Department of Nephrology, Xiangya Hospital of Central South University, Changsha, Hunan, China.
Department of Pediatrics, The Third Xiangya Hospital, Central South University, Changsha, China.
Front Immunol. 2023 Apr 3;14:1140755. doi: 10.3389/fimmu.2023.1140755. eCollection 2023.
Sepsis-associated acute kidney injury (S-AKI) is considered to be associated with high morbidity and mortality, a commonly accepted model to predict mortality is urged consequently. This study used a machine learning model to identify vital variables associated with mortality in S-AKI patients in the hospital and predict the risk of death in the hospital. We hope that this model can help identify high-risk patients early and reasonably allocate medical resources in the intensive care unit (ICU).
A total of 16,154 S-AKI patients from the Medical Information Mart for Intensive Care IV database were examined as the training set (80%) and the validation set (20%). Variables (129 in total) were collected, including basic patient information, diagnosis, clinical data, and medication records. We developed and validated machine learning models using 11 different algorithms and selected the one that performed the best. Afterward, recursive feature elimination was used to select key variables. Different indicators were used to compare the prediction performance of each model. The SHapley Additive exPlanations package was applied to interpret the best machine learning model in a web tool for clinicians to use. Finally, we collected clinical data of S-AKI patients from two hospitals for external validation.
In this study, 15 critical variables were finally selected, namely, urine output, maximum blood urea nitrogen, rate of injection of norepinephrine, maximum anion gap, maximum creatinine, maximum red blood cell volume distribution width, minimum international normalized ratio, maximum heart rate, maximum temperature, maximum respiratory rate, minimum fraction of inspired O, minimum creatinine, minimum Glasgow Coma Scale, and diagnosis of diabetes and stroke. The categorical boosting algorithm model presented significantly better predictive performance [receiver operating characteristic (ROC): 0.83] than other models [accuracy (ACC): 75%, Youden index: 50%, sensitivity: 75%, specificity: 75%, F1 score: 0.56, positive predictive value (PPV): 44%, and negative predictive value (NPV): 92%]. External validation data from two hospitals in China were also well validated (ROC: 0.75).
After selecting 15 crucial variables, a machine learning-based model for predicting the mortality of S-AKI patients was successfully established and the CatBoost model demonstrated best predictive performance.
脓毒症相关性急性肾损伤（S-AKI）被认为与高发病率和死亡率相关，因此迫切需要建立一种公认的预测死亡率的模型。本研究使用机器学习模型来识别与医院中 S-AKI 患者死亡率相关的重要变量，并预测住院期间的死亡风险。我们希望该模型能够帮助早期识别高危患者，并合理分配重症监护病房（ICU）的医疗资源。
从医疗信息集市重症监护 IV 数据库中检查了 16154 名 S-AKI 患者作为训练集（80%）和验证集（20%）。共收集了 129 个变量，包括基本患者信息、诊断、临床数据和药物记录。我们使用 11 种不同的算法开发和验证了机器学习模型，并选择了性能最佳的模型。然后，使用递归特征消除选择关键变量。使用不同的指标比较每个模型的预测性能。使用 SHapley Additive exPlanations 包在一个临床医生使用的网络工具中解释最佳机器学习模型。最后，我们从两家医院收集了 S-AKI 患者的临床数据进行外部验证。
本研究最终选择了 15 个关键变量，即尿量、最大血尿素氮、去甲肾上腺素注射率、最大阴离子间隙、最大肌酐、最大红细胞体积分布宽度、最小国际标准化比值、最大心率、最高温度、最大呼吸频率、最低吸入氧分数、最低肌酐、最低格拉斯哥昏迷量表和糖尿病和中风的诊断。分类提升算法模型的预测性能明显优于其他模型[接收者操作特征（ROC）：0.83] [准确性（ACC）：75%、约登指数：50%、灵敏度：75%、特异性：75%、F1 评分：0.56、阳性预测值（PPV）：44%、阴性预测值（NPV）：92%]。来自中国两家医院的外部验证数据也得到了很好的验证（ROC：0.75）。
在选择 15 个关键变量后，成功建立了基于机器学习的预测 S-AKI 患者死亡率的模型，CatBoost 模型表现出最佳的预测性能。
