ArticleInternational journal of chronic obstructive pulmonary disease2019
A clinical prediction model for hospitalized COPD exacerbations based on "treatable traits".
Article in International journal of chronic obstructive pulmonary disease, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers, 3 of them syntheses that pooled it.
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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
19 citing papers in PubMed, 3 syntheses or guidelines pooled it, 29 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
- Prognostic risk factors for moderate-to-severe exacerbations in patients with chronic obstructive pulmonary disease: a systematic literature review.Respiratory research · 2022Pooled it
- Pooled it
- Chronic Obstructive Pulmonary Disease in Singapore: Current Perspectives on Prevalence, Disease Burden, and Treatment.International journal of chronic obstructive pulmonary disease · 2026Review
- Prognostic Models for Disease Progression and Outcomes in Chronic Obstructive Pulmonary Disease: A Systematic Review and Meta-Analysis.Journal of clinical medicine · 2025Review
- Clinical and Paraclinical Characteristics and Predictive Factors of Chronic Obstructive Pulmonary Exacerbation.Materia socio-medica · 2025Article
- Risk Factors for Recurrent Exacerbations in the General-Practitioner-Based Swiss Chronic Obstructive Pulmonary Disease (COPD) Cohort.Journal of clinical medicine · 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
- Characteristics of Patients with Chronic Obstructive Pulmonary Disease Treated with Long-Acting Bronchodilators in a Real-World Setting in Singapore: A Single-Center Observational Study.International journal of chronic obstructive pulmonary disease · 2022Observational
- Prediction of readmission in patients with acute exacerbation of chronic obstructive pulmonary disease within one year after treatment and discharge.BMC pulmonary 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
- Comparison of Immediate and Sequential Withdrawal of a Systemic Glucocorticoid in the Treatment of Acute Exacerbations of Chronic Obstructive Pulmonary Disease: A Multicenter, Randomized, Double-Blind, Parallel-Controlled, Open-Label Study.Frontiers in molecular biosciences · 2021Article
- Disease-dependent variations in the timing and causes of readmissions in Germany: A claims data analysis for six different conditions.PloS one · 2021Article
- A Nomogram for Predicting Severe Exacerbations in Stable COPD Patients.International journal of chronic obstructive pulmonary disease · 2020Observational
- Predictive modeling of COPD exacerbation rates using baseline risk factors.Therapeutic advances in respiratory diseaseArticle
- Treatable Traits in COPD - A Proposed Approach.International journal of chronic obstructive pulmonary diseaseReview
- The EXACERBANTES Study (Open respiratory archivesArticle
Corrections and comments
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Authors and funding
10 authors at 6 institutions in 3 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Assessing risk of future exacerbations is an important component in COPD management. History of exacerbation is a strong and independent predictor of future exacerbations, and the criterion of ≥2 nonhospitalized or ≥1 hospitalized exacerbation is often used to identify high-risk patients in whom therapy should be intensified. However, other factors or "treatable traits" also contribute to risk of exacerbation. Objective: The objective of the study was to develop and externally validate a novel clinical prediction model for risk of hospitalized COPD exacerbations based on both exacerbation history and treatable traits. Patients and methods: A total of 237 patients from the COPD Registry of Changi General Hospital, Singapore, aged 75±9 years and with mean post-bronchodilator FEV Results: The final model included hospitalized exacerbation rate in the previous year, history of acute invasive/noninvasive ventilation, coronary artery disease, bronchiectasis, and sputum nontuberculous mycobacteria isolation. There was excellent agreement between predicted and observed annual hospitalized exacerbation rates. AUC was 0.789 indicating good discriminative accuracy, and was significantly higher than the AUC of the Global Initiative for Chronic Obstructive Lung Disease (GOLD) risk assessment criterion (history of ≥1 hospitalized exacerbation in the previous year) and the age, dyspnea, and obstruction index. When applied to the independent multicenter validation cohort, the model was well-calibrated and discrimination was good. Conclusion: We have derived and externally validated a novel risk prediction model for COPD hospitalizations which outperforms several other risk indices. Our model incorporates several treatable traits which can be targeted for intervention to reduce risk of future hospitalized exacerbations.
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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.