Archives of gerontology and geriatrics · 2026
Development and validation of a 90-day complication prediction model for geriatric orthopedic surgery: A multicenter prospective study.
СтатьяМногоцентровое исследованиеВалидационное исследованиеПоддержка исследования: вне правительства СШАНазвание и аннотация: Перевод в очереди
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Li Doudou, Ning Yanmei, Zhang Ting, Liu Xinwen, Yu Jiawen, Chen Guanghui, Wang Ronghui, Shi Jiana, Hu Ying
Аннотация
BACKGROUND: With the aging population, the demand for geriatric orthopedic surgery is increasing, and the rate of postoperative complications is escalating. This study aimed to construct a prediction model based on geriatric comprehensive assessment data and potential risk factors for surgery to assess the risk of complications in older patients within three months after various surgical procedures.
METHODS: Using a prospective cohort design, we split a central dataset (7:3 ratio) into training and internal validation sets. We identified relevant variables using LASSO regression and built predictive models using logistic regression. External validation was performed using data from four other centers. Performance was evaluated by AUC, calibration plots, and DCA, and a Shiny web calculator was subsequently deployed.
RESULTS: The study incorporated six variables related to frailty, nutrition, ADL, anesthesia modality, surgery complexity, and operation duration for modeling. To validate the model, we employed data from the training and validation sets independently. The AUC of the nomogram model based on the training set was 0.816 (95% CI: 0.769-0.863), and the AUC based on the internal validation set was 0.791 (95% CI: 0.716-0.866); the AUC for external validation was 0.789 (95% CI: 0.710-0.868). All calibration and DCA curves demonstrated good predictive performance.
CONCLUSIONS: The predictive model effectively identifies the risk of postoperative complications within three months in older individuals undergoing orthopedic surgery, aiding preoperative risk assessment and patient management to enhance clinical decision-making and improve patient outcomes.
METHODS: Using a prospective cohort design, we split a central dataset (7:3 ratio) into training and internal validation sets. We identified relevant variables using LASSO regression and built predictive models using logistic regression. External validation was performed using data from four other centers. Performance was evaluated by AUC, calibration plots, and DCA, and a Shiny web calculator was subsequently deployed.
RESULTS: The study incorporated six variables related to frailty, nutrition, ADL, anesthesia modality, surgery complexity, and operation duration for modeling. To validate the model, we employed data from the training and validation sets independently. The AUC of the nomogram model based on the training set was 0.816 (95% CI: 0.769-0.863), and the AUC based on the internal validation set was 0.791 (95% CI: 0.716-0.866); the AUC for external validation was 0.789 (95% CI: 0.710-0.868). All calibration and DCA curves demonstrated good predictive performance.
CONCLUSIONS: The predictive model effectively identifies the risk of postoperative complications within three months in older individuals undergoing orthopedic surgery, aiding preoperative risk assessment and patient management to enhance clinical decision-making and improve patient outcomes.