed to predict the occurrence of cardiovascular disease in people with type 2 diabetes, or when the model was designed for use in the general population but included diabetes as a predictor. Data extraction A standardized form was sued to extract all data of the CVD models. Results 45 prediction models were identified, of which 12 were specifically developed for patients with type 2 diabetes. Only 31% of the risk scores has been externally validated in a diabetes population, with an area under the curve ranging from 0.61 to 0.86 and 0.59 to 0.80 for models developed in a diabetes population and in the general population, respectively. Only one risk score has been studied for its effect on patient management and outcomes. 10% of the risk scores are advocated in national diabetes guidelines. Conclusion Many cardiovascular risk scores are available that can be applied to patients with type 2 diabetes. A minority of these risk scores has been validated and tested for its predictive accuracy, with only a few showing a discriminative value of >=0.80. The impact of applying these risk scores in clinical practice is almost completely unknown, but their use is recommended in various national guidelines. [ABSTRACT FROM AUTHOR]

Background: Clinical probability assessment is combined with d-dimer testing to exclude pulmonary embolism (PE). Purpose: To compare the test characteristics of gestalt (a physician's 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. Data Sources: Articles in MEDLINE and EMBASE in English, French, German, Italian, Spanish, or Dutch that were published between 1966 and June 2011. Study Selection: 3 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. Data Extraction: Data on study characteristics, test performance, and prevalence were extracted. Reviewers constructed 2 x 2 tables and assessed the methodological quality of the studies. Data Synthesis: 52 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. Limitations: Studies had substantial heterogeneity due to prevalence of PE and differences in threshold. Many studies (63%) had potential bias due to differential disease verification. Conclusion: Clinical 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. Primary Funding Source: Dutch Heart Foundation. [ABSTRACT FROM AUTHOR]

Purpose: We wanted to determine the diagnostic value of an oral prednisolone test for chronic obstructive pulmonary disorders.Methods: Two hundred thirty-three patients with cough for longer than 14 days, without known chronic obstructive pulmonary disease (COPD) or asthma, participated in a diagnostic study in Dutch primary care between 2006 and 2009. These patients used a 14-day prednisolone test of 30 mg/d and recorded before-after bronchodilator measurements of forced expiratory volume in 1 second (FEV(1)). An expert panel determined the presence or absence of COPD and asthma based on an extensive diagnostic workup. The proportion of responders to the prednisolone test (increased FEV(1) exceeding 200 mL or 12% of baseline) per diagnosis group was compared, and the diagnostic value of the test was quantified by logistic regression and analysis of the area under the receiver operating characteristic curve (ROC area).Results: In patients with COPD, 23% (14 of 61) responded to the test; in patients with asthma 4% (1 of 25) responded; in patients with asthma and COPD, 7% (1 of 14) responded; and in those without asthma or COPD, 11% (14 of 133) responded. Being a responder was, unexpectedly, associated with COPD (OR = 2.4; 95% confidence interval [CI], 1.1-5.2). After multivariate analysis, adjusting for age, sex, and smoking, the OR = 2.0 (95% CI, 0.8-5.0) and the ROC area did not increase (0.78; 95% CI, 0.72-0.85 vs 0.79; 95% CI, 0.72-0.85).Conclusion: A response to a prednisolone test was suggestive of COPD, but added no diagnostic value to more easily obtainable characteristics. [ABSTRACT FROM AUTHOR]

Background: Up to 90% of patients referred for ultrasonography with suspected deep venous thrombosis (DVT) of the leg do not have the disease. Objective: To evaluate the safety and efficiency of using a clinical decision rule that includes a point-of-care d-dimer assay at initial presentation in primary care to exclude DVT. Design: A prospective management study. Setting: Approximately 300 primary care practices in 3 regions of the Netherlands (Amsterdam, Maastricht, and Utrecht). Patients: 1028 consecutive patients with clinically suspected DVT. Intervention: Patients were managed on the basis of the result of the clinical decision rule, which included a d-dimer result. Patients with a score of 3 or less were not referred for ultrasonography and received no anticoagulant treatment; patients with a score of 4 or more were referred for ultrasonography. Measurements: The primary outcome was symptomatic, objectively confirmed, venous thromboembolism during 3-month follow-up. Results: The mean age of the 1028 study patients was 58 years, and 37% of patients were men. A valid score was obtained in 1002 patients (98%). In 500 patients (49%), with a score of 3 or less, 7 developed venous thromboembolism within 3 months (incidence, 1.4% [95% CI, 0.6% to 2.9%]). A total of 502 patients (49%) had a score of 4 or more; 3 did not have ultrasonography. Ultrasonography showed DVT in 125 patients (25%), for an overall prevalence in evaluable patients of 13% (125 of 1002). Of the 374 patients who had normal ultrasonography results, 4 developed venous thromboembolism within 3 months (1.1% [CI, 0.3% to 2.7%]). Limitation: The study lacked a randomized design and relied on clinical follow-up to detect missed thrombotic disease. Conclusion: A diagnostic management strategy in primary care by using a simple clinical decision rule and a point-of-care d-dimer assay reduces the need for referral to secondary care of patients with clinically suspected DVT by almost 50% and is associated with a low risk for subsequent venous thromboembolic events. Funding: The Netherlands Organization for Scientific Research. [ABSTRACT FROM AUTHOR]

