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Ye G, Zhu Y, Bao W, Zhou H, Lai J, Zhang Y, Xie J, Ma Q, Luo Z, Ma S, Guo Y, Zhang X, Zhang M, Niu X. The Long COVID Symptoms and Severity Score: Development, Validation, and Application. VALUE IN HEALTH : THE JOURNAL OF THE INTERNATIONAL SOCIETY FOR PHARMACOECONOMICS AND OUTCOMES RESEARCH 2024; 27:1085-1091. [PMID: 38641060 DOI: 10.1016/j.jval.2024.04.009] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/24/2023] [Revised: 03/17/2024] [Accepted: 04/01/2024] [Indexed: 04/21/2024]
Abstract
OBJECTIVES The primary focus of this research is the proposition of a methodological framework for the clinical application of the long COVID symptoms and severity score (LC-SSS). This tool is not just a self-reported assessment instrument developed and validated but serves as a standardized, quantifiable means to monitor the diverse and persistent symptoms frequently observed in individuals with long COVID. METHODS A 3-stage process was used to develop, validate, and establish scoring standards for the LC-SSS. Validation measures included correlations with other patient-reported measures, confirmatory factor analysis, Cronbach's α for internal consistency, and test-retest reliability. Scoring standards were determined using K-means clustering, with comparative assessments made against hierarchical clustering and the Gaussian Mixture Model. RESULTS The LC-SSS showed correlations with EuroQol 5-Dimension 5-Level (rs = -0.55), EuroQol visual analog scale (rs = -0.368), Patient Health Questionnaire-9 (rs = 0.538), Beck Anxiety Inventory (rs = 0.689), and Insomnia Severity Index (rs = 0.516), confirming its construct validity. Structural validity was good with a comparative fit index of 0.969, with Cronbach's α of 0.93 indicating excellent internal consistency. Test-retest reliability was also satisfactory (intraclass correlation coefficient 0.732). K-means clustering identified 3 distinct severity categories in individuals living with long COVID, providing a basis for personalized treatment strategies. CONCLUSIONS The LC-SSS provides a robust and valid tool for assessing long COVID. The severity categories established via K-means clustering demonstrate significant variation in symptom severity, informing personalized treatment and improving care quality for patients with long COVID.
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Affiliation(s)
- Gengchen Ye
- Department of Medical Imaging, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, Shaanxi Province, China
| | - Yanan Zhu
- Medical Imaging Centre, Ankang Central Hospital, Ankang, Shaanxi Province, China; School of Medicine, Ankang University, Ankang, Shaanxi Province, China
| | - Wenrui Bao
- School of Future Technology, Xi'an Jiaotong University, Xi'an, Shaanxi Province, China
| | - Heping Zhou
- Medical Imaging Centre, Ankang Central Hospital, Ankang, Shaanxi Province, China
| | - Jiandong Lai
- Medical Imaging Centre, Ankang Central Hospital, Ankang, Shaanxi Province, China
| | - Yuchen Zhang
- Department of Nuclear Medicine, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, Shaanxi Province, China
| | - Juanping Xie
- School of Medicine, Ankang University, Ankang, Shaanxi Province, China
| | - Qingbo Ma
- Master of Biomedical Engineering (Research-oriented), Ankang Vocational and Technical College, Ankang, Shaanxi Province, China
| | - Zhaoyao Luo
- Department of Medical Imaging, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, Shaanxi Province, China
| | - Shaohui Ma
- Department of Medical Imaging, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, Shaanxi Province, China
| | - Yichu Guo
- Department of Medical Imaging, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, Shaanxi Province, China
| | - Xuanting Zhang
- Department of Medical Imaging, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, Shaanxi Province, China
| | - Ming Zhang
- Department of Medical Imaging, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, Shaanxi Province, China.
| | - Xuan Niu
- Department of Medical Imaging, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, Shaanxi Province, China.
