1
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Hutch MR, Son J, Le TT, Hong C, Wang X, Shakeri Hossein Abad Z, Morris M, Gutiérrez-Sacristán A, Klann JG, Spiridou A, Batugo A, Bellazzi R, Benoit V, Bonzel CL, Bryant WA, Chiudinelli L, Cho K, Das P, González González T, Hanauer DA, Henderson DW, Ho YL, Loh NHW, Makoudjou A, Makwana S, Malovini A, Moal B, Mowery DL, Neuraz A, Samayamuthu MJ, Sanz Vidorreta FJ, Schriver ER, Schubert P, Talbert J, Tan ALM, Tan BWL, Tan BWQ, Tibollo V, Tippman P, Verdy G, Yuan W, Avillach P, Gehlenborg N, Omenn GS, Visweswaran S, Cai T, Luo Y, Xia Z. Neurological diagnoses in hospitalized COVID-19 patients associated with adverse outcomes: A multinational cohort study. PLOS DIGITAL HEALTH 2024; 3:e0000484. [PMID: 38620037 PMCID: PMC11018281 DOI: 10.1371/journal.pdig.0000484] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/17/2023] [Accepted: 03/06/2024] [Indexed: 04/17/2024]
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.
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Affiliation(s)
- Meghan R. Hutch
- Department of Preventive Medicine, Northwestern University, Chicago, Illinois, United States of America
| | - Jiyeon Son
- Department of Neurology, University of Pittsburgh, Pittsburgh, Pennsylvania, United States of America
| | - Trang T. Le
- Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania, United States of America
| | - Chuan Hong
- Department of Biostatistics and Bioinformatics, Duke University, Durham, North Carolina, United States of America
- Department of Population Health Sciences, University of Utah, Salt Lake City, Utah, United States of America
| | - Xuan Wang
- Department of Population Health Sciences, University of Utah, Salt Lake City, Utah, United States of America
| | - Zahra Shakeri Hossein Abad
- Department of Biomedical Informatics, Harvard Medical School, Boston, Massachusetts, United States of America
| | - Michele Morris
- Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, Pennsylvania, United States of America
| | - Alba Gutiérrez-Sacristán
- Department of Biomedical Informatics, Harvard Medical School, Boston, Massachusetts, United States of America
| | - Jeffrey G. Klann
- Department of Medicine, Massachusetts General Hospital, Boston, Massachusetts, United States of America
| | - Anastasia Spiridou
- Digital Research, Informatics and Virtual Environments (DRIVE), Great Ormond Street Hospital for Children, London, United Kingdom
| | - Ashley Batugo
- Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania, United States of America
| | - Riccardo Bellazzi
- Department of Electrical, Computer and Biomedical Engineering, University of Pavia, Pavia, Italy
| | - Vincent Benoit
- IT Department, Innovation & Data, APHP Greater Paris University Hospital, Paris, France
| | - Clara-Lea Bonzel
- Department of Biomedical Informatics, Harvard Medical School, Boston, Massachusetts, United States of America
| | - William A. Bryant
- Digital Research, Informatics and Virtual Environments (DRIVE), Great Ormond Street Hospital for Children, London, United Kingdom
| | - Lorenzo Chiudinelli
- UOC Ricerca, Innovazione e Brand reputation, ASST Papa Giovanni XXIII, Bergamo, Italy
| | - Kelly Cho
- Population Health and Data Science, VA Boston Healthcare System, Boston Massachusetts, United States of America
- Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC), VA Boston Healthcare System, Boston Massachusetts, United States of America
| | - Priyam Das
- Department of Biomedical Informatics, Harvard Medical School, Boston, Massachusetts, United States of America
| | | | - David A. Hanauer
- Department of Learning Health Sciences, University of Michigan Medical School, Ann Arbor, Michigan, United States of America
| | - Darren W. Henderson
- Center for Clinical and Translational Science, University of Kentucky, Lexington, Kentucky, United States of America
| | - Yuk-Lam Ho
- Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC), VA Boston Healthcare System, Boston Massachusetts, United States of America
| | - Ne Hooi Will Loh
- Department of Anaesthesia, National University Health System, Kent Ridge, Singapore
| | - Adeline Makoudjou
- Institute of Medical Biometry and Statistics, Faculty of Medicine and Medical Center, University of Freiburg, Freiburg, Germany
| | - Simran Makwana
- Department of Biomedical Informatics, Harvard Medical School, Boston, Massachusetts, United States of America
| | - Alberto Malovini
- Laboratory of Informatics and Systems Engineering for Clinical Research, Istituti Clinici Scientifici Maugeri SpA SB IRCCS, Pavia, Italy
| | - Bertrand Moal
- IAM Unit, Bordeaux University Hospital, Bordeaux, France
| | - Danielle L. Mowery
- Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania, United States of America
| | - Antoine Neuraz
- Department of biomedical informatics, Hôpital Necker-Enfants Malade, Assistance Publique Hôpitaux de Paris (APHP), University of Paris, Paris, France
