The spread of Coronavirus disease 19 (COVID-19) has led to many healthcare systems being overwhelmed by the rapid emergence of new cases. Here, we study the ramifications of hospital load due to COVID-19 morbidity on in-hospital mortality of patients with COVID-19 by analyzing records of all 22,636 COVID-19 patients hospitalized in Israel from mid-July 2020 to mid-January 2021. We show that even under moderately heavy patient load (>500 countrywide hospitalized severely-ill patients; the Israeli Ministry of Health defined 800 severely-ill patients as the maximum capacity allowing adequate treatment), in-hospital mortality rate of patients with COVID-19 significantly increased compared to periods of lower patient load (250–500 severely-ill patients): 14-day mortality rates were 22.1% (Standard Error 3.1%) higher (mid-September to mid-October) and 27.2% (Standard Error 3.3%) higher (mid-December to mid-January). We further show this higher mortality rate cannot be attributed to changes in the patient population during periods of heavier load.

Objective The spread of 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. Materials and Methods 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 1st to May 2nd, 2020 (n = 2,703). Results 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; AUCs 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. Discussion 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). Conclusion The multistate model we develop is a powerful tool for predicting individual-level patient outcomes and hospital-level utilization.

Importance: The spread of COVID-19 has led to a severe strain on hospital capacity in many countries. There is a need for a model to help planners assess expected COVID-19 hospital resource utilization. Objective: Provide publicly available tools for predicting future hospital-bed utilization given a succinct characterization of the status of currently hospitalized patients and scenarios for future incoming patients. Design: Retrospective cohort study following the day-by-day clinical status of all hospitalized COVID-19 patients in Israel from March 1st to May 2nd, 2020. Patient clinical course was modelled with a machine learning approach based on a set of multistate Cox regression-based models with adjustments for right censoring, recurrent events, competing events, left truncation, and time-dependent covariates. The model predicts the patient's entire disease course in terms of clinical states, from which we derive the patient's hospital length-of-stay, length-of-stay in critical state, risk of in-hospital mortality, and overall hospital-bed utilization. Accuracy assessed over 8 cross-validation cohorts of size 330, using per-day Mean Absolute Error (MAE) of predicted hospital utilization over time; and area under the receiver operating characteristics (AUROC) for individual risk of critical illness and in-hospital mortality, assessed on the first day of hospitalization. We present predicted hospital utilization under hypothetical incoming patient scenarios. Setting: 27 Israeli hospitals. Participants: During the study period, 2,703 confirmed COVID-19 patients were hospitalized in Israel for 1 day or more; 28 were excluded due to missing age or sex; the remaining 2,675 patients were included in the analysis. Main Outcomes and Measures: Primary outcome: per-day estimate of total number of hospitalized patients and number of patients in critical state; secondary outcome: risk of a single patient experiencing critical illness or in-hospital mortality. Results: For random validation samples of 330 patients, the per-day MAEs for total hospital-bed utilization and critical-bed utilization, averaged over 64 days, were 4.72 ± 1.07 and $1.68 ± 0.40 respectively; the AUROCs for prediction of the probabilities of critical illness and in-hospital mortality were 0.88 ± 0.04 and 0.96 ± 0.04, respectively. We further present the impact of several scenarios of patient influx on healthcare system utilization, demonstrating the ability to accurately plan ahead how to allocate healthcare resources. Conclusions and Relevance: We developed a model that, given basic easily obtained data as input, accurately predicts total and critical care hospital utilization. The model enables evaluating the impact of various patient influx scenarios on hospital utilization. Accurate predictions are also given for individual patients' probability of in-hospital mortality and critical illness. We further provide an R software package and a web-application for the model.

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