Prognostic models for outcome prediction in patients with chronic obstructive pulmonary disease: systematic review and critical appraisalBMJ 2019; 367 doi: https://doi.org/10.1136/bmj.l5358 (Published 04 October 2019) Cite this as: BMJ 2019;367:l5358
- Vanesa Bellou, PhD student1 2,
- Lazaros Belbasis, PhD student1,
- Athanasios K Konstantinidis, assistant professor2,
- Ioanna Tzoulaki, reader1 3 4,
- Evangelos Evangelou, associate professor1 3
- 1Department of Hygiene and Epidemiology, University of Ioannina Medical School, Ioannina, Greece
- 2Department of Respiratory Medicine, University Hospital of Ioannina, University of Ioannina Medical School, Ioannina, Greece
- 3Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, UK
- 4MRC-PHE Center for Environment, School of Public Health, Imperial College London, London, UK
- Correspondence to: E Evangelou (or @eevangelou on Twitter)
- Accepted 12 August 2019
Objective To map and assess prognostic models for outcome prediction in patients with chronic obstructive pulmonary disease (COPD).
Design Systematic review.
Data sources PubMed until November 2018 and hand searched references from eligible articles.
Eligibility criteria for study selection Studies developing, validating, or updating a prediction model in COPD patients and focusing on any potential clinical outcome.
Results The systematic search yielded 228 eligible articles, describing the development of 408 prognostic models, the external validation of 38 models, and the validation of 20 prognostic models derived for diseases other than COPD. The 408 prognostic models were developed in three clinical settings: outpatients (n=239; 59%), patients admitted to hospital (n=155; 38%), and patients attending the emergency department (n=14; 3%). Among the 408 prognostic models, the most prevalent endpoints were mortality (n=209; 51%), risk for acute exacerbation of COPD (n=42; 10%), and risk for readmission after the index hospital admission (n=36; 9%). Overall, the most commonly used predictors were age (n=166; 41%), forced expiratory volume in one second (n=85; 21%), sex (n=74; 18%), body mass index (n=66; 16%), and smoking (n=65; 16%). Of the 408 prognostic models, 100 (25%) were internally validated and 91 (23%) examined the calibration of the developed model. For 286 (70%) models a model presentation was not available, and only 56 (14%) models were presented through the full equation. Model discrimination using the C statistic was available for 311 (76%) models. 38 models were externally validated, but in only 12 of these was the validation performed by a fully independent team. Only seven prognostic models with an overall low risk of bias according to PROBAST were identified. These models were ADO, B-AE-D, B-AE-D-C, extended ADO, updated ADO, updated BODE, and a model developed by Bertens et al. A meta-analysis of C statistics was performed for 12 prognostic models, and the summary estimates ranged from 0.611 to 0.769.
Conclusions This study constitutes a detailed mapping and assessment of the prognostic models for outcome prediction in COPD patients. The findings indicate several methodological pitfalls in their development and a low rate of external validation. Future research should focus on the improvement of existing models through update and external validation, as well as the assessment of the safety, clinical effectiveness, and cost effectiveness of the application of these prognostic models in clinical practice through impact studies.
Systematic review registration PROSPERO CRD42017069247
Contributors: VB, LB, IT, and EE designed the study. VB and LB did the literature search and the data extraction and wrote the first draft of the manuscript. All the authors wrote the final version of the manuscript. EE accepts full responsibility for the work and conduct of the study, had access to the data, and controlled the decision to publish. The corresponding author attests that all listed authors meet authorship criteria and that no others meeting the criteria have been omitted. VB and EE are the guarantors.
Funding: VB and LB are supported by PhD scholarships funded by the Greek State Scholarships Foundation. No funding body has influenced data collection, analysis, or interpretation.
Competing interests: All authors have completed the ICMJE uniform disclosure form at www.icmje.org/coi_disclosure.pdf (available on request from the corresponding author) and declare: no support from any organisation for the submitted work; no financial relationships with any organisations that might have an interest in the submitted work in the previous three years; no other relationships or activities that could appear to have influenced the submitted work.
Ethical approval: Not needed.
Data sharing: Additional data for the eligible studies are available on request from the corresponding author at email@example.com.
Transparency: The corresponding author affirms that the manuscript is an honest, accurate, and transparent account of the study being reported; that no important aspects of the study have been omitted; and that any discrepancies from the study as planned (and, if relevant, registered) have been explained.
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