Supplementary tables of sepsis machine learning

Context: Non-thyroid disease syndrome (NTIS) occurs in various serious illnesses, with sepsis being a common cause. Early identification of NTIS risk in sepsis patients is crucial. Objective: This study aims to investigate the effectiveness of machine learning algorithms in predicting NTIS occurrence and mortality in sepsis patients.Methods: Clinical data, including biochemical markers, physiological parameters, and treatment information, were collected from sepsis patients to train and test eight machine learning models: eXtreme Gradient Boosting (XGBoost), generalized linear model (GLM), logistic regression, Poisson regression, random forest, support vector machine (SVM), decision tree, and Lasso regression. The area under the receiver operating characteristic curve (ROC AUC), accuracy, precision, recall, and balanced accuracy were used to assess the accuracy and efficacy of each model.Results: XGBoost showed the highest accuracy and sensitivity in predicting NTIS occurrence and mortality. Lower levels of T3, FT4, kappa/lambda (KAP/LAM) ratio, and anti-thymocyte globulin (ATG) were associated with increased NTIS risk. Mechanical ventilation, age, adrenaline, norepinephrine, lower CD3 counts, and lower fibrinogen (FIB) were indicative of worse outcomes.Conclusions: Machine learning algorithms, especially XGBoost, effectively predict NTIS occurrence and mortality in sepsis patients. XGBoost outperformed other models in accuracy and sensitivity, highlighting its potential in early identification and risk assessment of NTIS in critically ill patients.

背景：非甲状腺疾病综合征（NTIS）在各种严重疾病中均有发生，败血症是其常见诱因。早期识别败血症患者中的NTIS风险至关重要。研究目的：本研究旨在探讨机器学习算法在预测败血症患者NTIS发生和死亡率方面的有效性。方法：收集败血症患者的临床数据，包括生化标志物、生理参数和治疗信息，以训练和测试八种机器学习模型：XGBoost（极端梯度提升），广义线性模型（GLM），逻辑回归，泊松回归，随机森林，支持向量机（SVM），决策树和Lasso回归。使用受试者工作特征曲线下面积（ROC AUC）、准确率、精确率、召回率和平衡准确率来评估每个模型的准确性和有效性。结果：XGBoost在预测NTIS发生和死亡率方面表现出最高的准确性和灵敏度。T3、FT4、κ/λ（KAP/LAM）比率和抗胸腺细胞球蛋白（ATG）水平降低与NTIS风险增加相关。机械通气、年龄、肾上腺素、去甲肾上腺素、CD3计数降低和纤维蛋白原（FIB）水平降低是预后不良的指标。结论：机器学习算法，尤其是XGBoost，在预测败血症患者的NTIS发生和死亡率方面表现出显著效果。在准确性和灵敏度方面，XGBoost优于其他模型，凸显了其在重症患者早期识别和风险评估NTIS方面的潜力。