Department of Pediatric Intensive Care Unit, Wuhan Children's Hospital (Wuhan Maternal and Child Healthcare Hospital), Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
Department of Neonatal Intensive Care Unit, Wuhan Children's Hospital (Wuhan Maternal and Child Healthcare Hospital), Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
Medicine (Baltimore). 2024 Mar 8;103(10):e37419. doi: 10.1097/MD.0000000000037419.
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.
在这项研究中，我们构建并验证了一个评分预测模型，以识别因社区获得性肺炎（CAP）入住儿科重症监护病房（PICU）的儿童中，有早期死亡风险的患者。将入住 PICU 的 CAP 患儿纳入训练集，并根据入院后 30 天内是否死亡将其分为死亡组和存活组。对单变量和多变量分析，分别从两组中收集人口统计学特征、入院时生命体征和实验室检查结果，得出独立的危险因素，构建评分预测模型。通过纳入同期在另外 3 家中心住院的 CAP 患儿作为外部验证集，验证评分模型预测 CAP 相关死亡的能力。总体而言，训练集和验证集分别纳入了 296 例和 170 例患儿。单变量和多变量分析显示降钙素原（PCT）、乳酸脱氢酶（LDH）、活化部分凝血活酶时间（APTT）和纤维蛋白原（Fib）是独立的危险因素。构建的评分预测模型中，PCT≥0.375ng/mL、LDH≥490U/L、APTT≥31.8s 各计 2 分，Fib≤1.78g/L 计 1 分，总模型评分为 0-7 分。当评分≥5 分时，CAP 患儿死亡率诊断的敏感性和特异性分别为 72.7%和 87.5%。在外部验证集中，评分模型预测 CAP 相关死亡风险的敏感性、特异性和准确性分别为 64.0%、92.4%和 88.2%。构建评分预测模型值得推广，可帮助儿科医生简单快速地评估 CAP 患儿死亡风险，尤其是病情复杂的患儿。
