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Additional file 2. Data collection forms, quality checklists, and summary of findings tables.

Additional file 1. Search strategy.

Additional file 1. Hypothetical data example to illustrate the noncollapsibility effect.

Prediction models in health care use predictors to estimate for an individual the probability that a condition or disease is already present (diagnostic model) or will occur in the future (prognostic model). Publications on prediction models have become more common in recent years, and competing prediction models frequently exist for the same outcome or target population. Health care providers, guideline developers, and policymakers are often unsure which model to use or recommend, and in which persons or settings. Hence, systematic reviews of these studies are increasingly demanded, required, and performed. A key part of a systematic review of prediction models is examination of risk of bias and applicability to the intended population and setting. To help reviewers with this process, the authors developed PROBAST (Prediction model Risk Of Bias ASsessment Tool) for studies developing, validating, or updating (for example, extending) prediction models, both diagnostic and prognostic. PROBAST was developed through a consensus process involving a group of experts in the field. It includes 20 signaling questions across 4 domains (participants, predictors, outcome, and analysis). This explanation and elaboration document describes the rationale for including each domain and signaling question and guides researchers, reviewers, readers, and guideline developers in how to use them to assess risk of bias and applicability concerns. All concepts are illustrated with published examples across different topics. The latest version of the PROBAST checklist, accompanying documents, and filled-in examples can be downloaded from www.probast.org.

COPD exacerbation incidence rates are often ascertained retrospectively through patient recall and self-reports. We compared exacerbation ascertainment through patient self-reports and single-physician chart review to central adjudication by a committee and explored determinants and consequences of misclassification. Self-reported exacerbations (event-based definition) in 409 primary care patients with COPD participating in the International Collaborative Effort on Chronic Obstructive Lung Disease: Exacerbation Risk Index Cohorts (ICE COLD ERIC) cohort were ascertained every 6 months over 3 years. Exacerbations were adjudicated by single experienced physicians and an adjudication committee who had information from patient charts. We assessed the accuracy (sensitivities and specificities) of self-reports and single-physician chart review against a central adjudication committee (AC) (reference standard). We used multinomial logistic regression and bootstrap stability analyses to explore determinants of misclassifications. The AC identified 648 exacerbations, corresponding to an incidence rate of 0.60 ± 0.83 exacerbations/patient-year and a cumulative incidence proportion of 58.9%. Patients self-reported 841 exacerbations (incidence rate, 0.75 ± 1.01; incidence proportion, 59.7%). The sensitivity and specificity of self-reports were 84% and 76%, respectively, those of single-physician chart review were between 89% and 96% and 87% and 99%, respectively. The multinomial regression model and bootstrap selection showed that having experienced more exacerbations was the only factor consistently associated with underreporting and overreporting of exacerbations (underreporters: relative risk ratio [RRR], 2.16; 95% CI, 1.76-2.65 and overreporters: RRR, 1.67; 95% CI, 1.39-2.00). Patient 6-month recall of exacerbation events are inaccurate. This may lead to inaccurate estimates of incidence measures and underestimation of treatment effects. The use of multiple data sources combined with event adjudication could substantially reduce sample size requirements and possibly cost of studies. www.ClinicalTrials.gov, NCT00706602

Background: Although a large body of literature assessed the determinants of physical activity in patients with COPD, most of the studies were cross-sectional and focused on one or few determinants. We aimed to examine the determinants of physical activity using longitudinal models. Methods: In a prospective cohort study, 409 patients with COPD managed by general practitioners in the Netherlands or Switzerland were recruited and followed for 5 years. Patients completed a validated questionnaire (Longitudinal Aging Study Amsterdam Physical Activity Questionnaire) at baseline and every 6 months about their physical activity. We fitted linear mixed models to study the associations between predictors at baseline and physical activity (an adapted score on a 0-23 scale) across all study follow-up visits. Results: Unadjusted analysis suggested that there was a decrease in physical activity over time (coefficient = -0.22 per visit, 95% CI = -0.28, -0.17, p Conclusion: We identified in a cohort study several statistically significant determinants of physical activity in patients with COPD. It allows to identify patients who remain having low physical activity and who may benefit particularly from pulmonary rehabilitation that addresses the determinants of physical activity.