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

Harrison G Zhang^#¹, Arianna Dagliati^#², Zahra Shakeri Hossein Abad¹, Xin Xiong³, Clara-Lea Bonzel¹, Zongqi Xia⁴, Bryce W Q Tan⁵, Paul Avillach¹, Gabriel A Brat¹, Chuan Hong^{1

6}, Michele Morris⁷, Shyam Visweswaran⁷, Lav P Patel⁸, Alba Gutiérrez-Sacristán¹, David A Hanauer⁹, John H Holmes^{10

11}, Malarkodi Jebathilagam Samayamuthu⁷, Florence T Bourgeois¹², Sehi L'Yi¹, Sarah E Maidlow¹³, Bertrand Moal¹⁴, Shawn N Murphy¹⁵, Zachary H Strasser¹⁶, Antoine Neuraz¹⁷, Kee Yuan Ngiam¹⁸, Ne Hooi Will Loh¹⁹, Gilbert S Omenn²⁰, Andrea Prunotto²¹, Lauren A Dalvin²², Jeffrey G Klann¹⁶, Petra Schubert²³, Fernando J Sanz Vidorreta²⁴, Vincent Benoit²⁵, Guillaume Verdy¹⁴, Ramakanth Kavuluru²⁶, Hossein Estiri¹⁶, Yuan Luo²⁷, Alberto Malovini²⁸, Valentina Tibollo²⁸, Riccardo Bellazzi²⁹, Kelly Cho^{23

30}, Yuk-Lam Ho²³, Amelia L M Tan¹, Byorn W L Tan⁵, Nils Gehlenborg¹, Sara Lozano-Zahonero²¹, Vianney Jouhet³¹, Luca Chiovato³², Bruce J Aronow³³, Emma M S Toh³⁴, Wei Gen Scott Wong³⁵, Sara Pizzimenti³⁶, Kavishwar B Wagholikar¹⁶, Mauro Bucalo³⁷; Consortium for Clinical Characterization of COVID-19 by EHR (4CE); Tianxi Cai^#¹, Andrew M South^#³⁸, Isaac S Kohane^#¹, Griffin M Weber^#³⁹

Affiliations

¹ Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA.
² Department of Electrical Computer and Biomedical Engineering, University of Pavia, Pavia, Italy.
³ Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA.
⁴ Department of Neurology, University of Pittsburgh, Pittsburgh, PA, USA.
⁵ Department of Medicine, National University Hospital, Singapore, Singapore.
⁶ Department of Biostatistics and Bioinformatics, Duke University, Durham, NC, USA.
⁷ Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, PA, USA.
⁸ Department of Internal Medicine, Division of Medical Informatics, University Of Kansas Medical Center, Kansas City, MO, USA.
⁹ Department of Learning Health Sciences, University of Michigan Medical School, Ann Arbor, MI, USA.
¹⁰ Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA.
¹¹ Institute for Biomedical Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA.
¹² Department of Pediatrics, Harvard Medical School, Boston, MA, USA.
¹³ Michigan Institute for Clinical and Health Research (MICHR) Informatics, University of Michigan, Ann Arbor, MI, USA.
¹⁴ IAM unit, Bordeaux University Hospital, Bordeaux, France.
¹⁵ Department of Neurology, Massachusetts General Hospital, Boston, MA, USA.
¹⁶ Department of Medicine, Massachusetts General Hospital, Boston, MA, USA.
¹⁷ Department of biomedical informatics, Hôpital Necker-Enfants Malade, Assistance Publique Hôpitaux de Paris (APHP), University of Paris, Paris, France.
¹⁸ Department of Biomedical informatics, WiSDM, National University Health Systems Singapore, Singapore, Singapore.
¹⁹ Department of Anaesthesia, National University Health Systems Singapore, Singapore, Singapore.
²⁰ Department of Computational Medicine & Bioinformatics, Internal Medicine, Human Genetics, and School of Public Health, University of Michigan, Ann Arbor, MI, USA.
²¹ Institute of Medical Biometry and Statistics, Faculty of Medicine and Medical Center, University of Freiburg, Freiburg, Germany.
²² Department of Ophthalmology, Mayo Clinic, Rochester, NY, USA.
²³ Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC), VA Boston Healthcare System, Boston, MA, USA.
²⁴ Department of Medicine, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA.
²⁵ IT Department, Innovation & Data, APHP Greater Paris University Hospital, Paris, France.
²⁶ Division of Biomedical Informatics (Department of Internal Medicine), University of Kentucky, Lexington, KY, USA.
²⁷ Department of Preventive Medicine, Northwestern University, Chicago, IL, USA.
²⁸ Laboratory of Informatics and Systems Engineering for Clinical Research, Istituti Clinici Scientifici Maugeri SpA SB IRCCS, Pavia, Italy.
²⁹ Department of Electrical, Computer and Biomedical Engineering, University of Pavia, Pavia, Italy.
³⁰ Population Health and Data Science, VA Boston Healthcare System, Boston, MA, USA.
³¹ IAM unit, INSERM Bordeaux Population Health ERIAS TEAM, Bordeaux University Hospital / ERIAS - Inserm, U1219 BPH, Bordeaux, France.
³² Unit of Internal Medicine and Endocrinology, Istituti Clinici Scientifici Maugeri SpA SB IRCCS, Pavia, Italy.
³³ Departments of Biomedical Informatics, Pediatrics, Cincinnati Children's Hospital Medical Center, University of Cincinnati, Cincinnati, OH, USA.
³⁴ Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore.
³⁵ Department of Medicine, National University Health Systems Singapore, Singapore, Singapore.
³⁶ Scientific Direction, IRCCS Ca' Granda Ospedale Maggiore Policlinico di Milano, Milan, Italy.
³⁷ BIOMERIS (BIOMedical Research Informatics Solutions), Pavia, Italy.
³⁸ Department of Pediatrics-Section of Nephrology, Brenner Children's, Wake Forest School of Medicine, Winston Salem, NC, USA.
³⁹ Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA. weber@hms.harvard.edu.

