Honchar Oleksii, Ashcheulova Tetiana, Bobeiko Alla, Blazhko Viktor, Khodosh Eduard, Matiash Nataliia, Syrota Vladyslav
Department of Propedeutics of Internal Medicine, Nursing and Bioethics, Kharkiv National Medical University, Kharkiv, Ukraine.
Department of Pulmonology, MNE "Clinical City Hospital No.13" of Kharkiv City Council, Kharkiv, Ukraine.
Front Rehabil Sci. 2025 May 21;6:1568291. doi: 10.3389/fresc.2025.1568291. eCollection 2025.
Long COVID syndrome (LCS) represents a significant global health challenge due to its wide-ranging physical and cognitive symptoms that persist beyond 12 months in a substantial proportion of individuals recovering from SARS-CoV-2 infection. Developing tools for predicting long-term LCS persistence can improve patient management and resource allocation.
To evaluate the natural dynamics of symptoms over 12 months following hospitalization for COVID-19 and to establish the utility of survey-based symptoms assessment for predicting LCS at one year.
This prospective observational study included 166 hospitalized COVID-19 survivors who were evaluated pre-discharge and followed up at 1, 3, and 12 months. Assessments included surveys including physical and mental symptom scales (e.g., EFTER-COVID, SBQ-LC, PCFS, MRC Dyspnea, CAT, CCQ, and HADS) and machine learning modeling to predict LCS persistence at 12 months.
LCS symptoms were reported by 76% of patients at three months and 43% at 12 months. Physical symptom scores, particularly EFTER-COVID and PCFS, consistently differentiated LCS and LCS-free cohorts. CAT outperformed other respiratory scales in its discriminatory ability, while HADS subscales showed limited predictive value. Younger patients (<40 years) demonstrated faster recovery, whereas older patients (>60 years) exhibited persistent symptoms across respiratory and cognitive domains. A machine learning model combining EFTER-COVID, SBQ-LC, CAT, and MRC Dyspnea scores achieved 91% predictive accuracy for LCS persistence at 12 months.
Comprehensive survey-based symptoms assessment at three months post-discharge provides a practical and cost-effective tool for prediction of the long COVID persistence at 12 months, supporting targeted rehabilitation strategies.
长新冠综合征（LCS）是一项重大的全球健康挑战，因为在相当一部分从新冠病毒2型（SARS-CoV-2）感染中康复的个体中，其广泛的身体和认知症状会持续超过12个月。开发预测长新冠综合征长期持续存在的工具可以改善患者管理和资源分配。
评估新冠病毒病（COVID-19）住院后12个月内症状的自然动态变化，并确定基于调查的症状评估对预测1年后长新冠综合征的效用。
这项前瞻性观察性研究纳入了166名COVID-19住院幸存者，在出院前进行评估，并在1个月、3个月和12个月进行随访。评估包括问卷调查，其中有身体和精神症状量表（如EFTER-COVID、SBQ-LC、PCFS、医学研究委员会呼吸困难量表、CAT、CCQ和医院焦虑抑郁量表），以及用于预测12个月时长新冠综合征持续存在的机器学习建模。
76%的患者在3个月时报告有长新冠综合征症状，43%的患者在12个月时报告有该症状。身体症状评分，特别是EFTER-COVID和PCFS，始终能区分长新冠综合征患者组和无长新冠综合征患者组。CAT在其鉴别能力方面优于其他呼吸量表，而医院焦虑抑郁量表各子量表的预测价值有限。年轻患者（<40岁）恢复得更快，而老年患者（>60岁）在呼吸和认知领域均表现出持续症状。一个结合了EFTER-COVID、SBQ-LC、CAT和医学研究委员会呼吸困难量表评分的机器学习模型对12个月时长新冠综合征持续存在的预测准确率达到了91%。
出院后3个月基于全面调查的症状评估为预测12个月时长新冠综合征的持续存在提供了一种实用且具有成本效益的工具，有助于制定有针对性的康复策略。
