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Sharing science to find answers

Find RECOVER Publications

Researchers within the RECOVER Initiative share their progress to understand, treat, and prevent Long COVID through research publications. Follow the latest science from RECOVER’s research studies below.

Visit the Research Summaries page to learn about RECOVER’s Long COVID research in a format that’s easy to understand.

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53 Results

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EHR Adult
Reddy, NV; Yeh, HC; Tronieri, JS; et. al., Journal of Clinical and Translational Science
Published:
Journal: Journal of Clinical and Translational Science
Abstract: Long-term sequelae of severe acute respiratory coronavirus-2 (SARS-CoV-2) infection may include increased incidence of diabetes. Here we describe the temporal relationship between new type 2 diabetes and SARS-CoV-2 infection in a nationwide database. We found that while the proportion of newly diagnosed type 2 diabetes increased during the acute period of SARS-CoV-2 infection, the mean proportion of new diabetes cases in the 6 months post-infection was about 83% lower than the 6 months… Continue reading
Authors: Neha V Reddy, Hsin-Chieh Yeh, Jena S Tronieri, Til Stürmer, John B Buse, Jane E Reusch, Steven G Johnson, Rachel Wong, Richard Moffitt, Kenneth J Wilkins, Jeremy Harper, Carolyn T Bramante, N3C and RECOVER Consortiums
Keywords: Not available
EHR Adult
Zang, C; Hou, Y; Schenck, E; et. al., Research Square
Information
Caution: Preprints are preliminary reports of work that have not been certified by peer review. They should not be relied on to guide clinical practice or health-related behavior and should not be reported in news media as established information.
Published:
Journal: Research Square
Abstract: Background: Patients who were SARS-CoV-2 infected could suffer from newly incidental conditions in their post-acute infection period. These conditions, denoted as the post-acute sequelae of SARS-CoV-2 infection (PASC), are highly heterogeneous and involve a diverse set of organ systems. Limited studies have investigated the predictability of these conditions and their associated risk factors. Method: In this retrospective cohort study, we investigated two large-scale PCORnet clinical research… Continue reading
Authors: Chengxi Zang, Yu Hou, Edward Schenck, Zhenxing Xu, Yongkang Zhang, Jie Xu, Jiang Bian, Dmitry Morozyuk, Dhruv Khullar, Anna Nordvig, Elizabeth Shenkman, Russel Rothman, Jason Block, Kristin Lyman, Yiye Zhang, Jay Varma, Mark Weiner, Thomas Carton, Fei Wang, Rainu Kaushal, The Recover Consortium
Keywords: Not available
EHR Adult
Summary
Pfaff, ER; Madlock-Brown, C; Baratta, JM; et. al., BMC Medicine
Published:
Journal: BMC Medicine
Abstract: Naming a newly discovered disease is a difficult process; in the context of the COVID-19 pandemic and the existence of post-acute sequelae of SARS-CoV-2 infection (PASC), which includes long COVID, it has proven especially challenging. Disease definitions and assignment of a diagnosis code are often asynchronous and iterative. The clinical definition and our understanding of the underlying mechanisms of long COVID are still in flux, and the deployment of an ICD-10-CM code for long COVID in the… Continue reading
Authors: Emily R Pfaff, Charisse Madlock-Brown, John M Baratta, Abhishek Bhatia, Hannah Davis, Andrew Girvin, Elaine Hill, Elizabeth Kelly, Kristin Kostka, Johanna Loomba, Julie A McMurry, Rachel Wong, Tellen D Bennett, Richard Moffitt, Christopher G Chute, Melissa Haendel, N3C Consortium, RECOVER Consortium
Keywords: Humans; Female; Post-Acute COVID-19 Syndrome; International Classification of Diseases; Pandemics; COVID-19/diagnosis/epidemiology; SARS-CoV-2
Short Summary

RECOVER researchers looked at the use of a code to diagnose Long COVID in electronic health records (EHRs). They used EHR data from over 8,000 people and compared people who had the ICD-10 code for Long COVID (which is U09.9) in their EHR on or after October 1, 2021. They also looked at other codes in their EHR for health conditions, symptoms, tests, and treatments within 60 days after their Long COVID diagnosis.

Researchers found that many doctors use the Long COVID code along with other codes. People with Long COVID had a mix of symptoms and other health conditions, tests, and treatments. This suggests there may be different types of Long COVID. The researchers concluded that for now, doctors should look at a person’s mix of symptoms and other health conditions to diagnose Long COVID.

