Journal of cardiothoracic and vascular anesthesia · 2025
Predicting In-Hospital Acute Kidney Injury after Cardiac Surgery Using Machine Learning.
СтатьяНаблюдательное исследованиеПоддержка NIH: внешняя программаПоддержка исследования: вне правительства СШАНазвание и аннотация: Перевод пока не подготовлен
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Nezafati Kuroush, Cheruku Sreekanth, Wanyan Tingyi, Yang Donghan M, Zhou Qinbo, Jessen Michael E, Neyra Javier A, Zhan Xiaowei, Xie Yang, Fox Amanda
Аннотация
OBJECTIVE: We aimed to develop and evaluate cardiac surgery-associated acute kidney injury (CSA-AKI) machine learning prediction models that included clinical variables, biomarkers, and high-frequency hemodynamic measurements.
DESIGN: A prospective, observational study.
SETTING: A single-center university hospital.
PARTICIPANTS: Six hundred sixty-seven adult patients undergoing elective and urgent cardiac surgery between May 2015 and March 2023.
MEASUREMENTS AND MAIN RESULTS: After excluding patients for clinical and administrative reasons and those with missing data, 602 patients were included in the predictive models. Random forest (RF) and long short-term memory (LSTM) models were trained on data from 492 patients, which included hemodynamic data from throughout the perioperative period, and BNP and [TIMP-2] × [IGFBP7] biomarkers. These models were evaluated in a test set of 110 patients to predict CSA-AKI, which was defined as a serum creatinine rise of 0.3 mg/dL in the first 48 hours after surgery or 1.5 times baseline creatinine between postoperative day 1 to day 7. The RF model achieved an AUC of 0.73 (0.72-0.74) and accuracy of 0.77 (0.76-0.79) on the test set. The RF model achieved a sensitivity and specificity of 0.59 (0.57-0.62) and 0.81 (0.79-0.84), respectively. The LSTM model achieved an AUC of 0.68 (0.67-0.70) and accuracy of 0.72 (0.69-0.74) on the test set. The sensitivity of the LSTM model was 0.60 (0.57-0.64), and the specificity was 0.74 (0.71-0.78).
CONCLUSIONS: We developed two machine learning models that incorporated perioperative biomarkers and hemodynamics as input data to predict CSA-AKI. The models exhibited competitive performance for prediction of the outcome and laid the foundation for further advancement in this critical area of research in kidney injury prevention.
DESIGN: A prospective, observational study.
SETTING: A single-center university hospital.
PARTICIPANTS: Six hundred sixty-seven adult patients undergoing elective and urgent cardiac surgery between May 2015 and March 2023.
MEASUREMENTS AND MAIN RESULTS: After excluding patients for clinical and administrative reasons and those with missing data, 602 patients were included in the predictive models. Random forest (RF) and long short-term memory (LSTM) models were trained on data from 492 patients, which included hemodynamic data from throughout the perioperative period, and BNP and [TIMP-2] × [IGFBP7] biomarkers. These models were evaluated in a test set of 110 patients to predict CSA-AKI, which was defined as a serum creatinine rise of 0.3 mg/dL in the first 48 hours after surgery or 1.5 times baseline creatinine between postoperative day 1 to day 7. The RF model achieved an AUC of 0.73 (0.72-0.74) and accuracy of 0.77 (0.76-0.79) on the test set. The RF model achieved a sensitivity and specificity of 0.59 (0.57-0.62) and 0.81 (0.79-0.84), respectively. The LSTM model achieved an AUC of 0.68 (0.67-0.70) and accuracy of 0.72 (0.69-0.74) on the test set. The sensitivity of the LSTM model was 0.60 (0.57-0.64), and the specificity was 0.74 (0.71-0.78).
CONCLUSIONS: We developed two machine learning models that incorporated perioperative biomarkers and hemodynamics as input data to predict CSA-AKI. The models exhibited competitive performance for prediction of the outcome and laid the foundation for further advancement in this critical area of research in kidney injury prevention.