Yue Chaoyan, Zhang Chunyi, Ying Chunmei
Department of Laboratory Medicine, Obstetrics and Gynecology Hospital of Fudan University Shanghai, China.
Am J Transl Res. 2023 Jun 15;15(6):4172-4178. eCollection 2023.
We developed a new nomogram for the prediction of mortality risk in children in pediatric intensive care units (PICU).
We conducted a retrospective analysis using the PICU Public Database, a study that included a total of 10,538 children, to develop a new risk model for mortality in children in the intensive care units (ICU). The prediction model was analyzed using multivariate logistic regression with predictors including age and physiological indicators, and the prediction model was presented as a nomogram. The performance of the nomogram was evaluated based on its discriminative power and was internally validated.
Predictors contained in the individualized prediction nomogram included the neutrophils, platelets, albumin, lactate, oxygen saturation (<0.1). The area under the receiver operating characteristic (ROC) curve for this prediction model is 0.7638 (95% CI: 0.7415-0.7861), which has effective discriminatory power. The area under the ROC curve of the prediction model in the validation dataset is 0.7404 (95% CI: 0.7016-0.7793), which is still effectively discriminative.
The mortality risk prediction model constructed in this study can be easily used for individualized prediction of mortality risk in children in pediatric intensive care units.
我们开发了一种新的列线图，用于预测儿科重症监护病房（PICU）中儿童的死亡风险。
我们使用PICU公共数据库进行了一项回顾性分析，该研究共纳入10538名儿童，以开发一种用于重症监护病房（ICU）中儿童死亡的新风险模型。使用包括年龄和生理指标在内的预测变量进行多变量逻辑回归分析预测模型，并将预测模型呈现为列线图。基于其鉴别能力对列线图的性能进行评估，并进行内部验证。
个性化预测列线图中包含的预测变量有中性粒细胞、血小板、白蛋白、乳酸、氧饱和度（<0.1）。该预测模型的受试者操作特征（ROC）曲线下面积为0.7638（95%CI：0.7415 - 0.7861），具有有效的鉴别能力。验证数据集中预测模型的ROC曲线下面积为0.7404（95%CI：0.7016 - 0.7793），仍具有有效的鉴别能力。
本研究构建的死亡风险预测模型可方便地用于儿科重症监护病房中儿童死亡风险的个性化预测。
