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The spread of Coronavirus disease 19 (COVID-19) has led to many healthcare systems being overwhelmed by the rapid emergence of new cases within a short period of time. We explore the ramifications of hospital load due to COVID-19 morbidity on COVID-19 in-hospital patient mortality. We address this question with a nationwide study based on the 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.
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BackgroundThe 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. MethodsRetrospective nationwide 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 patients entire disease course in terms of clinical states, from which we derive the patients hospital length-of-stay, length-of-stay in critical state, the risk of in-hospital mortality, and total and critical care hospital-bed utilization. Accuracy assessed over eight cross-validation cohorts of size 330, using per-day Mean Absolute Error (MAE) of predicted hospital utilization averaged over 64 days; 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. FindingsDuring the study period, 2,703 confirmed COVID-19 patients were hospitalized in Israel. The per-day MAEs for total and critical-care hospital-bed utilization, were 4{middle dot}72 {+/-} 1{middle dot}07 and 1{middle dot}68 {+/-} 0{middle dot}40 respectively; the AUROCs for prediction of the probabilities of critical illness and in-hospital mortality were 0{middle dot}88 {+/-} 0{middle dot}04 and 0{middle dot}96 {+/-} 0{middle dot}04, respectively. We further present the impact of several scenarios of patient influx on healthcare system utilization, and provide an R software package for predicting hospital-bed utilization. InterpretationWe 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 and planning ahead of hospital resource allocation. FundingThe work was funded by the Israeli Ministry of Health. M.G. received support from the U.S.-Israel Binational Science Foundation (BSF, 2016126). O_TEXTBOXResearch in contextO_ST_ABSEvidence before this studyC_ST_ABSCOVID19 outbreaks are known to lead to severe case load in hospital systems, stretching resources, partially due to the long hospitalizations needed for some of the patients. There is a crucial need for tools helping planners assess future hospitalization load, taking into account the specific characteristics and heterogeneity of currently hospitalized COVID19 patients, as well as the characteristics of incoming patients. We searched PubMed for articles published up to September 9, 2020, containing the words "COVID19" and combinations of "hospital", "utilization", "resource", "capacity" and "predict". We found 145 studies; out of them, several included models that predict the future trend of hospitalizations using compartment models (e.g. SIR models), or by using exponential or logistic models. We discuss two of the more prominent ones, which model explicitly the passage of patients through the ICU. These models (i) do not take into account individual patient characteristics; (ii) do not consider length-of-stay heterogeneity, despite the fact that bed utilization is in part determined by a long tail of patients requiring significantly longer stays than others; (iii) do not correct for competing risks bias. We further searched for studies containing the words "COVID19" and "multistate", and "COVID19" and "length" and "stay". Out of 317 papers, we found two using multistate models focusing only on patients undergoing ECMO treatment. Added value of this studyWe present the first model predicting hospital load based on the individual characteristics of hospitalized patients: age, sex, clinical state, and time already spent in-hospital. We combine this with scenarios for incoming patients, allowing for variations by age, sex and clinical state. The models precise predictions are based on a large sample of complete, day-by-day disease trajectories of patients, with a full coverage of the entire COVID-19 hospitalized population in Israel up to early May, 2020 (n =2, 703). We provide the model, as well as software for fitting such a model to local data, and an anonymized version of the dataset used to create the model. Implications of all the available evidenceAccurate predictions for hospital utilization can be made based on easy to obtain patient data: age, sex, and patient clinical state (moderate, severe or critical). The model allows hospital-and regional-level planners to allocate resources in a timely manner, preparing for different patient influx scenarios. C_TEXTBOX
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With the global coronavirus disease 2019 (COVID-19) pandemic, there is an urgent need for risk stratification tools to support prevention and treatment decisions. The Centers for Disease Control and Prevention (CDC) listed several criteria that define high-risk individuals, but multivariable prediction models may allow for a more accurate and granular risk evaluation. In the early days of the pandemic, when individual level data required for training prediction models was not available, a large healthcare organization developed a prediction model for supporting its COVID-19 policy using a hybrid strategy. The model was constructed on a baseline predictor to rank patients according to their risk for severe respiratory infection or sepsis (trained using over one-million patient records) and was then post-processed to calibrate the predictions to reported COVID-19 case fatality rates. Since its deployment in mid-March, this predictor was integrated into many decision-processes in the organization that involved allocating limited resources. With the accumulation of enough COVID-19 patients, the predictor was validated for its accuracy in predicting COVID-19 mortality among all COVID-19 cases in the organization (3,176, 3.1% death rate). The predictor was found to have good discrimination, with an area under the receiver-operating characteristics curve of 0.942. Calibration was also good, with a marked improvement compared to the calibration of the baseline model when evaluated for the COVID-19 mortality outcome. While the CDC criteria identify 41% of the population as high-risk with a resulting sensitivity of 97%, a 5% absolute risk cutoff by the model tags only 14% to be at high-risk while still achieving a sensitivity of 90%. To summarize, we found that even in the midst of a pandemic, shrouded in epidemiologic "fog of war" and with no individual level data, it was possible to provide a useful predictor with good discrimination and calibration.