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

Lorman et al., medRxiv, doi:10.1101/2022.12.22.22283791
Dec 2022  
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1st treatment shown to increase risk in November 2020
*, now known with p = 0.00000029 from 28 studies, but still recommended in 46 countries.
* From meta analysis with ≥3 clinical studies.
4,000+ studies for 60+ treatments.
Retrospective 87,398 pediatric patients in the USA, reporting acetaminophen and aspirin associated with PASC, without specific details. Authors note that this could be related to use for MIS-C treatment.
Acetaminophen is also known as paracetamol, Tylenol, Panadol, Calpol, Tempra, Calprofen, Doliprane, Efferalgan, Grippostad C, Dolo, Acamol, Fevadol, Crocin, and Perfalgan.
Lorman et al., 26 Dec 2022, retrospective, USA, preprint, 18 authors, study period September 2021 - April 2022.
This PaperAcetaminophenAll
A machine learning-based phenotype for long COVID in children: an EHR-based study from the RECOVER program Authors
PhD Vitaly Lorman, MPH Hanieh Razzaghi, PhD Xing Song, MD, MBA Keith Morse, MD Levon Utidjian, MS Andrea J Allen, MBBS Suchitra Rao, MD, MPH Colin Rogerson, MD Tellen D Bennett, PhD Hiroki Morizono, MLIS Daniel Eckrich, MD Ravi Jhaveri, PhD, MBA Yungui Huang, MPH, MBA Daksha Ranade, MD, MS Nathan Pajor, MD, MPH Grace M Lee, MD, PhD Christopher B Forrest, MD, PhD L Charles Bailey
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.
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Rao, Lee, Razzaghi, Clinical features and burden of post-acute sequelae of SARS-CoV-2 infection in children and adolescents: an exploratory EHR-based cohort study from the RECOVER program, MedRxiv Prepr Serv Health Sci. Published online, doi:10.1101/2022.05.24.22275544
Reese, Blau, Bergquist, Generalizable Long COVID Subtypes: Findings from the NIH N3C and RECOVER Programs, MedRxiv Prepr Serv Health Sci. Published online, doi:10.1101/2022.05.24.22275398
Saito, Rehmsmeier, The Precision-Recall Plot Is More Informative than the ROC Plot When Evaluating Binary Classifiers on Imbalanced Datasets, PLoS ONE, doi:10.1371/journal.pone.0118432
Thallapureddy, Thallapureddy, Zerda, Long-Term Complications of COVID-19 Infection in Adolescents and Children, Curr Pediatr Rep, doi:10.1007/s40124-021-00260-x
Wang, Maro, Baro, Data Mining for Adverse Drug Events With a Propensity Scorematched Tree-based Scan Statistic, Epidemiol Camb Mass, doi:10.1097/EDE.0000000000000907
Yang, Varghese, Stephenson, Tu, Gronsbell, Machine learning approaches for electronic health records phenotyping: A methodical review, Published online, doi:10.1101/2022.04.23.22274218
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