| | | | - Fernando J. Sanz Vidorreta
- Department of Medicine, David Geffen School of Medicine at UCLA, Los Angeles, California, United States of America
| | - Emily R. Schriver
- Data Analytics Center, University of Pennsylvania Health System, Philadelphia, Pennsylvania, United States of America
| | - Petra Schubert
- Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC), VA Boston Healthcare System, Boston Massachusetts, United States of America
| | - Jeffery Talbert
- Division of Biomedical Informatics, University of Kentucky, Lexington, Kentucky, United States of America
| | - Amelia L. M. Tan
- Department of Biomedical Informatics, Harvard Medical School, Boston, Massachusetts, United States of America
| | - Byorn W. L. Tan
- Department of Medicine, National University Hospital, Singapore, Kent Ridge, Singapore
| | - Bryce W. Q. Tan
- Department of Medicine, National University Hospital, Singapore, Kent Ridge, Singapore
| | - Valentina Tibollo
- Laboratory of Informatics and Systems Engineering for Clinical Research, Istituti Clinici Scientifici Maugeri SpA SB IRCCS, Pavia, Italy
| | - Patric Tippman
- Institute of Medical Biometry and University of Freiburg, Medical Center, Freiburg, Germany
| | | | - William Yuan
- Department of Biomedical Informatics, Harvard Medical School, Boston, Massachusetts, United States of America
| | - Paul Avillach
- Department of Biomedical Informatics, Harvard Medical School, Boston, Massachusetts, United States of America
| | - Nils Gehlenborg
- Department of Biomedical Informatics, Harvard Medical School, Boston, Massachusetts, United States of America
| | - Gilbert S. Omenn
- Departments of Computational Medicine & Bioinformatics, Internal Medicine, Human Genetics, Public Health, University of Michigan, Ann Arbor, Michigan, United States of America
| | | | - Shyam Visweswaran
- Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, Pennsylvania, United States of America
| | - Tianxi Cai
- Department of Biomedical Informatics, Harvard Medical School, Boston, Massachusetts, United States of America
| | - Yuan Luo
- Department of Preventive Medicine, Northwestern University, Chicago, Illinois, United States of America
| | - Zongqi Xia
- Department of Neurology, University of Pittsburgh, Pittsburgh, Pennsylvania, United States of America
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2
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Strasser ZH, Dagliati A, Shakeri Hossein Abad Z, Klann JG, Wagholikar KB, Mesa R, Visweswaran S, Morris M, Luo Y, Henderson DW, Samayamuthu MJ, Omenn GS, Xia Z, Holmes JH, Estiri H, Murphy SN. A retrospective cohort analysis leveraging augmented intelligence to characterize long COVID in the electronic health record: A precision medicine framework. PLOS DIGITAL HEALTH 2023; 2:e0000301. [PMID: 37490472 PMCID: PMC10368277 DOI: 10.1371/journal.pdig.0000301] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/01/2022] [Accepted: 06/16/2023] [Indexed: 07/27/2023]
Abstract
Physical and psychological symptoms lasting months following an acute COVID-19 infection are now recognized as post-acute sequelae of COVID-19 (PASC). Accurate tools for identifying such patients could enhance screening capabilities for the recruitment for clinical trials, improve the reliability of disease estimates, and allow for more accurate downstream cohort analysis. In this retrospective cohort study, we analyzed the EHR of hospitalized COVID-19 patients across three healthcare systems to develop a pipeline for better identifying patients with persistent PASC symptoms (dyspnea, fatigue, or joint pain) after their SARS-CoV-2 infection. We implemented distributed representation learning powered by the Machine Learning for modeling Health Outcomes (MLHO) to identify novel EHR features that could suggest PASC symptoms outside of typical diagnosis codes. MLHO applies an entropy-based feature selection and boosting algorithms for representation mining. These improved definitions were then used for estimating PASC among hospitalized patients. 30,422 hospitalized patients were diagnosed with COVID-19 across three healthcare systems between March 13, 2020 and February 28, 2021. The mean age of the population was 62.3 years (SD, 21.0 years) and 15,124 (49.7%) were female. We implemented the distributed representation learning technique to augment PASC definitions. These definitions were found to have positive predictive values of 0.73, 0.74, and 0.91 for dyspnea, fatigue, and joint pain, respectively. We estimated that 25 percent (CI 95%: 6-48), 11 percent (CI 95%: 6-15), and 13 percent (CI 95%: 8-17) of hospitalized COVID-19 patients will have dyspnea, fatigue, and joint pain, respectively, 3 months or longer after a COVID-19 diagnosis. We present a validated framework for screening and identifying patients with PASC in the EHR and then use the tool to estimate its prevalence among hospitalized COVID-19 patients.
