Zhengyu Jiang1, Lulong Bo2, Zhenhua Xu3, Yubing Song3, Jiafeng Wang2, Pingshan Wen2, Xiaojian Wan2, Tao Yang2, Xiaoming Deng2, Jinjun Bian4. 1. Faculty of Anesthesiology, Changhai Hospital, Naval Medical University, 168 Changhai Road, Shanghai 200433, China; Department of Anesthesiology, Naval Medical Center, Naval Medical University, Shanghai 200052, China. 2. Faculty of Anesthesiology, Changhai Hospital, Naval Medical University, 168 Changhai Road, Shanghai 200433, China. 3. Heal Sci Technology Co., Ltd, 1606, Tower 5, 2 Rong Hua South Road, BDA, Beijing 100176, China. 4. Faculty of Anesthesiology, Changhai Hospital, Naval Medical University, 168 Changhai Road, Shanghai 200433, China. Electronic address: jinjunbicu@163.com.
Abstract
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.
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.