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Online interpretable dynamic prediction models for postoperative delirium after cardiac surgery under cardiopulmonary bypass developed based on machine learning algorithms: A retrospective cohort study.
Zhao, Xiuxiu; Li, Junlin; Xie, Xianhai; Fang, Zhaojing; Feng, Yue; Zhong, Yi; Chen, Chen; Huang, Kaizong; Ge, Chun; Shi, Hongwei; Si, Yanna; Zou, Jianjun.
Afiliación
  • Zhao X; Department of Anesthesiology, Perioperative and Pain Medicine, Nanjing First Hospital, Nanjing Medical University, Nanjing, China.
  • Li J; School of Basic Medicine and Clinical Pharmacy, China Pharmaceutical University, Nanjing, China; Department of Clinical Pharmacology, Nanjing First Hospital, Nanjing Medical University, Nanjing, China.
  • Xie X; School of Basic Medicine and Clinical Pharmacy, China Pharmaceutical University, Nanjing, China; Department of Clinical Pharmacology, Nanjing First Hospital, Nanjing Medical University, Nanjing, China.
  • Fang Z; Department of Anesthesiology, Perioperative and Pain Medicine, Nanjing First Hospital, Nanjing Medical University, Nanjing, China.
  • Feng Y; Department of Anesthesiology, Perioperative and Pain Medicine, Nanjing First Hospital, Nanjing Medical University, Nanjing, China.
  • Zhong Y; Department of Anesthesiology, Perioperative and Pain Medicine, Nanjing First Hospital, Nanjing Medical University, Nanjing, China.
  • Chen C; Department of Clinical Pharmacology, Nanjing First Hospital, Nanjing Medical University, Nanjing, China; Department of Pharmacy, Nanjing First Hospital, China Pharmaceutical University, Nanjing, China.
  • Huang K; Department of Clinical Pharmacology, Nanjing First Hospital, Nanjing Medical University, Nanjing, China; Department of Pharmacy, Nanjing First Hospital, China Pharmaceutical University, Nanjing, China.
  • Ge C; Department of Clinical Pharmacology, Nanjing First Hospital, Nanjing Medical University, Nanjing, China; Department of Pharmacy, Nanjing First Hospital, China Pharmaceutical University, Nanjing, China.
  • Shi H; Department of Anesthesiology, Perioperative and Pain Medicine, Nanjing First Hospital, Nanjing Medical University, Nanjing, China.
  • Si Y; Department of Anesthesiology, Perioperative and Pain Medicine, Nanjing First Hospital, Nanjing Medical University, Nanjing, China. Electronic address: siyanna@163.com.
  • Zou J; Department of Clinical Pharmacology, Nanjing First Hospital, Nanjing Medical University, Nanjing, China; Department of Pharmacy, Nanjing First Hospital, China Pharmaceutical University, Nanjing, China. Electronic address: zoujianjun100@126.com.
J Psychosom Res ; 176: 111553, 2024 01.
Article en En | MEDLINE | ID: mdl-37995429
OBJECTIVE: Postoperative delirium (POD) is strongly associated with poor early and long-term prognosis in cardiac surgery patients with cardiopulmonary bypass (CPB). This study aimed to develop dynamic prediction models for POD after cardiac surgery under CPB using machine learning (ML) algorithms. METHODS: From July 2021 to June 2022, clinical data were collected from patients undergoing cardiac surgery under CPB at Nanjing First Hospital. A dataset from the same center (October 2022 to November 2022) was also used for temporal external validation. We used ML and deep learning to build models in the training set, optimized parameters in the test set, and finally validated the best model in the validation set. The SHapley Additive exPlanations (SHAP) method was introduced to explain the best models. RESULTS: Of the 885 patients enrolled, 221 (25.0%) developed POD. 22 (22.0%) of 100 validation cohort patients developed POD. The preoperative and postoperative artificial neural network (ANN) models exhibited optimal performance. The validation results demonstrated satisfactory predictive performance of the ANN model, with area under the receiver operator characteristic curve (AUROC) values of 0.776 and 0.684 for the preoperative and postoperative models, respectively. Based on the ANN algorithm, we constructed dynamic, highly accurate, and interpretable web risk calculators for POD. CONCLUSIONS: We successfully developed online interpretable dynamic ANN models as clinical decision aids to identify patients at high risk of POD before and after cardiac surgery to facilitate early intervention or care.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Delirio del Despertar / Procedimientos Quirúrgicos Cardíacos Límite: Humans Idioma: En Revista: J Psychosom Res Año: 2024 Tipo del documento: Article País de afiliación: China Pais de publicación: Reino Unido
Texto completo
Añadir a Mi BVS
Imprimir
XML
PubMed Links
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Delirio del Despertar / Procedimientos Quirúrgicos Cardíacos Límite: Humans Idioma: En Revista: J Psychosom Res Año: 2024 Tipo del documento: Article País de afiliación: China Pais de publicación: Reino Unido