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Affiliation(s)
- Zachary H. Strasser
- Department of Medicine, Massachusetts General Hospital, Boston, Massachusetts, United States of America
| | - Arianna Dagliati
- Department of Electrical Computer and Biomedical Engineering, University of Pavia, Pavia, Italy
| | - Zahra Shakeri Hossein Abad
- Institute of Health Policy, Management and Evaluation, Dalla Lana School of Public Health, University of Toronto, Toronto, Canada
| | - Jeffrey G. Klann
- Department of Medicine, Massachusetts General Hospital, Boston, Massachusetts, United States of America
| | - Kavishwar B. Wagholikar
- Department of Medicine, Massachusetts General Hospital, Boston, Massachusetts, United States of America
| | - Rebecca Mesa
- Department of Electrical Computer and Biomedical Engineering, University of Pavia, Pavia, Italy
| | - Shyam Visweswaran
- Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, Pennsylvania, United States
| | - Michele Morris
- Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, Pennsylvania, United States
| | - Yuan Luo
- Department of Preventive Medicine, Northwestern University, Chicago, Illinois, United States of America
| | - Darren W. Henderson
- Center for Clinical and Translation Science, University of Kentucky, Lexington, Kentucky, United States of America
| | | | | | - Gilbert S. Omenn
- Dept of Computational Medicine & Bioinformatics, Internal Medicine, Human Genetics, and School of Public Health, University of Michigan, Ann Arbor, Michigan, United States of America
| | - Zongqi Xia
- Department of Neurology, University of Pittsburgh, Pittsburgh, Pennsylvania, United States of America
| | - John H. Holmes
- Department of Biostatistics, Epidemiology, and Informatics; Institute for Biomedical Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania, United States of America
| | - Hossein Estiri
- Department of Medicine, Massachusetts General Hospital, Boston, Massachusetts, United States of America
| | - Shawn N. Murphy
- Department of Neurology, Massachusetts General Hospital, Boston, Massachusetts, United States of America
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3
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Tan ALM, Getzen EJ, Hutch MR, Strasser ZH, Gutiérrez-Sacristán A, Le TT, Dagliati A, Morris M, Hanauer DA, Moal B, Bonzel CL, Yuan W, Chiudinelli L, Das P, Zhang HG, Aronow BJ, Avillach P, Brat GA, Cai T, Hong C, La Cava WG, Hooi Will Loh H, Luo Y, Murphy SN, Yuan Hgiam K, Omenn GS, Patel LP, Jebathilagam Samayamuthu M, Shriver ER, Shakeri Hossein Abad Z, Tan BWL, Visweswaran S, Wang X, Weber GM, Xia Z, Verdy B, Long Q, Mowery DL, Holmes JH. Informative missingness: What can we learn from patterns in missing laboratory data in the electronic health record? J Biomed Inform 2023; 139:104306. [PMID: 36738870 PMCID: PMC10849195 DOI: 10.1016/j.jbi.2023.104306] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2022] [Revised: 01/21/2023] [Accepted: 01/29/2023] [Indexed: 02/05/2023]
Abstract
BACKGROUND In electronic health records, patterns of missing laboratory test results could capture patients' course of disease as well as reflect clinician's concerns or worries for possible conditions. These patterns are often understudied and overlooked. This study aims to identify informative patterns of missingness among laboratory data collected across 15 healthcare system sites in three countries for COVID-19 inpatients. METHODS We collected and analyzed demographic, diagnosis, and laboratory data for 69,939 patients with positive COVID-19 PCR tests across three countries from 1 January 2020 through 30 September 2021. We analyzed missing laboratory measurements across sites, missingness stratification by demographic variables, temporal trends of missingness, correlations between labs based on missingness indicators over time, and clustering of groups of labs based on their missingness/ordering pattern. RESULTS With these analyses, we identified mapping issues faced in seven out of 15 sites. We also identified nuances in data collection and variable definition for the various sites. Temporal trend analyses may support the use of laboratory test result missingness patterns in identifying severe COVID-19 patients. Lastly, using missingness patterns, we determined relationships between various labs that reflect clinical behaviors. CONCLUSION In this work, we use computational approaches to relate missingness patterns to hospital treatment capacity and highlight the heterogeneity of looking at COVID-19 over time and at multiple sites, where there might be different phases, policies, etc. Changes in missingness could suggest a change in a patient's condition, and patterns of missingness among laboratory measurements could potentially identify clinical outcomes. This allows sites to consider missing data as informative to analyses and help researchers identify which sites are better poised to study particular questions.
