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Chinese Journal of Clinical Pharmacology and Therapeutics ›› 2024, Vol. 29 ›› Issue (11): 1272-1279.doi: 10.12092/j.issn.1009-2501.2024.11.009

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Construction and evaluation of dynamic nomogram model prediction model for early acute renal injury risk after heart transplantation

CHEN Ye1,2, JIANG Yingshuo3, ZHU Xinyue1,2, DU Wenxin1,2, CHEN Xin3, LOU Sheng1,2, SUN Jianguo4, ZHU Junrong1,2   

  1. 1Department of Pharmacy, Nanjing First Hospital, China Pharmaceutical University, Nanjing 210006, Jiangsu, China; 2Department of Pharmacy, Nanjing First Hospital, Nanjing Medical University, Nanjing 210006, Jiangsu, China; 3Department of Cardiothoracic Surgery, Nanjing First Hospital, Nanjing Medical University, Nanjing 210006, Jiangsu, China; 4Key Laboratory of Drug Metabolism and Pharmacokinetics, China Pharmaceutical University, Nanjing 210009, Jiangsu, China
  • Received:2024-07-22 Revised:2024-09-05 Online:2024-11-26 Published:2024-10-24

Abstract:

AIM: To analyze and screen the risk factors for acute kidney injury (AKI) following heart transplantation (HT), and to establish a dynamic nomograms prediction model to forecast early AKI after HT. METHODS: A retrospective analysis was conducted on clinical data from HT recipients at Nanjing First Hospital from October 2012 to June 2024. Patients were divided into AKI and non-AKI groups based on AKI occurrence within 7 days post-surgery, with a 8:2 ratio for training and testing sets. Lasso regression and multivariable logistic regression were used to select influencing factors. A dynamic nomogram model was visualized using R. Internal validation was performed using 1 000 bootstrap samples. Model accuracy and discrimination were evaluated using the area under the receiver operating characteristic curve (AUC-ROC), calibration curves, and the Hosmer-Lemeshow goodness-of-fit test. The nomogram model was compared with the Cleveland score. RESULTS: The results of a multivariable logistic regression indicate that a history of atrial fibrillation (OR=9.647, 95% CI=1.961-47.470), vasoactive inotropic score (OR=1.094, 95% CI=1.012-1.183), intraoperative transfusion of red blood cells or plasma (OR=10.200, 95% CI=1.727-60.238), postoperative central venous pressure (OR=1.548, 95% CI=1.186-2.021), and postoperative use of vancomycin (OR=25.082, 95% CI=2.122-296.417) are independent risk factors for HT-AKI. The dynamic nomogram model achieved an AUC of 0.842 (95% CI: 0.676-0.971) in the test set, with a calibration plot showing a slope close to 1 and a Brier score of 0.173. The Hosmer-Lemeshow goodness-of-fit test (χ2=5.658, P=0.685) suggests good predictive performance of the model. Moreover, this model demonstrates superior discriminative ability compared to the Cleveland score. CONCLUSION: This study identified preoperative, intraoperative, and postoperative risk factors influencing the occurrence of HT-AKI. The developed dynamic nomogram model accurately identifies high-risk individuals for early HT-AKI and is convenient for clinical use.

Key words: heart transplantation, acute kidney injury, prediction model, dynamic nomogram

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