ArticleInternational journal of chronic obstructive pulmonary disease2013
Development and validation of a model to predict the risk of exacerbations in chronic obstructive pulmonary disease.
Article in International journal of chronic obstructive pulmonary disease, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 35 papers, 3 of them syntheses that pooled it.
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The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
35 citing papers in PubMed, 3 syntheses or guidelines pooled it, 71 citations in OpenAlex.
- Community-based prediction models of cardiovascular events, acute exacerbations and all-cause mortality in individuals with chronic obstructive pulmonary disease: a systematic review and meta-analysis on behalf of the International Cardiovascular and Respiratory Alliance.BMJ open respiratory research · 2026Pooled it
- Palliative Care Initiation in Chronic Obstructive Pulmonary Disease: Prognosis-Based, Symptoms-Based or Needs-Based?International journal of chronic obstructive pulmonary disease · 2020Pooled it
- Prognostic models for outcome prediction in patients with chronic obstructive pulmonary disease: systematic review and critical appraisal.BMJ (Clinical research ed.) · 2019Pooled it
- Early Clinically Important Improvement (ECII) and Exacerbation Outcomes in COPD Patients.International journal of chronic obstructive pulmonary disease · 2020Trial
- Integrated Disease Management for Chronic Obstructive Pulmonary Disease in Primary Care, from the Controlled Trial to Clinical Program: A Cohort Study.International journal of chronic obstructive pulmonary diseaseTrial
- Development and validation of PRECISE-X model: predicting first severe exacerbation in COPD.Thorax · 2026Article
- Prognostic score for predicting respiratory admissions among patients with chronic obstructive pulmonary disease in primary care: development and validation in population cohorts (Birmingham Lung Improvement Studies (BLISS)).BMJ (Clinical research ed.) · 2026Observational
- Predictors of COPD exacerbations differ between grades of airflow limitation.ERJ open research · 2026Article
- Prognostic Models for Disease Progression and Outcomes in Chronic Obstructive Pulmonary Disease: A Systematic Review and Meta-Analysis.Journal of clinical medicine · 2025Review
- Leveraging Machine Learning and Real-World Data to Predict Chronic Obstructive Pulmonary Disease Exacerbations.International journal of chronic obstructive pulmonary disease · 2025Article
- Development and validation of a nomogram for assessing survival in acute exacerbation of chronic obstructive pulmonary disease patients.BMC pulmonary medicine · 2024Article
- Developing and validating prediction models for severe exacerbations and readmissions in patients hospitalised for COPD exacerbation (SERCO) in China: a prospective observational study.BMJ open respiratory research · 2024Observational
- Risk Factors for Recurrent Exacerbations in the General-Practitioner-Based Swiss Chronic Obstructive Pulmonary Disease (COPD) Cohort.Journal of clinical medicine · 2023Article
- Machine learning for screening of at-risk, mild and moderate COPD patients at risk of FEVFrontiers in physiology · 2023Article
- Development of the ESEx index: a tool for predicting risk of recurrent severe COPD exacerbations.Therapeutic advances in chronic disease · 2023Article
- Automatically Explaining Machine Learning Predictions on Severe Chronic Obstructive Pulmonary Disease Exacerbations: Retrospective Cohort Study.JMIR medical informatics · 2022Article
- Developing a Machine Learning Model to Predict Severe Chronic Obstructive Pulmonary Disease Exacerbations: Retrospective Cohort Study.Journal of medical Internet research · 2022Article
- Article
- How Well Does the Surprise Question Predict 1-year Mortality for Patients Admitted with COPD?Journal of general internal medicine · 2021Article
- Using Computational Methods to Improve Integrated Disease Management for Asthma and Chronic Obstructive Pulmonary Disease: Protocol for a Secondary Analysis.JMIR research protocols · 2021Article
Corrections and comments
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Authors and funding
8 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purposePrediction 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
methodsThe 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.
resultsThe 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]).
conclusionOur newly developed prediction model can help clinicians to predict the risk of future exacerbations in individual patients with COPD, including those with mild disease.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.