The Permanente Medical Group, Oakland, CA, USA.
Division of Research, Kaiser Permanente Northern California, Oakland, CA, USA.
Pediatr Res. 2024 Aug;96(3):759-765. doi: 10.1038/s41390-024-03141-3. Epub 2024 Apr 4.
Invasive bacterial infections (IBIs) in febrile infants are rare but potentially devastating. We aimed to derive and validate a predictive model for IBI among febrile infants age 7-60 days.
Data were abstracted retrospectively from electronic records of 37 emergency departments (EDs) for infants with a measured temperature >=100.4 F who underwent an ED evaluation with blood and urine cultures. Models to predict IBI were developed and validated respectively using a random 80/20 dataset split, including 10-fold cross-validation. We used precision recall curves as the classification metric.
Of 4411 eligible infants with a mean age of 37 days, 29% had characteristics that would likely have excluded them from existing risk stratification protocols. There were 196 patients with IBI (4.4%), including 43 (1.0%) with bacterial meningitis. Analytic approaches varied in performance characteristics (precision recall range 0.04-0.29, area under the curve range 0.5-0.84), with the XGBoost model demonstrating the best performance (0.29, 0.84). The five most important variables were serum white blood count, maximum temperature, absolute neutrophil count, absolute band count, and age in days.
A machine learning model (XGBoost) demonstrated the best performance in predicting a rare outcome among febrile infants, including those excluded from existing algorithms.
Several models for the risk stratification of febrile infants have been developed. There is a need for a preferred comprehensive model free from limitations and algorithm exclusions that accurately predicts IBIs. This is the first study to derive an all-inclusive predictive model for febrile infants aged 7-60 days in a community ED sample with IBI as a primary outcome. This machine learning model demonstrates potential for clinical utility in predicting IBI.
发热婴儿的侵袭性细菌感染（IBI）较为罕见，但可能具有破坏性。我们旨在为 7-60 日龄发热婴儿开发并验证一种用于 IBI 的预测模型。
数据从 37 个急诊部（ED）的电子病历中回顾性提取，纳入体温≥100.4°F 且接受 ED 评估（包括血培养和尿培养）的婴儿。分别使用随机 80/20 数据集分割和 10 折交叉验证来开发和验证预测 IBI 的模型。我们使用精度召回曲线作为分类指标。
在 4411 名符合条件的婴儿中，平均年龄为 37 天，29%的婴儿具有特征，这些特征可能使他们不符合现有风险分层方案。共有 196 名婴儿发生 IBI（4.4%），包括 43 名（1.0%）患有细菌性脑膜炎。分析方法的性能特征（精度召回范围 0.04-0.29，曲线下面积范围 0.5-0.84）有所不同，XGBoost 模型的表现最佳（0.29，0.84）。最重要的五个变量是血清白细胞计数、最高体温、绝对中性粒细胞计数、绝对带状细胞计数和日龄。
机器学习模型（XGBoost）在预测发热婴儿中罕见结局方面表现最佳，包括那些被现有算法排除的婴儿。
已经开发了几种用于发热婴儿风险分层的模型。需要有一种没有局限性和算法排除的首选综合模型，该模型能够准确预测 IBI。这是第一项针对社区 ED 样本中 7-60 日龄发热婴儿的涵盖所有病例的预测模型的研究，以 IBI 为主要结局。这种机器学习模型在预测 IBI 方面具有潜在的临床应用价值。
