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A machine learning-based phenotype for long COVID in children: an EHR-based study from the RECOVER program
Vitaly Lorman1, Hanieh Razzaghi1, Xing Song2
1Applied Clinical Research Center, Children's Hospital of Philadelphia, Philadelphia, PA, United States.
Insights
A machine learning algorithm was developed to identify pediatric Post-Acute Sequelae of SARS CoV-2 (PASC) in electronic health records. This tool aids in classifying PASC cases, distinguishing between MIS-C and non-MIS-C variants for research and clinical trial recruitment.
Area of Science:
- Pediatric Health Informatics
- Machine Learning in Clinical Research
- Epidemiology of Post-Acute Sequelae of SARS CoV-2 (PASC)
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.
- Distinguishing PASC from Multisystem Inflammatory Syndrome in Children (MIS-C) is important for accurate classification.
Approach:
- Developed and validated a machine learning algorithm using the PEDSnet Electronic Health Record (EHR) network.
- Selected patient features from conditions, procedures, diagnostics, and medications using a tree-based scan statistic.
- Employed an XGBoost model with hyperparameter tuning via cross-validated grid search and evaluated using 5-fold cross-validation.
- Utilized Shapley Additive exPlanations (SHAP) for model prediction and feature importance analysis.
Key Points:
- The algorithm effectively classifies pediatric patients with PASC from electronic health records.
- Feature importance analysis using SHAP values provides insights into characteristics associated with PASC.
- The model demonstrated robust performance through rigorous validation methods.
Conclusions:
- The developed machine learning model serves as a valuable tool for identifying and characterizing PASC in pediatric populations.
- The model's flexibility allows for precise patient identification for studies or broad screening for clinical trials.
- This approach enhances the utility of health systems data for PASC research, especially where diagnostic codes are unreliable.
Background:
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.
Methods And Findings:
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.
Conclusions:
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.
Funding Source:
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.
Disclaimer:
The content is solely the responsibility of the authors and does not necessarily represent the official views of the RECOVER Program, the NIH or other funders.
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