Continuous monitoring and patient acuity assessments are key aspects of Intensive Care Unit (ICU) practice, but both are limited by time constraints imposed on healthcare providers. Moreover, anticipating clinical trajectories remains imprecise. The objectives of this study are to (1) develop an electronic phenotype of acuity using automated variable retrieval within the electronic health records and (2) describe transitions between acuity states that illustrate the clinical trajectories of ICU patients. We gathered two single-center, longitudinal electronic health record datasets for 51,372 adult ICU patients admitted to the University of Florida Health (UFH) Gainesville (GNV) and Jacksonville (JAX). We developed algorithms to quantify acuity status at four-hour intervals for each ICU admission and identify acuity phenotypes using continuous acuity status and k-means clustering approach. 51,073 admissions for 38,749 patients in the UFH GNV dataset and 22,219 admissions for 12,623 patients in the UFH JAX dataset had at least one ICU stay lasting more than four hours. There were three phenotypes: persistently stable, persistently unstable, and transitioning from unstable to stable. For stable patients, approximately 0.7%-1.7% would transition to unstable, 0.02%-0.1% would expire, 1.2%-3.4% would be discharged, and the remaining 96%-97% would remain stable in the ICU every four hours. For unstable patients, approximately 6%-10% would transition to stable, 0.4%-0.5% would expire, and the remaining 89%-93% would remain unstable in the ICU in the next four hours. We developed phenotyping algorithms for patient acuity status every four hours while admitted to the ICU. This approach may be useful in developing prognostic and clinical decision-support tools to aid patients, caregivers, and providers in shared decision-making processes regarding escalation of care and patient values.

翻译：持续监测与病情程度评估是重症监护病房（ICU）实践的核心环节，但两者均受限于医护人员的时间约束。此外，临床轨迹的预测仍存在不精确性。本研究旨在：（1）利用电子健康记录中的自动化变量提取，开发病情程度的电子表型；（2）描述反映ICU患者临床轨迹的病情状态间的转换。我们收集了佛罗里达大学健康中心（UFH）盖恩斯维尔（GNV）及杰克逊维尔（JAX）两个单中心纵向电子健康记录数据集，涵盖51,372例成人ICU患者。我们开发了算法以每四小时为间隔量化每次ICU入院的病情状态，并利用连续病情状态与k-means聚类方法识别病情表型。UFH GNV数据集中的38,749名患者的51,073次入院及UFH JAX数据集中12,623名患者的22,219次入院，均至少有一次ICU停留时间超过四小时。存在三种表型：持续稳定型、持续不稳定型、从不稳定型向稳定型过渡。对于稳定患者，每四小时内约0.7%-1.7%会转为不稳定，0.02%-0.1%会死亡，1.2%-3.4%会出院，其余96%-97%在ICU保持稳定。对于不稳定患者，接下来四小时内约6%-10%会转为稳定，0.4%-0.5%会死亡，其余89%-93%在ICU保持不稳定。我们开发了ICU住院期间每四小时评估患者病情状态的表型算法。该方法有望用于开发预后及临床决策支持工具，辅助患者、照料者及医护人员在关于护理升级与患者价值观的共同决策过程中提供支持。