A machine learning-based phenotype for long COVID in children: An EHR-based study from the RECOVER program

Vitaly Lorman*, Hanieh Razzaghi, Xing Song, Keith Morse, Levon Utidjian, Andrea J. Allen, Suchitra Rao, Colin Rogerson, Tellen D. Bennett, Hiroki Morizono, Daniel Eckrich, Ravi Jhaveri, Yungui Huang, Daksha Ranade, Nathan Pajor, Grace M. Lee, Christopher B. Forrest, L. Charles Bailey

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

4 Scopus citations

Abstract

As clinical understanding of pediatric Post-Acute Sequelae of SARS CoV-2 (PASC) develops, and hence the clinical definition evolves, it is desirable to have a method to reliably identify patients who are likely to have post-acute sequelae of SARS CoV-2 (PASC) in health systems data. In this study, we developed and validated a machine learning algorithm to classify which patients have PASC (distinguishing between Multisystem Inflammatory Syndrome in Children (MIS-C) and non-MIS-C variants) from a cohort of patients with positive SARS- CoV-2 test results in pediatric health systems within the PEDSnet EHR network. Patient features included in the model were selected from conditions, procedures, performance of diagnostic testing, and medications using a tree-based scan statistic approach. We used an XGboost model, with hyperparameters selected through cross-validated grid search, and model performance was assessed using 5-fold cross-validation. Model predictions and feature importance were evaluated using Shapley Additive exPlanation (SHAP) values. The model provides a tool for identifying patients with PASC and an approach to characterizing PASC using diagnosis, medication, laboratory, and procedure features in health systems data. Using appropriate threshold settings, the model can be used to identify PASC patients in health systems data at higher precision for inclusion in studies or at higher recall in screening for clinical trials, especially in settings where PASC diagnosis codes are used less frequently or less reliably. Analysis of how specific features contribute to the classification process may assist in gaining a better understanding of features that are associated with PASC diagnoses.

Original languageEnglish (US)
Article numbere0289774
JournalPloS one
Volume18
Issue number8 August
DOIs
StatePublished - Aug 2023

Funding

“This research was funded by the National Institutes of Health (NIH) Agreement OT2HL161847-01 as part of the Researching COVID to Enhance Recovery (RECOVER) program of research. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.” The authors gratefully acknowledge the contributions of Miranda Higginbotham.

ASJC Scopus subject areas

  • General

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