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A new machine learning algorithm reliably identifies pediatric Post-Acute Sequelae of SARS CoV-2 (PASC) in electronic health records. This tool aids in understanding PASC and supports clinical trial recruitment by accurately classifying patients with or without PASC.

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Area of Science:

  • Pediatric Health Informatics
  • Computational Epidemiology
  • Machine Learning in Medicine

Background:

  • Clinical understanding and definitions of pediatric Post-Acute Sequelae of SARS CoV-2 (PASC) are evolving.
  • Reliable identification of PASC patients within health systems data is crucial for research and clinical care.
  • Existing diagnostic codes may not fully capture the spectrum of PASC in children.

Purpose of the Study:

  • To develop and validate a machine learning algorithm for identifying pediatric PASC patients in electronic health records (EHR).
  • To distinguish between Multisystem Inflammatory Syndrome in Children (MIS-C) and non-MIS-C variants of PASC.
  • To characterize PASC using diverse EHR features and understand contributing factors.

Main Methods:

  • A cohort of pediatric patients with positive SARS-CoV-2 tests from the PEDSnet EHR network was analyzed.
  • An XGBoost machine learning model was developed, utilizing conditions, procedures, diagnostic tests, and medications as features.
  • Feature selection employed a tree-based scan statistic; model performance was validated using 5-fold cross-validation and SHAP values.

Main Results:

  • A validated machine learning model was created to classify pediatric PASC patients.
  • The model effectively utilizes EHR data, including diagnoses, medications, labs, and procedures, for PASC identification.
  • SHAP values provided insights into feature importance for PASC classification.

Conclusions:

  • The developed machine learning algorithm offers a reliable tool for identifying pediatric PASC in health systems data.
  • The model supports precise patient identification for studies and efficient screening for clinical trials.
  • Analysis of model features can enhance the understanding of PASC characteristics and associated factors in children.