Development and Validation of a Sepsis Mortality Risk Score for Sepsis-3 Patients in Intensive Care Unit

Frontiers in Medicine

article

Kai Zhang¹  Shufang Zhang²  Wei Cui¹  Yucai Hong³  Gensheng Zhang¹  Zhongheng Zhang³ 

[1] Department of Critical Care Medicine, Second Affiliated Hospital, Zhejiang University School of Medicine;Department of Cardiology, Second Affiliated Hospital, Zhejiang University School of Medicine;Department of Emergency Medicine, Sir Run-Run Shaw Hospital, Zhejiang University School of Medicine

关键词: sepsis-3.0;    critical care;    intensive care unit (ICU);    machine learning;    mortality prediction model;    severity score system;   

DOI  :  10.3389/fmed.2020.609769

学科分类：社会科学、人文和艺术（综合）

来源: Frontiers

【 摘 要 】

Background: Many severity scores are widely used for clinical outcome prediction for critically ill patients in the intensive care unit (ICU). However, for patients identified by sepsis-3 criteria, none of these have been developed. This study aimed to develop and validate a risk stratification score for mortality prediction in sepsis-3 patients. Methods: In this retrospective cohort study, we employed the Medical Information Mart for Intensive Care III (MIMIC III) database for model development and the eICU database for external validation. We identified septic patients by sepsis-3 criteria on day 1 of ICU entry. The Least Absolute Shrinkage and Selection Operator (LASSO) technique was performed to select predictive variables. We also developed a sepsis mortality prediction model and associated risk stratification score. We then compared model discrimination and calibration with other traditional severity scores. Results: For model development, we enrolled a total of 5,443 patients fulfilling the sepsis-3 criteria. The 30-day mortality was 16.7%. With 5,658 septic patients in the validation set, there were 1,135 deaths (mortality 20.1%). The score had good discrimination in development and validation sets (area under curve: 0.789 and 0.765). In the validation set, the calibration slope was 0.862, and the Brier value was 0.140. In the development dataset, the score divided patients according to mortality risk of low (3.2%), moderate (12.4%), high (30.7%), and very high (68.1%). The corresponding mortality in the validation dataset was 2.8, 10.5, 21.1, and 51.2%. As shown by the decision curve analysis, the score always had a positive net benefit. Conclusion: We observed moderate discrimination and calibration for the score termed Sepsis Mortality Risk Score (SMRS), allowing stratification of patients according to mortality risk. However, we still require further modification and external validation.

【 授权许可】

CC BY