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Validation of an internationally derived patient severity phenotype to support COVID-19 analytics from electronic health record data.
Klann, Jeffrey G; Estiri, Hossein; Weber, Griffin M; Moal, Bertrand; Avillach, Paul; Hong, Chuan; Tan, Amelia L M; Beaulieu-Jones, Brett K; Castro, Victor; Maulhardt, Thomas; Geva, Alon; Malovini, Alberto; South, Andrew M; Visweswaran, Shyam; Morris, Michele; Samayamuthu, Malarkodi J; Omenn, Gilbert S; Ngiam, Kee Yuan; Mandl, Kenneth D; Boeker, Martin; Olson, Karen L; Mowery, Danielle L; Follett, Robert W; Hanauer, David A; Bellazzi, Riccardo; Moore, Jason H; Loh, Ne-Hooi Will; Bell, Douglas S; Wagholikar, Kavishwar B; Chiovato, Luca; Tibollo, Valentina; Rieg, Siegbert; Li, Anthony L L J; Jouhet, Vianney; Schriver, Emily; Xia, Zongqi; Hutch, Meghan; Luo, Yuan; Kohane, Isaac S; Brat, Gabriel A; Murphy, Shawn N.
  • Klann JG; Laboratory of Computer Science, Department of Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, USA.
  • Estiri H; Laboratory of Computer Science, Department of Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, USA.
  • Weber GM; Department of Biomedical Informatics, Department of Medicine, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, Massachusetts, USA.
  • Moal B; IAM Unit, Public Health Department , Bordeaux University Hospital, Bordeaux, France.
  • Avillach P; Department of Biomedical Informatics, Harvard Medical School, Boston, Massachusetts, USA.
  • Hong C; Department of Biomedical Informatics, Harvard Medical School, Boston, Massachusetts, USA.
  • Tan ALM; Department of Biomedical Informatics, Harvard Medical School, Boston, Massachusetts, USA.
  • Beaulieu-Jones BK; Department of Biomedical Informatics, Harvard Medical School, Boston, Massachusetts, USA.
  • Castro V; Research Information Science and Computing, Mass General Brigham, Boston, Massachusetts, USA.
  • Maulhardt T; Institute of Medical Biometry and Statistics, Medical Center Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany.
  • Geva A; Department of Anesthesiology, Critical Care, and Pain Medicine, Boston Children's Hospital, Boston, Massachusetts, USA.
  • Malovini A; Computational Health Informatics Program, Boston Children's Hospital, Boston, Massachusetts, USA.
  • South AM; Laboratory of Informatics and Systems Engineering for Clinical Research, Istituti Clinici Scientifici Maugeri IRCCS, Pavia, Italy.
  • Visweswaran S; Section of Nephrology, Department of Pediatrics, Brenner Children's Hospital, Wake Forest School of Medicine, Winston Salem, North Carolina, USA.
  • Morris M; Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, Pennsylvania, USA.
  • Samayamuthu MJ; Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, Pennsylvania, USA.
  • Omenn GS; Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, Pennsylvania, USA.
  • Ngiam KY; Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, Michigan, USA.
  • Mandl KD; Department of Biomedical Informatics-WisDM, National University Health System, Singapore.
  • Boeker M; Computational Health Informatics Program, Boston Children's Hospital, Boston, Massachusetts, USA.
  • Olson KL; Institute of Medical Biometry and Statistics, Medical Center Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany.
  • Mowery DL; Computational Health Informatics Program, Boston Children's Hospital, Boston, Massachusetts, USA.
  • Follett RW; Department of Biostatistics, Epidemiology, and Informatics, Institute for Biomedical Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania, USA.
  • Hanauer DA; Department of Medicine, David Geffen School of Medicine at UCLA, Los Angeles, California, USA.
  • Bellazzi R; Department of Learning Health Sciences, University of Michigan Medical School, Ann Arbor, Michigan, USA.
  • Moore JH; Laboratory of Informatics and Systems Engineering for Clinical Research, Istituti Clinici Scientifici Maugeri IRCCS, Pavia, Italy.
  • Loh NW; Department of Electrical, Computer and Biomedical Engineering, University of Pavia, Italy.
  • Bell DS; Department of Biostatistics, Epidemiology, and Informatics, Institute for Biomedical Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania, USA.
  • Wagholikar KB; Division of Critical Care, National University Health System, Singapore.
  • Chiovato L; Department of Medicine, David Geffen School of Medicine at UCLA, Los Angeles, California, USA.
  • Tibollo V; Department of Medicine, Massachusetts General Hospital, Boston, Massachusetts, USA.
  • Rieg S; Laboratory of Informatics and Systems Engineering for Clinical Research, Istituti Clinici Scientifici Maugeri IRCCS, Pavia, Italy.
  • Li ALLJ; Department of Internal Medicine and Medical Therapy, University of Pavia, Pavia, Italy.
  • Jouhet V; Laboratory of Informatics and Systems Engineering for Clinical Research, Istituti Clinici Scientifici Maugeri IRCCS, Pavia, Italy.
  • Schriver E; Division of Infectious Diseases, Department of Medicine II, Medical Center Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany.
  • Xia Z; National Center for Infectious Diseases, Tan Tock Seng Hospital, Singapore.
  • Hutch M; ERIAS-INSERM U1219 BPH, Bordeaux University Hospital, Bordeaux, France.
  • Luo Y; Data Analytics Center, Penn Medicine, Philadelphia, Pennsylvania, USA.
  • Kohane IS; Department of Neurology, University of Pittsburgh, Pittsburgh, Pennsylvania, USA.
  • Brat GA; Department of Preventive Medicine, Northwestern University Feinberg School of Medicine, Chicago, Illinois, USA.
  • Murphy SN; Department of Biomedical Informatics, Harvard Medical School, Boston, Massachusetts, USA.
J Am Med Inform Assoc ; 28(7): 1411-1420, 2021 07 14.
Article in English | MEDLINE | ID: covidwho-1075534
Semantic information from SemMedBD (by NLM)
1. COVID-19 PROCESS_OF hospitalized patients
Subject
COVID-19
Predicate
PROCESS_OF
Object
hospitalized patients
2. COVID-19 PROCESS_OF hospitalized patients
Subject
COVID-19
Predicate
PROCESS_OF
Object
hospitalized patients
ABSTRACT

