ArticleThe Lancet. Respiratory medicine2020
The Acute COPD Exacerbation Prediction Tool (ACCEPT): a modelling study.
Article in The Lancet. Respiratory medicine, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 74 papers, 4 of them syntheses that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
74 citing papers in PubMed, 4 syntheses or guidelines pooled it, 140 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
- Quantitative computed tomography and predictive modelling for COPD exacerbations.European respiratory review : an official journal of the European Respiratory Society · 2025Pooled it
- Applications of machine learning approaches for pediatric asthma exacerbation management: a systematic review.BMC medical informatics and decision making · 2025Pooled it
- A 7-point evidence-based care discharge protocol for patients hospitalized for exacerbation of COPD: consensus strategy and expert recommendation.NPJ primary care respiratory medicine · 2024Pooled it
- Trial
- Development of a daily predictive model for the exacerbation of chronic obstructive pulmonary disease.Scientific reports · 2023Trial
- 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
- ACCEPT 3.0: Recalibrating ACute COPD Exacerbation Prediction Tool (ACCEPT) for global use.Diagnostic and prognostic research · 2026Article
- Development and validation of PRECISE-X model: predicting first severe exacerbation in COPD.Thorax · 2026Article
- Short-term prediction of COPD exacerbations based on wearable vital sign monitoring.PLOS digital health · 2026Article
- Predictive models for mortality readmission events and cardiovascular complications in patients with COPD: a systematic review and meta-analysis.ERJ open research · 2026Article
- Development and validation of a risk stratification tool for predicting COPD exacerbations in patients without prior exacerbation history.BMJ open respiratory research · 2026Article
- Tackling NHS winter pressures due to respiratory disease: the UK Centre for Applied Respiratory Research Innovation and Impact (CARRii).NPJ primary care respiratory medicine · 2026Review
- 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
- Prolonged expiratory time constant and risk of moderate-to-severe exacerbations in stable COPD.Scientific reports · 2026Article
- Prediction of Chronic Obstructive Pulmonary Disease Using Machine Learning, Clinical Summary Notes, and Vital Signs: A Single-Center Retrospective Cohort Study in the United States.Advances in respiratory medicine · 2026Article
- Predictors of COPD exacerbations differ between grades of airflow limitation.ERJ open research · 2026Article
- Construction and Validation of a Prediction Model for Sustained Smoking Cessation in Patients with Chronic Obstructive Pulmonary Disease.International journal of chronic obstructive pulmonary disease · 2026Article
- The BREATH-TRACHER 2 Trial: Protocol for a Retrospective Mixed Methods Study to Establish the Utility of a Wearable Device in the Detection of Chronic Obstructive Pulmonary Disease Exacerbations.JMIR research protocols · 2025Article
- Impact of dynapenia, presarcopenia and sarcopenia in chronic obstructive pulmonary disease: a prospective cohort study.BMJ open respiratory research · 2025Article
14 more citing papers are in PubMed but not listed here.
Corrections and comments
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Authors and funding
7 authors at 4 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundAccurate prediction of exacerbation risk enables personalised care for patients with chronic obstructive pulmonary disease (COPD). We developed and validated a generalisable model to predict individualised rate and severity of COPD exacerbations.
methodsIn this risk modelling study, we pooled data from three COPD trials on patients with a history of exacerbations. We developed a mixed-effect model to predict exacerbations over 1 year. Severe exacerbations were those requiring inpatient care. Predictors were history of exacerbations, age, sex, body-mass index, smoking status, domiciliary oxygen therapy, lung function, symptom burden, and current medication use. Evaluation of COPD Longitudinally to Identify Predictive Surrogate End-points (ECLIPSE), a multicentre cohort study, was used for external validation.
resultsThe development dataset included 2380 patients, 1373 (58%) of whom were men. Mean age was 64·7 years (SD 8·8). Mean exacerbation rate was 1·42 events per year and 0·29 events per year were severe. When validated against all patients with COPD in ECLIPSE (mean exacerbation rate was 1·20 events per year, 0·27 events per year were severe), the area-under-curve (AUC) was 0·81 (95% CI 0·79-0·83) for at least two exacerbations and 0·77 (95% CI 0·74-0·80) for at least one severe exacerbation. Predicted exacerbation and observed exacerbation rates were similar (1·31 events per year for all exacerbations and 0·25 events per year for severe exacerbations vs 1·20 events per year and 0·27 events per year). In ECLIPSE, in patients with previous exacerbation history (mean exacerbation rate was 1·82 events per year, 0·40 events per year were severe), AUC was 0·73 (95% CI 0·70-0·76) for two or more exacerbations and 0·74 (95% CI 0·70-0·78) for at least one severe exacerbation. Calibration was accurate for severe exacerbations (predicted 0·37 events per year vs observed 0·40 events per year) and all exacerbations (predicted 1·80 events per year vs observed 1·82 events per year).
interpretationThis model can be used as a decision tool to personalise COPD treatment and prevent exacerbations.
fundingCanadian Institutes of Health Research.
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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.