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Jing N, Liu X, Wu Q, Rao S, Mejias A, Maltenfort M, Schuchard J, Lorman V, Razzaghi H, Webb R, Zhou C, Jhaveri R, Lee GM, Pajor NM, Thacker D, Charles Bailey L, Forrest CB, Chen Y. Development and validation of a federated learning framework for detection of subphenotypes of multisystem inflammatory syndrome in children. medRxiv 2024:2024.01.26.24301827. [PMID: 38343837 PMCID: PMC10854314 DOI: 10.1101/2024.01.26.24301827] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 02/19/2024]
Abstract
Background Multisystem inflammatory syndrome in children (MIS-C) is a severe post-acute sequela of SARS-CoV-2 infection. The highly diverse clinical features of MIS-C necessities characterizing its features by subphenotypes for improved recognition and treatment. However, jointly identifying subphenotypes in multi-site settings can be challenging. We propose a distributed multi-site latent class analysis (dMLCA) approach to jointly learn MIS-C subphenotypes using data across multiple institutions. Methods We used data from the electronic health records (EHR) systems across nine U.S. children's hospitals. Among the 3,549,894 patients, we extracted 864 patients < 21 years of age who had received a diagnosis of MIS-C during an inpatient stay or up to one day before admission. Using MIS-C conditions, laboratory results, and procedure information as input features for the patients, we applied our dMLCA algorithm and identified three MIS-C subphenotypes. As validation, we characterized and compared more granular features across subphenotypes. To evaluate the specificity of the identified subphenotypes, we further compared them with the general subphenotypes identified in the COVID-19 infected patients. Findings Subphenotype 1 (46.1%) represents patients with a mild manifestation of MIS-C not requiring intensive care, with minimal cardiac involvement. Subphenotype 2 (25.3%) is associated with a high risk of shock, cardiac and renal involvement, and an intermediate risk of respiratory symptoms. Subphenotype 3 (28.6%) represents patients requiring intensive care, with a high risk of shock and cardiac involvement, accompanied by a high risk of >4 organ system being impacted. Importantly, for hospital-specific clinical decision-making, our algorithm also revealed a substantial heterogeneity in relative proportions of these three subtypes across hospitals. Properly accounting for such heterogeneity can lead to accurate characterization of the subphenotypes at the patient-level. Interpretation Our identified three MIS-C subphenotypes have profound implications for personalized treatment strategies, potentially influencing clinical outcomes. Further, the proposed algorithm facilitates federated subphenotyping while accounting for the heterogeneity across hospitals.
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Affiliation(s)
- Naimin Jing
- Perelman School of Medicine, The University of Pennsylvania, Philadelphia, PA
- Current affiliation: Biostatistics and Research Decision Sciences, Merck & Co., Inc, Kenilworth, NJ
| | - Xiaokang Liu
- Perelman School of Medicine, The University of Pennsylvania, Philadelphia, PA
| | - Qiong Wu
- Perelman School of Medicine, The University of Pennsylvania, Philadelphia, PA
| | - Suchitra Rao
- Department of Pediatrics, University of Colorado School of Medicine and Children’s Hospital Colorado, Aurora, CO
| | - Asuncion Mejias
- Division of Infectious Diseases, Department of Pediatrics, Nationwide Children’s Hospital and The Ohio State University, Columbus, OH
| | - Mitchell Maltenfort
- Applied Clinical Research Center, Children’s Hospital of Philadelphia, Philadelphia, PA
| | - Julia Schuchard