OBJECTIVE:

The Consortium for Clinical Characterization of COVID-19 by EHR (4CE) is an international collaboration addressing coronavirus disease 2019 (COVID-19) with federated analyses of electronic health record (EHR) data. We sought to develop and validate a computable phenotype for COVID-19 severity. MATERIALS AND

METHODS:

Twelve 4CE sites participated. First, we developed an EHR-based severity phenotype consisting of 6 code classes, and we validated it on patient hospitalization data from the 12 4CE clinical sites against the outcomes of intensive care unit (ICU) admission and/or death. We also piloted an alternative machine learning approach and compared selected predictors of severity with the 4CE phenotype at 1 site.

RESULTS:

The full 4CE severity phenotype had pooled sensitivity of 0.73 and specificity 0.83 for the combined outcome of ICU admission and/or death. The sensitivity of individual code categories for acuity had high variability-up to 0.65 across sites. At one pilot site, the expert-derived phenotype had mean area under the curve of 0.903 (95% confidence interval, 0.886-0.921), compared with an area under the curve of 0.956 (95% confidence interval, 0.952-0.959) for the machine learning approach. Billing codes were poor proxies of ICU admission, with as low as 49% precision and recall compared with chart review.

DISCUSSION:

We developed a severity phenotype using 6 code classes that proved resilient to coding variability across international institutions. In contrast, machine learning approaches may overfit hospital-specific orders. Manual chart review revealed discrepancies even in the gold-standard outcomes, possibly owing to heterogeneous pandemic conditions.

CONCLUSIONS:

We developed an EHR-based severity phenotype for COVID-19 in hospitalized patients and validated it at 12 international sites.
Subject(s)
Keywords

Full text: Available Collection: International databases Database: MEDLINE Document Type: Article Main subject: Severity of Illness Index / Electronic Health Records / COVID-19 Subject: Severity of Illness Index / Electronic Health Records / COVID-19 Type of study: Diagnostic study / Prognostic study Language: English Journal: J Am Med Inform Assoc Clinical aspect: Diagnosis / Prediction / Prognosis Year: 2021

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Full text: Available Collection: International databases Database: MEDLINE Document Type: Article Main subject: Severity of Illness Index / Electronic Health Records / COVID-19 Subject: Severity of Illness Index / Electronic Health Records / COVID-19 Type of study: Diagnostic study / Prognostic study Language: English Journal: J Am Med Inform Assoc Clinical aspect: Diagnosis / Prediction / Prognosis Year: 2021
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