Construction and external validation of a scoring prediction model for mortality risk within 30 days of community-acquired pneumonia in children admitted to the pediatric intensive care unit: A multicenter retrospective case-control study

Abstract

In this study, we constructed and validated a scoring prediction model to identify children admitted to the pediatric intensive care unit (PICU) with community-acquired pneumonia (CAP) at risk for early death. Children with CAP who were admitted to the PICU were included in the training set and divided into death and survival groups according to whether they died within 30 days of admission. For univariate and multifactorial analyses, demographic characteristics, vital signs at admission, and laboratory test results were collected separately from the 2 groups, and independent risk factors were derived to construct a scoring prediction model. The ability of the scoring model to predict CAP-related death was validated by including children with CAP hospitalized at 3 other centers during the same period in the external validation set. Overall, the training and validation sets included 296 and 170 children, respectively. Univariate and multifactorial analyses revealed that procalcitonin (PCT), lactate dehydrogenase (LDH), activated partial thromboplastin time (APTT), and fibrinogen (Fib) were independent risk factors. The constructed scoring prediction model scored 2 points each for PCT ≥ 0.375 ng/mL, LDH ≥ 490 U/L, and APTT ≥ 31.8 s and 1 point for Fib ≤ 1.78 g/L, with a total model score of 0–7 points. When the score was ≥ 5 points, the sensitivity and specificity of mortality diagnosis in children with CAP were 72.7% and 87.5%, respectively. In the external validation set, the sensitivity, specificity, and accuracy of the scoring model for predicting the risk of CAP-related death were 64.0%, 92.4%, and 88.2%, respectively. Constructing a scoring prediction model is worth promoting and can aid pediatricians in simply and rapidly evaluating the risk of death in children with CAP, particularly those with complex conditions.