Clinical features and predictive nomogram for fatigue sequelae in non-severe patients infected with SARS-CoV-2 Omicron variant in Shanghai, China
Background: Patients with coronavirus disease 2019(COVID-2019) infections may still experience long-term effects, with fatigue being one of the most frequent ones. Clinical research on the long COVID in the Chinese population after infection is comparatively lacking. Objective: To collect and analyz...
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Elsevier
2024-12-01
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| Series: | Brain, Behavior, & Immunity - Health |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2666354624001674 |
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| author | Xiao-Lei Shen Yu-Han Jiang Shen-Jie Li Xin-Yi Xie Yu Cheng Li Wu Jun Shen Wei Chen Jian-Ren Liu |
| author_facet | Xiao-Lei Shen Yu-Han Jiang Shen-Jie Li Xin-Yi Xie Yu Cheng Li Wu Jun Shen Wei Chen Jian-Ren Liu |
| author_sort | Xiao-Lei Shen |
| collection | DOAJ |
| description | Background: Patients with coronavirus disease 2019(COVID-2019) infections may still experience long-term effects, with fatigue being one of the most frequent ones. Clinical research on the long COVID in the Chinese population after infection is comparatively lacking. Objective: To collect and analyze the long-term effects of non-severe COVID-19 infection patients and to develop a model for the prediction of fatigue symptoms. Methods: 223 non-severe COVID-19 patients admitted to one designated hospital were enrolled after finish all the self-designed clinical information registration form and nine-month follow-up. We explored the frequency and symptom types of long COVID. Correlation analysis was done on the neuropsychological scale results. After cluster analysis, lasoo regression and logistic regressions, a nomogram prediction model was produced as a result of investigating the risk factors for fatigue. Results: A total of 108 (48.4%) of the 223 non-severe COVID-19 patients reported sequelae for more than 4 weeks, and of these, 35 (15.7%) had fatigue sequelae that were scale-confirmed. Other sequelae of more than 10% were brain fog (n = 37,16.6%), cough (n = 26,11.7%) and insomnia (n = 23,10.3%). A correlation between depression and fatigue was discovered following the completion of neuropsychological scale. The duration of hospitalization, the non-use of antiviral medications in treatment, IL-6 and CD16+CD56+ cell levels in blood are the main independent risk factors and predictors of fatigue sequelae in long COVID. Additionally, the neurology diseases and vaccination status may also influence the fatigue sequelae. Conclusion: Nearly half of the patients infected with COVID-19 Omicron variant complained of sequelae, and fatigue was the most common symptom, which was correlated with depression. Significant predictors of fatigue sequelae included length of hospitalization, non-use of antiviral drug, and immune-related serum markers of IL-6 and CD16+CD56+ NK cell levels. The presence of neurology diseases and a lack of vaccination could also predict the occurrence of fatigue sequelae. |
| format | Article |
| id | doaj-art-4ecbc3c82a6346bcbeb53a6100d656c9 |
| institution | DOAJ |
| issn | 2666-3546 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Brain, Behavior, & Immunity - Health |
| spelling | doaj-art-4ecbc3c82a6346bcbeb53a6100d656c92025-08-20T02:51:14ZengElsevierBrain, Behavior, & Immunity - Health2666-35462024-12-014210088910.1016/j.bbih.2024.100889Clinical features and predictive nomogram for fatigue sequelae in non-severe patients infected with SARS-CoV-2 Omicron variant in Shanghai, ChinaXiao-Lei Shen0Yu-Han Jiang1Shen-Jie Li2Xin-Yi Xie3Yu Cheng4Li Wu5Jun Shen6Wei Chen7Jian-Ren Liu8Department of Neurology, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200011, ChinaDepartment of Neurology, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200011, ChinaDepartment of Neurology, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200011, ChinaDepartment of Neurology, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200011, ChinaDepartment of Neurology, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200011, ChinaDepartment of Neurology, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200011, ChinaDepartment of Neurology, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200011, ChinaCorresponding author.; Department of Neurology, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200011, ChinaCorresponding author.; Department of Neurology, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200011, ChinaBackground: Patients with coronavirus disease 2019(COVID-2019) infections may still experience long-term effects, with fatigue being one of the most frequent ones. Clinical research on the long COVID in the Chinese population after infection is comparatively lacking. Objective: To collect and analyze the long-term effects of non-severe COVID-19 infection patients and to develop a model for the prediction of fatigue symptoms. Methods: 223 non-severe COVID-19 patients admitted to one designated hospital were enrolled after finish all the self-designed clinical information registration form and nine-month follow-up. We explored the frequency and symptom types of long COVID. Correlation analysis was done on the neuropsychological scale results. After cluster analysis, lasoo regression and logistic regressions, a nomogram prediction model was produced as a result of investigating the risk factors for fatigue. Results: A total of 108 (48.4%) of the 223 non-severe COVID-19 patients reported sequelae for more than 4 weeks, and of these, 35 (15.7%) had fatigue sequelae that were scale-confirmed. Other sequelae of more than 10% were brain fog (n = 37,16.6%), cough (n = 26,11.7%) and insomnia (n = 23,10.3%). A correlation between depression and fatigue was discovered following the completion of neuropsychological scale. The duration of hospitalization, the non-use of antiviral medications in treatment, IL-6 and CD16+CD56+ cell levels in blood are the main independent risk factors and predictors of fatigue sequelae in long COVID. Additionally, the neurology diseases and vaccination status may also influence the fatigue sequelae. Conclusion: Nearly half of the patients infected with COVID-19 Omicron variant complained of sequelae, and fatigue was the most common symptom, which was correlated with depression. Significant predictors of fatigue sequelae included length of hospitalization, non-use of antiviral drug, and immune-related serum markers of IL-6 and CD16+CD56+ NK cell levels. The presence of neurology diseases and a lack of vaccination could also predict the occurrence of fatigue sequelae.http://www.sciencedirect.com/science/article/pii/S2666354624001674SARS-CoV-2COVID-19OmicronLong COVID syndromeFatigue |
| spellingShingle | Xiao-Lei Shen Yu-Han Jiang Shen-Jie Li Xin-Yi Xie Yu Cheng Li Wu Jun Shen Wei Chen Jian-Ren Liu Clinical features and predictive nomogram for fatigue sequelae in non-severe patients infected with SARS-CoV-2 Omicron variant in Shanghai, China Brain, Behavior, & Immunity - Health SARS-CoV-2 COVID-19 Omicron Long COVID syndrome Fatigue |
| title | Clinical features and predictive nomogram for fatigue sequelae in non-severe patients infected with SARS-CoV-2 Omicron variant in Shanghai, China |
| title_full | Clinical features and predictive nomogram for fatigue sequelae in non-severe patients infected with SARS-CoV-2 Omicron variant in Shanghai, China |
| title_fullStr | Clinical features and predictive nomogram for fatigue sequelae in non-severe patients infected with SARS-CoV-2 Omicron variant in Shanghai, China |
| title_full_unstemmed | Clinical features and predictive nomogram for fatigue sequelae in non-severe patients infected with SARS-CoV-2 Omicron variant in Shanghai, China |
| title_short | Clinical features and predictive nomogram for fatigue sequelae in non-severe patients infected with SARS-CoV-2 Omicron variant in Shanghai, China |
| title_sort | clinical features and predictive nomogram for fatigue sequelae in non severe patients infected with sars cov 2 omicron variant in shanghai china |
| topic | SARS-CoV-2 COVID-19 Omicron Long COVID syndrome Fatigue |
| url | http://www.sciencedirect.com/science/article/pii/S2666354624001674 |
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