人工智能对使用呋塞米的住院患者发生急性肾损伤及死亡风险的个性化预测价值

PERSONALIZED PREDICTIVE VALUE OF ARTIFICIAL INTELLIGENCE FOR ACUTE KIDNEY INJURY AND MORTALITY RISK IN HOSPITALIZED PATIENTS RECEIVING FUROSEMIDE

图提示使用呋塞米后患者血肌酐、血清胱抑素C、尿微量白蛋白/肌酐比值、联合使用肾上腺素及联合使用肝素是影响AKI发生的5个最主要因素;使用呋塞米后的24 h尿蛋白定量、血浆凝血酶原时间国际标准化比值、血浆血小板计数、联合应用肾上腺素及住院期间发生AKI是导致患者死亡的5个最主要因素。结论通过机器学习算法建立了使用呋塞米的住院患者发生AKI以及死亡风险的评估模型,该模型可以有效预测患者AKI发生及死亡风险的影响因素。

Objective To analyze the association between the use of furosemide in hospitalized patients and the risk of acute kidney injury(AKI)and death based on machine learning and to construct a predictive model.Methods The study inclu-ded 18998 hospitalized patients who had used furosemide in our hospital from October 2017 to October 2020.The predictive model for evaluating furosemide-associated AKI and mortality risks was established using eight machine learning algorithms including Light Gradient Boosting Machine(LightGBM).The receiver operating characteristic(ROC)curve and decision curve analysis(DCA)were used to assess the discrimination,calibration,and clinical utility of the model.Feature importance analysis of the predictive model with the highest area under the curve(AUC)was conducted using SHAP summary plots,while SHAP force and decision plots were used to explain the individualized decision-making process for predicting AKI and mortality.Results Among the eight machine learning models,the LightGBM model exhibited an AUC of 0.814 for predicting AKI and 0.949 for predicting mortality risk,and the ROC and DCA curves confirmed its strong performance in terms of calibration and clinical application.SHAP summary plots revealed that after using furosemide,crucial factors affecting the occurrence of AKI included serum creatinine,se-rum cystatin C,urine microalbumin/creatinine ratio,and the concurrent use of adrenaline and heparin;meanwhile,the most significant factors leading to mortality in patients using furosemide encompassed 24-hour urinary protein quantification,plasma prothrombin time and international normalized ratio,plasma platelet count,the concurrent use of adrenaline,and the development of AKI du-ring hospitalization.Conclusion A predictive model has been established to assess the risk of AKI occurrence and mortality in hospitalized patients using furosemide by employing machine learning algorithms.This model effectively identifies the influencing factors in predicting the risk of AKI and mortality in these patients.