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Song X, Song W, Cui L, Duong TQ, Pandy R, Liu H, Zhou Q, Sun J, Liu Y, Li T. A Comprehensive Review of the Global Epidemiology, Clinical Management, Socio-Economic Impacts, and National Responses to Long COVID with Future Research Directions. Diagnostics (Basel) 2024; 14:1168. [PMID: 38893693 PMCID: PMC11171614 DOI: 10.3390/diagnostics14111168] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2024] [Revised: 05/14/2024] [Accepted: 05/24/2024] [Indexed: 06/21/2024] Open
Abstract
Background: Long COVID, characterized by a persistent symptom spectrum following SARS-CoV-2 infection, poses significant health, social, and economic challenges. This review aims to consolidate knowledge on its epidemiology, clinical features, and underlying mechanisms to guide global responses; Methods: We conducted a literature review, analyzing peer-reviewed articles and reports to gather comprehensive data on long COVID's epidemiology, symptomatology, and management approaches; Results: Our analysis revealed a wide array of long COVID symptoms and risk factors, with notable demographic variability. The current understanding of its pathophysiology suggests a multifactorial origin yet remains partially understood. Emerging diagnostic criteria and potential therapeutic strategies were identified, highlighting advancements in long COVID management; Conclusions: This review highlights the multifaceted nature of long COVID, revealing a broad spectrum of symptoms, diverse risk factors, and the complex interplay of physiological mechanisms underpinning the condition. Long COVID symptoms and disorders will continue to weigh on healthcare systems in years to come. Addressing long COVID requires a holistic management strategy that integrates clinical care, social support, and policy initiatives. The findings underscore the need for increased international cooperation in research and health planning to address the complex challenges of long COVID. There is a call for continued refinement of diagnostic and treatment modalities, emphasizing a multidisciplinary approach to manage the ongoing and evolving impacts of the condition.
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Affiliation(s)
- Xiufang Song
- National Science Library, Chinese Academy of Sciences, Beijing 100190, China;
- Department of Information Resources Management, School of Economics and Management, University of Chinese Academy of Sciences, Beijing 100190, China
| | - Weiwei Song
- Jiangsu Taizhou People’s Hospital, Taizhou 225306, China;
- School of Integrative Medicine, Nanjing University of Traditional Chinese Medicine, Nanjing 210023, China
| | - Lizhen Cui
- College of Life Sciences, University of Chinese Academy of Sciences, Beijing 100049, China;
| | - Tim Q. Duong
- Albert Einstein College of Medicine and Montefiore Medical Center, Bronx, NY 10461, USA;
| | - Rajiv Pandy
- Indian Council of Forestry Research & Education, Dehradun 248006, India;
| | - Hongdou Liu
- Centre for Planetary Health and Food Security, School of Environment and Science, Griffith University, Nathan, Brisbane, QLD 4111, Australia;
| | - Qun Zhou
- Department of Library, China Agricultural University (East Campus), 17 Qinghua East Road, Haidian District, Beijing 100193, China; (Q.Z.); (J.S.)
| | - Jiayao Sun
- Department of Library, China Agricultural University (East Campus), 17 Qinghua East Road, Haidian District, Beijing 100193, China; (Q.Z.); (J.S.)
| | - Yanli Liu
- National Science Library, Chinese Academy of Sciences, Beijing 100190, China;
- Department of Information Resources Management, School of Economics and Management, University of Chinese Academy of Sciences, Beijing 100190, China
| | - Tong Li
- School of Agriculture and Food Sustainability, The University of Queensland, St. Lucia, Brisbane, QLD 4072, Australia
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Demko ZO, Yu T, Mullapudi SK, Varela Heslin MG, Dorsey CA, Payton CB, Tornheim JA, Blair PW, Mehta SH, Thomas DL, Manabe YC, Antar AAR. Two-Year Longitudinal Study Reveals That Long COVID Symptoms Peak and Quality of Life Nadirs at 6-12 Months Postinfection. Open Forum Infect Dis 2024; 11:ofae027. [PMID: 38449921 PMCID: PMC10917418 DOI: 10.1093/ofid/ofae027] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/07/2023] [Accepted: 01/11/2024] [Indexed: 03/08/2024] Open
Abstract
Background Few longitudinal studies available characterize long COVID outcomes out to 24 months, especially in people with nonsevere acute coronavirus disease 2019 (COVID-19). This study sought to prospectively characterize incidence and duration of long COVID symptoms and their association with quality of life (QoL) from 1-24 months after mild-to-moderate COVID-19 using validated tools in a diverse cohort of unvaccinated people infected with SARS-CoV-2 in 2020. Methods At 1-3, 6, 12, 18, and 24 months post-COVID-19, 70 participants had orthostatic vital signs measured, provided blood, and completed surveys characterizing symptoms, QoL, and return to pre-COVID-19 health and activities using validated tools (FLU-PRO+, Fatigue Severity Scale, Insomnia Severity Index, General Practitioner Assessment of Cognition, Patient Health Questionnaire Depression 8-Item, Generalized Anxiety Disorder 7-Item, 36-Item Short-Form Health Survey, EuroQol EQ-5D-5L). Results During the study period, 33% of participants experienced long COVID (had not returned to pre-COVID-19 health status and reported at least 1 symptom >90 days postinfection); 8% had not returned to their pre-COVID-19 health status 24 months postinfection. Long COVID symptoms peaked 6 months post-COVID-19, frequently causing activity limitations. Having long COVID was significantly associated with decreased QoL in multiple domains. Frequencies of orthostatic hypotension and tachycardia reflected levels reported in the general population. Within-person weight increased significantly between months 1 and 6. Long COVID was associated with pre-COVID-19 obesity and hyperlipidemia, but not with high-sensitivity C-reactive protein levels 1-3 months postinfection. Conclusions Long COVID occurs in a significant proportion of unvaccinated people, even if the acute illness was not severe. Long COVID prevalence peaked 6-12 months post-COVID-19, and a small proportion of participants still reported not returning to their pre-COVID-19 health status 24 months post-COVID-19.