^# Contributed equally.

PMID: 35768548
PMCID: PMC9242995
DOI: 10.1038/s41746-022-00623-8

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

Harrison G Zhang et al. NPJ Digit Med. 2022.

. 2022 Jun 29;5(1):81.

doi: 10.1038/s41746-022-00623-8.

Authors

Affiliations

¹ Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA.
² Department of Electrical Computer and Biomedical Engineering, University of Pavia, Pavia, Italy.
³ Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA.
⁴ Department of Neurology, University of Pittsburgh, Pittsburgh, PA, USA.
⁵ Department of Medicine, National University Hospital, Singapore, Singapore.
⁶ Department of Biostatistics and Bioinformatics, Duke University, Durham, NC, USA.
⁷ Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, PA, USA.
⁸ Department of Internal Medicine, Division of Medical Informatics, University Of Kansas Medical Center, Kansas City, MO, USA.
⁹ Department of Learning Health Sciences, University of Michigan Medical School, Ann Arbor, MI, USA.
¹⁰ Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA.
¹¹ Institute for Biomedical Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA.
¹² Department of Pediatrics, Harvard Medical School, Boston, MA, USA.
¹³ Michigan Institute for Clinical and Health Research (MICHR) Informatics, University of Michigan, Ann Arbor, MI, USA.
¹⁴ IAM unit, Bordeaux University Hospital, Bordeaux, France.
¹⁵ Department of Neurology, Massachusetts General Hospital, Boston, MA, USA.
¹⁶ Department of Medicine, Massachusetts General Hospital, Boston, MA, USA.
¹⁷ Department of biomedical informatics, Hôpital Necker-Enfants Malade, Assistance Publique Hôpitaux de Paris (APHP), University of Paris, Paris, France.
¹⁸ Department of Biomedical informatics, WiSDM, National University Health Systems Singapore, Singapore, Singapore.
¹⁹ Department of Anaesthesia, National University Health Systems Singapore, Singapore, Singapore.
²⁰ Department of Computational Medicine & Bioinformatics, Internal Medicine, Human Genetics, and School of Public Health, University of Michigan, Ann Arbor, MI, USA.
²¹ Institute of Medical Biometry and Statistics, Faculty of Medicine and Medical Center, University of Freiburg, Freiburg, Germany.
²² Department of Ophthalmology, Mayo Clinic, Rochester, NY, USA.
²³ Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC), VA Boston Healthcare System, Boston, MA, USA.
²⁴ Department of Medicine, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA.
²⁵ IT Department, Innovation & Data, APHP Greater Paris University Hospital, Paris, France.
²⁶ Division of Biomedical Informatics (Department of Internal Medicine), University of Kentucky, Lexington, KY, USA.
²⁷ Department of Preventive Medicine, Northwestern University, Chicago, IL, USA.
²⁸ Laboratory of Informatics and Systems Engineering for Clinical Research, Istituti Clinici Scientifici Maugeri SpA SB IRCCS, Pavia, Italy.
²⁹ Department of Electrical, Computer and Biomedical Engineering, University of Pavia, Pavia, Italy.
³⁰ Population Health and Data Science, VA Boston Healthcare System, Boston, MA, USA.
³¹ IAM unit, INSERM Bordeaux Population Health ERIAS TEAM, Bordeaux University Hospital / ERIAS - Inserm, U1219 BPH, Bordeaux, France.
³² Unit of Internal Medicine and Endocrinology, Istituti Clinici Scientifici Maugeri SpA SB IRCCS, Pavia, Italy.
³³ Departments of Biomedical Informatics, Pediatrics, Cincinnati Children's Hospital Medical Center, University of Cincinnati, Cincinnati, OH, USA.
³⁴ Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore.
³⁵ Department of Medicine, National University Health Systems Singapore, Singapore, Singapore.
³⁶ Scientific Direction, IRCCS Ca' Granda Ospedale Maggiore Policlinico di Milano, Milan, Italy.
³⁷ BIOMERIS (BIOMedical Research Informatics Solutions), Pavia, Italy.
³⁸ Department of Pediatrics-Section of Nephrology, Brenner Children's, Wake Forest School of Medicine, Winston Salem, NC, USA.
³⁹ Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA. weber@hms.harvard.edu.

