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Abstract：
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.
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.
摘要：
2019年冠状病毒病(COVID-19)的传播导致许多国家的医院产能严重紧张。我们的目标是开发一种模型，帮助计划人员根据个体患者特征评估预期的COVID-19医院资源利用率。
我们基于先进的多状态生存模型开发了患者临床病程模型。该模型根据临床危急状态预测患者的病程，严重,或中等。该模型还可以预测整个医院或医疗保健系统的医院利用率。我们在2020年3月1日至5月2日在以色列的所有住院COVID-19患者的日常临床状况（n=2703）后，使用全国注册表对该模型进行了交叉验证。
预计总病床利用率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，超过330例住院患者的队列;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.我们进一步介绍了患者涌入情况对日常医疗保健系统利用率的影响。我们提供随附的R软件包。
所提出的模型可以准确预测医院的整体和重症监护利用率。该模型能够评估患者涌入情景对利用率的影响，考虑目前住院患者的状况和来华患者的特征。我们表明，准确的医院负荷预测是可能的，仅使用患者的年龄，性别,和日常临床状态(危重，严重,或中等)。
我们开发的多状态模型是预测个体水平患者结果和医院水平利用率的强大工具。
我们基于先进的多状态生存模型开发了患者临床病程模型。该模型根据临床危急状态预测患者的病程，严重,或中等。该模型还可以预测整个医院或医疗保健系统的医院利用率。我们在2020年3月1日至5月2日在以色列的所有住院COVID-19患者的日常临床状况（n=2703）后，使用全国注册表对该模型进行了交叉验证。
预计总病床利用率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，超过330例住院患者的队列;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.我们进一步介绍了患者涌入情况对日常医疗保健系统利用率的影响。我们提供随附的R软件包。
所提出的模型可以准确预测医院的整体和重症监护利用率。该模型能够评估患者涌入情景对利用率的影响，考虑目前住院患者的状况和来华患者的特征。我们表明，准确的医院负荷预测是可能的，仅使用患者的年龄，性别,和日常临床状态(危重，严重,或中等)。
我们开发的多状态模型是预测个体水平患者结果和医院水平利用率的强大工具。
