Wang Guan, Gao Yan, Fu Yanan, Huo Qin, Guo Enyu, Jiang Qin, Liu Jing, Jiang Xinzhu, Liu Xinjie
Department of Pediatrics, Qilu Hospital of Shandong University, Jinan, Shandong, China.
Qilu Hospital of Shandong University, Jinan, Shandong, China.
Front Neurol. 2024 Jul 29;15:1418405. doi: 10.3389/fneur.2024.1418405. eCollection 2024.
As one of the serious complications of sepsis in children, sepsis-associated encephalopathy (SAE) is associated with significantly poor prognosis and increased mortality. However, predictors of outcomes for pediatric SAE patients have yet to be identified. The aim of this study was to develop nomograms to predict the 14-day and 90-day mortality of children with SAE, providing early warning to take effective measures to improve prognosis and reduce mortality.
In this multicenter, retrospective study, we screened 291 patients with SAE admitted to the PICU between January 2017 and September 2022 in Shandong Province. A least absolute shrinkage and selector operation (LASSO) method was used to identify the optimal prognostic factors predicting the outcomes in pediatric patients with SAE. Then, multivariable logistic regression analysis was performed based on these variables, and two nomograms were built for visualization. We used the area under the curve (AUC), calibration curves and decision curves to test the accuracy and discrimination of the nomograms in predicting outcomes.
There were 129 patients with SAE in the training cohort, and there were 103 and 59 patients in the two independent validation cohorts, respectively. Vasopressor use, procalcitonin (PCT), lactate and pediatric critical illness score (PCIS) were independent predictive factors for 14-day mortality, and vasopressor use, PCT, lactate, PCIS and albumin were independent predictive factors for 90-day mortality. Based on the variables, we generated two nomograms for the early identification of 14-day mortality (AUC 0.853, 95% CI 0.787-0.919, sensitivity 72.4%, specificity 84.5%) and 90-day mortality (AUC 0.857, 95% CI 0.792-0.923, sensitivity 72.3%, specificity 90.6%), respectively. The calibration plots for nomograms showed excellent agreement of mortality probabilities between the observed and predicted values in both training and validation cohorts. Decision curve analyses (DCA) indicated that nomograms conferred high clinical net benefit.
The nomograms in this study revealed optimal prognostic factors for the mortality of pediatric patients with SAE, and individualized quantitative risk evaluation by the models would be practical for treatment management.
作为儿童脓毒症的严重并发症之一，脓毒症相关脑病（SAE）与显著不良预后及死亡率增加相关。然而，儿童SAE患者预后的预测因素尚未明确。本研究的目的是构建列线图以预测SAE患儿的14天和90天死亡率，为采取有效措施改善预后及降低死亡率提供早期预警。
在这项多中心回顾性研究中，我们筛选了2017年1月至2022年9月期间山东省PICU收治的291例SAE患者。采用最小绝对收缩和选择算子（LASSO）方法识别预测儿童SAE患者预后的最佳预测因素。然后，基于这些变量进行多变量逻辑回归分析，并构建两个列线图用于可视化。我们使用曲线下面积（AUC）、校准曲线和决策曲线来检验列线图预测预后的准确性和辨别力。
训练队列中有129例SAE患者，两个独立验证队列中分别有103例和59例患者。血管活性药物使用、降钙素原（PCT）、乳酸和儿科危重病评分（PCIS）是14天死亡率的独立预测因素，血管活性药物使用、PCT、乳酸、PCIS和白蛋白是90天死亡率的独立预测因素。基于这些变量，我们分别生成了两个用于早期识别14天死亡率（AUC 0.853，95%CI 0.787 - 0.919，敏感性72.4%，特异性84.5%）和90天死亡率（AUC 0.857，95%CI 0.792 - 0.923，敏感性72.3%，特异性90.6%）的列线图。列线图的校准图显示，在训练和验证队列中，观察值与预测值之间的死亡概率具有良好的一致性。决策曲线分析（DCA）表明列线图具有较高的临床净效益。
本研究中的列线图揭示了儿童SAE患者死亡的最佳预测因素，通过模型进行个体化定量风险评估对治疗管理具有实际意义。
