Tracing In-Hospital COVID-19 Outcomes: A Multistate Model Exploration (TRACE).

COVID-19 comorbidity diabetes mellitus hospital length of stay lymphocytes mortality multistate model prognosis risk factors

Journal

Life (Basel, Switzerland)
ISSN: 2075-1729
Titre abrégé: Life (Basel)
Pays: Switzerland
ID NLM: 101580444

Informations de publication

Date de publication:
21 Sep 2024
Historique:
received: 05 08 2024
revised: 07 09 2024
accepted: 18 09 2024
medline: 28 9 2024
pubmed: 28 9 2024
entrez: 28 9 2024
Statut: epublish

Résumé

This study aims to develop and apply multistate models to estimate, forecast, and manage hospital length of stay during the COVID-19 epidemic without using any external packages. Data from Bellvitge University Hospital in Barcelona, Spain, were analyzed, involving 2285 hospitalized COVID-19 patients with moderate to severe conditions. The implemented multistate model includes transition probabilities and risk rates calculated from transitions between defined states, such as admission, ICU transfer, discharge, and death. In addition to examining key factors like age and gender, diabetes, lymphocyte count, comorbidity burden, symptom duration, and different COVID-19 waves were analyzed. Based on the model, patients hospitalized stay an average of 11.90 days before discharge, 2.84 days before moving to the ICU, or 34.21 days before death. ICU patients remain for about 24.08 days, with subsequent stays of 124.30 days before discharge and 35.44 days before death. These results highlight hospital stays' varying durations and trajectories, providing critical insights into patient flow and healthcare resource utilization. Additionally, it can predict ICU peak loads for specific subgroups, aiding in preparedness. Future work will integrate the developed code into the hospital's Health Information System (HIS) following ISO 13606 EHR standards and implement recursive methods to enhance the model's efficiency and accuracy.

Identifiants

pubmed: 39337977
pii: life14091195
doi: 10.3390/life14091195
pii:
doi:

Types de publication

Journal Article

Langues

eng

Subventions

Organisme : Beatriu de Pinós post-doctoral programme from the Office of the Secretary of Universities and Research from the Ministry of Business and Knowledge of the Government of Catalonia
ID : 2020 BP 00261

Auteurs

Hamed Mohammadi (H)

Biomedical Engineering Department, Engineering Faculty, University of Isfahan, Isfahan 81746-73441, Iran.

Hamid Reza Marateb (HR)

Biomedical Engineering Department, Engineering Faculty, University of Isfahan, Isfahan 81746-73441, Iran.
Department of Automatic Control (ESAII), Biomedical Engineering Research Centre (CREB), Universitat Politèncicna de Catalunya (UPC), 08028 Barcelona, Spain.

Mohammadreza Momenzadeh (M)

Department of Artificial Intelligence, Smart University of Medical Sciences, Tehran 1553-1, Iran.

Martin Wolkewitz (M)

Institute of Medical Biometry and Statistics, Faculty of Medicine and Medical Center, University of Freiburg, 79104 Freiburg, Germany.

Manuel Rubio-Rivas (M)

Department of Internal Medicine, Bellvitge University Hospital, Hospitalet de Llobregat, 08907 Barcelona, Spain.

Classifications MeSH