Zhu Jia-Liang, Xu Xiao-Mei, Yin Hai-Yan, Wei Jian-Rui, Lyu Jun
Department of Intensive Care Unit, The First Affiliated Hospital of Jinan University, Guangzhou, China.
Guangzhou Women and Children's Medical Center, Guangzhou Medical University, Guangzhou, China.
Front Physiol. 2023 Jul 4;14:1182719. doi: 10.3389/fphys.2023.1182719. eCollection 2023.
Ventricular septal defect is a common congenital heart disease. As the disease progresses, the likelihood of lung infection and heart failure increases, leading to prolonged hospital stays and an increased likelihood of complications such as nosocomial infections. We aimed to develop a nomogram for predicting hospital stays over 14 days in pediatric patients with ventricular septal defect and to evaluate the predictive power of the nomogram. We hope that nomogram can provide clinicians with more information to identify high-risk groups as soon as possible and give early treatment to reduce hospital stay and complications. The population of this study was pediatric patients with ventricular septal defect, and data were obtained from the Pediatric Intensive Care Database. The resulting event was a hospital stay longer than 14 days. Variables with a variance inflation factor (VIF) greater than 5 were excluded. Variables were selected using the least absolute shrinkage and selection operator (Lasso), and the selected variables were incorporated into logistic regression to construct a nomogram. The performance of the nomogram was assessed by using the area under the receiver operating characteristic curve (AUC), Decision Curve Analysis (DCA) and calibration curve. Finally, the importance of variables in the model is calculated based on the XGboost method. A total of 705 patients with ventricular septal defect were included in the study. After screening with VIF and Lasso, the variables finally included in the statistical analysis include: Brain Natriuretic Peptide, bicarbonate, fibrinogen, urea, alanine aminotransferase, blood oxygen saturation, systolic blood pressure, respiratory rate, heart rate. The AUC values of nomogram in the training cohort and validation cohort were 0.812 and 0.736, respectively. The results of the calibration curve and DCA also indicated that the nomogram had good performance and good clinical application value. The nomogram established by BNP, bicarbonate, fibrinogen, urea, alanine aminotransferase, blood oxygen saturation, systolic blood pressure, respiratory rate, heart rate has good predictive performance and clinical applicability. The nomogram can effectively identify specific populations at risk for adverse outcomes.
室间隔缺损是一种常见的先天性心脏病。随着病情进展，肺部感染和心力衰竭的可能性增加，导致住院时间延长以及医院感染等并发症的可能性增加。我们旨在开发一种列线图，用于预测小儿室间隔缺损患者超过14天的住院时间，并评估该列线图的预测能力。我们希望列线图能够为临床医生提供更多信息，以便尽快识别高危人群并尽早给予治疗，以缩短住院时间并减少并发症。本研究的人群为小儿室间隔缺损患者，数据来自儿科重症监护数据库。最终事件为住院时间超过14天。排除方差膨胀因子（VIF）大于5的变量。使用最小绝对收缩和选择算子（Lasso）选择变量，并将所选变量纳入逻辑回归以构建列线图。通过受试者操作特征曲线（AUC）下面积、决策曲线分析（DCA）和校准曲线评估列线图的性能。最后，基于XGboost方法计算模型中变量的重要性。本研究共纳入705例室间隔缺损患者。经VIF和Lasso筛选后，最终纳入统计分析的变量包括：脑钠肽、碳酸氢盐、纤维蛋白原、尿素、丙氨酸氨基转移酶、血氧饱和度、收缩压、呼吸频率、心率。列线图在训练队列和验证队列中的AUC值分别为0.812和0.736。校准曲线和DCA的结果也表明列线图具有良好的性能和良好的临床应用价值。由脑钠肽、碳酸氢盐、纤维蛋白原、尿素、丙氨酸氨基转移酶、血氧饱和度、收缩压、呼吸频率、心率建立的列线图具有良好的预测性能和临床适用性。该列线图可以有效地识别有不良结局风险的特定人群。