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Affiliation(s)
| | - Emily J Getzen
- University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
| | | | | | | | - Trang T Le
- University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
| | | | | | | | | | | | | | | | - Priam Das
- Harvard Medical School, Cambridge, MA, USA
| | | | - Bruce J Aronow
- Cincinnati Children's Hospital Medical Center, University of Cincinnati, Cincinnati, OH, USA
| | | | | | - Tianxi Cai
- Harvard Medical School, Cambridge, MA, USA
| | - Chuan Hong
- Harvard Medical School, Cambridge, MA, USA; Duke University, Durham, NC, USA
| | - William G La Cava
- Harvard Medical School, Cambridge, MA, USA; Boston Children's Hospital, Boston, MA, USA
| | | | - Yuan Luo
- Northwestern University, Chicago, IL, USA
| | | | | | | | - Lav P Patel
- University of Kansas Medical Center, United States
| | | | - Emily R Shriver
- University of Pennsylvania Health System, Philadelphia, PA, USA
| | | | | | | | - Xuan Wang
- Harvard Medical School, Cambridge, MA, USA
| | | | - Zongqi Xia
- University of Pittsburgh, Pittsburgh, PA, USA
| | | | - Qi Long
- University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
| | - Danielle L Mowery
- University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
| | - John H Holmes
- University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
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4
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Zhang HG, Dagliati A, Shakeri Hossein Abad Z, Xiong X, Bonzel CL, Xia Z, Tan BWQ, Avillach P, Brat GA, Hong C, Morris M, Visweswaran S, Patel LP, Gutiérrez-Sacristán A, Hanauer DA, Holmes JH, Samayamuthu MJ, Bourgeois FT, L'Yi S, Maidlow SE, Moal B, Murphy SN, Strasser ZH, Neuraz A, Ngiam KY, Loh NHW, Omenn GS, Prunotto A, Dalvin LA, Klann JG, Schubert P, Vidorreta FJS, Benoit V, Verdy G, Kavuluru R, Estiri H, Luo Y, Malovini A, Tibollo V, Bellazzi R, Cho K, Ho YL, Tan ALM, Tan BWL, Gehlenborg N, Lozano-Zahonero S, Jouhet V, Chiovato L, Aronow BJ, Toh EMS, Wong WGS, Pizzimenti S, Wagholikar KB, Bucalo M, Cai T, South AM, Kohane IS, Weber GM. International electronic health record-derived post-acute sequelae profiles of COVID-19 patients. NPJ Digit Med 2022; 5:81. [PMID: 35768548 PMCID: PMC9242995 DOI: 10.1038/s41746-022-00623-8] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2022] [Accepted: 05/19/2022] [Indexed: 11/10/2022] Open
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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Affiliation(s)
- Harrison G Zhang
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
| | - Arianna Dagliati
- Department of Electrical Computer and Biomedical Engineering, University of Pavia, Pavia, Italy
| | | | - Xin Xiong
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Clara-Lea Bonzel
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
| | - Zongqi Xia
- Department of Neurology, University of Pittsburgh, Pittsburgh, PA, USA
| | - Bryce W Q Tan
- Department of Medicine, National University Hospital, Singapore, Singapore
| | - Paul Avillach
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
| | - Gabriel A Brat
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
| | - Chuan Hong
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA.,Department of Biostatistics and Bioinformatics, Duke University, Durham, NC, USA
| | - Michele Morris
- Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, PA, USA
| | - Shyam Visweswaran
- Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, PA, USA
| | - Lav P Patel
- Department of Internal Medicine, Division of Medical Informatics, University Of Kansas Medical Center, Kansas City, MO, USA
| | | | - David A Hanauer
- Department of Learning Health Sciences, University of Michigan Medical School, Ann Arbor, MI, USA
| | - John H Holmes
- Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA.,Institute for Biomedical Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
| | | | | | - Sehi L'Yi
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
| | - Sarah E Maidlow
- Michigan Institute for Clinical and Health Research (MICHR) Informatics, University of Michigan, Ann Arbor, MI, USA