In meta-analysis, the assessment of graphs is widely used in an attempt to identify or rule out heterogeneity and publication bias. A variety of graphs are available for this purpose. To date, however, there has been no comparative evaluation of the performance of these graphs. With the objective of assessing the reproducibility and validity of graph ratings, the authors simulated 100 meta-analyses from 4 scenarios that covered situations with and without heterogeneity and publication bias. From each meta-analysis, the authors produced 11 types of graphs (box plot, weighted box plot, standardized residual histogram, normal quantile plot, forest plot, 3 kinds of funnel plots, trim-and-fill plot, Galbraith plot, and L'Abbé plot), and 3 reviewers assessed the resulting 1,100 plots. The intraclass correlation coefficients (ICCs) for reproducibility of the graph ratings ranged from poor (ICC = 0.34) to high (ICC = 0.91). Ratings of the forest plot and the standardized residual histogram were best associated with parameter heterogeneity. Association between graph ratings and publication bias (censorship of studies) was poor. Meta-analysts should be selective in the graphs they choose for the exploration of their data. [ABSTRACT FROM AUTHOR]

Background. Recently, a new, simple diagnostic rule was introduced to enable GPs to safely refrain from referring a considerable proportion of the patients suspected of having deep vein thrombosis (DVT). The rule (which includes seven patient history and physical examination items plus the result of a D-dimer test) discriminates ‘very low’ risk patients (not to be referred) from patients with an increased risk of DVT (to be referred). However, the rules ‘efficiency’ (proportion of patients designated by the rule as very low risk) and safety (DVT prevalence among these very low risk patients) may change according to patient characteristics. Objective. To test the rules safety and efficiency in clinically relevant subgroups; i.e. across three age groups, in men and women, and in patients with and without a history of DVT, separately. Methods. We retrospectively analysed data of 2086 primary care patients suspected of DVT, in whom all rule items and the result of the reference (‘gold’) standard (compression ultrasonography) were collected. Results. The rules efficiency decreased with age from 38.1% in the relatively young (Conclusion. The rule can safely exclude DVT in primary care patients suspected of DVT, irrespective of age, gender and history of DVT. [ABSTRACT FROM AUTHOR]

Loes CM Bertens,1 Johannes B Reitsma,1 Karel GM Moons,1 Yvonne van Mourik,1 Jan Willem J Lammers,2 Berna DL Broekhuizen,1 Arno W Hoes,1 Frans H Rutten1 1Julius Center for Health Sciences and Primary Care, 2Department of Pulmonology, Heart-Lung Center, University Medical Center Utrecht, Utrecht, the Netherlands Purpose: Prediction models for exacerbations in patients with chronic obstructive pulmonary disease (COPD) are scarce. Our aim was to develop and validate a new model to predict exacerbations in patients with COPD. Patients and methods: The derivation cohort consisted of patients aged 65 years or over, with a COPD diagnosis, who were followed up over 24 months. The external validation cohort consisted of another cohort of COPD patients, aged 50 years or over. Exacerbations of COPD were defined as symptomatic deterioration requiring pulsed oral steroid use or hospitalization. Logistic regression analysis including backward selection and shrinkage were used to develop the final model and to adjust for overfitting. The adjusted regression coefficients were applied in the validation cohort to assess calibration of the predictions and calculate changes in discrimination applying C-statistics. Results: The derivation and validation cohort consisted of 240 and 793 patients with COPD, of whom 29% and 28%, respectively, experienced an exacerbation during follow-up. The final model included four easily assessable variables: exacerbations in the previous year, pack years of smoking, level of obstruction, and history of vascular disease, with a C-statistic of 0.75 (95% confidence interval [CI]: 0.69–0.82). Predictions were well calibrated in the validation cohort, with a small loss in discrimination potential (C-statistic 0.66 [95% CI 0.61–0.71]). Conclusion: Our newly developed prediction model can help clinicians to predict the risk of future exacerbations in individual patients with COPD, including those with mild disease. Keywords: exacerbation of COPD, risk prediction, external validation, vascular disease

