An explainable machine learning algorithm for risk factor analysis of in-hospital mortality in sepsis survivors with ICU readmission
An explainable machine learning algorithm for risk factor analysis of in-hospital mortality in sepsis survivors with ICU readmission
复制标题
一种可解释的机器学习算法，用于对再入 ICU 的脓毒症幸存者院内死亡率进行危险因素分析
DOI：
10.1016/j.cmpb.2021.106040
复制
发表时间：
2021-03-27
影响因子：
6.1
通讯作者：
Bian, Jinjun
中科院分区：
文献类型：
--
作者：
Jiang, Zhengyu;Bo, Lulong;Bian, Jinjun
背景和目的：重症监护病房(ICU)的脓毒症幸存者(脓毒症幸存者)的长期死亡率和ICU再入院风险增加。我们的目标是通过应用机器学习(ML)算法，识别较晚再住ICU的脓毒症幸存者的住院死亡率的危险因素，并可视化个体危险因素与死亡率之间的定量关系。方法：数据来自第三代重症监护医学信息集市(MIMIC-III)数据库，其中包括后来再次住进ICU的脓毒症和非脓毒症ICU幸存者。结果：在2,970例入选患者中，脓毒症存活者的住院死亡率显著高于非脓毒症存活者(50.4%比30.7%，P<；0.001)。ML算法确定了这些组中与死亡风险相关的18个特征；其中，BUN、年龄、体重和最低心率是两组共同的，剩余的平均收缩压、尿量、白蛋白、血小板、乳酸、活化的部分凝血活酶时间(APTT)、钾、二氧化碳分压、氧分压、呼吸频率、格拉斯哥昏迷评分(GCS)睁眼评分、阴离子间隙、性别和温度是既往脓毒症幸存者特有的。ML算法还计算了每个因素对脓毒症幸存者死亡风险的数量贡献和值得注意的阈值。结论：在ICU再入院期间，发现14个具有对应阈值的特定参数与脓毒症幸存者的住院死亡率相关。先进ML技术的构建可以支持预测模型的分析和开发，这些模型可以用于支持在危重护理患者的临床环境中做出的决定和治疗策略。(C)2021由Elsevier B.V.出版。
Background and objective: Patients who survive sepsis in the intensive care unit (ICU) (sepsis survivors) have an increased risk of long-term mortality and ICU readmission. We aim to identify the risk factors for in-hospital mortality in sepsis survivors with later ICU readmission and visualize the quantitative relationship between the individual risk factors and mortality by applying machine learning (ML) algorithm.Methods: Data were obtained from the Medical Information Mart for Intensive Care III (MIMIC-III) database for sepsis and non-sepsis ICU survivors who were later readmitted to the ICU. The data on the first day of ICU readmission and the in-hospital mortality was combined for the ML algorithm modeling and the SHapley Additive exPlanations (SHAP) value of the correlation between the risk factors and the outcome.Results: Among the 2970 enrolled patients, in-hospital mortality during ICU readmission was significantly higher in sepsis survivors ( n = 2228) than nonsepsis survivors ( n = 742) (50.4% versus 30.7%, P < 0.001). The ML algorithm identified 18 features that were associated with a risk of mortality in these groups; among these, BUN, age, weight, and minimum heart rate were shared by both groups, and the remaining mean systolic pressure, urine output, albumin, platelets, lactate, activated partial thromboplastin time (APTT), potassium, pCO2, pO2, respiration rate, Glasgow Coma Scale (GCS) score for eye-opening, anion gap, sex and temperature were specific to previous sepsis survivors. The ML algorithm also calculated the quantitative contribution and noteworthy threshold of each factor to the risk of mortality in sepsis survivors.Conclusion: 14 specific parameters with corresponding thresholds were found to be associated with the in-hospital mortality of sepsis survivors during the ICU readmission. The construction of advanced ML techniques could support the analysis and development of predictive models that can be used to support the decisions and treatment strategies made in a clinical setting in critical care patients.(c) 2021 Published by Elsevier B.V.