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      A multistate model and its standalone tool to predict hospital and ICU occupancy by patients with COVID-19

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          Abstract

          Objective

          This study aims to build a multistate model and describe a predictive tool for estimating the daily number of intensive care unit (ICU) and hospital beds occupied by patients with coronavirus 2019 disease (COVID-19).

          Material and methods

          The estimation is based on the simulation of patient trajectories using a multistate model where the transition probabilities between states are estimated via competing risks and cure models. The input to the tool includes the dates of COVID-19 diagnosis, admission to hospital, admission to ICU, discharge from ICU and discharge from hospital or death of positive cases from a selected initial date to the current moment. Our tool is validated using 98,496 cases positive for severe acute respiratory coronavirus 2 extracted from the Aragón Healthcare Records Database from July 1, 2020 to February 28, 2021.

          Results

          The tool demonstrates good performance for the 7- and 14-days forecasts using the actual positive cases, and shows good accuracy among three scenarios corresponding to different stages of the pandemic: 1) up-scenario, 2) peak-scenario and 3) down-scenario. Long term predictions (two months) also show good accuracy, while those using Holt-Winters positive case estimates revealed acceptable accuracy to day 14 onwards, with relative errors of 8.8%.

          Discussion

          In the era of the COVID-19 pandemic, hospitals must evolve in a dynamic way. Our prediction tool is designed to predict hospital occupancy to improve healthcare resource management without information about clinical history of patients.

          Conclusions

          Our easy-to-use and freely accessible tool ( https://github.com/peterman65) shows good performance and accuracy for forecasting the daily number of hospital and ICU beds required for patients with COVID-19.

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          Most cited references44

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          Risk stratification of patients admitted to hospital with covid-19 using the ISARIC WHO Clinical Characterisation Protocol: development and validation of the 4C Mortality Score

          Stephen R. Knight, Antonia Ho, Riinu Pius … (2020)
          Objective To develop and validate a pragmatic risk score to predict mortality in patients admitted to hospital with coronavirus disease 2019 (covid-19). Design Prospective observational cohort study. Setting International Severe Acute Respiratory and emerging Infections Consortium (ISARIC) World Health Organization (WHO) Clinical Characterisation Protocol UK (CCP-UK) study (performed by the ISARIC Coronavirus Clinical Characterisation Consortium—ISARIC-4C) in 260 hospitals across England, Scotland, and Wales. Model training was performed on a cohort of patients recruited between 6 February and 20 May 2020, with validation conducted on a second cohort of patients recruited after model development between 21 May and 29 June 2020. Participants Adults (age ≥18 years) admitted to hospital with covid-19 at least four weeks before final data extraction. Main Outcome Measure In-hospital mortality. Results 35 463 patients were included in the derivation dataset (mortality rate 32.2%) and 22 361 in the validation dataset (mortality rate 30.1%). The final 4C Mortality Score included eight variables readily available at initial hospital assessment: age, sex, number of comorbidities, respiratory rate, peripheral oxygen saturation, level of consciousness, urea level, and C reactive protein (score range 0-21 points). The 4C Score showed high discrimination for mortality (derivation cohort: area under the receiver operating characteristic curve 0.79, 95% confidence interval 0.78 to 0.79; validation cohort: 0.77, 0.76 to 0.77) with excellent calibration (validation: calibration-in-the-large=0, slope=1.0). Patients with a score of at least 15 (n=4158, 19%) had a 62% mortality (positive predictive value 62%) compared with 1% mortality for those with a score of 3 or less (n=1650, 7%; negative predictive value 99%). Discriminatory performance was higher than 15 pre-existing risk stratification scores (area under the receiver operating characteristic curve range 0.61-0.76), with scores developed in other covid-19 cohorts often performing poorly (range 0.63-0.73). Conclusions An easy-to-use risk stratification score has been developed and validated based on commonly available parameters at hospital presentation. The 4C Mortality Score outperformed existing scores, showed utility to directly inform clinical decision making, and can be used to stratify patients admitted to hospital with covid-19 into different management groups. The score should be further validated to determine its applicability in other populations. Study Registration ISRCTN66726260
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            COVID-19 length of hospital stay: a systematic review and data synthesis

