Long COVID Clinical Phenotypes up to 6 Months After Infection Identified by Latent Class Analysis of Self-Reported Symptoms

Author:

Gottlieb Michael1ORCID,Spatz Erica S234,Yu Huihui24,Wisk Lauren E5ORCID,Elmore Joann G5ORCID,Gentile Nicole L6ORCID,Hill Mandy7ORCID,Huebinger Ryan M7ORCID,Idris Ahamed H8ORCID,Kean Efrat R9,Koo Katherine10,Li Shu-Xia4,McDonald Samuel811ORCID,Montoy Juan Carlos C12ORCID,Nichol Graham13,O’Laughlin Kelli N14,Plumb Ian D15ORCID,Rising Kristin L916ORCID,Santangelo Michelle10,Saydah Sharon15,Wang Ralph C12ORCID,Venkatesh Arjun417ORCID,Stephens Kari A18,Weinstein Robert A192021ORCID,Weinstein Robert A,Gottlieb Michael,Santangelo Michelle,Koo Katherine,Derden Antonia,Gottlieb Michael,Gatling Kristyn,Guzman Diego,Yang Geoffrey,Kaadan Marshall,Hassaballa Minna,Jerger Ryan,Ahmed Zohaib,Choi Michael,Venkatesh Arjun,Spatz Erica,Lin Zhenqiu,Li Shu-Xia,Yu Huihui,Liu Mengni,Venkatesh Arjun,Spatz Erica,Ulrich Andrew,Kinsman Jeremiah,Dorney Jocelyn,Pierce Senyte,Puente Xavier,Nichol Graham,Stephens Kari,Anderson Jill,Morse Dana,Adams Karen,Maat Zenoura,Stober Tracy,O’Laughlin Kelli N,Gentile Nikki,Geyer Rachel E,Willis Michael,Ruiz Luis,Malone Kerry,Park Jasmine,Rising Kristin,Kean Efrat,Kelly Morgan,Schaeffer Kevin,Hannikainen Paavali,Shughart Lindsey,Shughart Hailey,Renzi Nicole,Amadio Grace,Grau Dylan,Watts Phillip,Cheng David,Miao Jessica,Shutty Carly,Charlton Alex,Hill Mandy,Chavez Summer,Kane Arun,Nikonowicz Peter,Idris Ahamed H,McDonald Samuel,Gallegos David,Martin Riley,Elmore Joann,Wisk Lauren,L’Hommedieu Michelle,Chandler Chris,Eguchi Megan,Roldan Kate Diaz,Villegas Nicole,Moreno Raul,Rodrigue Robertz,Wang Ralph C,Montoy Juan Carlos,Kemball Robin,Chan Virginia,Chavez Cecilia Lara,Wong Angela,Arreguin Mireya,

Affiliation:

1. Department of Emergency Medicine, Rush University Medical Center , Chicago, Illinois , USA

2. Section of Cardiovascular Medicine, Yale School of Medicine, New Haven, Connecticut , USA

3. Department of Epidemiology, Yale School of Public Health, New Haven, Connecticut , USA

4. Yale Center for Outcomes Research and Evaluation, Yale School of Medicine, New Haven, Connecticut , USA

5. Division of General Internal Medicine and Health Services Research, David Geffen School of Medicine, University of California, Los Angeles , Los Angeles, California , USA

6. Post-COVID Rehabilitation and Recovery Clinic, Department of Family Medicine, Department of Laboratory Medicine and Pathology, University of Washington , Seattle, Washington , USA

7. Department of Emergency Medicine, UTHealth , Houston, Texas , USA

8. Department of Emergency Medicine, University of Texas Southwestern Medical Center , Dallas, Texas , USA

9. Department of Emergency Medicine, Thomas Jefferson University , Philadelphia, Pennsylvania , USA

10. Department of Internal Medicine, Rush University Medical Center , Chicago, Illinois , USA

11. Clinical Informatics Center, University of Texas Southwestern Medical Center , Dallas, Texas , USA

12. Department of Emergency Medicine, University of California, San Francisco , San Francisco, California , USA

13. Departments of Medicine and Emergency Medicine, University of Washington , Seattle, Washington , USA

14. Departments of Emergency Medicine and Global Health, University of Washington , Seattle, Washington , USA

15. National Center for Immunizations and Respiratory Diseases, Centers for Disease Control and Prevention , Atlanta, Georgia , USA

16. Center for Connected Care, Sidney Kimmel Medical School, Thomas Jefferson University , Philadelphia, Pennsylvania , USA

17. Department of Emergency Medicine, Yale School of Medicine , New Haven, Connecticut , USA

18. Departments of Family Medicine, Biomedical Informatics and Medical Education, University of Washington , Seattle, Washington , USA

19. Division of Infectious Diseases, Department of Internal Medicine, Rush University Medical Center , Chicago, Illinois , USA

20. Department of Internal Medicine, Cook County Hospital , Chicago, Illinois , USA

21. The CORE Center , Chicago, Illinois , USA

Abstract

Abstract Background The prevalence, incidence, and interrelationships of persistent symptoms after severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection vary. There are limited data on specific phenotypes of persistent symptoms. Using latent class analysis (LCA) modeling, we sought to identify whether specific phenotypes of COVID-19 were present 3 months and 6 months post-infection. Methods This was a multicenter study of symptomatic adults tested for SARS-CoV-2 with prospectively collected data on general symptoms and fatigue-related symptoms up to 6 months postdiagnosis. Using LCA, we identified symptomatically homogenous groups among COVID-positive and COVID-negative participants at each time period for both general and fatigue-related symptoms. Results Among 5963 baseline participants (4504 COVID-positive and 1459 COVID-negative), 4056 had 3-month and 2856 had 6-month data at the time of analysis. We identified 4 distinct phenotypes of post-COVID conditions (PCCs) at 3 and 6 months for both general and fatigue-related symptoms; minimal-symptom groups represented 70% of participants at 3 and 6 months. When compared with the COVID-negative cohort, COVID-positive participants had higher occurrence of loss of taste/smell and cognition problems. There was substantial class-switching over time; those in 1 symptom class at 3 months were equally likely to remain or enter a new phenotype at 6 months. Conclusions We identified distinct classes of PCC phenotypes for general and fatigue-related symptoms. Most participants had minimal or no symptoms at 3 and 6 months of follow-up. Significant proportions of participants changed symptom groups over time, suggesting that symptoms present during the acute illness may differ from prolonged symptoms and that PCCs may have a more dynamic nature than previously recognized. Clinical Trials Registration. NCT04610515.

Funder

CDC

National Center of Immunization and Respiratory Diseases

Publisher

Oxford University Press (OUP)

Subject

Infectious Diseases,Oncology

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3