| | - Bertrand Moal
- IAM unit, Bordeaux University Hospital, Bordeaux, France
| | - Shawn N Murphy
- Department of Neurology, Massachusetts General Hospital, Boston, MA, USA
| | | | - Antoine Neuraz
- Department of biomedical informatics, Hôpital Necker-Enfants Malade, Assistance Publique Hôpitaux de Paris (APHP), University of Paris, Paris, France
| | - Kee Yuan Ngiam
- Department of Biomedical informatics, WiSDM, National University Health Systems Singapore, Singapore, Singapore
| | - Ne Hooi Will Loh
- Department of Anaesthesia, National University Health Systems Singapore, Singapore, Singapore
| | - Gilbert S Omenn
- Department of Computational Medicine & Bioinformatics, Internal Medicine, Human Genetics, and School of Public Health, University of Michigan, Ann Arbor, MI, USA
| | - Andrea Prunotto
- Institute of Medical Biometry and Statistics, Faculty of Medicine and Medical Center, University of Freiburg, Freiburg, Germany
| | - Lauren A Dalvin
- Department of Ophthalmology, Mayo Clinic, Rochester, NY, USA
| | - Jeffrey G Klann
- Department of Medicine, Massachusetts General Hospital, Boston, MA, USA
| | - Petra Schubert
- Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC), VA Boston Healthcare System, Boston, MA, USA
| | | | - Vincent Benoit
- IT Department, Innovation & Data, APHP Greater Paris University Hospital, Paris, France
| | | | - Ramakanth Kavuluru
- Division of Biomedical Informatics (Department of Internal Medicine), University of Kentucky, Lexington, KY, USA
| | - Hossein Estiri
- Department of Medicine, Massachusetts General Hospital, Boston, MA, USA
| | - Yuan Luo
- Department of Preventive Medicine, Northwestern University, Chicago, IL, USA
| | - Alberto Malovini
- Laboratory of Informatics and Systems Engineering for Clinical Research, Istituti Clinici Scientifici Maugeri SpA SB IRCCS, Pavia, Italy
| | - Valentina Tibollo
- Laboratory of Informatics and Systems Engineering for Clinical Research, Istituti Clinici Scientifici Maugeri SpA SB IRCCS, Pavia, Italy
| | - Riccardo Bellazzi
- Department of Electrical, Computer and Biomedical Engineering, University of Pavia, Pavia, Italy
| | - Kelly Cho
- Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC), VA Boston Healthcare System, Boston, MA, USA.,Population Health and Data Science, VA Boston Healthcare System, Boston, MA, USA
| | - Yuk-Lam Ho
- Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC), VA Boston Healthcare System, Boston, MA, USA
| | - Amelia L M Tan
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
| | - Byorn W L Tan
- Department of Medicine, National University Hospital, Singapore, Singapore
| | - Nils Gehlenborg
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
| | - Sara Lozano-Zahonero
- Institute of Medical Biometry and Statistics, Faculty of Medicine and Medical Center, University of Freiburg, Freiburg, Germany
| | - Vianney Jouhet
- IAM unit, INSERM Bordeaux Population Health ERIAS TEAM, Bordeaux University Hospital / ERIAS - Inserm, U1219 BPH, Bordeaux, France
| | - Luca Chiovato
- Unit of Internal Medicine and Endocrinology, Istituti Clinici Scientifici Maugeri SpA SB IRCCS, Pavia, Italy
| | - Bruce J Aronow
- Departments of Biomedical Informatics, Pediatrics, Cincinnati Children's Hospital Medical Center, University of Cincinnati, Cincinnati, OH, USA
| | - Emma M S Toh
- Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
| | - Wei Gen Scott Wong
- Department of Medicine, National University Health Systems Singapore, Singapore, Singapore
| | - Sara Pizzimenti
- Scientific Direction, IRCCS Ca' Granda Ospedale Maggiore Policlinico di Milano, Milan, Italy
| | | | - Mauro Bucalo
- BIOMERIS (BIOMedical Research Informatics Solutions), Pavia, Italy
| | | | - Tianxi Cai
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
| | - Andrew M South
- Department of Pediatrics-Section of Nephrology, Brenner Children's, Wake Forest School of Medicine, Winston Salem, NC, USA
| | - Isaac S Kohane
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
| | - Griffin M Weber
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA.