            Eleanor Rees, Emily S Nightingale, Yalda Jafari … (2020)
            Background The COVID-19 pandemic has placed an unprecedented strain on health systems, with rapidly increasing demand for healthcare in hospitals and intensive care units (ICUs) worldwide. As the pandemic escalates, determining the resulting needs for healthcare resources (beds, staff, equipment) has become a key priority for many countries. Projecting future demand requires estimates of how long patients with COVID-19 need different levels of hospital care. Methods We performed a systematic review of early evidence on length of stay (LoS) of patients with COVID-19 in hospital and in ICU. We subsequently developed a method to generate LoS distributions which combines summary statistics reported in multiple studies, accounting for differences in sample sizes. Applying this approach, we provide distributions for total hospital and ICU LoS from studies in China and elsewhere, for use by the community. Results We identified 52 studies, the majority from China (46/52). Median hospital LoS ranged from 4 to 53 days within China, and 4 to 21 days outside of China, across 45 studies. ICU LoS was reported by eight studies—four each within and outside China—with median values ranging from 6 to 12 and 4 to 19 days, respectively. Our summary distributions have a median hospital LoS of 14 (IQR 10–19) days for China, compared with 5 (IQR 3–9) days outside of China. For ICU, the summary distributions are more similar (median (IQR) of 8 (5–13) days for China and 7 (4–11) days outside of China). There was a visible difference by discharge status, with patients who were discharged alive having longer LoS than those who died during their admission, but no trend associated with study date. Conclusion Patients with COVID-19 in China appeared to remain in hospital for longer than elsewhere. This may be explained by differences in criteria for admission and discharge between countries, and different timing within the pandemic. In the absence of local data, the combined summary LoS distributions provided here can be used to model bed demands for contingency planning and then updated, with the novel method presented here, as more studies with aggregated statistics emerge outside China.
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              Forecasting Sales by Exponentially Weighted Moving Averages

              Peter Winters (1960)
              Article 0 comments Cited 158 times – based on 0 reviews     Review now
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                Author and article information

                Journal
                Journal ID (nlm-ta): Heliyon
                Journal ID (iso-abbrev): Heliyon
                Title: Heliyon
                Publisher: The Authors. Published by Elsevier Ltd.
                ISSN (Electronic): 2405-8440
                Publication date PMC-release: 5 February 2023
                Publication date (Electronic): 5 February 2023
                Electronic Location Identifier: e13545
                Affiliations
                [a ]Department of Statistical Methods, Universidad de Zaragoza, C. Pedro Cerbuna 12, 50009 Zaragoza, Spain
                [b ]Institute for Biocomputation and Physics of Complex Systems-BIFI, Universidad de Zaragoza. C. de Mariano Esquillor Gómez, Edificio I+D, 50018 Zaragoza, Spain
                [c ]Centre Q-UPHS. Quantitative Methods for Uplifting the Performance of Health Services, Spain
                [d ]Department of Statistics and Operational Research, Universidad Pública de Navarra, Campus Arrosadía S/n, 31006 Pamplona, Spain
                [e ]Department of Urology, Miguel Servet University Hospital and IIS Aragón, Paseo Isabel La Católica 1-3, 50009 Zaragoza, Spain
                [f ]Department of Applied Mathematics, Escuela Universitaria Politécnica de La Almunia, University of Zaragoza, C/ Mayor 5, 50100 La Almunia de Doña Godina, Spain
                Author notes
                [∗ ]Corresponding author. Department of Statistical Methods, Universidad de Zaragoza, C. Pedro Cerbuna 12, 50009 Zaragoza, Spain.
                [∗∗ ]Corresponding author.
                Article
                Publisher Item ID: S2405-8440(23)00752-1 Publisher ID: e13545
                DOI: 10.1016/j.heliyon.2023.e13545
                PMC ID: 9899510
                SO-VID: 32648642-b5ac-4342-959b-2230cb0e0f59
                Copyright © © 2023 The Authors. Published by Elsevier Ltd.
                License:

                Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active.

                History
                Date received : 28 June 2022
                Date revision received : 28 January 2023
                Date accepted : 2 February 2023
                Categories
                Subject: Research Article

                Keywords: covid-19,health resources,multistate models,hospital and icu occupancy,predictive tool
                Data availability:
                Keywords: covid-19, health resources, multistate models, hospital and icu occupancy, predictive tool

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