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International Changes in COVID-19 Clinical Trajectories Across 315 Hospitals and 6 Countries: Retrospective Cohort Study.
Weber, Griffin M; Zhang, Harrison G; L'Yi, Sehi; Bonzel, Clara-Lea; Hong, Chuan; Avillach, Paul; Gutiérrez-Sacristán, Alba; Palmer, Nathan P; Tan, Amelia Li Min; Wang, Xuan; Yuan, William; Gehlenborg, Nils; Alloni, Anna; Amendola, Danilo F; Bellasi, Antonio; Bellazzi, Riccardo; Beraghi, Michele; Bucalo, Mauro; Chiovato, Luca; Cho, Kelly; Dagliati, Arianna; Estiri, Hossein; Follett, Robert W; García Barrio, Noelia; Hanauer, David A; Henderson, Darren W; Ho, Yuk-Lam; Holmes, John H; Hutch, Meghan R; Kavuluru, Ramakanth; Kirchoff, Katie; Klann, Jeffrey G; Krishnamurthy, Ashok K; Le, Trang T; Liu, Molei; Loh, Ne Hooi Will; Lozano-Zahonero, Sara; Luo, Yuan; Maidlow, Sarah; Makoudjou, Adeline; Malovini, Alberto; Martins, Marcelo Roberto; Moal, Bertrand; Morris, Michele; Mowery, Danielle L; Murphy, Shawn N; Neuraz, Antoine; Ngiam, Kee Yuan; Okoshi, Marina P; Omenn, Gilbert S.
  • Weber GM; Department of Biomedical Informatics, Harvard Medical School, Boston, MA, United States.
  • Zhang HG; Department of Biomedical Informatics, Harvard Medical School, Boston, MA, United States.
  • L'Yi S; Department of Biomedical Informatics, Harvard Medical School, Boston, MA, United States.
  • Bonzel CL; Department of Biomedical Informatics, Harvard Medical School, Boston, MA, United States.
  • Hong C; Department of Biomedical Informatics, Harvard Medical School, Boston, MA, United States.
  • Avillach P; Department of Biomedical Informatics, Harvard Medical School, Boston, MA, United States.
  • Gutiérrez-Sacristán A; Department of Biomedical Informatics, Harvard Medical School, Boston, MA, United States.
  • Palmer NP; Department of Biomedical Informatics, Harvard Medical School, Boston, MA, United States.
  • Tan ALM; Department of Biomedical Informatics, Harvard Medical School, Boston, MA, United States.
  • Wang X; Department of Biomedical Informatics, Harvard Medical School, Boston, MA, United States.
  • Yuan W; Department of Biomedical Informatics, Harvard Medical School, Boston, MA, United States.
  • Gehlenborg N; Department of Biomedical Informatics, Harvard Medical School, Boston, MA, United States.
  • Alloni A; BIOMERIS (BIOMedical Research Informatics Solutions), Pavia, Italy.
  • Amendola DF; Clinical Research Unit, Botucatu Medical School, São Paulo State University, Botucatu, Brazil.
  • Bellasi A; Division of Nephrology, Department of Medicine, Ente Ospedaliero Cantonale, Lugano, Switzerland.
  • Bellazzi R; Department of Electrical, Computer and Biomedical Engineering, University of Pavia, Pavia, Italy.
  • Beraghi M; Information Technology Department, Azienda Socio-Sanitaria Territoriale di Pavia, Pavia, Italy.
  • Bucalo M; BIOMERIS (BIOMedical Research Informatics Solutions), Pavia, Italy.
  • Chiovato L; Unit of Internal Medicine and Endocrinology, Istituti Clinici Scientifici Maugeri SpA SB IRCCS, Pavia, Italy.
  • Cho K; Massachusetts Veterans Epidemiology Research and Information Center, Veterans Affairs Boston Healthcare System, Boston, MA, United States.
  • Dagliati A; Department of Electrical Computer and Biomedical Engineering, University of Pavia, Pavia, Italy.
  • Estiri H; Department of Medicine, Massachusetts General Hospital, Boston, MA, United States.
  • Follett RW; Department of Medicine, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, United States.
  • García Barrio N; Health Informatics, Hospital Universitario 12 de Octubre, Madrid, Spain.
  • Hanauer DA; Department of Learning Health Sciences, University of Michigan Medical School, Ann Arbor, MI, United States.
  • Henderson DW; Department of Biomedical Informatics, University of Kentucky, Lexington, KY, United States.
  • Ho YL; Massachusetts Veterans Epidemiology Research and Information Center, Veterans Affairs Boston Healthcare System, Boston, MA, United States.
  • Holmes JH; Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, United States.
  • Hutch MR; Institute for Biomedical Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, United States.
  • Kavuluru R; Department of Preventive Medicine, Northwestern University, Chicago, IL, United States.
  • Kirchoff K; Institute for Biomedical Informatics, University of Kentucky, Lexington, KY, United States.
  • Klann JG; Medical University of South Carolina, Charleston, SC, United States.
  • Krishnamurthy AK; Department of Medicine, Massachusetts General Hospital, Boston, MA, United States.
  • Le TT; Department of Computer Science, Renaissance Computing Institute, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States.
  • Liu M; Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, United States.
  • Loh NHW; Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, United States.
  • Lozano-Zahonero S; Department of Anaesthesia, National University Health System, Singapore, Singapore.
  • Luo Y; Institute of Medical Biometry and Statistics, Faculty of Medicine and Medical Center, University of Freiburg, Freiburg, Germany.
  • Maidlow S; Department of Preventive Medicine, Northwestern University, Chicago, IL, United States.
  • Makoudjou A; Michigan Institute for Clinical & Health Research Informatics, University of Michigan, Ann Arbor, MI, United States.
  • Malovini A; Institute of Medical Biometry and Statistics, Faculty of Medicine and Medical Center, University of Freiburg, Freiburg, Germany.
  • Martins MR; Laboratory of Informatics and Systems Engineering for Clinical Research, Istituti Clinici Scientifici Maugeri SpA SB IRCCS, Pavia, Italy.
  • Moal B; Clinical Hospital of Botucatu Medical School, São Paulo State University, Botucatu, Brazil.
  • Morris M; Informatique et archivistique médicales unit, Bordeaux University Hospital, Bordeaux, France.
  • Mowery DL; Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, PA, United States.
  • Murphy SN; Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, United States.
  • Neuraz A; Department of Neurology, Massachusetts General Hospital, Boston, MA, United States.
  • Ngiam KY; Department of Biomedical Informatics, Hôpital Necker-Enfants Malade, Assistance Publique Hôpitaux de Paris, University of Paris, Paris, France.
  • Okoshi MP; Department of Biomedical Informatics, Institute for Digital Medicine, National University Health System, Singapore, Singapore.
  • Omenn GS; Internal Medicine Department, Botucatu Medical School, São Paulo State University, Botucatu, Brazil.
J Med Internet Res ; 23(10): e31400, 2021 10 11.
Article in English | MEDLINE | ID: covidwho-1463405
ABSTRACT