- Applied Clinical Research Center, Children’s Hospital of Philadelphia, Philadelphia, PA
| | - Vitaly Lorman
- Applied Clinical Research Center, Children’s Hospital of Philadelphia, Philadelphia, PA
| | - Hanieh Razzaghi
- Applied Clinical Research Center, Children’s Hospital of Philadelphia, Philadelphia, PA
| | - Ryan Webb
- Applied Clinical Research Center, Children’s Hospital of Philadelphia, Philadelphia, PA
| | - Chuan Zhou
- Center for Child Health, Behavior and Development, Seattle Children’s Hospital, Seattle, WA
| | - Ravi Jhaveri
- Division of Infectious Diseases, Ann & Robert H. Lurie Children’s Hospital of Chicago, Chicago, IL
| | - Grace M. Lee
- Department of Pediatrics (Infectious Diseases), Stanford University School of Medicine, Stanford, CA
| | - Nathan M. Pajor
- Division of Pulmonary Medicine, Cincinnati Children’s Hospital Medical Center and University of Cincinnati College of Medicine, Cincinnati, OH
| | - Deepika Thacker
- Division of Cardiology, Nemours Children’s Health, Wilmington, DE
| | - L. Charles Bailey
- Applied Clinical Research Center, Children’s Hospital of Philadelphia, Philadelphia, PA
- Department of Pediatrics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA
| | - Christopher B. Forrest
- Applied Clinical Research Center, Children’s Hospital of Philadelphia, Philadelphia, PA
- Department of Pediatrics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA
| | - Yong Chen
- Perelman School of Medicine, The University of Pennsylvania, Philadelphia, PA
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Rao S, Jing N, Liu X, Lorman V, Maltenfort M, Schuchard J, Wu Q, Tong J, Razzaghi H, Mejias A, Lee GM, Pajor NM, Schulert GS, Thacker D, Jhaveri R, Christakis DA, Bailey LC, Forrest CB, Chen Y. Spectrum of severity of multisystem inflammatory syndrome in children: an EHR-based cohort study from the RECOVER program. Sci Rep 2023; 13:21005. [PMID: 38017007 PMCID: PMC10684592 DOI: 10.1038/s41598-023-47655-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2023] [Accepted: 11/16/2023] [Indexed: 11/30/2023] Open
Abstract
Multi-system inflammatory syndrome in children (MIS-C) is a severe post-acute sequela of SARS-CoV-2 infection in children, and there is a critical need to unfold its highly heterogeneous disease patterns. Our objective was to characterize the illness spectrum of MIS-C for improved recognition and management. We conducted a retrospective cohort study using data from March 1, 2020-September 30, 2022, in 8 pediatric medical centers from PEDSnet. We included 1139 children hospitalized with MIS-C and used their demographics, symptoms, conditions, laboratory values, and medications for analyses. We applied heterogeneity-adaptive latent class analyses and identified three latent classes. We further characterized the sociodemographic and clinical characteristics of the latent classes and evaluated their temporal patterns. Class 1 (47.9%) represented children with the most severe presentation, with more admission to the ICU, higher inflammatory markers, hypotension/shock/dehydration, cardiac involvement, acute kidney injury and respiratory involvement. Class 2 (23.3%) represented a moderate presentation, with 4-6 organ systems involved, and some overlapping features with acute COVID-19. Class 3 (28.8%) represented a mild presentation. Our results indicated that MIS-C has a spectrum of clinical severity ranging from mild to severe and the proportion of severe or critical MIS-C decreased over time.
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Affiliation(s)
- Suchitra Rao
- Department of Pediatrics, University of Colorado School of Medicine and Children's Hospital Colorado, 13123 E 16th Ave Box 090, Aurora, CO, 80045, USA.
