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. 2023 Nov 29:11:1276211.
doi: 10.3389/fpubh.2023.1276211. eCollection 2023.

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

Oleksii Honchar  1 , Tetiana Ashcheulova  1 , Tetyana Chumachenko  2 , Dmytro Chumachenko  3 , Alla Bobeiko  4 , Viktor Blazhko  4 , Eduard Khodosh  4 , Nataliia Matiash  4 , Tetiana Ambrosova  1 , Nina Herasymchuk  1 , Oksana Kochubiei  1 , Viktoriia Smyrnova  1
Affiliations

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

Oleksii Honchar et al. Front Public Health. .

Abstract

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.

Keywords: COVID-19; clinical decision rules; convalescence; echocardiography; hospitalization; machine learning; post-acute COVID-19 syndrome; walk test.

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Conflict of interest statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. The author(s) declared that they were an editorial board member of Frontiers, at the time of submission. This had no impact on the peer review process and the final decision.

Figures

Figure 1
Figure 1
SANN-based classification model to predict post-COVID-19 syndrome at 3 months post-discharge. (A) Receiver operator characteristic analysis. Area under curve = 0,955. (B) 13–7-2 SANN architecture. (C) Lift chart for predicting PCS-positive cases. (D) Gains chart for predicting PCS-positive cases.
See this image and copyright information in PMC

References

    1. Covid-19 Excess Mortality Collaborators . Estimating excess mortality due to the COVID-19 pandemic: a systematic analysis of COVID-19-related mortality, 2020-21. Lancet. (2022) 399:1513–36. doi: 10.1016/S0140-6736(21)02796-3, PMID: - DOI - PMC - PubMed
    1. Delardas O, Kechagias KS, Pontikos PN, Giannos P. Socio-economic impacts and challenges of the coronavirus pandemic (COVID-19): an updated review. Sustainability. (2022) 14:13. doi: 10.3390/su14159699 - DOI
    1. Doroftei B, Ciobica A, Ilie OD, Maftei R, Ilea C. Mini-review discussing the reliability and efficiency of COVID-19 vaccines. Diagnostics (Basel). (2021) 11. doi: 10.3390/diagnostics11040579 - DOI - PMC - PubMed
    1. Worldometers.info . (2023). Delaware, U.S.A.: Dover. Available at: https://www.worldometers.info/coronavirus/worldwide-graphs/ (Accessed March 1, 2023).
    1. Astin R, Banerjee A, Baker MR, Dani M, Ford E, Hull JH, et al. Long COVID: mechanisms, risk factors and recovery. Exp Physiol. (2023) 108:12–27. doi: 10.1113/EP090802 - DOI - PMC - PubMed