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5
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Hong C, Zhang HG, L'Yi S, Weber G, Avillach P, Tan BWQ, Gutiérrez-Sacristán A, Bonzel CL, Palmer NP, Malovini A, Tibollo V, Luo Y, Hutch MR, Liu M, Bourgeois F, Bellazzi R, Chiovato L, Sanz Vidorreta FJ, Le TT, Wang X, Yuan W, Neuraz A, Benoit V, Moal B, Morris M, Hanauer DA, Maidlow S, Wagholikar K, Murphy S, Estiri H, Makoudjou A, Tippmann P, Klann J, Follett RW, Gehlenborg N, Omenn GS, Xia Z, Dagliati A, Visweswaran S, Patel LP, Mowery DL, Schriver ER, Samayamuthu MJ, Kavuluru R, Lozano-Zahonero S, Zöller D, Tan ALM, Tan BWL, Ngiam KY, Holmes JH, Schubert P, Cho K, Ho YL, Beaulieu-Jones BK, Pedrera-Jiménez M, García-Barrio N, Serrano-Balazote P, Kohane I, South A, Brat GA, Cai T. Changes in laboratory value improvement and mortality rates over the course of the pandemic: an international retrospective cohort study of hospitalised patients infected with SARS-CoV-2. BMJ Open 2022; 12:e057725. [PMID: 35738646 PMCID: PMC9226470 DOI: 10.1136/bmjopen-2021-057725] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/30/2021] [Accepted: 06/12/2022] [Indexed: 01/08/2023] Open
Abstract
OBJECTIVE To assess changes in international mortality rates and laboratory recovery rates during hospitalisation for patients hospitalised with SARS-CoV-2 between the first wave (1 March to 30 June 2020) and the second wave (1 July 2020 to 31 January 2021) of the COVID-19 pandemic. DESIGN, SETTING AND PARTICIPANTS This is a retrospective cohort study of 83 178 hospitalised patients admitted between 7 days before or 14 days after PCR-confirmed SARS-CoV-2 infection within the Consortium for Clinical Characterization of COVID-19 by Electronic Health Record, an international multihealthcare system collaborative of 288 hospitals in the USA and Europe. The laboratory recovery rates and mortality rates over time were compared between the two waves of the pandemic. PRIMARY AND SECONDARY OUTCOME MEASURES The primary outcome was all-cause mortality rate within 28 days after hospitalisation stratified by predicted low, medium and high mortality risk at baseline. The secondary outcome was the average rate of change in laboratory values during the first week of hospitalisation. RESULTS Baseline Charlson Comorbidity Index and laboratory values at admission were not significantly different between the first and second waves. The improvement in laboratory values over time was faster in the second wave compared with the first. The average C reactive protein rate of change was -4.72 mg/dL vs -4.14 mg/dL per day (p=0.05). The mortality rates within each risk category significantly decreased over time, with the most substantial decrease in the high-risk group (42.3% in March-April 2020 vs 30.8% in November 2020 to January 2021, p<0.001) and a moderate decrease in the intermediate-risk group (21.5% in March-April 2020 vs 14.3% in November 2020 to January 2021, p<0.001). CONCLUSIONS Admission profiles of patients hospitalised with SARS-CoV-2 infection did not differ greatly between the first and second waves of the pandemic, but there were notable differences in laboratory improvement rates during hospitalisation. Mortality risks among patients with similar risk profiles decreased over the course of the pandemic. The improvement in laboratory values and mortality risk was consistent across multiple countries.
