Development and validation of artificial intelligence models for early detection of postoperative infections (PERISCOPE): a multicentre study using electronic health record data
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Le résumé fourni par la source
Background: Postoperative infections significantly impact patient outcomes and costs, exacerbated by late diagnoses, yet early reliable predictors are scarce. Existing artificial intelligence (AI) models for postoperative infection prediction often lack external validation or perform poorly in local settings when validated. We aimed to develop locally valid models as part of the PERISCOPE AI system to enable early detection, safer discharge, and more timely treatment of patients. Methods: We developed and validated XGBoost models to predict postoperative infections within 7 and 30 days of surgery. Using retrospective pre-operative and intra-operative electronic health record data from 2014 to 2023 across various surgical specialities, the models were developed at Hospital A and validated and updated at Hospitals B and C in the Netherlands and Belgium. Model performance was evaluated before and after updating using the two most recent years of data as temporal validation datasets. Main outcome measures were model discrimination (area under the receiver operating characteristic curve (AUROC)), calibration (slope, intercept, and plots), and clinical utility (decision curve analysis with net benefit). Findings: The study included 253,010 surgical procedures with 23,903 infections within 30-days. Discriminative performance, calibration properties, and clinical utility significantly improved after updating. Final AUROCs after updating for Hospitals A, B, and C were 0.82 (95% confidence interval (CI) 0.81-0.83), 0.82 (95% CI 0.81-0.83), and 0.91 (95% CI 0.90-0.91) respectively for 30-day predictions on the temporal validation datasets (2022-2023). Calibration plots demonstrated adequate correspondence between observed outcomes and predicted risk. All local models were deemed clinically useful as the net benefit was higher than default strategies (treat all and treat none) over a wide range of clinically relevant decision thresholds. Interpretation: PERISCOPE can accurately predict overall postoperative infections within 7- and 30-days post-surgery. The robust performance implies potential for improving clinical care in diverse clinical target populations. This study supports the need for approaches to local updating of AI models to account for domain shifts in patient populations and data distributions across different clinical settings. Funding: This study was funded by a REACT EU grant from European Regional Development Fund (ERDF) and Kansen voor West.
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Le contrôle bibliographique ouvert
DOI retrouvé dans Crossref DOI retrouvé ; titre concordant.
- Titre Crossref
- Development and validation of artificial intelligence models for early detection of postoperative infections (PERISCOPE): a multicentre study using electronic health record data
- Date Crossref
- 01/02/2025
- Éditeur
- Elsevier BV
- Type
- journal-article
Ce recoupement confirme des métadonnées liées au DOI. Il ne confirme ni la méthode ni les conclusions de l’étude, et il ne compte pas comme une seconde source scientifique indépendante.
Où se fait cette recherche
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Leiden University Medical Center pays non établi dans la noticeOrganisme public
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Radboud University Nijmegen pays non établi dans la noticeUniversité ou école supérieure
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Radboud University Medical Center pays non établi dans la noticeOrganisme public
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Transnational University Limburg pays non établi dans la noticeUniversité ou école supérieure
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Ziekenhuis Oost-Limburg pays non établi dans la noticeÉtablissement de santé
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Hasselt University pays non établi dans la noticeUniversité ou école supérieure
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Healthplus.ai B.V. pays non établi dans la noticeInstitution
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Leiden University Medical Centre Intensive Care Unit pays non établi dans la noticeUniversité ou école supérieure
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Radboud University Medical Centre General Surgery Department pays non établi dans la noticeUniversité ou école supérieure
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Department of Anaesthesiology pays non établi dans la noticeInstitution
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Faculty of Medicine and Life Sciences pays non établi dans la noticeUniversité ou école supérieure
Leiden University Medical Center, Radboud University Nijmegen et Radboud University Medical Center, avec 8 autres affiliations.
Une affiliation ne permet pas de déduire la nationalité d’un auteur.