{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,21]],"date-time":"2026-04-21T16:26:09Z","timestamp":1776788769351,"version":"3.51.2"},"reference-count":24,"publisher":"Public Library of Science (PLoS)","issue":"9","license":[{"start":{"date-parts":[[2021,9,20]],"date-time":"2021-09-20T00:00:00Z","timestamp":1632096000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["www.plosone.org"],"crossmark-restriction":false},"short-container-title":["PLoS ONE"],"abstract":"<jats:p>This paper analyses COVID-19 patients\u2019 dynamics during the first wave in the region of Castilla y Le\u00f3n (Spain) with around 2.4 million inhabitants using multi-state competing risk survival models. From the date registered as the start of the clinical process, it is assumed that a patient can progress through three intermediate states until reaching an absorbing state of recovery or death. Demographic characteristics, epidemiological factors such as the time of infection and previous vaccinations, clinical history, complications during the course of the disease and drug therapy for hospitalised patients are considered as candidate predictors. Regarding risk factors associated with mortality and severity, consistent results with many other studies have been found, such as older age, being male, and chronic diseases. Specifically, the hospitalisation (death) rate for those over 69 is 27.2% (19.8%) versus 5.3% (0.7%) for those under 70, and for males is 14.5%(7%) versus 8.3%(4.6%)for females. Among patients with chronic diseases the highest rates of hospitalisation are 26.1% for diabetes and 26.3% for kidney disease, while the highest death rate is 21.9% for cerebrovascular disease. Moreover, specific predictors for different transitions are given, and estimates of the probability of recovery and death for each patient are provided by the model. Some interesting results obtained are that for patients infected at the end of the period the hazard of transition from hospitalisation to ICU is significatively lower (<jats:italic>p<\/jats:italic>&lt; 0.001) and the hazard of transition from hospitalisation to recovery is higher (<jats:italic>p<\/jats:italic>&lt; 0.001). For patients previously vaccinated against pneumococcus the hazard of transition to recovery is higher (<jats:italic>p<\/jats:italic>&lt; 0.001). Finally, internal validation and calibration of the model are also performed.<\/jats:p>","DOI":"10.1371\/journal.pone.0257613","type":"journal-article","created":{"date-parts":[[2021,9,20]],"date-time":"2021-09-20T18:27:09Z","timestamp":1632162429000},"page":"e0257613","update-policy":"https:\/\/doi.org\/10.1371\/journal.pone.corrections_policy","source":"Crossref","is-referenced-by-count":8,"title":["Predicting COVID-19 progression from diagnosis to recovery or death linking primary care and hospital records in Castilla y Le\u00f3n (Spain)"],"prefix":"10.1371","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8818-0194","authenticated-orcid":true,"given":"Pedro C.","family":"\u00c1lvarez-Esteban","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Eustasio","family":"del Barrio","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0008-4884","authenticated-orcid":true,"given":"Oscar M.","family":"Rueda","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Cristina","family":"Rueda","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"340","published-online":{"date-parts":[[2021,9,20]]},"reference":[{"issue":"11","key":"pone.0257613.ref001","doi-asserted-by":"crossref","first-page":"e0242127","DOI":"10.1371\/journal.pone.0242127","article-title":"COVID-19 in-hospital mortality and mode of death in a dynamic and non-restricted tertiary care model in Germany","volume":"15","author":"S Rieg","year":"2020","journal-title":"PloS One"},{"key":"pone.0257613.ref002","article-title":"The Clinical Course of COVID-19 Disease in a US Hospital System: a Multi-state Analysis","author":"A Mody","year":"2020","journal-title":"American Journal of Epidemiology"},{"key":"pone.0257613.ref003","doi-asserted-by":"crossref","DOI":"10.3389\/fpubh.2020.585850","article-title":"Patient Flow Dynamics in Hospital Systems During Times of COVID-19: Cox Proportional Hazard Regression Analysis","volume":"8","author":"S Bhandari","year":"2020","journal-title":"Frontiers in Public Health"},{"key":"pone.0257613.ref004","volume-title":"The prognostic value of the SOFA score in patients with COVID-19: a retrospective, observational study","author":"Z Yang","year":"2020"},{"key":"pone.0257613.ref005","article-title":"OpenSAFELY: factors associated with COVID-19 death in 17 million patients","author":"EJ Williamson","year":"2020","journal-title":"Nature"},{"key":"pone.0257613.ref006","article-title":"The natural history of symptomatic COVID-19 in Catalonia, Spain: a multi-state model including 109,367 outpatient diagnoses, 18,019 hospitalisations, and 5,585 COVID-19 deaths among 5,627,520 people","author":"E Burn","year":"2020","journal-title":"MedRxiv"},{"key":"pone.0257613.ref007","article-title":"Factors associated with hospital admission and critical illness among 5279 people with coronavirus disease 2019 in New York City: prospective cohort study","volume":"369","author":"CM Petrilli","year":"2020","journal-title":"BMJ"},{"key":"pone.0257613.ref008","unstructured":"Calman YBL, Gelbshtein U, Liverant-Taub S, Ziv A, Eytan D, Gorfine M, et al. 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