Mollura Maximiliano, Chicco Davide, Paglialonga Alessia, Barbieri Riccardo
Dipartimento di Elettronica Informazione e Bioingegneria, Politecnico di Milano, Milan, Italy.
Institute of Health Policy Management and Evaluation, University of Toronto, Toronto, Ontario, Canada.
PLOS Digit Health. 2024 Mar 15;3(3):e0000459. doi: 10.1371/journal.pdig.0000459. eCollection 2024 Mar.
Systemic inflammatory response syndrome (SIRS) and sepsis are the most common causes of in-hospital death. However, the characteristics associated with the improvement in the patient conditions during the ICU stay were not fully elucidated for each population as well as the possible differences between the two.
The aim of this study is to highlight the differences between the prognostic clinical features for the survival of patients diagnosed with SIRS and those of patients diagnosed with sepsis by using a multi-variable predictive modeling approach with a reduced set of easily available measurements collected at the admission to the intensive care unit (ICU).
Data were collected from 1,257 patients (816 non-sepsis SIRS and 441 sepsis) admitted to the ICU. We compared the performance of five machine learning models in predicting patient survival. Matthews correlation coefficient (MCC) was used to evaluate model performances and feature importance, and by applying Monte Carlo stratified Cross-Validation.
Extreme Gradient Boosting (MCC = 0.489) and Logistic Regression (MCC = 0.533) achieved the highest results for SIRS and sepsis cohorts, respectively. In order of importance, APACHE II, mean platelet volume (MPV), eosinophil counts (EoC), and C-reactive protein (CRP) showed higher importance for predicting sepsis patient survival, whereas, SOFA, APACHE II, platelet counts (PLTC), and CRP obtained higher importance in the SIRS cohort.
By using complete blood count parameters as predictors of ICU patient survival, machine learning models can accurately predict the survival of SIRS and sepsis ICU patients. Interestingly, feature importance highlights the role of CRP and APACHE II in both SIRS and sepsis populations. In addition, MPV and EoC are shown to be important features for the sepsis population only, whereas SOFA and PLTC have higher importance for SIRS patients.
全身炎症反应综合征（SIRS）和脓毒症是院内死亡的最常见原因。然而，对于入住重症监护病房（ICU）期间患者病情改善相关的特征，尚未针对每个群体以及两者之间可能存在的差异进行充分阐明。
本研究的目的是通过使用多变量预测建模方法，利用在重症监护病房（ICU）入院时收集的一组简化的易于获取的测量指标，突出诊断为SIRS的患者与诊断为脓毒症的患者在生存预后临床特征上的差异。
收集了1257例入住ICU的患者（816例非脓毒症SIRS患者和441例脓毒症患者）的数据。我们比较了五种机器学习模型在预测患者生存方面的性能。使用马修斯相关系数（MCC）来评估模型性能和特征重要性，并应用蒙特卡洛分层交叉验证。
极端梯度提升（MCC = 0.489）和逻辑回归（MCC = 0.533）分别在SIRS和脓毒症队列中取得了最高结果。按重要性排序，急性生理学与慢性健康状况评分系统II（APACHE II）、平均血小板体积（MPV）、嗜酸性粒细胞计数（EoC）和C反应蛋白（CRP）在预测脓毒症患者生存方面显示出更高的重要性，而序贯器官衰竭评估（SOFA）、APACHE II、血小板计数（PLTC）和CRP在SIRS队列中具有更高的重要性。
通过使用全血细胞计数参数作为ICU患者生存的预测指标，机器学习模型可以准确预测SIRS和脓毒症ICU患者的生存情况。有趣的是，特征重要性突出了CRP和APACHE II在SIRS和脓毒症人群中的作用。此外，MPV和EoC仅显示为脓毒症人群的重要特征，而SOFA和PLTC对SIRS患者具有更高的重要性。
