Department of Biomedical Informatics, Emory University, Atlanta, GA.
Department of Pediatrics, Emory University School of Medicine, Atlanta, GA.
Pediatr Crit Care Med. 2024 Mar 1;25(3):212-221. doi: 10.1097/PCC.0000000000003410. Epub 2023 Nov 13.
To develop and externally validate an intubation prediction model for children admitted to a PICU using objective and routinely available data from the electronic medical records (EMRs).
Retrospective observational cohort study.
Two PICUs within the same healthcare system: an academic, quaternary care center (36 beds) and a community, tertiary care center (56 beds).
Children younger than 18 years old admitted to a PICU between 2010 and 2022.
None.
Clinical data was extracted from the EMR. PICU stays with at least one mechanical ventilation event (≥ 24 hr) occurring within a window of 1-7 days after hospital admission were included in the study. Of 13,208 PICU stays in the derivation PICU cohort, 1,175 (8.90%) had an intubation event. In the validation cohort, there were 1,165 of 17,841 stays (6.53%) with an intubation event. We trained a Categorical Boosting (CatBoost) model using vital signs, laboratory tests, demographic data, medications, organ dysfunction scores, and other patient characteristics to predict the need of intubation and mechanical ventilation using a 24-hour window of data within their hospital stay. We compared the CatBoost model to an extreme gradient boost, random forest, and a logistic regression model. The area under the receiving operating characteristic curve for the derivation cohort and the validation cohort was 0.88 (95% CI, 0.88-0.89) and 0.92 (95% CI, 0.91-0.92), respectively.
We developed and externally validated an interpretable machine learning prediction model that improves on conventional clinical criteria to predict the need for intubation in children hospitalized in a PICU using information readily available in the EMR. Implementation of our model may help clinicians optimize the timing of endotracheal intubation and better allocate respiratory and nursing staff to care for mechanically ventilated children.
利用电子病历（EMR）中客观且常规可用的数据，为入住儿科重症监护病房（PICU）的儿童开发并验证一种插管预测模型。
回顾性观察队列研究。
同一医疗系统内的两个 PICU：一个是学术性、四级保健中心（36 张床位），另一个是社区性、三级保健中心（56 张床位）。
2010 年至 2022 年期间入住 PICU 的年龄小于 18 岁的儿童。
无。
从 EMR 中提取临床数据。研究纳入了 PICU 住院患者，其 PICU 入住期间至少有一次机械通气事件（≥24 小时）发生在入院后 1-7 天的窗口期内。在推导 PICU 队列的 13208 例 PICU 入住中，有 1175 例（8.90%）发生插管事件。在验证队列中，有 17841 例入住中，有 1165 例（6.53%）发生插管事件。我们使用生命体征、实验室检查、人口统计学数据、药物、器官功能障碍评分和其他患者特征，通过其住院期间 24 小时的数据窗，训练了一种分类提升（CatBoost）模型，以预测插管和机械通气的需求。我们将 CatBoost 模型与极端梯度提升、随机森林和逻辑回归模型进行了比较。推导队列和验证队列的接收者操作特征曲线下面积分别为 0.88（95%CI，0.88-0.89）和 0.92（95%CI，0.91-0.92）。
我们开发并验证了一种可解释的机器学习预测模型，该模型基于电子病历中易于获得的信息，对传统临床标准进行了改进，以预测入住 PICU 的儿童插管的需求。实施我们的模型可以帮助临床医生优化气管插管的时机，并更好地分配呼吸和护理人员来照顾机械通气的儿童。