EHR Adult
Varma, JK; Zang, C; Carton, TW; et. al., medRxiv
Information
Caution: Preprints are preliminary reports of work that have not been certified by peer review. They should not be relied on to guide clinical practice or health-related behavior and should not be reported in news media as established information.
Published:
Journal: medRxiv
Abstract: The frequency and characteristics of post-acute sequelae of SARS-CoV-2 infection (PASC) may vary by SARS-CoV-2 variant. To characterize PASC-related conditions among individuals likely infected by the ancestral strain in 2020 and individuals likely infected by the Delta variant in 2021. Retrospective cohort study of electronic medical record data for approximately 27 million patients from March 1, 2020-November 30, 2021. Healthcare facilities in New York and Florida. Patients who were at least… Continue reading
Authors: Jay K Varma, Chengxi Zang, Thomas W Carton, Jason P Block, Dhruv J Khullar, Yongkang Zhang, Mark G Weiner, Russell L Rothman, Edward J Schenck, Zhenxing Xu, Kristin Lyman, Jiang Bian, Jie Xu, Elizabeth A Shenkman, Christine Maughan, Leah Castro-Baucom, Lisa Oâ Brien, Fei Wang, Rainu Kaushal
Keywords: Not available
EHR Adult
Summary
Zhang, H; Zang, C; Xu, Z; et. al., Nature Medicine
Published:
Journal: Nature Medicine
Abstract: The post-acute sequelae of SARS-CoV-2 infection (PASC) refers to a broad spectrum of symptoms and signs that are persistent, exacerbated or newly incident in the period after acute SARS-CoV-2 infection. Most studies have examined these conditions individually without providing evidence on co-occurring conditions. In this study, we leveraged the electronic health record data of two large cohorts, INSIGHT and OneFlorida+, from the national Patient-Centered Clinical Research Network. We created a… Continue reading
Authors: Hao Zhang, Chengxi Zang, Zhenxing Xu, Yongkang Zhang, Jie Xu, Jiang Bian, Dmitry Morozyuk, Dhruv Khullar, Yiye Zhang, Anna S Nordvig, Edward J Schenck, Elizabeth A Shenkman, Russell L Rothman, Jason P Block, Kristin Lyman, Mark G Weiner, Thomas W Carton, Fei Wang, Rainu Kaushal
Keywords: Humans; COVID-19/epidemiology; SARS-CoV-2; Post-Acute COVID-19 Syndrome; Anxiety; Anxiety Disorders; Disease Progression
Short Summary

RECOVER researchers wanted to learn if there are different types of Long COVID based on symptoms and health problems that often happen together. Researchers used data from electronic health records (EHRs) of about 35,000 people diagnosed with COVID. The EHRs were from 2 healthcare systems in PCORnet, the National Patient-Centered Clinical Research Network. They used a computer program to look for patterns in people’s new symptoms and health problems that started 30 to 180 days after having COVID.

The researchers found 4 main types of Long COVID based on symptoms and health problems that happen together. The 4 types of Long COVID are related to the 1) heart and kidneys, 2) breathing, sleep, and anxiety, 3) muscles and nerves, and 4) digestive tract and breathing. This research could help define types of Long COVID to give people more specific diagnoses and treatment plans.

EHR Adult
Summary
Reese, JT; Blau, H; Casiraghi, E; et. al., eBioMedicine
Published:
Journal: eBioMedicine
Abstract: Stratification of patients with post-acute sequelae of SARS-CoV-2 infection (PASC, or long COVID) would allow precision clinical management strategies. However, long COVID is incompletely understood and characterised by a wide range of manifestations that are difficult to analyse computationally. Additionally, the generalisability of machine learning classification of COVID-19 clinical outcomes has rarely been tested. We present a method for computationally modelling PASC phenotype data based… Continue reading
Authors: Justin T Reese, Hannah Blau, Elena Casiraghi, Timothy Bergquist, Johanna J Loomba, Tiffany J Callahan, Bryan Laraway, Corneliu Antonescu, Ben Coleman, Michael Gargano, Kenneth J Wilkins, Luca Cappelletti, Tommaso Fontana, Nariman Ammar, Blessy Antony, T M Murali, J Harry Caufield, Guy Karlebach, Julie A McMurry, Andrew Williams, Richard Moffitt, Jineta Banerjee, Anthony E Solomonides, Hannah Davis, Kristin Kostka, Giorgio Valentini, David Sahner, Christopher G Chute, Charisse Madlock-Brown, Melissa A Haendel, Peter N Robinson, N3C Consortium, RECOVER Consortium
Keywords: Humans; COVID-19; Disease Progression; Post-Acute COVID-19 Syndrome; SARS-CoV-2
Short Summary