| | - Naimin Jing
- Department of Biostatistics, Epidemiology and Informatics, The Perelman School of Medicine, University of Pennsylvania, 423 Guardian Dr, Blockley Hall 602, Philadelphia, PA, 19104, USA
- Biostatistics and Research Decision Sciences, Merck & Co., Inc, Kenilworth, NJ, USA
| | - Xiaokang Liu
- Department of Biostatistics, Epidemiology and Informatics, The Perelman School of Medicine, University of Pennsylvania, 423 Guardian Dr, Blockley Hall 602, Philadelphia, PA, 19104, USA
| | - Vitaly Lorman
- Applied Clinical Research Center, Children's Hospital of Philadelphia, Philadelphia, PA, USA
| | - Mitchell Maltenfort
- Applied Clinical Research Center, Children's Hospital of Philadelphia, Philadelphia, PA, USA
| | - Julia Schuchard
- Applied Clinical Research Center, Children's Hospital of Philadelphia, Philadelphia, PA, USA
| | - Qiong Wu
- Department of Biostatistics, Epidemiology and Informatics, The Perelman School of Medicine, University of Pennsylvania, 423 Guardian Dr, Blockley Hall 602, Philadelphia, PA, 19104, USA
| | - Jiayi Tong
- Department of Biostatistics, Epidemiology and Informatics, The Perelman School of Medicine, University of Pennsylvania, 423 Guardian Dr, Blockley Hall 602, Philadelphia, PA, 19104, USA
| | - Hanieh Razzaghi
- Applied Clinical Research Center, Children's Hospital of Philadelphia, Philadelphia, PA, USA
| | - Asuncion Mejias
- Division of Infectious Diseases, Department of Pediatrics, Nationwide Children's Hospital and The Ohio State University, Columbus, OH, USA
| | - Grace M Lee
- Department of Pediatrics (Infectious Diseases), Stanford University School of Medicine, Stanford, CA, USA
| | - Nathan M Pajor
- Division of Pulmonary Medicine, Cincinnati Children's Hospital Medical Center and University of Cincinnati College of Medicine, Cincinnati, OH, USA
| | - Grant S Schulert
- Division of Rheumatology, Cincinnati Children's Hospital Medical Center and Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, OH, USA
| | - Deepika Thacker
- Division of Cardiology, Nemours Children's Health, Wilmington, DE, USA
| | - Ravi Jhaveri
- Division of Infectious Diseases, Ann & Robert H. Lurie Children's Hospital of Chicago, Chicago, IL, USA
| | - Dimitri A Christakis
- Center for Child Health, Behavior and Development, Seattle Children's Hospital, Seattle, WA, USA
| | - L Charles Bailey
- Applied Clinical Research Center, Children's Hospital of Philadelphia, Philadelphia, PA, USA
- Department of Pediatrics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Christopher B Forrest
- Applied Clinical Research Center, Children's Hospital of Philadelphia, Philadelphia, PA, USA
- Department of Pediatrics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Yong Chen
- Department of Biostatistics, Epidemiology and Informatics, The Perelman School of Medicine, University of Pennsylvania, 423 Guardian Dr, Blockley Hall 602, Philadelphia, PA, 19104, USA.
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L Mandel H, Colleen G, Abedian S, Ammar N, Charles Bailey L, Bennett TD, Daniel Brannock M, Brosnahan SB, Chen Y, Chute CG, Divers J, Evans MD, Haendel M, Hall MA, Hirabayashi K, Hornig M, Katz SD, Krieger AC, Loomba J, Lorman V, Mazzotti DR, McMurry J, Moffitt RA, Pajor NM, Pfaff E, Radwell J, Razzaghi H, Redline S, Seibert E, Sekar A, Sharma S, Thaweethai T, Weiner MG, Jae Yoo Y, Zhou A, Thorpe LE. Risk of post-acute sequelae of SARS-CoV-2 infection associated with pre-coronavirus disease obstructive sleep apnea diagnoses: an electronic health record-based analysis from the RECOVER initiative. Sleep 2023; 46:zsad126. [PMID: 37166330 PMCID: PMC10485569 DOI: 10.1093/sleep/zsad126] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2022] [Revised: 03/20/2023] [Indexed: 05/12/2023] Open
Abstract
STUDY OBJECTIVES Obstructive sleep apnea (OSA) has been associated with more severe acute coronavirus disease-2019 (COVID-19) outcomes. We assessed OSA as a potential risk factor for Post-Acute Sequelae of SARS-CoV-2 (PASC). METHODS We assessed the impact of preexisting OSA on the risk for probable PASC in adults and children using electronic health record data from multiple research networks. Three research networks within the REsearching COVID to Enhance Recovery initiative (PCORnet Adult, PCORnet Pediatric, and the National COVID Cohort Collaborative [N3C]) employed a harmonized analytic approach to examine the risk of probable PASC in COVID-19-positive patients with and without a diagnosis of OSA prior to pandemic onset. Unadjusted odds ratios (ORs) were calculated as well as ORs adjusted for age group, sex, race/ethnicity, hospitalization status, obesity, and preexisting comorbidities. RESULTS Across networks, the unadjusted OR for probable PASC associated with a preexisting OSA diagnosis in adults and children ranged from 1.41 to 3.93. Adjusted analyses found an attenuated association that remained significant among adults only. Multiple sensitivity analyses with expanded inclusion criteria and covariates yielded results consistent with the primary analysis. CONCLUSIONS Adults with preexisting OSA were found to have significantly elevated odds of probable PASC. This finding was consistent across data sources, approaches for identifying COVID-19-positive patients, and definitions of PASC. Patients with OSA may be at elevated risk for PASC after SARS-CoV-2 infection and should be monitored for post-acute sequelae.
