ArticleThorax2026
Development and validation of PRECISE-X model: predicting first severe exacerbation in COPD.
Article in Thorax, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- 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
- A machine learning model for predicting short-term in-hospital mortality in acute myocardial infarction with coexisting chronic obstructive pulmonary disease.Frontiers in cardiovascular medicine · 2026Article
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Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectivesIn patients with chronic obstructive pulmonary disease (COPD), severe exacerbations (ECOPDs) impose significant morbidity and mortality. Current guidelines emphasise using ECOPD history to inform preventive treatments but offer limited guidance for risk stratification for the first severe ECOPD.
methodsWe developed and validated PRECISE-X using a cohort of newly diagnosed COPD patients from the UK's Clinical Practice Research Datalink (2004-2022), to predict first severe ECOPD over 5 years (primary outcome) and 12 months (secondary outcome). Predictors were selected via clinical expertise and data-driven methods. Internal-external cross-validation was performed across practice regions to evaluate the model's out-of-sample performance in terms of discrimination (c-statistic), calibration and net benefit.
resultsThe study included 2 19 015 patients (mean age 66.0; 42.4% female). Observed risk of first severe ECOPD was 29.5% at 5 years (4.2% at 1 year). The final model included four mandatory predictors (sex, age, Medical Research Council dyspnoea score and forced expiratory volume in 1 second) and 28 optional predictors. In internal-external cross-validation, the average out-of-sample c-statistic was 0.836 (95% CI 0.827 to 0.846) for 5-year prediction and 0.756 (95% CI 0.746 to 0.766) for 1-year prediction. Calibration across regions was robust, and the model showed positive NB across a wide range of risk thresholds. In a secondary validation assessment among those with available spirometry data with confirmed airflow obstruction, the model was well calibrated and had only a modest decline in discriminatory performance.
conclusionsPRECISE-X accurately predicts the first severe COPD exacerbation using routine clinical data, supporting earlier risk stratification and proactive disease management.
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