Zhang Tiancong, Wang Shuang, Meng Qiang, Li Liman, Yuan Mengxue, Guo Shuo, Fu Yang
Department of Laboratory Medicine, West China Hospital, Sichuan University, Chengdu, Sichuan, 610041, China.
Sichuan Clinical Research Center for Laboratory Medicine, Chengdu, Sichuan, 610041, China.
Heliyon. 2024 Jul 11;10(14):e34498. doi: 10.1016/j.heliyon.2024.e34498. eCollection 2024 Jul 30.
Sepsis, a severe infectious disease, carries a high mortality rate. Early detection and prompt treatment are crucial for reducing mortality and improving prognosis. The aim of this research is to develop a clinical prediction model using machine learning algorithms, leveraging complete blood cell (CBC) parameters, to detect sepsis at an early stage.
The study involved 572 patients admitted to West China Hospital of Sichuan University between July 2020 and September 2021. Among them, 215 were diagnosed with sepsis, while 357 had local infections. Demographic information was collected, and 57 CBC parameters were analyzed to identify potential predictors using techniques such as the Least Absolute Shrinkage and Selection Operator (LASSO), Random Forest (RF), Support Vector Machine (SVM), and eXtreme Gradient Boosting (XGBoost). The prediction model was built using Logistic Regression and evaluated for diagnostic specificity, discrimination, and clinical applicability including metrics such as the area under the curve (AUC), calibration curve, clinical impact curve, and clinical decision curve. Additionally, the model's diagnostic performance was assessed on a separate validation cohort. Shapley's additive explanations (SHAP), and breakdown (BD) profiles were used to explain the contribution of each variable in predicting the outcome.
Among all the machine learning methods' prediction models, the LASSO-based model (λ = min) demonstrated the highest diagnostic performance in both the discovery cohort (AUC = 0.9446, < 0.001) and the validation cohort (AUC = 0.9001, < 0.001). Furthermore, upon local analysis and interpretation of the model, we demonstrated that LY-Z, MO-Z, and PLT-I had the most significant impact on the outcome.
The predictive model based on CBC parameters can be utilized as an effective approach for the early detection of sepsis.
脓毒症是一种严重的传染病，死亡率很高。早期检测和及时治疗对于降低死亡率和改善预后至关重要。本研究的目的是利用机器学习算法，借助全血细胞（CBC）参数，开发一种临床预测模型，以早期检测脓毒症。
该研究纳入了2020年7月至2021年9月期间在四川大学华西医院住院的572例患者。其中，215例被诊断为脓毒症，357例有局部感染。收集了人口统计学信息，并分析了57项CBC参数，使用最小绝对收缩和选择算子（LASSO）、随机森林（RF）、支持向量机（SVM）和极端梯度提升（XGBoost）等技术来识别潜在的预测因素。使用逻辑回归构建预测模型，并评估其诊断特异性、区分度和临床适用性，包括曲线下面积（AUC）、校准曲线、临床影响曲线和临床决策曲线等指标。此外，在一个单独的验证队列中评估了该模型的诊断性能。使用夏普利值加法解释（SHAP）和分解（BD）概况来解释每个变量在预测结果中的贡献。
在所有机器学习方法的预测模型中，基于LASSO的模型（λ = min）在发现队列（AUC = 0.9446，<0.001）和验证队列（AUC = 0.9001，<0.001）中均表现出最高的诊断性能。此外，在对模型进行局部分析和解释时，我们发现LY-Z、MO-Z和PLT-I对结果的影响最为显著。
基于CBC参数的预测模型可作为早期检测脓毒症的有效方法。