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Affiliation(s)
- Hannah L Mandel
- Department of Population Health, New York University Grossman School of Medicine, New York, NY, USA
| | - Gunnar Colleen
- Department of Population Health, New York University Grossman School of Medicine, New York, NY, USA
| | - Sajjad Abedian
- Information Technologies and Services Department, Weill Cornell Medicine, New York, NY, USA
| | - Nariman Ammar
- Department of Pediatrics, University of Tennessee Health Science Center College of Medicine Memphis, Memphis, TN, USA
| | - L Charles Bailey
- Applied Clinical Research Center, The Children’s Hospital of Philadelphia, Philadelphia, PA, USA
| | - Tellen D Bennett
- Department of Pediatrics, Children’s Hospital Colorado, Aurora, CO, USA
| | | | - Shari B Brosnahan
- Division of Pulmonary, Department of Medicine, Critical Care and Sleep Medicine, NYU Langone Health, New York, NY, USA¸
| | - Yu Chen
- Department of Population Health, New York University Grossman School of Medicine, New York, NY, USA
| | - Christopher G Chute
- Department of Medicine, Johns Hopkins School of Medicine, Baltimore, MD, USA
| | - Jasmin Divers
- Department of Foundations of Medicine, New York University Long Island School of Medicine, Mineola, NY, USA
| | - Michael D Evans
- Clinical and Translational Science Institute, University of Minnesota, Minneapolis, MN, USA
| | - Melissa Haendel
- Biomedical Informatics, University of Colorado Anschutz Medical Campus, Aurora, CO, USA
| | - Margaret A Hall
- Department of Biomedical Informatics, Stony Brook University, Stony Brook, NY, USA
| | - Kathryn Hirabayashi
- Applied Clinical Research Center, The Children’s Hospital of Philadelphia, Philadelphia, PA, USA
| | - Mady Hornig
- Department of Epidemiology, Columbia University Mailman School of Public Health, New York, NY, USA
| | - Stuart D Katz
- Leon H. Charney Division of Cardiology, Department of Medicine, NYU Langone Health, New York, NY, USA
| | - Ana C Krieger
- Departments of Medicine, Neurology, and Genetic Medicine, Weill Cornell Medical College, New York, NY, USA
| | - Johanna Loomba
- Integrated Translational Health Research Institute, University of Virginia, Charlottesville, VA, USA
| | - Vitaly Lorman
- Applied Clinical Research Center, The Children’s Hospital of Philadelphia, Philadelphia, PA, USA
| | - Diego R Mazzotti
- Division of Pulmonary Critical Care and Sleep Medicine, Department of Internal Medicine, University of Kansas Medical Center, Kansas City, KS, USA
| | - Julie McMurry
- Biomedical Informatics, University of Colorado Anschutz Medical Campus, Aurora, CO, USA
| | - Richard A Moffitt
- Department of Biomedical Informatics, Stony Brook University, Stony Brook, NY, USA
| | - Nathan M Pajor
- Division of Pulmonary Medicine Cincinnati Children’s Hospital Medical Center and University of Cincinnati College of Medicine, Cincinnati, OH, USA
| | - Emily Pfaff
- Department of Medicine, University of North Carolina at Chapel Hill School of Medicine, Chapel Hill, NC, USA
| | - Jeff Radwell
- Department of Population Health, New York University Grossman School of Medicine, New York, NY, USA
| | - Hanieh Razzaghi
- Applied Clinical Research Center, The Children’s Hospital of Philadelphia, Philadelphia, PA, USA
| | - Susan Redline
- Division of Sleep and Circadian Disorders, Department of Medicine, Brigham and Women’s Hospital, Boston, MA, USA
| | | | | | - Suchetha Sharma
- Integrated Translational Health Research Institute, University of Virginia, Charlottesville, VA, USA
| | - Tanayott Thaweethai
- Biostatistics Center, Massachusetts General Hospital, Boston, MA, USA