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Affiliation(s)
- Zoe O Demko
- Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | - Tong Yu
- Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | - Sarika K Mullapudi
- Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | | | - Chamia A Dorsey
- Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | - Christine B Payton
- Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | - Jeffrey A Tornheim
- Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | - Paul W Blair
- Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
- Austere Environments Consortium for Enhanced Sepsis Outcomes, Henry M. Jackson Foundation for the Advancement of Military Medicine, Bethesda, Maryland, USA
| | - Shruti H Mehta
- Department of Epidemiology, Johns Hopkins University Bloomberg School of Public Health, Baltimore, Maryland, USA
| | - David L Thomas
- Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | - Yukari C Manabe
- Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | - Annukka A R Antar
- Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
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Newhouse A, Kritzer MD, Eryilmaz H, Praschan N, Camprodon JA, Fricchione G, Chemali Z. Neurocircuitry Hypothesis and Clinical Experience in Treating Neuropsychiatric Symptoms of Postacute Sequelae of Severe Acute Respiratory Syndrome Coronavirus 2. J Acad Consult Liaison Psychiatry 2022; 63:619-627. [PMID: 36030055 PMCID: PMC9404079 DOI: 10.1016/j.jaclp.2022.08.007] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/19/2022] [Revised: 07/26/2022] [Accepted: 08/04/2022] [Indexed: 12/15/2022]
Abstract
Persistent symptoms following COVID-19 infection have been termed postacute sequelae of severe acute respiratory syndrome coronavirus 2 infection. Many of these symptoms are neuropsychiatric, such as inattention, impaired memory, and executive dysfunction; these are often colloquially termed "brain fog". These symptoms are common and often persist long after the acute phase. The pattern of these deficits combined with laboratory, neuroimaging, electroencephalographic, and neuropsychological data suggest that these symptoms may be driven by direct and indirect damage to the frontal-subcortical neural networks. Here, we review this evidence, share our clinical experience at an academic medical center, and discuss potential treatment implications. While the exact etiology remains unknown, a neurocircuit-informed understanding of postacute sequelae of severe acute respiratory syndrome coronavirus 2 infection can help guide pharmacology, neuromodulation, and physical and psychological therapeutic approaches.
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Affiliation(s)
- Amy Newhouse
- Department of Psychiatry, Massachusetts General Hospital and Harvard Medical School, Boston, MA; Department of Medicine, Massachusetts General Hospital, Boston, MA.
| | - Michael D Kritzer
- Department of Psychiatry, Massachusetts General Hospital and Harvard Medical School, Boston, MA
| | - Hamdi Eryilmaz
- Department of Psychiatry, Massachusetts General Hospital and Harvard Medical School, Boston, MA
| | - Nathan Praschan
- Department of Psychiatry, Massachusetts General Hospital and Harvard Medical School, Boston, MA
| | - Joan A Camprodon
- Department of Psychiatry, Massachusetts General Hospital and Harvard Medical School, Boston, MA; Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, MA
| | - Gregory Fricchione
- Department of Psychiatry, Massachusetts General Hospital and Harvard Medical School, Boston, MA
| | - Zeina Chemali
- Department of Psychiatry, Massachusetts General Hospital and Harvard Medical School, Boston, MA; Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, MA
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