PDF(Pubmed)
Abstract：
UNASSIGNED: 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.
UNASSIGNED: 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.
UNASSIGNED: 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.
UNASSIGNED: 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.
UNASSIGNED: 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.
UNASSIGNED: 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.
UNASSIGNED: 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.
摘要：
■作为儿童败血症的严重并发症之一，脓毒症相关性脑病(SAE)与显著的不良预后和增加的死亡率相关.然而,儿科SAE患者结局的预测因子尚未确定.这项研究的目的是开发列线图来预测SAE儿童的14天和90天死亡率。提供早期预警，采取有效措施，改善预后，降低死亡率。
■在这个多中心中，回顾性研究,我们筛选了2017年1月至2022年9月山东省PICU收治的291例SAE患者.使用最小绝对收缩和选择程序（LASSO）方法来确定预测SAE儿科患者预后的最佳预后因素。然后，基于这些变量进行多变量逻辑回归分析，并为可视化建立了两个列线图。我们使用曲线下面积（AUC），校准曲线和决策曲线，以测试列线图在预测结果中的准确性和区分度。
■训练队列中有129名SAE患者，在两个独立的验证队列中有103和59名患者，分别。血管加压药的使用,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，和血管加压药的使用，PCT,乳酸，PCIS和白蛋白是90天死亡率的独立预测因素。根据变量，我们生成了两个列线图，用于早期识别14天死亡率（AUC0.853，95％CI0.787-0.919，灵敏度72.4％，特异性84.5%）和90天死亡率（AUC0.857，95%CI0.792-0.923，敏感性72.3%，特异性90.6%)，分别。列线图的校准图显示了训练和验证队列中观测值和预测值之间的死亡率概率的极好一致性。决策曲线分析（DCA）表明，列线图具有较高的临床净收益。
■本研究中的列线图揭示了儿童SAE患者死亡率的最佳预后因素，通过模型进行个性化的定量风险评估对于治疗管理将是实用的。
■在这个多中心中，回顾性研究,我们筛选了2017年1月至2022年9月山东省PICU收治的291例SAE患者.使用最小绝对收缩和选择程序（LASSO）方法来确定预测SAE儿科患者预后的最佳预后因素。然后，基于这些变量进行多变量逻辑回归分析，并为可视化建立了两个列线图。我们使用曲线下面积（AUC），校准曲线和决策曲线，以测试列线图在预测结果中的准确性和区分度。
■训练队列中有129名SAE患者，在两个独立的验证队列中有103和59名患者，分别。血管加压药的使用,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，和血管加压药的使用，PCT,乳酸，PCIS和白蛋白是90天死亡率的独立预测因素。根据变量，我们生成了两个列线图，用于早期识别14天死亡率（AUC0.853，95％CI0.787-0.919，灵敏度72.4％，特异性84.5%）和90天死亡率（AUC0.857，95%CI0.792-0.923，敏感性72.3%，特异性90.6%)，分别。列线图的校准图显示了训练和验证队列中观测值和预测值之间的死亡率概率的极好一致性。决策曲线分析（DCA）表明，列线图具有较高的临床净收益。
■本研究中的列线图揭示了儿童SAE患者死亡率的最佳预后因素，通过模型进行个性化的定量风险评估对于治疗管理将是实用的。
