ArticleEClinicalMedicine2022
ACCEPT 2·0: Recalibrating and externally validating the Acute COPD exacerbation prediction tool (ACCEPT).
Article in EClinicalMedicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers, 4 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
18 citing papers in PubMed, 4 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
- Pooled it
- AI/ML driven prediction of COPD exacerbations and readmissions: a systematic review and meta-analysis.Frontiers in digital health · 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 peripheral blood transcriptomic gene signature to predict bronchopulmonary dysplasia.American journal of physiology. Lung cellular and molecular physiology · 2023Trial
- ACCEPT 3.0: Recalibrating ACute COPD Exacerbation Prediction Tool (ACCEPT) for global use.Diagnostic and prognostic research · 2026Article
- Nonparametric Assessment of the Calibration of Individualized Treatment Effects.Statistics in medicine · 2026Article
- 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
- Innovative Applications and Challenges of Artificial Intelligence in the Whole-Course Management of Chronic Obstructive Pulmonary Disease.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
- From Logistic Regression to Foundation Models: Factors Associated With Improved Forecasts.Cureus · 2025Review
- AORI-HAP: a multidimensional risk index to predict in-hospital adverse outcomes in asthma exacerbations.Frontiers in medicine · 2025Article
- Risk Factors for Recurrent Exacerbations in the General-Practitioner-Based Swiss Chronic Obstructive Pulmonary Disease (COPD) Cohort.Journal of clinical medicine · 2023Article
- Digital health in chronic obstructive pulmonary disease.Chronic diseases and translational medicine · 2023Review
- IMplementing Predictive Analytics towards efficient COPD Treatments (IMPACT): protocol for a stepped-wedge cluster randomized impact study.Diagnostic and prognostic research · 2023Article
- Prediction value of neutrophil and eosinophil count at risk of COPD exacerbation.Annals of medicine · 2023Article
Corrections and comments
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Authors and funding
6 authors at 1 institution in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: The Acute Chronic Obstructive Pulmonary Disease (COPD) Exacerbation Prediction Tool (ACCEPT) was developed for individualised prediction of COPD exacerbations. ACCEPT was well calibrated overall and had a high discriminatory power, but overestimated risk among individuals without recent exacerbations. The objectives of this study were to 1) fine-tune ACCEPT to make better predictions for individuals with a negative exacerbation history, 2) develop more parsimonious models, and 3) externally validate the models in a new dataset. Methods: We recalibrated ACCEPT using data from the Evaluation of COPD Longitudinally to Identify Predictive Surrogate End-points (ECLIPSE, a three-year observational study, 1,803 patients, 2,117 exacerbations) study by applying non-parametric regression splines to the predicted rates. We developed three reduced versions of ACCEPT by removing symptom score and/or baseline medications as predictors. We examined the discrimination, calibration, and net benefit of ACCEPT 2·0 in the placebo arm of the Towards a Revolution in COPD Health (TORCH, a three-year randomised clinical trial of inhaled therapies in COPD, 1,091 patients, 1,064 exacerbations) study. The primary outcome for prediction was the occurrence of ≥2 moderate or ≥1 severe exacerbation in the next 12 months; the secondary outcomes were prediction of the occurrence of any moderate/severe exacerbation or any severe exacerbation. Findings: ACCEPT 2·0 had an area-under-the-curve (AUC) of 0·76 for predicting the primary outcome. Exacerbation history alone (current standard of care) had an AUC of 0·68. The model was well calibrated in patients with positive or negative exacerbation histories. Changes in AUC in reduced versions were minimal for the primary outcome as well as for predicting the occurrence of any moderate/severe exacerbations (ΔAUC<0·011), but more substantial for predicting the occurrence of any severe exacerbations (ΔAUC<0·020). All versions of ACCEPT 2·0 provided positive net benefit over the use of exacerbation history alone for some range of thresholds. Interpretation: ACCEPT 2·0 showed good calibration regardless of exacerbation history, and predicts exacerbation risk better than current standard of care for a range of thresholds. Future studies need to investigate the utility of exacerbation prediction in various subgroups of patients. Funding: This study was funded by a team grant from the Canadian Institutes of Health Research (PHT 178432).
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Registered trials
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