Zhang Qitian, Xu Lizhen, He Weibin, Lai Xinqi, Huang Xiaohong
Department of Cardiology, Zhangzhou Affiliated Hospital of Fujian Medical University, Zhangzhou, Fujian, China.
Department of Endocrinology, Shengli Clinical Medical College of Fujian Medical University, Fujian Provincial Hospital, Fuzhou University Affiliated Provincial Hospital, Fuzhou, China.
Front Med (Lausanne). 2024 Oct 3;11:1410702. doi: 10.3389/fmed.2024.1410702. eCollection 2024.
Heart failure is a cardiovascular disorder, while sepsis is a common non-cardiac cause of mortality. Patients with combined heart failure and sepsis have a significantly higher mortality rate and poor prognosis, making early identification of high-risk patients and appropriate allocation of medical resources critically important.
We constructed a survival prediction model for patients with heart failure and sepsis using the eICU-CRD database and externally validated it using the MIMIC-IV database. Our primary outcome is the 28-day all-cause mortality rate. The Boruta method is used for initial feature selection, followed by feature ranking using the XGBoost algorithm. Four machine learning models were compared, including Logistic Regression (LR), eXtreme Gradient Boosting (XGBoost), Adaptive Boosting (AdaBoost), and Gaussian Naive Bayes (GNB). Model performance was assessed using metrics such as area under the curve (AUC), accuracy, sensitivity, and specificity, and the SHAP method was utilized to visualize feature importance and interpret model results. Additionally, we conducted external validation using the MIMIC-IV database.
We developed a survival prediction model for heart failure complicated by sepsis using data from 3891 patients in the eICU-CRD and validated it externally with 2928 patients from the MIMIC-IV database. The LR model outperformed all other machine learning algorithms with a validation set AUC of 0.746 (XGBoost: 0.726, AdaBoost: 0.744, GNB: 0.722), alongside accuracy (0.685), sensitivity (0.666), and specificity (0.712). The final model incorporates 10 features: age, ventilation, norepinephrine, white blood cell count, total bilirubin, temperature, phenylephrine, respiratory rate, neutrophil count, and systolic blood pressure. We employed the SHAP method to enhance the interpretability of the model based on the LR algorithm. Additionally, external validation was conducted using the MIMIC-IV database, with an external validation AUC of 0.699.
Based on the LR algorithm, a model was constructed to effectively predict the 28-day all-cause mortality rate in patients with heart failure complicated by sepsis. Utilizing our model predictions, clinicians can promptly identify high-risk patients and receive guidance for clinical practice.
心力衰竭是一种心血管疾病，而脓毒症是常见的非心脏性死亡原因。合并心力衰竭和脓毒症的患者死亡率显著更高，预后较差，因此早期识别高危患者并合理分配医疗资源至关重要。
我们使用eICU-CRD数据库构建了心力衰竭合并脓毒症患者的生存预测模型，并使用MIMIC-IV数据库进行外部验证。我们的主要结局是28天全因死亡率。使用Boruta方法进行初始特征选择，随后使用XGBoost算法进行特征排序。比较了四种机器学习模型，包括逻辑回归（LR）、极端梯度提升（XGBoost）、自适应提升（AdaBoost）和高斯朴素贝叶斯（GNB）。使用曲线下面积（AUC）、准确率、敏感性和特异性等指标评估模型性能，并利用SHAP方法可视化特征重要性并解释模型结果。此外，我们使用MIMIC-IV数据库进行了外部验证。
我们使用eICU-CRD中3891例患者的数据开发了心力衰竭合并脓毒症的生存预测模型，并使用MIMIC-IV数据库中的2928例患者进行了外部验证。LR模型在验证集AUC方面优于所有其他机器学习算法，为0.746（XGBoost：0.726，AdaBoost：0.744，GNB：0.722），同时具有准确率（0.685）、敏感性（0.666）和特异性（0.712）。最终模型纳入了10个特征：年龄、通气、去甲肾上腺素、白细胞计数、总胆红素、体温、去氧肾上腺素、呼吸频率、中性粒细胞计数和收缩压。我们采用SHAP方法增强基于LR算法的模型的可解释性。此外，使用MIMIC-IV数据库进行了外部验证，外部验证AUC为0.699。
基于LR算法构建了一个模型，可有效预测心力衰竭合并脓毒症患者的28天全因死亡率。利用我们的模型预测，临床医生可以及时识别高危患者并获得临床实践指导。
