ArticleBMC medicine2021
Evolving phenotypes of non-hospitalized patients that indicate long COVID.
Article in BMC medicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 85 papers, 5 of them syntheses that pooled it.
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Who cites it
85 citing papers in PubMed, 5 syntheses or guidelines pooled it, 150 citations in OpenAlex.
- New onset of type 1 and type 2 diabetes post-COVID-19 infection: a systematic review.Emerging microbes & infections · 2025Pooled it
- Gender Disparities in Neurological Symptoms of Long COVID: A Systematic Review and Meta-Analysis.Neuroepidemiology · 2025Pooled it
- Prevalence and risk of new-onset diabetes mellitus after COVID-19: a systematic review and meta-analysis.Frontiers in endocrinology · 2023Pooled it
- ESCMID rapid guidelines for assessment and management of long COVID.Clinical microbiology and infection : the official publication of the European Society of Clinical Microbiology and Infectious Diseases · 2022Guideline
- Mid and long-term neurological and neuropsychiatric manifestations of post-COVID-19 syndrome: A meta-analysis.Journal of the neurological sciences · 2022Pooled it
- Incident diabetes within the first two years after SARS-CoV-2 infection: a population-based retrospective cohort study of the Agency for Health Protection of Milan, Italy.BMC infectious diseases · 2026Article
- "Bridging the clinical, molecular and genetic perspectives on myocarditis in post-COVID-19 era".International journal of cardiology. Cardiovascular risk and prevention · 2026Review
- Identifying subtypes of Long COVID: a systematic review.EClinicalMedicine · 2026Article
- Coronavirus disease 2019-related risk factors for postoperative delirium: a secondary analysis of an observational cohort study.International journal of surgery (London, England) · 2025Article
- Blood diagnostic biomarkers for neurologic manifestations of long COVID.Brain, behavior, & immunity - health · 2025Article
- Poor Association Between Clinical Characteristics and Seropositivity in Children With Suspected Long COVID-A Single-Centre Study.Acta paediatrica (Oslo, Norway : 1992) · 2025Article
- Label efficient phenotyping for Long COVID using electronic health records.NPJ digital medicine · 2025Article
- Predictors of Post-Acute Sequelae of COVID-19 in a Diverse Urban Population.Journal of general internal medicine · 2025Observational
- Review
- Precision phenotyping for curating research cohorts of patients with unexplained post-acute sequelae of COVID-19.Med (New York, N.Y.) · 2025Article
- Strategies for population-level identification of post-acute sequelae of COVID-19 through health administrative data.Frontiers in public health · 2025Review
- Ocular neuroinflammatory response secondary to SARS-CoV-2 infection-a review.Frontiers in immunology · 2025Review
- Tree-based classification model for Long-COVID infection prediction with age stratification using data from the National COVID Cohort Collaborative.JAMIA open · 2024Article
- The Influence of COVID-19 in Glycemic Control: Predictive Value of Inflammation and Metabolic Parameters.Biomedicines · 2024Article
- Wearable Devices for Long COVID: Prospects, Challenges and Options.Asian bioethics review · 2024Article
25 more citing papers are in PubMed but not listed here.
Corrections and comments
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Authors and funding
7 authors at 3 institutions in 2 countries.
Funding
Abstract
backgroundFor some SARS-CoV-2 survivors, recovery from the acute phase of the infection has been grueling with lingering effects. Many of the symptoms characterized as the post-acute sequelae of COVID-19 (PASC) could have multiple causes or are similarly seen in non-COVID patients. Accurate identification of PASC phenotypes will be important to guide future research and help the healthcare system focus its efforts and resources on adequately controlled age- and gender-specific sequelae of a COVID-19 infection.
methodsIn this retrospective electronic health record (EHR) cohort study, we applied a computational framework for knowledge discovery from clinical data, MLHO, to identify phenotypes that positively associate with a past positive reverse transcription-polymerase chain reaction (RT-PCR) test for COVID-19. We evaluated the post-test phenotypes in two temporal windows at 3-6 and 6-9 months after the test and by age and gender. Data from longitudinal diagnosis records stored in EHRs from Mass General Brigham in the Boston Metropolitan Area was used for the analyses. Statistical analyses were performed on data from March 2020 to June 2021. Study participants included over 96 thousand patients who had tested positive or negative for COVID-19 and were not hospitalized.
resultsWe identified 33 phenotypes among different age/gender cohorts or time windows that were positively associated with past SARS-CoV-2 infection. All identified phenotypes were newly recorded in patients' medical records 2 months or longer after a COVID-19 RT-PCR test in non-hospitalized patients regardless of the test result. Among these phenotypes, a new diagnosis record for anosmia and dysgeusia (OR 2.60, 95% CI [1.94-3.46]), alopecia (OR 3.09, 95% CI [2.53-3.76]), chest pain (OR 1.27, 95% CI [1.09-1.48]), chronic fatigue syndrome (OR 2.60, 95% CI [1.22-2.10]), shortness of breath (OR 1.41, 95% CI [1.22-1.64]), pneumonia (OR 1.66, 95% CI [1.28-2.16]), and type 2 diabetes mellitus (OR 1.41, 95% CI [1.22-1.64]) is one of the most significant indicators of a past COVID-19 infection. Additionally, more new phenotypes were found with increased confidence among the cohorts who were younger than 65.
conclusionsThe findings of this study confirm many of the post-COVID-19 symptoms and suggest that a variety of new diagnoses, including new diabetes mellitus and neurological disorder diagnoses, are more common among those with a history of COVID-19 than those without the infection. Additionally, more than 63% of PASC phenotypes were observed in patients under 65 years of age, pointing out the importance of vaccination to minimize the risk of debilitating post-acute sequelae of COVID-19 among younger adults.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.