In this study, RECOVER researchers used a computer program to identify possible types of Long COVID based on electronic health records (EHRs). They used the computer program to review EHRs of people diagnosed with Long COVID and group them based on patterns in their symptoms and health conditions.

The computer program found 6 different types of Long COVID, which were related to 1) many symptoms and health conditions with unusual lab test results, 2) the lungs, 3) the brain, 4) the heart, 5) pain and feeling weak and tired (fatigue), and 6) many symptoms and conditions with pain. Each type of Long COVID also differed based on health conditions people had before COVID and how severe their COVID infection was. This research could help identify people with different types of Long COVID to better diagnose and treat them and invite them to join research studies.

EHR Adult
Hadley, E; Yoo, YJ; Patel, S; et. al., medRxiv
Information
Caution: Preprints are preliminary reports of work that have not been certified by peer review. They should not be relied on to guide clinical practice or health-related behavior and should not be reported in news media as established information.
Published:
Journal: medRxiv
Abstract: Although the COVID-19 pandemic has persisted for over 2 years, reinfections with SARS-CoV-2 are not well understood. We use the electronic health record (EHR)-based study cohort from the National COVID Cohort Collaborative (N3C) as part of the NIH Researching COVID to Enhance Recovery (RECOVER) Initiative to characterize reinfection, understand development of Long COVID after reinfection, and compare severity of reinfection with initial infection. We validate previous findings of reinfection… Continue reading
Authors: Emily Hadley, Yun Jae Yoo, Saaya Patel, Andrea Zhou, Bryan Laraway, Rachel Wong, Alexander Preiss, Rob Chew, Hannah Davis, Christopher G Chute, Emily R Pfaff, Johanna Loomba, Melissa Haendel, Elaine Hill, Richard Moffitt, N3C and RECOVER consortia
Keywords: Not available
Pathobiology Adult
Finlay, JB; Brann, DH; Abi Hachem, R; et. al., Science Translational Medicine
Information
Epub ahead of print indicates that the article has completed the peer review process and has been published online in advance of the actual print journal issue being released.
Published:
Journal: Science Translational Medicine
Abstract: SARS-CoV-2 causes profound changes in the sense of smell, including total smell loss. Although these alterations are often transient, many patients with COVID-19 exhibit olfactory dysfunction that lasts months to years. Although animal and human autopsy studies have suggested mechanisms driving acute anosmia, it remains unclear how SARS-CoV-2 causes persistent smell loss in a subset of patients. To address this question, we analyzed olfactory epithelial samples collected from 24 biopsies,… Continue reading
Authors: John B Finlay, David H Brann, Ralph Abi Hachem, David W Jang, Allison D Oliva, Tiffany Ko, Rupali Gupta, Sebastian A Wellford, E Ashley Moseman, Sophie S Jang, Carol H Yan, Hiroaki Matsunami, Tatsuya Tsukahara, Sandeep Robert Datta, Bradley J Goldstein
Keywords: Animals; Humans; COVID-19/complications; Anosmia; SARS-CoV-2; RNA, Viral/metabolism; Olfaction Disorders/epidemiology/etiology; Olfactory Mucosa; Gene Expression
Pathobiology Adult
Jason, LA; Dorri, JA, Neurology International
Published:
Journal: Neurology International
Abstract: This study sought to ascertain the prevalence of Myalgic Encephalomyelitis/Chronic Fatigue Syndrome (ME/CFS) among a sample of 465 patients with Long COVID. The participants completed three questionnaires: (1) a new questionnaire measuring both the frequency and severity of 38 common symptoms of COVID and Long COVID, (2) a validated short form questionnaire assessing ME/CFS, and (3) a validated questionnaire measuring post-exertional malaise. The population was predominantly white, female, and… Continue reading
Authors: Leonard A Jason, Joseph A Dorri
Keywords: COVID-19; Long COVID; ME/CFS; case definition
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