Zhang Kai, Zhang Shufang, Cui Wei, Hong Yucai, Zhang Gensheng, Zhang Zhongheng
Department of Critical Care Medicine, Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China.
Department of Cardiology, Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China.
Front Med (Lausanne). 2021 Jan 21;7:609769. doi: 10.3389/fmed.2020.609769. eCollection 2020.
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. 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. 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. 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.
许多严重程度评分被广泛用于预测重症监护病房（ICU）中危重症患者的临床结局。然而，对于符合脓毒症-3标准的患者，尚未开发出此类评分。本研究旨在开发并验证一种用于预测脓毒症-3患者死亡率的风险分层评分。在这项回顾性队列研究中，我们使用重症监护医学信息集市III（MIMIC III）数据库进行模型开发，并使用电子ICU数据库进行外部验证。我们在患者入住ICU的第1天根据脓毒症-3标准确定脓毒症患者。采用最小绝对收缩和选择算子（LASSO）技术选择预测变量。我们还开发了一个脓毒症死亡率预测模型及相关的风险分层评分。然后，我们将模型的区分度和校准度与其他传统严重程度评分进行了比较。在模型开发过程中，我们共纳入了5443例符合脓毒症-3标准的患者。30天死亡率为16.7%。验证集中有5658例脓毒症患者，其中1135例死亡（死亡率20.1%）。该评分在开发集和验证集中均具有良好的区分度（曲线下面积分别为0.789和0.765）。在验证集中，校准斜率为0.862，Brier值为0.140。在开发数据集中，该评分根据死亡率风险将患者分为低（3.2%）、中（12.4%）、高（30.7%）和极高（68.1%）四类。验证数据集中相应的死亡率分别为2.8%、10.5%、21.1%和51.2%。决策曲线分析表明，该评分始终具有正的净效益。我们观察到名为脓毒症死亡风险评分（SMRS）的该评分具有中等的区分度和校准度，能够根据死亡率风险对患者进行分层。然而，我们仍需要进一步修改和外部验证。