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Affiliation(s)
- Chuan Hong
- Department of Biomedical Informatics, Harvard Medical School, Boston, Massachusetts, USA
| | - Harrison G Zhang
- Department of Biomedical Informatics, Harvard Medical School, Boston, Massachusetts, USA
| | - Sehi L'Yi
- Department of Biomedical Informatics, Harvard Medical School, Boston, Massachusetts, USA
| | - Griffin Weber
- Department of Biomedical Informatics, Harvard Medical School, Boston, Massachusetts, USA
| | - Paul Avillach
- Department of Biomedical Informatics, Harvard Medical School, Boston, Massachusetts, USA
| | - Bryce W Q Tan
- Department of Medicine, National University Hospital, Singapore
| | | | - Clara-Lea Bonzel
- Department of Biomedical Informatics, Harvard Medical School, Boston, Massachusetts, USA
| | - Nathan P Palmer
- Department of Biomedical Informatics, Harvard Medical School, Boston, Massachusetts, USA
| | - Alberto Malovini
- Laboratory of Informatics and Systems Engineering for Clinical Research, Istituti Clinici Scientifici Maugeri SpA SB IRCCS, Pavia, Lombardia, Italy
| | - Valentina Tibollo
- Laboratory of Informatics and Systems Engineering for Clinical Research, Istituti Clinici Scientifici Maugeri SpA SB IRCCS, Pavia, Lombardia, Italy
| | - Yuan Luo
- Department of Preventive Medicine, Northwestern University, Evanston, Illinois, USA
| | - Meghan R Hutch
- Department of Preventive Medicine, Northwestern University, Evanston, Illinois, USA
| | - Molei Liu
- Department of Biostatistics, Harvard University T H Chan School of Public Health, Boston, Massachusetts, USA
| | - Florence Bourgeois
- Department of Pediatrics, Harvard Medical School, Boston, Massachusetts, USA
| | - Riccardo Bellazzi
- Department of Electrical, Computer and Biomedical Engineering, University of Pavia, Pavia, Italy
| | - Luca Chiovato
- Unit of Internal Medicine and Endocrinology, Istituti Clinici Scientifici Maugeri SpA SB IRCCS, Pavia, Lombardia, Italy
| | | | - Trang T Le
- Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania, USA
| | - Xuan Wang
- Department of Biomedical Informatics, Harvard Medical School, Boston, Massachusetts, USA
| | - William Yuan
- Department of Biomedical Informatics, Harvard Medical School, Boston, Massachusetts, USA
| | - Antoine Neuraz
- Department of Biomedical Informatics, Hopital Universitaire Necker-Enfants Malades, Paris, Île-de-France, France
| | - Vincent Benoit
- IT department, Innovation & Data, APHP Greater Paris University Hospital, Paris, France
| | - Bertrand Moal
- IAM unit, Bordeaux University Hospital, Bordeaux, France
| | - Michele Morris
- Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, Pennsylvania, USA
| | - David A Hanauer
- Department of Learning Health Sciences, University of Michigan Medical School, Ann Arbor, Michigan, USA
| | - Sarah Maidlow
- MICHR Informatics, University of Michigan, Ann Arbor, Michigan, USA
| | - Kavishwar Wagholikar
- Department of Medicine, Massachusetts General Hospital, Boston, Massachusetts, USA
| | - Shawn Murphy
- Neurology, Massachusetts General Hospital, Boston, Massachusetts, USA
| | - Hossein Estiri
- Department of Medicine, Massachusetts General Hospital, Boston, Massachusetts, USA
| | - Adeline Makoudjou
- Institute of Medical Biometry and Statistics, University of Freiburg Faculty of Medicine, Freiburg, Baden-Württemberg, Germany
| | - Patric Tippmann
- Institute of Medical Biometry and Statistics, Medical Center-University of Freiburg, Freiburg, Baden-Württemberg, Germany
| | - Jeffery Klann
- Department of Medicine, Massachusetts General Hospital, Boston, Massachusetts, USA
| | - Robert W Follett
- Department of Medicine, David Geffen School of Medicine, Los Angeles, California, USA
| | - Nils Gehlenborg
- Department of Biomedical Informatics, Harvard Medical School, Boston, Massachusetts, USA
| | - Gilbert S Omenn
- Computational Medicine & Bioinformatics, University of Michigan, Ann Arbor, Michigan, USA
| | - Zongqi Xia
- Department of Neurology, University of Pittsburgh, Pittsburgh, Pennsylvania, USA
| | - Arianna Dagliati
- Department of Electrical, Computer and Biomedical Engineering, University of Pavia, Pavia, Italy
| | - Shyam Visweswaran
- Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, Kansas, USA
| | - Lav P Patel
