Ni Peifeng, Xu Shurui, Zhang Weidong, Wu Chenxi, Zhang Gensheng, Gu Qiao, Hu Xin, Zhu Ying, Hu Wei, Diao Mengyuan
Department of Critical Care Medicine, Zhejiang University School of Medicine, 310058 Hangzhou, Zhejiang, China.
Department of Critical Care Medicine, Hangzhou First People's Hospital, Westlake University School of Medicine, 310006 Hangzhou, Zhejiang, China.
Rev Cardiovasc Med. 2025 Apr 10;26(4):33387. doi: 10.31083/RCM33387. eCollection 2025 Apr.
Cardiac arrest (CA) is associated with high incidence and mortality rates. Hence, assessing the prognosis of CA patients is crucial for optimizing clinical treatment. This study aimed to develop and validate a clinically applicable nomogram for predicting the risk of in-hospital mortality in CA patients.
We retrospectively collected the clinical data of CA patients admitted to two hospitals in Zhejiang Province between January 2018 and June 2024. These patients were randomly assigned to the training set (70%) and the internal validation set (30%). Variables of interest included demographics, comorbidities, CA-related characteristics, vital signs, and laboratory results, and the outcome was defined as in-hospital death. Variables were selected using least absolute shrinkage and selection operator (LASSO) regression, recursive feature elimination (RFE), and eXtremely Gradient Boosting (XGBoost). Meanwhile, multivariate regression analysis was used to identify independent risk factors. Subsequently, prediction models were developed in the training set and validated in the internal validation set. Receiver operating characteristic (ROC) curves were plotted and the area under these curves (AUC) was calculated to compare the discriminative ability of the models. The model with the highest performance was further validated in an independent external cohort and was subsequently represented as a nomogram for predicting the risk of in-hospital mortality in CA patients.
This study included 996 CA patients, with an in-hospital mortality rate of 49.9% (497/996). The LASSO regression model significantly outperformed the RFE and XGBoost models in predicting in-hospital mortality, with an AUC value of 0.81 (0.78, 0.84) in the training set and 0.85 (0.80, 0.89) in the internal validation set. The AUC values for these sets in the RFE model were 0.74 (0.70, 0.78) and 0.77 (0.72, 0.83), respectively, and those for the XGBoost model were 0.75 (0.71, 0.79) and 0.77 (0.72, 0.83), respectively. For the optimal prediction model, the AUC value of the LASSO regression model in the external validation set was 0.84 (0.78, 0.90). The LASSO regression model was represented as a nomogram incorporating several independent risk factors, namely age, hypertension, cause of arrest, initial heart rhythm, vasoactive drugs, continuous renal replacement therapy (CRRT), temperature, blood urea-nitrogen (BUN), lactate, and Sequential Organ Failure Assessment (SOFA) scores. Calibration and decision curves confirmed the predictive accuracy and clinical utility of the model.
We developed a nomogram to predict the risk of in-hospital mortality in CA patients, using variables selected via LASSO regression. This nomogram demonstrated strong discriminative ability and clinical practicality.
心脏骤停（CA）的发病率和死亡率都很高。因此，评估CA患者的预后对于优化临床治疗至关重要。本研究旨在开发并验证一种临床适用的列线图，用于预测CA患者的院内死亡风险。
我们回顾性收集了2018年1月至2024年6月期间浙江省两家医院收治的CA患者的临床数据。这些患者被随机分配到训练集（70%）和内部验证集（30%）。感兴趣的变量包括人口统计学、合并症、与CA相关的特征、生命体征和实验室检查结果，结局定义为院内死亡。使用最小绝对收缩和选择算子（LASSO）回归、递归特征消除（RFE）和极端梯度提升（XGBoost）选择变量。同时，采用多因素回归分析确定独立危险因素。随后，在训练集中开发预测模型，并在内部验证集中进行验证。绘制受试者工作特征（ROC）曲线并计算曲线下面积（AUC），以比较模型的判别能力。在独立的外部队列中进一步验证性能最佳的模型，随后将其表示为预测CA患者院内死亡风险的列线图。
本研究纳入996例CA患者，院内死亡率为49.9%（497/996）。在预测院内死亡方面，LASSO回归模型显著优于RFE和XGBoost模型，训练集的AUC值为0.81（0.78，0.84），内部验证集的AUC值为0.85（0.80，0.89）。RFE模型在这些数据集中的AUC值分别为0.74（0.70，0.78）和0.77（0.72，0.83），XGBoost模型的AUC值分别为0.75（0.71，0.79）和0.77（0.72，0.83）。对于最佳预测模型，LASSO回归模型在外部验证集中的AUC值为0.84（0.78，0.90）。LASSO回归模型被表示为一个包含多个独立危险因素的列线图，即年龄、高血压、骤停原因、初始心律、血管活性药物、持续肾脏替代治疗（CRRT）、体温血尿素氮（BUN）、乳酸和序贯器官衰竭评估（SOFA）评分。校准曲线和决策曲线证实了该模型的预测准确性和临床实用性。
我们开发了一种列线图，使用通过LASSO回归选择的变量来预测CA患者的院内死亡风险。该列线图显示出强大的判别能力和临床实用性。