BACKGROUND:

Many countries have experienced 2 predominant waves of COVID-19-related hospitalizations. Comparing the clinical trajectories of patients hospitalized in separate waves of the pandemic enables further understanding of the evolving epidemiology, pathophysiology, and health care dynamics of the COVID-19 pandemic.

OBJECTIVE:

In this retrospective cohort study, we analyzed electronic health record (EHR) data from patients with SARS-CoV-2 infections hospitalized in participating health care systems representing 315 hospitals across 6 countries. We compared hospitalization rates, severe COVID-19 risk, and mean laboratory values between patients hospitalized during the first and second waves of the pandemic.

METHODS:

Using a federated approach, each participating health care system extracted patient-level clinical data on their first and second wave cohorts and submitted aggregated data to the central site. Data quality control steps were adopted at the central site to correct for implausible values and harmonize units. Statistical analyses were performed by computing individual health care system effect sizes and synthesizing these using random effect meta-analyses to account for heterogeneity. We focused the laboratory analysis on C-reactive protein (CRP), ferritin, fibrinogen, procalcitonin, D-dimer, and creatinine based on their reported associations with severe COVID-19.

RESULTS:

Data were available for 79,613 patients, of which 32,467 were hospitalized in the first wave and 47,146 in the second wave. The prevalence of male patients and patients aged 50 to 69 years decreased significantly between the first and second waves. Patients hospitalized in the second wave had a 9.9% reduction in the risk of severe COVID-19 compared to patients hospitalized in the first wave (95% CI 8.5%-11.3%). Demographic subgroup analyses indicated that patients aged 26 to 49 years and 50 to 69 years; male and female patients; and black patients had significantly lower risk for severe disease in the second wave than in the first wave. At admission, the mean values of CRP were significantly lower in the second wave than in the first wave. On the seventh hospital day, the mean values of CRP, ferritin, fibrinogen, and procalcitonin were significantly lower in the second wave than in the first wave. In general, countries exhibited variable changes in laboratory testing rates from the first to the second wave. At admission, there was a significantly higher testing rate for D-dimer in France, Germany, and Spain.

CONCLUSIONS:

Patients hospitalized in the second wave were at significantly lower risk for severe COVID-19. This corresponded to mean laboratory values in the second wave that were more likely to be in typical physiological ranges on the seventh hospital day compared to the first wave. Our federated approach demonstrated the feasibility and power of harmonizing heterogeneous EHR data from multiple international health care systems to rapidly conduct large-scale studies to characterize how COVID-19 clinical trajectories evolve.
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Full text: Available Collection: International databases Database: MEDLINE Document Type: Article Main subject: Pandemics / COVID-19 Subject: Pandemics / COVID-19 Type of study: Etiology study / Observational study / Risk factors Language: English Journal: J Med Internet Res Clinical aspect: Etiology Year: 2021

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Full text: Available Collection: International databases Database: MEDLINE Document Type: Article Main subject: Pandemics / COVID-19 Subject: Pandemics / COVID-19 Type of study: Etiology study / Observational study / Risk factors Language: English Journal: J Med Internet Res Clinical aspect: Etiology Year: 2021
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