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A prognostic model and pre-discharge predictors of post-Covid-19 syndrome after hospitalization for Sars-CoV-2 infection

Bibliographic Data

ID22073256
AuthorsOleksii Honchar (0000-0001-6834-8845, Kharkiv National Medical University, corresponding author), Tetiana Ashcheulova, Тетяна Вадимівна Ащеулова (0000-0002-8860-652X, Kharkiv National Medical University), Tetyana Chumachenko (0000-0002-4175-2941, Kharkiv National Medical University), Dmytro Chumachenko (0000-0003-2623-3294, National Aerospace University – Kharkiv Aviation Institute), Alla Bobeiko (Kharkiv State Academy of Culture), Viktor Blazhko (Kharkiv State Academy of Culture), Eduard Khodosh (0000-0003-0572-4932, Kharkiv State Academy of Culture), Nataliia Matiash (Kharkiv State Academy of Culture), Tetiana Ambrosova, Тетяна Миколаївна Амбросова (Kharkiv National Medical University), Nina Herasymchuk, N M Herasymchuk (Kharkiv National Medical University), Oksana Kochubiei (0000-0001-7932-7310, Kharkiv National Medical University), Viktoriia Smyrnova (Kharkiv National Medical University)
Year2023
Volume11
Pages1276211-1276211
Publication date2023-11-29
Peer ReviewedYes
Open AccessYes
TypeARTICLE
VenueFrontiers in Public Health (JOURNAL)
Journal identifiersISSN: 2296-2565 • E-ISSN: 2296-2565
PublisherFrontiers Media SA (PUBLISHER • CH)
DOI10.3389/fpubh.2023.1276211
PMID38094237
OpenAlexW4389149663
LanguageEN
References cited45

Background: Post-COVID-19 syndrome (PCS) has been increasingly recognized as an emerging problem: 50% of patients report ongoing symptoms 1 year after acute infection, with most typical manifestations (fatigue, dyspnea, psychiatric and neurological symptoms) having potentially debilitating effect. Early identification of high-risk candidates for PCS development would facilitate the optimal use of resources directed to rehabilitation of COVID-19 convalescents. Objective: To study the in-hospital clinical characteristics of COVID-19 survivors presenting with self-reported PCS at 3 months and to identify the early predictors of its development. Methods: 221 hospitalized COVID-19 patients underwent symptoms assessment, 6-min walk test, and echocardiography pre-discharge and at 1 month; presence of PCS was assessed 3 months after discharge. Unsupervised machine learning was used to build a SANN-based binary classification model of PCS development. Results: PCS at 3 months has been detected in 75% patients. Higher symptoms level in the PCS group was not associated with worse physical functional recovery or significant echocardiographic changes. Despite identification of a set of pre-discharge predictors, inclusion of parameters obtained at 1 month proved necessary to obtain a high accuracy model of PCS development, with inputs list including age, sex, in-hospital levels of CRP, eGFR and need for oxygen supplementation, and level of post-exertional symptoms at 1 month after discharge (fatigue and dyspnea in 6MWT and MRC Dyspnea score). Conclusion: Hospitalized COVID-19 survivors at 3 months were characterized by 75% prevalence of PCS, the development of which could be predicted with an 89% accuracy using the derived neural network-based classification model

Disease · Hospital discharge · Physical therapy · COVID-19 Clinical Research Studies · Heart Rate Variability and Autonomic Control · Long-Term Effects of COVID-19 · Medicine · Emergency Medicine · Internal Medicine · Pediatrics · Rehabilitation

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