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Predictive models of long COVID.
Antony, Blessy; Blau, Hannah; Casiraghi, Elena; Loomba, Johanna J; Callahan, Tiffany J; Laraway, Bryan J; Wilkins, Kenneth J; Antonescu, Corneliu C; Valentini, Giorgio; Williams, Andrew E; Robinson, Peter N; Reese, Justin T; Murali, T M.
Afiliação
  • Antony B; Department of Computer Science, Virginia Polytechnic Institute and State University (Virginia Tech), Blacksburg, VA, 24061, USA.
  • Blau H; The Jackson Laboratory for Genomic Medicine, Farmington, CT, 06032, USA.
  • Casiraghi E; AnacletoLab, Computer Science Department, Dipartimento di Informatica, Università degli Studi di Milano, Milan, 20133, Italy; Division of Environmental Genomics and Systems Biology, Lawrence Berkeley National Laboratory, Berkeley, CA, 94720, USA; ELLIS - European Laboratory for Learning and Intellig
  • Loomba JJ; Integrated Translational Health Research Institute of Virginia, University of Virginia, Charlottesville, VA, 22904, USA.
  • Callahan TJ; Department of Biomedical Informatics, Columbia University Irving Medical Center, New York, NY, 10032, USA.
  • Laraway BJ; Department of Biomedical Informatics, University of Colorado Anschutz Medical Campus, Aurora, CO, 80045, USA.
  • Wilkins KJ; Biostatistics Program, Office of the Director, National Institute of Diabetes and Digestive and Kidney Diseases, National Institutes of Health, Bethesda, MD, 20814, USA.
  • Antonescu CC; Banner Health, University of Arizona, Phoenix, AZ, 85006, USA.
  • Valentini G; AnacletoLab, Computer Science Department, Dipartimento di Informatica, Università degli Studi di Milano, Milan, 20133, Italy; ELLIS - European Laboratory for Learning and Intelligent Systems, Milan Unit, Milan, 20133, Italy.
  • Williams AE; Institute for Clinical Research and Health Policy Studies, Tufts University School of Medicine, Boston, MA, 02111, USA.
  • Robinson PN; The Jackson Laboratory for Genomic Medicine, Farmington, CT, 06032, USA; Institute for Systems Genomics, University of Connecticut, Farmington, CT, 06269, USA.
  • Reese JT; Division of Environmental Genomics and Systems Biology, Lawrence Berkeley National Laboratory, Berkeley, CA, 94720, USA.
  • Murali TM; Department of Computer Science, Virginia Polytechnic Institute and State University (Virginia Tech), Blacksburg, VA, 24061, USA. Electronic address: murali@cs.vt.edu.
EBioMedicine ; 96: 104777, 2023 Oct.
Article em En | MEDLINE | ID: mdl-37672869
ABSTRACT

BACKGROUND:

The cause and symptoms of long COVID are poorly understood. It is challenging to predict whether a given COVID-19 patient will develop long COVID in the future.

METHODS:

We used electronic health record (EHR) data from the National COVID Cohort Collaborative to predict the incidence of long COVID. We trained two machine learning (ML) models - logistic regression (LR) and random forest (RF). Features used to train predictors included symptoms and drugs ordered during acute infection, measures of COVID-19 treatment, pre-COVID comorbidities, and demographic information. We assigned the 'long COVID' label to patients diagnosed with the U09.9 ICD10-CM code. The cohorts included patients with (a) EHRs reported from data partners using U09.9 ICD10-CM code and (b) at least one EHR in each feature category. We analysed three cohorts all patients (n = 2,190,579; diagnosed with long COVID = 17,036), inpatients (149,319; 3,295), and outpatients (2,041,260; 13,741).

FINDINGS:

LR and RF models yielded median AUROC of 0.76 and 0.75, respectively. Ablation study revealed that drugs had the highest influence on the prediction task. The SHAP method identified age, gender, cough, fatigue, albuterol, obesity, diabetes, and chronic lung disease as explanatory features. Models trained on data from one N3C partner and tested on data from the other partners had average AUROC of 0.75.

INTERPRETATION:

ML-based classification using EHR information from the acute infection period is effective in predicting long COVID. SHAP methods identified important features for prediction. Cross-site analysis demonstrated the generalizability of the proposed methodology.

FUNDING:

NCATS U24 TR002306, NCATS UL1 TR003015, Axle Informatics Subcontract NCATS-P00438-B, NIH/NIDDK/OD, PSR2015-1720GVALE_01, G43C22001320007, and Director, Office of Science, Office of Basic Energy Sciences of the U.S. Department of Energy Contract No. DE-AC02-05CH11231.
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Texto completo: 1 Base de dados: MEDLINE Assunto principal: COVID-19 / Síndrome de COVID-19 Pós-Aguda Idioma: En Ano de publicação: 2023 Tipo de documento: Article

Texto completo: 1 Base de dados: MEDLINE Assunto principal: COVID-19 / Síndrome de COVID-19 Pós-Aguda Idioma: En Ano de publicação: 2023 Tipo de documento: Article