Department of Critical Care Medicine, Rambam Health Care Campus, Haifa, Israel.
Faculty of Industrial Engineering and Management, Technion - Israel Institute of Technology, Haifa, Israel.
J Am Med Inform Assoc. 2021 Jun 12;28(6):1188-1196. doi: 10.1093/jamia/ocab005.
The spread of coronavirus disease 2019 (COVID-19) has led to severe strain on hospital capacity in many countries. We aim to develop a model helping planners assess expected COVID-19 hospital resource utilization based on individual patient characteristics.
We develop a model of patient clinical course based on an advanced multistate survival model. The model predicts the patient's disease course in terms of clinical states-critical, severe, or moderate. The model also predicts hospital utilization on the level of entire hospitals or healthcare systems. We cross-validated the model using a nationwide registry following the day-by-day clinical status of all hospitalized COVID-19 patients in Israel from March 1 to May 2, 2020 (n = 2703).
Per-day mean absolute errors for predicted total and critical care hospital bed utilization were 4.72 ± 1.07 and 1.68 ± 0.40, respectively, over cohorts of 330 hospitalized patients; areas under the curve for prediction of critical illness and in-hospital mortality were 0.88 ± 0.04 and 0.96 ± 0.04, respectively. We further present the impact of patient influx scenarios on day-by-day healthcare system utilization. We provide an accompanying R software package.
The proposed model accurately predicts total and critical care hospital utilization. The model enables evaluating impacts of patient influx scenarios on utilization, accounting for the state of currently hospitalized patients and characteristics of incoming patients. We show that accurate hospital load predictions were possible using only a patient's age, sex, and day-by-day clinical state (critical, severe, or moderate).
The multistate model we develop is a powerful tool for predicting individual-level patient outcomes and hospital-level utilization.
2019 年冠状病毒病（COVID-19）的传播导致许多国家的医院容量严重紧张。我们旨在开发一种模型，帮助规划者根据患者个体特征评估 COVID-19 医院资源的预期利用情况。
我们基于先进的多状态生存模型开发了一种患者临床过程模型。该模型根据临床状态（危急、严重或中度）预测患者的疾病进程。该模型还预测整个医院或医疗系统的医院利用情况。我们使用全国性登记处，根据 2020 年 3 月 1 日至 5 月 2 日期间以色列所有住院 COVID-19 患者的每日临床状况（n=2703），对模型进行了交叉验证。
对于 330 名住院患者的队列，预测总重症监护病床和重症监护病床利用率的每日平均绝对误差分别为 4.72±1.07 和 1.68±0.40；预测危重病和住院死亡率的曲线下面积分别为 0.88±0.04 和 0.96±0.04。我们进一步展示了患者流入情景对每日医疗系统利用的影响。我们提供了一个配套的 R 软件包。
所提出的模型准确预测了总重症监护病房和重症监护病房的利用率。该模型能够评估患者流入情景对利用率的影响，同时考虑到目前住院患者的状态和即将入院患者的特征。我们表明，仅使用患者的年龄、性别和每日临床状态（危急、严重或中度）就可以进行准确的医院负荷预测。
我们开发的多状态模型是预测个体患者结局和医院利用水平的强大工具。
