Qilu Hospital of Shandong University, No.107 West Wenhua Road, Jinan 250012, Shandong Province, China.
Department of Medical Engineering, Qilu Hospital of Shandong University, No.107 West Wenhua Road, Jinan 250012, Shandong Province, China.
Thromb Res. 2024 Nov;243:109152. doi: 10.1016/j.thromres.2024.109152. Epub 2024 Sep 13.
Sepsis-induced coagulopathy (SIC) is a severe complication of sepsis, characterized by poor prognosis and high mortality. However, the predictors of SIC in pediatric patients have yet to be identified. Our aim was to develop a user-friendly and efficient nomogram for predicting SIC in sepsis patients admitted to the pediatric intensive care unit (PICU).
We screened 948 sepsis patients admitted to the PICU in three hospitals located in Shandong, China. Least absolute shrinkage and selector operation (LASSO) regression was used in the training cohort for variable selection and regularization. The selected variables were utilized to construct a nomogram for predicting the risk of SIC among sepsis patients admitted to the PICU.
Overall, SIC was observed in 324 (40.3 %) patients. The morbidity of SIC in sepsis patients is associated with age, fibrinogen, prothrombin time, C-reactive protein, lactate and the pediatric sequential organ failure assessment score. We developed a nomogram for the early identification of SIC in the training cohort (area under the curve [AUC] 0.869, 95 % confidence interval [CI] 0.830-0.907, sensitivity 75.7 %, specificity 84.8 %) and validation cohorts (validation cohort 1: AUC 0.854, 95 % CI 0.805-0.903, sensitivity 72.0 %, specificity 86.9 %; validation cohort 2: AUC 0.853, 95 % CI 0.796-0.910, sensitivity 70.1 %, specificity 87.8 %). The calibration plots of the nomogram demonstrated a high level of concordance in the SIC probabilities between the observed and predicted values.
The novel nomogram showed excellent predictive performance for the morbidity of SIC among sepsis patients admitted to the PICU, potentially assisting healthcare professionals in early identification and intervention for SIC.
脓毒症相关性凝血病(SIC)是脓毒症的一种严重并发症,其预后差、死亡率高。然而,儿科患者 SIC 的预测因素尚未确定。我们的目的是开发一个便于使用且高效的列线图,以预测入住儿科重症监护病房(PICU)的脓毒症患者发生 SIC 的风险。
我们筛选了来自中国山东三家医院的 948 例入住 PICU 的脓毒症患者。在训练队列中,使用最小绝对收缩和选择操作(LASSO)回归进行变量选择和正则化。使用所选变量构建预测 PICU 入住的脓毒症患者 SIC 风险的列线图。
总体而言,324 例(40.3%)患者发生 SIC。SIC 在脓毒症患者中的发病率与年龄、纤维蛋白原、凝血酶原时间、C 反应蛋白、乳酸和儿科序贯器官衰竭评估评分相关。我们在训练队列中开发了一个用于 SIC 早期识别的列线图(曲线下面积 [AUC] 0.869,95%置信区间 [CI] 0.830-0.907,敏感性 75.7%,特异性 84.8%)和验证队列(验证队列 1:AUC 0.854,95%CI 0.805-0.903,敏感性 72.0%,特异性 86.9%;验证队列 2:AUC 0.853,95%CI 0.796-0.910,敏感性 70.1%,特异性 87.8%)。该列线图的校准图表明,观察值和预测值之间 SIC 概率的一致性很高。
该新列线图对入住 PICU 的脓毒症患者 SIC 的发病率具有出色的预测性能,可能有助于医疗保健专业人员早期识别和干预 SIC。