- Department of Medicine, Harvard Medical School, Boston, MA, USA
| | - Mark G Weiner
- Department of Medicine, Weill Cornell Medical College, New York, NY, USA
| | - Yun Jae Yoo
- Department of Biomedical Informatics, Stony Brook University, Stony Brook, NY, USA
| | - Andrea Zhou
- Integrated Translational Health Research Institute, University of Virginia, Charlottesville, VA, USA
| | - Lorna E Thorpe
- Department of Population Health, New York University Grossman School of Medicine, New York, NY, USA
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4
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Lorman V, Razzaghi H, Song X, Morse K, Utidjian L, Allen AJ, Rao S, Rogerson C, Bennett TD, Morizono H, Eckrich D, Jhaveri R, Huang Y, Ranade D, Pajor N, Lee GM, Forrest CB, Bailey LC. A machine learning-based phenotype for long COVID in children: An EHR-based study from the RECOVER program. PLoS One 2023; 18:e0289774. [PMID: 37561683 PMCID: PMC10414557 DOI: 10.1371/journal.pone.0289774] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2023] [Accepted: 07/25/2023] [Indexed: 08/12/2023] Open
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.
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Affiliation(s)
- Vitaly Lorman
- Applied Clinical Research Center, Children’s Hospital of Philadelphia, Philadelphia, Pennsylvania, United States of America
| | - Hanieh Razzaghi
- Applied Clinical Research Center, Children’s Hospital of Philadelphia, Philadelphia, Pennsylvania, United States of America
| | - Xing Song
- Department of Health Management and Informatics, University of Missouri School of Medicine, Columbia, Missouri, United States of America
| | - Keith Morse
- Division of Pediatric Hospital Medicine, Department of Pediatrics, Stanford University School of Medicine, Stanford, California, United States of America
| | - Levon Utidjian
- Applied Clinical Research Center, Children’s Hospital of Philadelphia, Philadelphia, Pennsylvania, United States of America
| | - Andrea J. Allen
- Applied Clinical Research Center, Children’s Hospital of Philadelphia, Philadelphia, Pennsylvania, United States of America
| | - Suchitra Rao
- Department of Pediatrics, University of Colorado School of Medicine and Children’s Hospital of Colorado, Aurora, Colorado, United States of America
| | - Colin Rogerson
- Division of Critical Care, Department of Pediatrics, Indiana University School of Medicine, Indianapolis, Indiana, United States of America
| | - Tellen D. Bennett
- Departments of Biomedical Informatics and Pediatrics, University of Colorado School of Medicine and Children’s Hospital Colorado, Aurora, Colorado, United States of America
| | - Hiroki Morizono
- Center for Genetic Medicine Research, Children’s National Hospital, Washington, DC, United States of America
| | - Daniel Eckrich
- Biomedical Research Informatics Center, Nemours Children’s Health, Wilmington, Delaware, United States of America
| | - Ravi Jhaveri
- Division of Infectious Diseases, Ann & Robert H. Lurie Children’s Hospital of Chicago, Chicago, Illinois, United States of America
| | - Yungui Huang
- IT Research and Innovation, The Research Institute at Nationwide Children’s Hospital, Columbus, Ohio, United States of America
| | - Daksha Ranade
- Research Informatics Department, Seattle Children’s Hospital, Seattle, Washington, United States of America
| | - Nathan Pajor
- Division of Pulmonary Medicine, Cincinnati Children’s Hospital Medical Center and University of Cincinnati College of Medicine, Cincinnati, Ohio, United States of America
| | - Grace M. Lee
