George Washington University School of Medicine and Health Sciences, Washington, DC.
Department of Pediatrics, Division of Critical Care Medicine, Children's National Hospital and George Washington University School of Medicine and Health Sciences, Washington, DC.
Pediatr Crit Care Med. 2021 Jan 1;22(1):e33-e43. doi: 10.1097/PCC.0000000000002560.
To validate the conceptual framework of "criticality," a new pediatric inpatient severity measure based on physiology, therapy, and therapeutic intensity calibrated to care intensity, operationalized as ICU care.
Deep neural network analysis of a pediatric cohort from the Health Facts (Cerner Corporation, Kansas City, MO) national database.
Hospitals with pediatric routine inpatient and ICU care.
Children cared for in the ICU (n = 20,014) and in routine care units without an ICU admission (n = 20,130) from 2009 to 2016. All patients had laboratory, vital sign, and medication data.
None.
A calibrated, deep neural network used physiology (laboratory tests and vital signs), therapy (medications), and therapeutic intensity (number of physiology tests and medications) to model care intensity, operationalized as ICU (versus routine) care every 6 hours of a patient's hospital course. The probability of ICU care is termed the Criticality Index. First, the model demonstrated excellent separation of criticality distributions from a severity hierarchy of five patient groups: routine care, routine care for those who also received ICU care, transition from routine to ICU care, ICU care, and high-intensity ICU care. Second, model performance assessed with statistical metrics was excellent with an area under the curve for the receiver operating characteristic of 0.95 for 327,189 6-hour time periods, excellent calibration, sensitivity of 0.817, specificity of 0.892, accuracy of 0.866, and precision of 0.799. Third, the performance in individual patients with greater than one care designation indicated as 88.03% (95% CI, 87.72-88.34) of the Criticality Indices in the more intensive locations was higher than the less intense locations.
The Criticality Index is a quantification of severity of illness for hospitalized children using physiology, therapy, and care intensity. This new conceptual model is applicable to clinical investigations and predicting future care needs.
验证“关键性”这一全新儿科住院患者严重度测量指标的概念框架，该指标基于生理学、治疗和治疗强度，根据与照护强度相匹配的 ICU 护理进行校准。
利用来自健康事实（Cerner 公司，堪萨斯城，密苏里州）全国数据库的儿科队列进行深度神经网络分析。
有儿科常规住院和 ICU 护理的医院。
2009 年至 2016 年期间在 ICU 接受治疗的患者（n=20014）和未入住 ICU 的常规护理病房的患者（n=20130）。所有患者均有实验室、生命体征和用药数据。
无。
使用校准后的深度神经网络，基于生理学（实验室检查和生命体征）、治疗（药物）和治疗强度（生理检查和药物的数量）来模拟照护强度，具体表现为患者住院期间每 6 小时的 ICU（与常规护理）护理。ICU 护理的概率称为关键性指数。首先，该模型很好地分离了关键性分布和五个患者群体的严重度分层：常规护理、常规护理+ ICU 护理、从常规护理到 ICU 护理的过渡、ICU 护理和高强度 ICU 护理。其次，使用统计指标评估的模型性能非常出色，327189 个 6 小时时间段的接收者操作特征曲线下面积为 0.95，校准效果好，敏感性为 0.817，特异性为 0.892，准确性为 0.866，精密度为 0.799。第三，在大于一个护理指定的个体患者中，表现出 88.03%（95%置信区间，87.72-88.34）的关键性指数在更密集的位置高于在不密集的位置。
关键性指数是使用生理学、治疗和照护强度对住院儿童进行疾病严重度量化的指标。这一新的概念模型适用于临床研究和预测未来的护理需求。