^# Contributed equally.

PMID: 35768548
PMCID: PMC9242995
DOI: 10.1038/s41746-022-00623-8

Abstract

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.

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Conflict of interest statement

The authors declare no competing interests.

Figures

**Fig. 1. Demographic trends for age and sex across calendar time in the study population.**
a Age trends among inpatient COVID-19 cases and outpatient COVID-19 cases. b Sex trends among inpatient COVID-19 cases and outpatient COVID-19 cases.

**Fig. 2. Clinical characteristics of the study population.**
a Most prevalent preexisting conditions in COVID-19 patients compared to the controls stratified by hospitalization status. b Conditions with the highest cumulative incidence during the acute stage up to 29 days after initial infection.

**Fig. 3. Statistically significant risk ratios and their 95% confidence intervals of health conditions in the inpatient COVID-19 cohort compared to the control inpatient cohort.**
Left panel shows diseases of increased risk at the mid-stage post-acute period (30 to 89 days after initial infection) among the inpatient COVID-19 cohort. Right panel shows diseases of increased risk at the latestage post-acute period (90+ days after initial infection). P values were corrected for multiple comparisons with a 5% false discovery rate.

**Fig. 4. Statistically significant risk ratios with 95% confidence intervals of health conditions in the outpatient COVID-19 cohort compared to the control outpatient cohort.**
Left panel shows diseases of increased risk at the mid-stage post-acute period (30 to 89 days after initial infection) among the outpatient COVID-19 cohort. Right panel shows diseases of increased risk at the late-stage post-acute period (90+ days after initial infection). P values were corrected for multiple comparisons with a 5% false discovery rate.

**Fig. 5. Cumulative incidence of various conditions at the mid-stage post-acute period (30 to 89 days after initial infection) by the calendar quarter of their initial infection date.**
Left panel shows cumulative incidence among inpatient COVID-19 cases. Right panel shows cumulative incidence among outpatient COVID-19 cases.

**Fig. 6. Study schematic of diagnosis code recording periods relative to the defined index date.**
Diagnosis codes in the post-acute period are defined as diagnosis codes recorded 30 days after initial infection. First occurrence diagnosis codes were defined as diagnosis codes which were not observed up to 365 days prior to the infection date.

See this image and copyright information in PMC

References

1. Bellan M, et al. Respiratory and psychophysical sequelae among patients with COVID-19 four months after hospital discharge. JAMA Netw. Open. 2021;4:e2036142. doi: 10.1001/jamanetworkopen.2020.36142. - DOI - PMC - PubMed
1. Logue JK, et al. Sequelae in adults at 6 months after COVID-19 infection. JAMA Netw. Open. 2021;4:e210830. doi: 10.1001/jamanetworkopen.2021.0830. - DOI - PMC - PubMed
1. Nalbandian A, et al. Post-acute COVID-19 syndrome. Nat. Med. 2021;27:601–615. doi: 10.1038/s41591-021-01283-z. - DOI - PMC - PubMed
1. Sudre CH, et al. Attributes and predictors of long COVID. Nat. Med. 2021;27:626–631. doi: 10.1038/s41591-021-01292-y. - DOI - PMC - PubMed
1. Garg P, Arora U, Kumar A, Wig N. The ‘post-COVID’ syndrome: how deep is the damage? J. Med. Virol. 2021;93:673–674. doi: 10.1002/jmv.26465. - DOI - PMC - PubMed

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

Affiliations

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

Authors

Affiliations

Abstract

Conflict of interest statement

Figures

References

Grants and funding

LinkOut - more resources

Full Text Sources