- Department of Internal Medicine, Division of Medical Informatics, University of Kansas Medical Center, Kansas City, Kansas, USA
| | - Danielle L Mowery
- Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania, USA
| | - Emily R Schriver
- Data Analytics Center, University of Pennsylvania Health System, Philadelphia, Pennsylvania, USA
| | | | - Ramakanth Kavuluru
- Institute for Biomedical Informatics, University of Kentucky, Lexington, Kentucky, USA
| | - Sara Lozano-Zahonero
- Institute of Medical Biometry and Statistics, University of Freiburg Faculty of Medicine, Freiburg, Baden-Württemberg, Germany
| | - Daniela Zöller
- Institute of Medical Biometry and Statistics, University of Freiburg Faculty of Medicine, Freiburg, Baden-Württemberg, Germany
| | - Amelia L M Tan
- Department of Biomedical Informatics, Harvard Medical School, Boston, Massachusetts, USA
| | - Byorn W L Tan
- Department of Medicine, National University Hospital, Singapore
| | - Kee Yuan Ngiam
- Department of Surgery, National University Hospital, Singapore
| | - John H Holmes
- Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania, USA
- Institute for Biomedical Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania, USA
| | - Petra Schubert
- Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC), VA Boston Healthcare System, Boston, Massachusetts, USA
| | - Kelly Cho
- Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC), VA Boston Healthcare System, Boston, Massachusetts, USA
| | - Yuk-Lam Ho
- Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC), VA Boston Healthcare System, Boston, Massachusetts, USA
| | | | - Miguel Pedrera-Jiménez
- Health Informatics, Hospital Universitario 12 de Octubre, Madrid, Comunidad de Madrid, Spain
| | - Noelia García-Barrio
- Health Informatics, Hospital Universitario 12 de Octubre, Madrid, Comunidad de Madrid, Spain
| | - Pablo Serrano-Balazote
- Health Informatics, Hospital Universitario 12 de Octubre, Madrid, Comunidad de Madrid, Spain
| | - Isaac Kohane
- Department of Biomedical Informatics, Harvard Medical School, Boston, Massachusetts, USA
| | - Andrew South
- Department of Pediatrics, Section of Nephrology, Wake Forest University, Winston Salem, North Carolina, USA
| | - Gabriel A Brat
- Department of Biomedical Informatics, Harvard Medical School, Boston, Massachusetts, USA
| | - T Cai
- Department of Biomedical Informatics, Harvard Medical School, Boston, Massachusetts, USA
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The relationship between COVID-19 infection and intracranial hemorrhage: A systematic review. BRAIN HEMORRHAGES 2021; 2:141-150. [PMID: 34786548 PMCID: PMC8582085 DOI: 10.1016/j.hest.2021.11.003] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2021] [Accepted: 11/04/2021] [Indexed: 01/05/2023] Open
Abstract
INTRODUCTION In addition to the deleterious effects Covid-19 has on the pulmonary and cardiovascular systems, COVID-19 can also result in damage to the nervous system. This review aims to explore current literature on the association between COVID-19 and intracranial hemorrhage (ICH). METHODS We conducted a systematic review of PubMed for literature published on COVID-19 and ICH. Ninety-four of 295 screened papers met inclusion criteria. RESULTS The literature addressed incidence and mortality of ICH associated with Covid-19. It also revealed cases of COVID-19 patients with subarachnoid hemorrhage, intraparenchymal hemorrhage, subdural hematomas, and hemorrhage secondary to cerebral venous thrombosis and ischemic stroke. ICH during COVID-19 infections was associated with increased morbidity and mortality. Risk factors for ICH appeared to be therapeutic anticoagulation, ECMO, and mechanical ventilation. Outcomes varied widely, depending on the severity of COVID-19 infection and neurologic injury. CONCLUSION Although treatment for severe Covid-19 infections is often aimed at addressing acute respiratory distress syndrome, vasculopathy, and coagulopathy, neurologic injury can also occur. Evidence-based treatments that improve COVID-19 mortality may also increase risk for developing ICH. Providers should be aware of potential neurologic sequelae of COVID-19, diagnostic methods to rule out other causes of ICH, and treatment regimens.
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