Background: Despite its benefits, it is uncommon to apply the nested case-control design in diagnostic research. We aim to show advantages of this design for diagnostic accuracy studies.Methods: We used data from a full cross-sectional diagnostic study comprising a cohort of 1295 consecutive patients who were selected on their suspicion of having deep vein thrombosis (DVT). We draw nested case-control samples from the full study population with case:control ratios of 1:1, 1:2, 1:3 and 1:4 (per ratio 100 samples were taken). We calculated diagnostic accuracy estimates for two tests that are used to detect DVT in clinical practice.Results: Estimates of diagnostic accuracy in the nested case-control samples were very similar to those in the full study population. For example, for each case:control ratio, the positive predictive value of the D-dimer test was 0.30 in the full study population and 0.30 in the nested case-control samples (median of the 100 samples). As expected, variability of the estimates decreased with increasing sample size.Conclusion: Our findings support the view that the nested case-control study is a valid and efficient design for diagnostic studies and should also be (re)appraised in current guidelines on diagnostic accuracy research. [ABSTRACT FROM AUTHOR]

Background and Objective: Multiple tools exist for assessing the methodological quality of diagnosis and prognosis research. It can be challenging to decide on when to use which tool. We aimed to provide an overview of existing methodological quality assessment (QA) tools for diagnosis and prognosis studies, highlight the overlap and differences among these tools, and to provide guidance for choosing the appropriate tool., Study Design and Setting: We performed a methodological review of tools designed for assessing risk of bias, applicability, or other aspects related to methodological quality in studies investigating tests/factors/markers/models for classifying or predicting a current (diagnosis) and/or future (prognosis) health state. Tools focusing exclusively on causal research or on reporting quality were excluded. Guidance was subsequently developed to assist in choosing an appropriate QA tool., Results: We identified 14 QA tools, eight of which were developed for assessment of diagnosis studies, four for prognosis studies, and two addressing both. We propose a set of five questions to help guide the process of choosing a QA tool based on the purpose or question of the user: whether the focus is on (1) diagnosis, prognosis, or another domain; (2) a prediction model versus a test/factor/marker; (3) evaluating simply the performance of a test/factor/marker versus assessing its added value over other variables; (4) comparing two or more tests/factors/markers/models; and (5) whether the user aims to assess only risk of bias or also other quality aspects., Conclusion: Existing QA tools for appraising diagnosis and prognosis studies vary in purpose, scope, and contents. Our guidance may help researchers, systematic reviewers, health policy makers, and guideline developers in specifying their purpose and question to select the most appropriate QA tool for their assessment., (Copyright © 2024. Published by Elsevier Inc.)

Competing Interests: We declare no competing interests. TL declares funding from the Dutch Research Council, outside this publication.

Objective: Risk of bias assessments are important in meta-analyses of both aggregate and individual participant data (IPD). There is limited evidence on whether and how risk of bias of included studies or datasets in IPD meta-analyses (IPDMAs) is assessed. We review how risk of bias is currently assessed, reported, and incorporated in IPDMAs of test accuracy and clinical prediction model studies, and provide recommendations for improvement., Methods and Analysis: We searched PubMed (January 2018-May 2020) to identify IPDMAs of test accuracy and prediction models, then elicited whether each IPDMA assessed risk of bias of included studies and, if so, how assessments were reported and subsequently incorporated into the IPDMAs., Results: Forty-nine IPDMAs were included. Nineteen of 27 (70%) test accuracy IPDMAs assessed risk of bias, compared to 5 of 22 (23%) prediction model IPDMAs. Seventeen of 19 (89%) test accuracy IPDMAs used QUADAS-2, but no tool was used consistently among prediction model IPDMAs. Of IPDMAs assessing risk of bias, seven (37%) test accuracy IPDMAs and one (20%) prediction model IPDMA provided details on the information sources (e.g., the original manuscript, IPD, primary investigators) used to inform judgments, and four (21%) test accuracy IPDMAs and one (20%) prediction model IPDMA provided information or whether assessments were done before or after obtaining the IPD of the included studies or datasets. Of all included IPDMAs, only seven test accuracy IPDMAs (26%) and one prediction model IPDMA (5%) incorporated risk of bias assessments into their meta-analyses. For future IPDMA projects, we provide guidance on how to adapt tools such as PROBAST (for prediction models) and QUADAS-2 (for test accuracy) to assess risk of bias of included primary studies and their IPD., Conclusion: Risk of bias assessments and their reporting need to be improved in IPDMAs of test accuracy and, especially, prediction model studies. Using recommended tools, both before and after IPD are obtained, will address this., (Copyright © 2023. Published by Elsevier Inc.)