International electronic health record-derived post-acute sequelae profiles of COVID-19 patients

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11 Scopus citations


The risk profiles of post-acute sequelae of COVID-19 (PASC) have not been well characterized in multi-national settings with appropriate controls. We leveraged electronic health record (EHR) data from 277 international hospitals representing 414,602 patients with COVID-19, 2.3 million control patients without COVID-19 in the inpatient and outpatient settings, and over 221 million diagnosis codes to systematically identify new-onset conditions enriched among patients with COVID-19 during the post-acute period. Compared to inpatient controls, inpatient COVID-19 cases were at significant risk for angina pectoris (RR 1.30, 95% CI 1.09–1.55), heart failure (RR 1.22, 95% CI 1.10–1.35), cognitive dysfunctions (RR 1.18, 95% CI 1.07–1.31), and fatigue (RR 1.18, 95% CI 1.07–1.30). Relative to outpatient controls, outpatient COVID-19 cases were at risk for pulmonary embolism (RR 2.10, 95% CI 1.58–2.76), venous embolism (RR 1.34, 95% CI 1.17–1.54), atrial fibrillation (RR 1.30, 95% CI 1.13–1.50), type 2 diabetes (RR 1.26, 95% CI 1.16–1.36) and vitamin D deficiency (RR 1.19, 95% CI 1.09–1.30). Outpatient COVID-19 cases were also at risk for loss of smell and taste (RR 2.42, 95% CI 1.90–3.06), inflammatory neuropathy (RR 1.66, 95% CI 1.21–2.27), and cognitive dysfunction (RR 1.18, 95% CI 1.04–1.33). The incidence of post-acute cardiovascular and pulmonary conditions decreased across time among inpatient cases while the incidence of cardiovascular, digestive, and metabolic conditions increased among outpatient cases. Our study, based on a federated international network, systematically identified robust conditions associated with PASC compared to control groups, underscoring the multifaceted cardiovascular and neurological phenotype profiles of PASC.

Original languageEnglish
Article number81
Journalnpj Digital Medicine
Issue number1
StatePublished - Dec 2022

Bibliographical note

Funding Information:
Z.X. is supported by the NIH National Institute of Neurological Disorders and Stroke (NINDS) R01NS098023. B.W.Q.T. is supported by National Medical Research Council Research Training Fellowship (MOH-000195-00). M.M. is supported by NIH National Center for Advancing Translational Sciences (NCATS) UL1TR001857. S.V. is supported by NCATS UL1TR001857. L.P.P. is supported by NCATS CTSA Award #UL1TR002366. D.A.H. is supported by NCATS UL1TR002240. S.E.M. is supported by NCATS UL1TR002240. S.N.M. is supported by NCATS 5UL1TR001857-05 and NIH National Human Genome Research Institute (NHGRI) 5R01HG009174-04. G.S.O. is supported by NIH grants P30ES017885 and U24CA210967. F.J.S.V. is supported by NCATS Grant #UL1TR001881. R.K. is supported by NCATS UL1TR001998. Y.L. is supported by the NIH National Library of Medicine (NLM) R01LM013337. R.B. is supported by EU PROJECT H2020 PERISCOPE—101016233. K.C. is supported by VA MVP000 and CIPHER. N.G., Z.S.H.A., and S.L. are supported by NLM T15 LM007092. B.J.A. is supported by NIH National Heart, Lung, and Blood Institute (NHLBI) U24 HL148865. K.B.W. is supported by NHLBI R01 HL151643-01. A.M.S. is supported by NHLBI K23HL148394 and L40HL148910, and NCATS UL1TR001420. G.M.W. is supported by NCATS UL1TR002541 and UL1TR000005, NLM R01LM013345, and NHGRI 3U01HG008685-05S2.

Publisher Copyright:
© 2022, The Author(s).

ASJC Scopus subject areas

  • Medicine (miscellaneous)
  • Health Informatics
  • Computer Science Applications
  • Health Information Management


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