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Zhao X, Li J, Xie X, Fang Z, Feng Y, Zhong Y, Chen C, Huang K, Ge C, Shi H, Si Y, Zou J. Online interpretable dynamic prediction models for postoperative delirium after cardiac surgery under cardiopulmonary bypass developed based on machine learning algorithms: A retrospective cohort study. J Psychosom Res 2024; 176:111553. [PMID: 37995429 DOI: 10.1016/j.jpsychores.2023.111553] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/09/2023] [Revised: 11/12/2023] [Accepted: 11/12/2023] [Indexed: 11/25/2023]
Abstract
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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Affiliation(s)
- Xiuxiu Zhao
- Department of Anesthesiology, Perioperative and Pain Medicine, Nanjing First Hospital, Nanjing Medical University, Nanjing, China
| | - Junlin Li
- School of Basic Medicine and Clinical Pharmacy, China Pharmaceutical University, Nanjing, China; Department of Clinical Pharmacology, Nanjing First Hospital, Nanjing Medical University, Nanjing, China
| | - Xianhai Xie
- School of Basic Medicine and Clinical Pharmacy, China Pharmaceutical University, Nanjing, China; Department of Clinical Pharmacology, Nanjing First Hospital, Nanjing Medical University, Nanjing, China
| | - Zhaojing Fang
- Department of Anesthesiology, Perioperative and Pain Medicine, Nanjing First Hospital, Nanjing Medical University, Nanjing, China
| | - Yue Feng
- Department of Anesthesiology, Perioperative and Pain Medicine, Nanjing First Hospital, Nanjing Medical University, Nanjing, China
| | - Yi Zhong
- Department of Anesthesiology, Perioperative and Pain Medicine, Nanjing First Hospital, Nanjing Medical University, Nanjing, China
| | - Chen Chen
- Department of Clinical Pharmacology, Nanjing First Hospital, Nanjing Medical University, Nanjing, China; Department of Pharmacy, Nanjing First Hospital, China Pharmaceutical University, Nanjing, China
| | - Kaizong Huang
- Department of Clinical Pharmacology, Nanjing First Hospital, Nanjing Medical University, Nanjing, China; Department of Pharmacy, Nanjing First Hospital, China Pharmaceutical University, Nanjing, China
| | - Chun Ge
- Department of Clinical Pharmacology, Nanjing First Hospital, Nanjing Medical University, Nanjing, China; Department of Pharmacy, Nanjing First Hospital, China Pharmaceutical University, Nanjing, China
| | - Hongwei Shi
- Department of Anesthesiology, Perioperative and Pain Medicine, Nanjing First Hospital, Nanjing Medical University, Nanjing, China
| | - Yanna Si
- Department of Anesthesiology, Perioperative and Pain Medicine, Nanjing First Hospital, Nanjing Medical University, Nanjing, China.
| | - Jianjun Zou
- Department of Clinical Pharmacology, Nanjing First Hospital, Nanjing Medical University, Nanjing, China; Department of Pharmacy, Nanjing First Hospital, China Pharmaceutical University, Nanjing, China.
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