Xue Lisha, Lian Huanjie, Wu Yong, Guo Shuangyi
Department of Pediatrics, Taihe Hospital, Hubei University of Medicine, Shiyan, China.
Department of Neurosurgery, Taihe Hospital, Hubei University of Medicine, Shiyan, China.
Transl Pediatr. 2025 Apr 30;14(4):533-544. doi: 10.21037/tp-2024-506. Epub 2025 Apr 27.
The incidence of congenital heart disease (CHD) has remained constant in recent years. The mortality rate is high in CHD patients admitted to the intensive care unit (ICU), but there is limited research on risk factors for in-hospital mortality. Therefore, the aim of this study was to identify risk factors for in-hospital mortality of CHD children in the ICU and develop a nomogram model to predict in-hospital mortality.
Patient demographics, comorbidities, surgical history, laboratory indicators, and in-hospital mortality were extracted from the paediatric intensive care unit (PICU) database. These patients were divided into training and validation cohorts in a 7:3 ratio. Variable selection was performed using single-factor Cox regression and stepwise Cox regression based on Akaike information criterion (AIC) in the training cohort. The selected variables were used to build a nomogram model, and calibration curves and receiver operator characteristic (ROC) curves were generated to evaluate the predictive performance of the model. Subsequently, an external validation was also carried out in the Medical Information Mart for Intensive Care III (MIMIC-III) database.
A total of 2,231 patients were included in the analysis. Lymphocyte percentage [hazard ratio (HR): 1.097, 95% confidence interval (CI): 1.038-1.160], magnesium ion (HR: 1.002, 95% CI: 1.001-1.002), neutrophil percentage (HR: 1.111, 95% CI: 1.050-1.175), oxygen partial pressure (pO) (HR: 0.987, 95% CI: 0.981-0.993), partial thromboplastin time (HR: 1.033, 95% CI: 1.020-1.047), and ventricular septal defect repair surgery (HR: 0.117, 95% CI: 0.028-0.494) were identified as independent predictors and were used to construct the nomogram model. ROC curves showed that the model had good discriminative ability with area under the curves (AUCs) of 0.940, 0.857, and 0.776 for predicting in-hospital mortality at 7-, 14-, and 30-days in the training cohort, and AUCs of 0.921, 0.858, and 0.699 in the validation cohort, respectively. In the external dataset, the AUC of the model for predicting 7-, 14-, and 30-day in-hospital mortality were 0.732, 0.722, and 0.629, respectively. The calibration curves demonstrated favorable consistency of the model.
Neutrophil percentage in the model exhibits the strongest predictive power, followed by lymphocyte percentage and pO. The model shows favorable performance and can provide effective predictive information for clinical practitioners.
近年来先天性心脏病（CHD）的发病率一直保持稳定。入住重症监护病房（ICU）的CHD患者死亡率很高，但关于院内死亡危险因素的研究有限。因此，本研究的目的是确定ICU中CHD儿童院内死亡的危险因素，并建立一个列线图模型来预测院内死亡率。
从儿科重症监护病房（PICU）数据库中提取患者的人口统计学资料、合并症、手术史、实验室指标和院内死亡率。这些患者按7:3的比例分为训练队列和验证队列。在训练队列中，基于赤池信息准则（AIC），使用单因素Cox回归和逐步Cox回归进行变量选择。所选变量用于构建列线图模型，并生成校准曲线和受试者工作特征（ROC）曲线以评估模型的预测性能。随后，还在重症监护医学信息集市III（MIMIC-III）数据库中进行了外部验证。
共纳入2231例患者进行分析。淋巴细胞百分比[风险比（HR）：1.097，95%置信区间（CI）：1.038 - 1.160]、镁离子（HR：1.002，95% CI：1.001 - 1.002）、中性粒细胞百分比（HR：1.111，95% CI：1.050 - 1.175）、氧分压（pO）（HR：0.987，95% CI：0.981 - 0.993）、部分凝血活酶时间（HR：1.033，95% CI：1.020 - 1.047）和室间隔缺损修复手术（HR：0.117，95% CI：0.028 - 0.494）被确定为独立预测因素，并用于构建列线图模型。ROC曲线显示，该模型在训练队列中预测7天、14天和30天院内死亡率的曲线下面积（AUC）分别为0.940、0.857和0.776，具有良好的辨别能力，在验证队列中的AUC分别为0.921、0.858和0.699。在外部数据集中，该模型预测7天、14天和
