Use patient demographic and clinical characteristics at admission and time-varying in-hospital measures of patient mobility to predict patient post-acute care (PAC) discharge. Retrospective cohort analysis of electronic medical records. Patients admitted to the two participating Hospitals from November 2016 through December 2019 with ≥72 hours in a general medicine service. Discharge location (PAC vs home) was the primary outcome, and 2 time-varying measures of patient mobility, Activity Measure for Post-Acute Care (AM-PAC) Mobility “6-clicks” and Johns Hopkins Highest Level of Mobility, were the primary predictors. Other predictors included demographic and clinical characteristics. For each day of hospitalization, we predicted discharge to PAC using the demographic and clinical characteristics and most recent mobility data within a random forest (RF) for survival, longitudinal, and multivariate (RF-SLAM) data. A regression tree for the daily predicted probabilities of discharge to PAC was constructed to represent a global summary of the RF. There were 23,090 total patients and compared to PAC, those discharged home were younger (64 vs 71), had shorter length of stay (5 vs 8 days), higher AM-PAC at admission (43 vs 32), and average AM-PAC throughout hospitalization (45 vs 35). AM-PAC was the most important predictor, followed by age, and whether the patient lives alone. The area under the hospital day–specific receiver operating characteristic curve ranged from 0.76 to 0.79 during the first 5 days. The global summary tree explained 75% of the variation in predicted probabilities for PAC from the RF. Sensitivity (75%), specificity (70%), and accuracy (72%) were maximized at a PAC probability threshold of 40%. Daily assessment of patient mobility should be part of routine practice to help inform care planning by hospital teams. Our prediction model could be used as a valuable tool by multidisciplinary teams in the discharge planning process.

中文翻译：

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住院患者急性后护理需求的动态预测

使用入院时的患者人口统计和临床特征以及患者活动能力随时间变化的院内测量来预测患者急性后护理 (PAC) 出院。电子病历的回顾性队列分析。 2016 年 11 月至 2019 年 12 月期间入住两家参与医院且接受普通医学服务时间≥72 小时的患者。出院地点（PAC 与家庭）是主要结果，患者活动能力的 2 项随时间变化的测量结果是急性后护理活动测量 (AM-PAC) 活动能力“6 次点击”和约翰霍普金斯大学最高活动水平主要预测因素。其他预测因素包括人口统计和临床特征。对于住院的每一天，我们使用人口统计和临床特征以及生存、纵向和多变量 (RF-SLAM) 数据的随机森林 (RF) 内的最新活动数据来预测出院到 PAC。构建了每日预测的 PAC 放电概率的回归树来表示 RF 的全局摘要。共有 23,090 名患者，与 PAC 相比，出院回家的患者更年轻（64 比 71），住院时间更短（5 比 8 天），入院时 AM-PAC 更高（43 比 32），平均 AM-PAC整个住院期间（45 比 35）。 AM-PAC 是最重要的预测因素，其次是年龄和患者是否独居。前 5 天内，住院日特定受试者工作特征曲线下面积的范围为 0.76 至 0.79。全局汇总树解释了 PAC 与 RF 预测概率差异的 75%。 PAC 概率阈值为 40% 时，敏感性 (75%)、特异性 (70%) 和准确性 (72%) 达到最大。对患者活动能力的日常评估应成为日常实践的一部分，以帮助医院团队制定护理计划。我们的预测模型可以作为多学科团队在出院计划过程中的宝贵工具。