In the United States, more than 5 million patients are admitted annually to ICUs, with ICU mortality of 10%-29% and costs over $82 billion. Acute brain dysfunction status, delirium, is often underdiagnosed or undervalued. This study's objective was to develop automated computable phenotypes for acute brain dysfunction states and describe transitions among brain dysfunction states to illustrate the clinical trajectories of ICU patients. We created two single-center, longitudinal EHR datasets for 48,817 adult patients admitted to an ICU at UFH Gainesville (GNV) and Jacksonville (JAX). We developed algorithms to quantify acute brain dysfunction status including coma, delirium, normal, or death at 12-hour intervals of each ICU admission and to identify acute brain dysfunction phenotypes using continuous acute brain dysfunction status and k-means clustering approach. There were 49,770 admissions for 37,835 patients in UFH GNV dataset and 18,472 admissions for 10,982 patients in UFH JAX dataset. In total, 18% of patients had coma as the worst brain dysfunction status; every 12 hours, around 4%-7% would transit to delirium, 22%-25% would recover, 3%-4% would expire, and 67%-68% would remain in a coma in the ICU. Additionally, 7% of patients had delirium as the worst brain dysfunction status; around 6%-7% would transit to coma, 40%-42% would be no delirium, 1% would expire, and 51%-52% would remain delirium in the ICU. There were three phenotypes: persistent coma/delirium, persistently normal, and transition from coma/delirium to normal almost exclusively in first 48 hours after ICU admission. We developed phenotyping scoring algorithms that determined acute brain dysfunction status every 12 hours while admitted to the ICU. This approach may be useful in developing prognostic and decision-support tools to aid patients and clinicians in decision-making on resource use and escalation of care.

翻译：在美国，每年有超过500万患者入住ICU，ICU死亡率达10%-29%，相关费用超过820亿美元。急性脑功能障碍状态（谵妄）常被低估或诊断不足。本研究旨在开发急性脑功能障碍状态的可自动化计算表型，并描述脑功能障碍状态间的转换过程，以呈现ICU患者的临床轨迹。我们利用弗罗里达大学盖恩斯维尔医院（UFH GNV）与杰克逊维尔医院（UFH JAX）两个单中心纵向电子健康记录数据集，纳入48,817名成年ICU住院患者。我们开发了量化急性脑功能障碍状态的算法，按每12小时间隔识别昏迷、谵妄、正常或死亡状态，并基于连续脑功能障碍状态与k-means聚类方法确定表型。UFH GNV数据集包含37,835名患者的49,770次入院记录，UFH JAX数据集包含10,982名患者的18,472次入院记录。总体而言，18%患者以昏迷为最严重脑功能障碍状态；每12小时，约4%-7%转为谵妄，22%-25%康复，3%-4%死亡，67%-68%持续昏迷。另有7%患者以谵妄为最严重状态；每12小时，约6%-7%转为昏迷，40%-42%无谵妄，1%死亡，51%-52%持续谵妄。共识别出三种表型：持续性昏迷/谵妄、持续性正常，以及仅于ICU入院前48小时内从昏迷/谵妄转为正常。我们开发了每12小时判定急性脑功能障碍状态的表型评分算法。该方法有助于开发预后与决策支持工具，协助患者与临床医生在资源使用与护理升级方面进行决策。