J Med Internet Res. 2023;25:e42259.

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.

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제1저자 최정연, 유수영, 송원근 · 교신저자 김광일

PMID 37955965 PMC10682929 DOI 10.2196/42259 IF 8.2 노인병내과

소속 (9)

Departmentof Internal Medicine, Seoul National University Bundang Hospital, Seongnam-si, Republic of Korea.
Office of eHealth Research and Business, Seoul National University Bundang Hospital, Seongnam-si, Republic of Korea.
Department of Health Science and Technology, Graduate School of Convergence Science and Technology, Seoul National University, Seongnam-si, Republic of Korea.
Department of Surgery, G Sam Hospital, Gunpo, Republic of Korea.
Department of Surgery, Seoul National University Bundang Hospital, Seongnam-si, Republic of Korea.
Department of Surgery, Seoul National University College of Medicine, Seoul, Republic of Korea.
Department of Clinical Pharmacology and Therapeutic, Seoul National University Bundang Hospital, Seongnam-si, Republic of Korea.
Department of Internal Medicine, Seoul National University Hospital, Seoul, Republic of Korea.
Department of Internal Medicine, Seoul National University College of Medicine, Seoul, Republic of Korea.

Abstract

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.

©Jung-Yeon Choi, Sooyoung Yoo, Wongeun Song, Seok Kim, Hyunyoung Baek, Jun Suh Lee, Yoo-Seok Yoon, Seonghae Yoon, Hae-Young Lee, Kwang-il Kim. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 13.11.2023.

Keywords

CDMOHDSIObservational Health Data Sciences and Informaticsadverse eventadverse outcomeageingalgorithmcommon data modelelderelderlygeriatricmodelolder adultpatient-level predictionpostoperativepostoperative outcomepredictsurgery

Figures

**Figure 1** Study design for predicting postoperative outcomes in older patients. (A) Prediction of postoperative delirium, prolonged postoperative stay, and a composite of all-cause mortality and emergency department visits on the operation date. (B) Prediction of prolonged hospital stay on the admission date.

**Figure 2** Receiver operating characteristic plots of least absolute shrinkage and selection operator logistic regression of the SNUBH training, SNUBH test (interval validation), and SNUH test (external validation) data sets for the adverse outcomes of (A) composite outcome of all-cause mortality and emergency department visits, (B) postoperative delirium, (C) prolonged postoperative stay, and (D) prolonged hospital stay. The y-axis is specificity and the x-axis is 1-specificity. SNUBH: Seoul National University Bundang Hospital; SNUH: Seoul National University Hospital.

**Figure 3** Comparison of primary outcome prediction between our model of least absolute shrinkage and selection operator logistic regression and (A) age or (B) CCI. AUROC: area under the receiver operating characteristic curve; CCI: Charlson comorbidity index; ROC: receiver operating characteristic.

그림 출처: PubMed Central 공개 논문 (PMC10682929)

Conflict of interest statement

Conflicts of Interest: None declared.

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