Neurological diagnoses in hospitalized COVID-19 patients associated with adverse outcomes: A multinational cohort study

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Abstract

Few studies examining the patient outcomes of concurrent neurological manifestations during acute COVID-19 leveraged multinational cohorts of adults and children or distinguished between central and peripheral nervous system (CNS vs. PNS) involvement. Using a federated multinational network in which local clinicians and informatics experts curated the electronic health records data, we evaluated the risk of prolonged hospitalization and mortality in hospitalized COVID-19 patients from 21 healthcare systems across 7 countries. For adults, we used a federated learning approach whereby we ran Cox proportional hazard models locally at each healthcare system and performed a meta-analysis on the aggregated results to estimate the overall risk of adverse outcomes across our geographically diverse populations. For children, we reported descriptive statistics separately due to their low frequency of neurological involvement and poor outcomes. Among the 106,229 hospitalized COVID-19 patients (104,031 patients _>18 years; 2,198 patients <18 years, January 2020-October 2021), 15,101 (14%) had at least one CNS diagnosis, while 2,788 (3%) had at least one PNS diagnosis. After controlling for demographics and pre-existing conditions, adults with CNS involvement had longer hospital stay (11 versus 6 days) and greater risk of (Hazard Ratio = 1.78) and faster time to death (12 versus 24 days) than patients with no neurological condition (NNC) during acute COVID-19 hospitalization. Adults with PNS involvement also had longer hospital stay but lower risk of mortality than the NNC group. Although children had a low frequency of neurological involvement during COVID-19 hospitalization, a substantially higher proportion of children with CNS involvement died compared to those with NNC (6% vs 1%). Overall, patients with concurrent CNS manifestation during acute COVID-19 hospitalization faced greater risks for adverse clinical outcomes than patients without any neurological diagnosis. Our global informatics framework using a federated approach (versus a centralized data collection approach) has utility for clinical discovery beyond COVID-19.

Original languageEnglish
Article numbere0000484
JournalPLOS digital health
Volume3
Issue number4
DOIs
StatePublished - Apr 2024

Bibliographical note

Publisher Copyright:
© 2024 Public Library of Science. All rights reserved.

Funding

MM and SV are supported by National Institutes of Health (NIH) National Center for Advancing Translational Sciences (NCATS) UL1 TR001857. RB is supported by EU PROJECT H2020 PERISCOPE \u2013 101016233. KC is supported by VA MVP000 and CIPHER. DAH is supported by NCATS UL1TR002240. DWH and JT are supported by NIH UL1TR001998. DLM is supported by NCATS UL1-TR001878. FJSV is supported by NCATS UL1TR001881. BWQT is supported by National Medical Research Council Research Training Fellowship (MOH-000195-00). WY is supported by NIH T32HD040128. GSO is supported by NIH P30ES017885; U24CA210967. YL is supported by NCATS U01TR003528 and National Library of Medicine (NLM) 1R01LM013337. ZX is supported by National Institute of Neurological Disorders and Stroke (NINDS) R01NS098023 and R01NS124882. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. We would like to thank Margaret Vella for her extraordinary administrative assistance.

FundersFunder number
National Institutes of Health (NIH)
Institute of Neurological Disorders and Stroke National Advisory Neurological Disorders and Stroke CouncilR01NS124882, R01NS098023
National Center for Advancing Translational Sciences (NCATS)UL1 TR001857
VA MVP000UL1TR001998, UL1TR001881, UL1-TR001878, UL1TR002240
National Medical Research Council SingaporeP30ES017885, U24CA210967, T32HD040128, U01TR003528, MOH-000195-00
U.S. National Library of Medicine1R01LM013337
University Research Committee, Emory University101016233

    ASJC Scopus subject areas

    • Health Informatics

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