- Division of Infectious Diseases, Department of Pediatrics, Stanford University School of Medicine, Stanford, California, United States of America
| | - Christopher B. Forrest
- Applied Clinical Research Center, Children’s Hospital of Philadelphia, Philadelphia, Pennsylvania, United States of America
| | - L. Charles Bailey
- Applied Clinical Research Center, Children’s Hospital of Philadelphia, Philadelphia, Pennsylvania, United States of America
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Lorman V, Rao S, Jhaveri R, Case A, Mejias A, Pajor NM, Patel P, Thacker D, Bose-Brill S, Block J, Hanley PC, Prahalad P, Chen Y, Forrest CB, Bailey LC, Lee GM, Razzaghi H. Understanding pediatric long COVID using a tree-based scan statistic approach: an EHR-based cohort study from the RECOVER Program. JAMIA Open 2023; 6:ooad016. [PMID: 36926600 PMCID: PMC10013630 DOI: 10.1093/jamiaopen/ooad016] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2022] [Revised: 02/23/2023] [Accepted: 03/06/2023] [Indexed: 03/09/2023] Open
Abstract
Objectives Post-acute sequalae of SARS-CoV-2 infection (PASC) is not well defined in pediatrics given its heterogeneity of presentation and severity in this population. The aim of this study is to use novel methods that rely on data mining approaches rather than clinical experience to detect conditions and symptoms associated with pediatric PASC. Materials and Methods We used a propensity-matched cohort design comparing children identified using the new PASC ICD10CM diagnosis code (U09.9) (N = 1309) to children with (N = 6545) and without (N = 6545) SARS-CoV-2 infection. We used a tree-based scan statistic to identify potential condition clusters co-occurring more frequently in cases than controls. Results We found significant enrichment among children with PASC in cardiac, respiratory, neurologic, psychological, endocrine, gastrointestinal, and musculoskeletal systems, the most significant related to circulatory and respiratory such as dyspnea, difficulty breathing, and fatigue and malaise. Discussion Our study addresses methodological limitations of prior studies that rely on prespecified clusters of potential PASC-associated diagnoses driven by clinician experience. Future studies are needed to identify patterns of diagnoses and their associations to derive clinical phenotypes. Conclusion We identified multiple conditions and body systems associated with pediatric PASC. Because we rely on a data-driven approach, several new or under-reported conditions and symptoms were detected that warrant further investigation.
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Affiliation(s)
- Vitaly Lorman
- Applied Clinical Research Center, Children's Hospital of Philadelphia, Philadelphia, Pennsylvania, USA
| | - Suchitra Rao
- Department of Pediatrics, University of Colorado School of Medicine and Children's Hospital of Colorado, Aurora, Colorado, USA
| | - Ravi Jhaveri
- Division of Infectious Diseases, Ann & Robert H. Lurie Children's Hospital of Chicago, Chicago, Illinois, USA
| | - Abigail Case
- Division of Physical Medicine & Rehabilitation, Children's Hospital of Philadelphia, Philadelphia, Pennsylvania, USA
| | - Asuncion Mejias
- Division of Infectious Diseases, Department of Pediatrics, Nationwide Children's Hospital and The Ohio State University, Columbus, Ohio, USA
| | - Nathan M Pajor
- Division of Pulmonary Medicine, Cincinnati Children's Hospital Medical Center, University of Cincinnati College of Medicine, Cincinnati, Ohio, USA
| | - Payal Patel
- Department of Neurology, University of Washington, Seattle, Washington, USA
| | - Deepika Thacker
- Nemours Cardiac Center, Nemours Children's Health, Wilmington, Delaware, USA
