TY  - JOUR
AU  - Choi, Jung-Yeon
AU  - Yoo, Sooyoung
AU  - Song, Wongeun
AU  - Kim, Seok
AU  - Baek, Hyunyoung
AU  - Lee, Jun Suh
AU  - Yoon, Yoo-Seok
AU  - Yoon, Seonghae
AU  - Lee, Hae-Young
AU  - Kim, Kwang-il
PY  - 2023
DA  - 2023/11/13
TI  - 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
JO  - J Med Internet Res
SP  - e42259
VL  - 25
KW  - CDM
KW  - common data model
KW  - patient-level prediction
KW  - OHDSI
KW  - Observational Health Data Sciences and Informatics
KW  - postoperative outcome
KW  - postoperative
KW  - surgery
KW  - elderly
KW  - elder
KW  - predict
KW  - adverse event
KW  - adverse outcome
KW  - geriatric
KW  - older adult
KW  - ageing
KW  - model
KW  - algorithm
AB  - Background: Older adults are at an increased risk of postoperative morbidity. Numerous risk stratification tools exist, but effort and manpower are required. Objective: This 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. Methods: We 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. Results: Data 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. Conclusions: The derived model shall assist clinicians and patients in understanding the individualized risks and benefits of surgery. 
SN  - 1438-8871
UR  - https://www.jmir.org/2023/1/e42259
UR  - https://doi.org/10.2196/42259
UR  - http://www.ncbi.nlm.nih.gov/pubmed/37955965
DO  - 10.2196/42259
ID  - info:doi/10.2196/42259
ER  -