The Acute COPD Exacerbation Prediction Tool (ACCEPT): development and external validation study of a personalised prediction model
Notice bibliographique
Résumé
Abstract Background Accurate prediction of exacerbation risk enables personalised chronic obstructive pulmonary disease (COPD) care. We developed and validated a generalisable model to predict the individualised rate and severity of COPD exacerbations. Methods We pooled data from three COPD trials on patients with a history of exacerbations. We developed a mixed-effect model to predict exacerbations over one-year. Severe exacerbations were those requiring inpatient care. Predictors were a history of exacerbations, age, sex, body mass index, smoking status, domiciliary oxygen therapy, lung function, symptom burden, and current medication use. ECLIPSE, a multicentre cohort study, was used for external validation. Results The development dataset included 2,380 patients (mean 64·7 years, 1373 [57·7%] men, mean exacerbation rate 1·42/year, 0·29/year [20·5%] severe). When validated against all COPD patients in ECLIPSE (n=1819, mean 63·3 years, 1186 [65·2%] men, mean exacerbation rate 1·20/year, 0·27/year [22·2%] severe), the area-under-curve was 0·81 (95%CI 0·79–0·83) for ≥2 exacerbations and 0·77 (95%CI 0·74–0·80) for ≥1 severe exacerbation. Predicted rates were 0·25/year for severe and 1·31/year for all exacerbations, close to the observed rates (0·27/year and 1·20/year, respectively). In ECLIPSE patients with a prior exacerbation history (n=996, mean 63·6 years, 611 (61·3%) men, mean exacerbation rate 1·82/year, 0·40/year [22·0%] severe), the area-under-curve was 0·73 (95%CI 0·70–0·76) for ≥2 exacerbations and 0·74 (95%CI 0·70–0·78) for ≥1 severe exacerbation. Calibration was accurate for severe exacerbations (predicted=0·37/year, observed=0·40/year) and all exacerbations (predicted=1·80/year, observed=1·82/year). The model is accessible at http://resp.core.ubc.ca/ipress/accept . Interpretation This model can be used as a decision tool to personalise COPD treatment and prevent exacerbations. Research in context Evidence before this study Preventing future exacerbations is a major goal in COPD care. Because of adverse effects, preventative treatments should be reserved for those at a higher risk of future exacerbations. Predicting exacerbation risk in individual patients can guide these clinical decisions. A 2017 systematic review reported that of the 27 identified COPD exacerbation prediction tools, only two had reported external validation and none was ready for clinical implementation. To find the studies that were published afterwards, we searched PubMed for articles on development and validation of COPD exacerbation prediction since 2015, using the search terms “COPD”, “exacerbation”, “model”, and “validation”. We included studies that reported prediction of either the risk or the rate of exacerbations and excluded studies that did not report external validation. Our literature search revealed two more prediction models neither of which was deemed generalisable due to lack of methodological rigour, or local and limited nature of the data available to investigators. Added value of this study We used data from three randomised trials to develop ACCEPT, a clinical prediction tool based on routinely available predictors for COPD exacerbations. We externally validated ACCEPT in a large, multinational prospective cohort. To our knowledge, ACCEPT is the first COPD exacerbation prediction tool that jointly estimates the individualised rate and severity of exacerbations. Successful external validation of ACCEPT showed that its generalisability can be expanded across geography and beyond the setting of therapeutic trials. ACCEPT is designed to be easily applicable in clinical practice and is readily accessible as a web application. Implications of all the available evidence Current guidelines rely on a history of exacerbations as the sole predictor of future exacerbations. Simple clinical and demographic variables, in aggregate, can be used to predict COPD exacerbations with improved accuracy. ACCEPT enables a more personalised approach to treatment based on routinely collected clinical data by allowing clinicians to objectively differentiate risk profiles of patients with similar exacerbation history. Care providers and patients can use individualised exacerbation risk estimates to decide on preventive therapies based on objectively-established or patient-specific thresholds for treatment benefit and harm. COPD clinical researchers can use this tool to target enriched populations for enrolment in clinical trials.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,060 | 0,080 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,002 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,001 |
| Communication savante | 0,002 | 0,001 |
| Science ouverte | 0,002 | 0,002 |
| Intégrité de la recherche | 0,001 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».