| | - Seuli Bose-Brill
- Internal Medicine and Pediatrics Section, Division of General Internal Medicine, Department of Internal Medicine, Ohio State University College of Medicine and Ohio State University Wexner Medical Center, Columbus, Ohio, USA
| | - Jason Block
- Division of Chronic Disease Research Across the Lifecourse, Department of Population Medicine, Harvard Pilgrim Health Care Institute, Harvard Medical School, Boston, Massachusetts, USA
| | - Patrick C Hanley
- Division of Endocrinology, Nemours Children's Hospital, Wilmington, Delaware, USA
| | - Priya Prahalad
- Division of Endocrinology, Department of Pediatrics, Stanford University, Stanford, California, USA
| | - Yong Chen
- Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Christopher B Forrest
- Applied Clinical Research Center, Children's Hospital of Philadelphia, Philadelphia, Pennsylvania, USA
| | - L Charles Bailey
- Applied Clinical Research Center, Children's Hospital of Philadelphia, Philadelphia, Pennsylvania, USA
| | - Grace M Lee
- Division of Infectious Diseases, Department of Pediatrics, Stanford University School of Medicine, Stanford, California, USA
| | - Hanieh Razzaghi
- Applied Clinical Research Center, Children's Hospital of Philadelphia, Philadelphia, Pennsylvania, USA
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6
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Bose-Brill S, Hirabayashi K, Pajor NM, Rao S, Mejias A, Jhaveri R, Forrest CB, Bailey C, Christakis DA, Thacker D, Hanley PC, Patel PB, Cogen JD, Block JP, Prahalad P, Lorman V, Lee GM. Pediatric Nirmatrelvir/Ritonavir Prescribing Patterns During the COVID-19 Pandemic. medRxiv 2022:2022.12.23.22283868. [PMID: 36597537 PMCID: PMC9810217 DOI: 10.1101/2022.12.23.22283868] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/28/2022]
Abstract
Objective This study was conducted to identify rates of pediatric nirmatrelvir/ritonavir (Paxlovid) prescriptions overall and by patient characteristics. Methods Patients up to 23 years old with a clinical encounter and a nirmatrelvir/ritonavir (Paxlovid, n/r) prescription in a PEDSnet-affiliated institution between December 1, 2021 and September 14, 2022 were identified using electronic health record (EHR) data. Results Of the 1,496,621 patients with clinical encounters during the study period, 920 received a nirmatrelvir/ritonavir prescription (mean age 17.2 years; SD 2.76 years). 40% (367/920) of prescriptions were provided to individuals aged 18-23, and 91% (838/920) of prescriptions occurred after April 1, 2022. The majority of patients (70%; 648/920) had received at least one COVID-19 vaccine dose at least 28 days before nirmatrelvir/ritonavir prescription. Only 40% (371/920) of individuals had documented COVID-19 within the 0 to 6 days prior to receiving a nirmatrelvir/ritonavir prescription. 53% (485/920) had no documented COVID-19 infection in the EHR. Among nirmatrelvir/ritonavir prescription recipients, 64% (586/920) had chronic or complex chronic disease and 9% (80/920) had malignant disease. 38/920 (4.5%) were hospitalized within 30 days of receiving nirmatrelvir/ritonavir. Conclusion Clinicians prescribe nirmatrelvir/ritonavir infrequently to children. While individuals receiving nirmatrelvir/ritonavir generally have significant chronic disease burden, a majority are receiving nirmatrelvir/ritonavir prescriptions without an EHR-recorded COVID-19 positive test or diagnosis. Development and implementation of concerted pediatric nirmatrelvir/ritonavir prescribing workflows can help better capture COVID-19 presentation, response, and adverse events at the population level.
