[Construction of a predictive model for early acute kidney injury risk in intensive care unit septic shock patients based on machine learning].

降钙素原 医学 感染性休克 急性肾损伤 重症监护室 接收机工作特性 阿帕奇II 机械通风 重症监护医学 血液滤过 沙发评分 肾脏替代疗法 肌酐 休克(循环) 急诊医学 内科学 败血症 血液透析
作者
Suzhen Zhang,Sujuan Tang,Rong Shan,Manchen Zhu,Jianguo Liu,Qinghe Hu,Cuiping Hao
出处
期刊:PubMed 卷期号:34 (3): 255-259 被引量:3
标识
DOI:10.3760/cma.j.cn121430-20211126-01790
摘要

To analyze the risk factors of acute kidney injury (AKI) in patients with septic shock in intensive care unit (ICU), construct a predictive model, and explore the predictive value of the predictive model.The clinical data of patients with septic shock who were hospitalized in the ICU of the Affiliated Hospital of Jining Medical College from April 2015 to June 2019 were retrospectively analyzed. According to whether the patients had AKI within 7 days of admission to the ICU, they were divided into AKI group and non-AKI group. 70% of the cases were randomly selected as the training set for building the model, and the remaining 30% of the cases were used as the validation set. XGBoost model was used to integrate relevant parameters to predict the risk of AKI in patients with septic shock. The predictive ability was assessed through receiver operator characteristic curve (ROC curve), and was correlated with acute physiology and chronic health evaluation II (APACHE II), sequential organ failure assessment (SOFA), procalcitonin (PCT) and other comparative verification models to verify the predictive value.A total of 303 patients with septic shock were enrolled, including 153 patients with AKI and 150 patients without AKI. The incidence of AKI was 50.50%. Compared with the non-AKI group, the AKI group had higher APACHE II score, SOFA score and blood lactate (Lac), higher dose of norepinephrine (NE), higher proportion of mechanical ventilation, and tachycardiac. In the XGBoost prediction model of AKI risk in septic shock patients, the top 10 features were serum creatinine (SCr) level at ICU admission, NE use, drinking history, albumin, serum sodium, C-reactive protein (CRP), Lac, body mass index (BMI), platelet count (PLT), and blood urea nitrogen (BUN) levels. Area under the ROC curve (AUC) of the XGBoost model for predicting the risk of AKI in patients with septic shock was 0.816, with a sensitivity of 73.3%, a specificity of 71.7%, and an accuracy of 72.5%. Compared with the APACHE II score, SOFA score and PCT, the performance of the model improved significantly. The calibration curve of the model showed that the goodness of fit of the XGBoost model was higher than the other scores (the calibration curve had the lowest score, with a score of 0.205).Compared with the commonly used clinical scores, the XGBoost model can more accurately predict the risk of AKI in patients with septic shock, which helps to make appropriate diagnosis, treatment and follow-up strategies while predicting the prognosis of patients.
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