Department of Pediatrics, University of Chicago, Chicago, IL.
Department of Pediatrics, Ann & Robert H. Lurie Children's Hospital of Chicago, Chicago, IL.
Pediatr Crit Care Med. 2022 Jul 1;23(7):514-523. doi: 10.1097/PCC.0000000000002965. Epub 2022 Apr 21.
Unrecognized clinical deterioration during illness requiring hospitalization is associated with high risk of mortality and long-term morbidity among children. Our objective was to develop and externally validate machine learning algorithms using electronic health records for identifying ICU transfer within 12 hours indicative of a child's condition.
Observational cohort study.
Two urban, tertiary-care, academic hospitals (sites 1 and 2).
Pediatric inpatients (age <18 yr).
None.
Our primary outcome was direct ward to ICU transfer. Using age, vital signs, and laboratory results, we derived logistic regression with regularization, restricted cubic spline regression, random forest, and gradient boosted machine learning models. Among 50,830 admissions at site 1 and 88,970 admissions at site 2, 1,993 (3.92%) and 2,317 (2.60%) experienced the primary outcome, respectively. Site 1 data were split longitudinally into derivation (2009-2017) and validation (2018-2019), whereas site 2 constituted the external test cohort. Across both sites, the gradient boosted machine was the most accurate model and outperformed a modified version of the Bedside Pediatric Early Warning Score that only used physiologic variables in terms of discrimination ( C -statistic site 1: 0.84 vs 0.71, p < 0.001; site 2: 0.80 vs 0.74, p < 0.001), sensitivity, specificity, and number needed to alert.
We developed and externally validated a novel machine learning model that identifies ICU transfers in hospitalized children more accurately than current tools. Our model enables early detection of children at risk for deterioration, thereby creating opportunities for intervention and improvement in outcomes.
住院治疗期间未被识别的临床恶化与儿童的高死亡率和长期发病风险相关。我们的目的是使用电子健康记录开发和外部验证机器学习算法，以识别 12 小时内提示儿童病情的 ICU 转科。
观察性队列研究。
两家城市三级保健学术医院（站点 1 和 2）。
儿科住院患者（年龄<18 岁）。
无。
我们的主要结局是直接从病房转至 ICU。使用年龄、生命体征和实验室结果，我们推导出了具有正则化、限制立方样条回归、随机森林和梯度提升机的逻辑回归模型。在站点 1 的 50830 次入院和站点 2 的 88970 次入院中，分别有 1993（3.92%）和 2317（2.60%）例患者发生了主要结局。站点 1 的数据纵向分为推导（2009-2017 年）和验证（2018-2019 年），而站点 2 则构成外部测试队列。在两个站点中，梯度提升机模型的准确性最高，其在区分度（站点 1：C 统计量为 0.84 对 0.71，p<0.001；站点 2：0.80 对 0.74，p<0.001）、敏感性、特异性和需要警示的人数方面均优于仅使用生理变量的改良床边儿科预警评分。
我们开发并外部验证了一种新的机器学习模型，该模型比当前工具更准确地识别住院儿童的 ICU 转科。我们的模型能够早期发现有恶化风险的儿童，从而为干预和改善结局创造机会。
