ArticleNature communications2023
Data-driven analysis to understand long COVID using electronic health records from the RECOVER initiative.
Article in Nature communications, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 39 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
39 citing papers in PubMed, 54 citations in OpenAlex.
- Antecedent and persistent symptoms in COVID-19 and other respiratory illnesses: Insights from prospectively collected data in the BRACE trial.The Journal of infection · 2024Trial
- Characterization and validation of EHR computable phenotypes for Long COVID using patient-reported symptoms: insights from the nationwide RECOVER program.Journal of the American Medical Informatics Association : JAMIA · 2026Article
- Immunothrombosis in hospitalized COVID-19 patients identified by multiomics profiling and linked to postacute complications.iScience · 2026Article
- A mixed-methods study comparing clinical and patient-reported perspectives on long COVID: insights from electronic health records and narrative journal entries.BMC infectious diseases · 2026Observational
- Differential Factors Associated With the Presence of Persistent Symptoms in Individuals Diagnosed With Long COVID: Protocol for a Longitudinal Matched Case-Control Study.JMIR research protocols · 2026Observational
- Algorithm dependence of patient phenotypes in Long COVID: a patient-led, multi-method clustering of 6031 patients using 162 self-reported symptoms.Oxford open immunology · 2026Article
- HIV Infection and Long COVID: A RECOVER Program, Electronic Health Record-Based Cohort Study.Clinical infectious diseases : an official publication of the Infectious Diseases Society of America · 2025Article
- Comparative risk of post-acute sequelae following SARS-CoV-2 or influenza virus infection: A retrospective cohort study among United States adults.PLoS medicine · 2025Article
- A machine learning model exploring the relationship between chronic medication and COVID-19 clinical outcomes.International journal of clinical pharmacy · 2025Article
- Long COVID Incidence Proportion in Adults and Children Between 2020 and 2024: An Electronic Health Record-Based Study From the RECOVER Initiative.Clinical infectious diseases : an official publication of the Infectious Diseases Society of America · 2025Article
- Federated target trial emulation using distributed observational data for treatment effect estimation.NPJ digital medicine · 2025Article
- Active Learning for Forecasting Severity among Patients with Post Acute Sequelae of SARS-CoV-2.ArXiv · 2025Article
- Federated Target Trial Emulation using Distributed Observational Data for Treatment Effect Estimation.medRxiv : the preprint server for health sciences · 2025Article
- Distinct type 1 immune networks underlie the severity of restrictive lung disease after COVID-19.Nature immunology · 2025Article
- Long COVID after SARS-CoV-2 during pregnancy in the United States.Nature communications · 2025Article
- Opportunities and Challenges in Using Electronic Health Record Systems to Study Postacute Sequelae of SARS-CoV-2 Infection: Insights From the NIH RECOVER Initiative.Journal of medical Internet research · 2025Article
- Social Networks as a Key Health Determinant in Acute Illness Recovery: A Lesson from the COVID-19 Pandemic.The American journal of medicine · 2025Article
- Cerebromicrovascular mechanisms contributing to long COVID: implications for neurocognitive health.GeroScience · 2025Review
- Article
- Severity of acute SARS-CoV-2 infection and risk of new-onset autoimmune disease: A RECOVER initiative study in nationwide U.S. cohorts.PloS one · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
16 authors at 5 institutions in 1 country.
Funding
Abstract
Recent studies have investigated post-acute sequelae of SARS-CoV-2 infection (PASC, or long COVID) using real-world patient data such as electronic health records (EHR). Prior studies have typically been conducted on patient cohorts with specific patient populations which makes their generalizability unclear. This study aims to characterize PASC using the EHR data warehouses from two large Patient-Centered Clinical Research Networks (PCORnet), INSIGHT and OneFlorida+, which include 11 million patients in New York City (NYC) area and 16.8 million patients in Florida respectively. With a high-throughput screening pipeline based on propensity score and inverse probability of treatment weighting, we identified a broad list of diagnoses and medications which exhibited significantly higher incidence risk for patients 30-180 days after the laboratory-confirmed SARS-CoV-2 infection compared to non-infected patients. We identified more PASC diagnoses in NYC than in Florida regarding our screening criteria, and conditions including dementia, hair loss, pressure ulcers, pulmonary fibrosis, dyspnea, pulmonary embolism, chest pain, abnormal heartbeat, malaise, and fatigue, were replicated across both cohorts. Our analyses highlight potentially heterogeneous risks of PASC in different populations.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.