Observational studyJournal of medical Internet research2023
Development and Validation of a Prognostic Classification Model Predicting Postoperative Adverse Outcomes in Older Surgical Patients Using a Machine Learning Algorithm: Retrospective Observational Network Study.
Observational study in Journal of medical Internet research, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers, 2 of them syntheses that pooled it.
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Who cites it
10 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Machine learning application in the prediction of postoperative delirium among elderly patients: a systematic review and meta-analysis.Langenbeck's archives of surgery · 2026Pooled it
- Machine learning models for predicting postoperative delirium in non-cardiac surgery patients - systematic review and meta-analysis.GeroScience · 2026Pooled it
- Development and Internal Validation of a Prognostic Model for Delayed Ambulation Recovery After Major Abdominal Surgery in Northwest Ethiopia. A Prospective Multicenter Study.Journal of epidemiology and global health · 2026Article
- Determining the ground truth for the prediction of delirium in adult patients in acute care: a scoping review.JAMIA open · 2025Review
- Machine Learning Multimodal Model for Delirium Risk Stratification.JAMA network open · 2025Article
- Development and validation comparison of multiple models for perioperative neurocognitive disorders during hip arthroplasty.Scientific reports · 2025Article
- Machine learning based on nutritional assessment to predict adverse events in older inpatients with possible sarcopenia.Aging clinical and experimental research · 2025Article
- Development and Validation of a Risk Predictive Model for Adverse Postoperative Health Status of Elderly Patients Undergoing Major Abdominal Surgery Using Lasso-Logistic Regression.Clinical interventions in aging · 2025Article
- Hospital Length of Stay Prediction for Planned Admissions Using Observational Medical Outcomes Partnership Common Data Model: Retrospective Study.Journal of medical Internet research · 2024Article
- Development and Validation of a Prognostic Classification Model Predicting Postoperative Adverse Outcomes in Older Surgical Patients Using a Machine Learning Algorithm: Retrospective Observational Network Study.Journal of medical Internet research · 2023Observational
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Authors and funding
10 authors.
Funding
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Abstract
backgroundOlder adults are at an increased risk of postoperative morbidity. Numerous risk stratification tools exist, but effort and manpower are required.
objectiveThis study aimed to develop a predictive model of postoperative adverse outcomes in older patients following general surgery with an open-source, patient-level prediction from the Observational Health Data Sciences and Informatics for internal and external validation.
methodsWe used the Observational Medical Outcomes Partnership common data model and machine learning algorithms. The primary outcome was a composite of 90-day postoperative all-cause mortality and emergency department visits. Secondary outcomes were postoperative delirium, prolonged postoperative stay (≥75th percentile), and prolonged hospital stay (≥21 days). An 80% versus 20% split of the data from the Seoul National University Bundang Hospital (SNUBH) and Seoul National University Hospital (SNUH) common data model was used for model training and testing versus external validation. Model performance was evaluated using the area under the receiver operating characteristic curve (AUC) with a 95% CI.
resultsData from 27,197 (SNUBH) and 32,857 (SNUH) patients were analyzed. Compared to the random forest, Adaboost, and decision tree models, the least absolute shrinkage and selection operator logistic regression model showed good internal discriminative accuracy (internal AUC 0.723, 95% CI 0.701-0.744) and transportability (external AUC 0.703, 95% CI 0.692-0.714) for the primary outcome. The model also possessed good internal and external AUCs for postoperative delirium (internal AUC 0.754, 95% CI 0.713-0.794; external AUC 0.750, 95% CI 0.727-0.772), prolonged postoperative stay (internal AUC 0.813, 95% CI 0.800-0.825; external AUC 0.747, 95% CI 0.741-0.753), and prolonged hospital stay (internal AUC 0.770, 95% CI 0.749-0.792; external AUC 0.707, 95% CI 0.696-0.718). Compared with age or the Charlson comorbidity index, the model showed better prediction performance.
conclusionsThe derived model shall assist clinicians and patients in understanding the individualized risks and benefits of surgery.
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