Tracing In-Hospital COVID-19 Outcomes: A Multistate Model Exploration (TRACE)
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...
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-09-01
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| Series: | Life |
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| Online Access: | https://www.mdpi.com/2075-1729/14/9/1195 |
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| author | Hamed Mohammadi Hamid Reza Marateb Mohammadreza Momenzadeh Martin Wolkewitz Manuel Rubio-Rivas |
| author_facet | Hamed Mohammadi Hamid Reza Marateb Mohammadreza Momenzadeh Martin Wolkewitz Manuel Rubio-Rivas |
| author_sort | Hamed Mohammadi |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-81eb634a4531433aa4e0403153fcc677 |
| institution | OA Journals |
| issn | 2075-1729 |
| language | English |
| publishDate | 2024-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Life |
| spelling | doaj-art-81eb634a4531433aa4e0403153fcc6772025-08-20T01:55:38ZengMDPI AGLife2075-17292024-09-01149119510.3390/life14091195Tracing In-Hospital COVID-19 Outcomes: A Multistate Model Exploration (TRACE)Hamed Mohammadi0Hamid Reza Marateb1Mohammadreza Momenzadeh2Martin Wolkewitz3Manuel Rubio-Rivas4Biomedical Engineering Department, Engineering Faculty, University of Isfahan, Isfahan 81746-73441, IranBiomedical Engineering Department, Engineering Faculty, University of Isfahan, Isfahan 81746-73441, IranDepartment of Artificial Intelligence, Smart University of Medical Sciences, Tehran 1553-1, IranInstitute of Medical Biometry and Statistics, Faculty of Medicine and Medical Center, University of Freiburg, 79104 Freiburg, GermanyDepartment of Internal Medicine, Bellvitge University Hospital, Hospitalet de Llobregat, 08907 Barcelona, SpainThis 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.https://www.mdpi.com/2075-1729/14/9/1195COVID-19comorbiditydiabetes mellitushospital length of staylymphocytesmortality |
| spellingShingle | Hamed Mohammadi Hamid Reza Marateb Mohammadreza Momenzadeh Martin Wolkewitz Manuel Rubio-Rivas Tracing In-Hospital COVID-19 Outcomes: A Multistate Model Exploration (TRACE) Life COVID-19 comorbidity diabetes mellitus hospital length of stay lymphocytes mortality |
| title | Tracing In-Hospital COVID-19 Outcomes: A Multistate Model Exploration (TRACE) |
| title_full | Tracing In-Hospital COVID-19 Outcomes: A Multistate Model Exploration (TRACE) |
| title_fullStr | Tracing In-Hospital COVID-19 Outcomes: A Multistate Model Exploration (TRACE) |
| title_full_unstemmed | Tracing In-Hospital COVID-19 Outcomes: A Multistate Model Exploration (TRACE) |
| title_short | Tracing In-Hospital COVID-19 Outcomes: A Multistate Model Exploration (TRACE) |
| title_sort | tracing in hospital covid 19 outcomes a multistate model exploration trace |
| topic | COVID-19 comorbidity diabetes mellitus hospital length of stay lymphocytes mortality |
| url | https://www.mdpi.com/2075-1729/14/9/1195 |
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