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

• Published in: Frontiers in public health 2023-11, Vol.11, p.1276211-1276211
• Main Authors: Honchar, Oleksii, Ashcheulova, Tetiana, Chumachenko, Tetyana, Chumachenko, Dmytro, Bobeiko, Alla, Blazhko, Viktor, Khodosh, Eduard, Matiash, Nataliia, Ambrosova, Tetiana, Herasymchuk, Nina, Kochubiei, Oksana, Smyrnova, Viktoriia
• Format: Article
• Language: English
• Subjects: convalescence; COVID-19; COVID-19 - epidemiology; Dyspnea - etiology; echocardiography; Fatigue - etiology; Hospitalization; Humans; Patient Discharge; Post-Acute COVID-19 Syndrome; Prognosis; Public Health; SARS-CoV-2; walk test
• ISSN: 2296-2565; 2296-2565
• DOI: 10.3389/fpubh.2023.1276211

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. 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. 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. 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). 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.