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7
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Lorman V, Razzaghi H, Song X, Morse K, Utidjian L, Allen AJ, Rao S, Rogerson C, Bennett TD, Morizono H, Eckrich D, Jhaveri R, Huang Y, Ranade D, Pajor N, Lee GM, Forrest CB, Bailey LC. A machine learning-based phenotype for long COVID in children: an EHR-based study from the RECOVER program. medRxiv 2022:2022.12.22.22283791. [PMID: 36597534 PMCID: PMC9810222 DOI: 10.1101/2022.12.22.22283791] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/28/2022]
Abstract
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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Affiliation(s)
- Vitaly Lorman
- Applied Clinical Research Center, Children's Hospital of Philadelphia, Philadelphia, PA, United States
| | - Hanieh Razzaghi
- Applied Clinical Research Center, Children's Hospital of Philadelphia, Philadelphia, PA, United States
| | - Xing Song
- Department of Health Management and Informatics, University of Missouri School of Medicine, Columbia, MO, United States
| | - Keith Morse
- Division of Pediatric Hospital Medicine, Department of Pediatrics, Stanford University School of Medicine, Stanford, California, United States
| | - Levon Utidjian
- Applied Clinical Research Center, Children's Hospital of Philadelphia, Philadelphia, PA, United States
| | - Andrea J Allen
- Applied Clinical Research Center, Children's Hospital of Philadelphia, Philadelphia, PA, United States
| | - Suchitra Rao
- Department of Pediatrics, University of Colorado School of Medicine and Children's Hospital of Colorado, Aurora, CO, United States
| | - Colin Rogerson
- Division of Critical Care, Department of Pediatrics, Indiana University School of Medicine, Indianapolis, IN, United States
| | - Tellen D Bennett
- Departments of Biomedical Informatics and Pediatrics, University of Colorado School of Medicine and Children's Hospital Colorado, Aurora, CO, United States
| | - Hiroki Morizono
- Center for Genetic Medicine Research, Children's National Hospital, Washington DC, United States
| | - Daniel Eckrich
- Biomedical Research Informatics Center, Nemours Children's Health, Wilmington, DE, United States
| | - Ravi Jhaveri
- Division of Infectious Diseases, Ann & Robert H. Lurie Children's Hospital of Chicago, Chicago, IL, United States
| | - Yungui Huang
- IT Research and Innovation, The Research Institute at Nationwide Children's Hospital, Columbus, OH, United States
| | - Daksha Ranade
- Research Informatics Department, Seattle Children's Hospital, Seattle, WA, United States
| | - Nathan Pajor
- Division of Pulmonary Medicine, Cincinnati Children's Hospital Medical Center and University of Cincinnati College of Medicine, Cincinnati, OH, United States
| | - Grace M Lee
- Division of Infectious Diseases, Department of Pediatrics, Stanford University School of Medicine, Stanford, CA, United States
| | - Christopher B Forrest
- Applied Clinical Research Center, Children's Hospital of Philadelphia, Philadelphia, PA, United States
| | - L Charles Bailey
- Applied Clinical Research Center, Children's Hospital of Philadelphia, Philadelphia, PA, United States
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Rao S, Lee G, Lorman V, Razzaghi H, Mejias A, Pajor N, Thacker D, Jhaveri R, Christakis D, Webb R, Dickinson K, Bailey C, Forrest C. 76. Clinical Features and Burden of Post-Acute Sequelae of SARS-CoV-2 Infection in Children and Adolescents. Open Forum Infect Dis 2022. [DOI: 10.1093/ofid/ofac492.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022] Open
Abstract
Abstract
Background
The post-acute sequelae of SARS-CoV-2 (PASC) has emerged as a long-term complication in adults, but current understanding of the clinical presentation of PASC in children is limited. Our study objectives were to identify symptoms, health conditions, and medications associated with PASC in children.
Methods
We conducted a retrospective cohort study using electronic health records from 9 US children’s hospitals for individuals < 21 years who underwent polymerase chain reaction (PCR) testing for SARS-CoV-2 between March 1, 2020 – October 31, 2021 and had at least 1 encounter in the 3 years before testing. Our exposure of interest was SARS-CoV-2 PCR positivity.
We identified syndromic (symptoms), systemic (conditions), and medication PASC features in the 28–179 days following the initial test date. Adjusted hazard ratios (aHRs) were obtained for 151 clinically predicted PASC features by contrasting PCR-positive with PCR-negative groups using proportional hazards models, adjusting for site, age, sex, testing location, race/ethnicity, and time-period of cohort entrance. We estimated the incidence proportion for any syndromic, systemic or medication PASC feature in the two groups to estimate PASC burden.
Results
Among 659,286 children in the study sample, 59,893 (9.1%) tested positive by PCR for SARS-CoV-2. Most were tested in outpatient testing facility (50.3%) or office (24.6%) settings (Table 1). The most common syndromic, systemic, and medication features were loss of taste or smell (aHR 1.96 [95% CI 1.16–3.32), myocarditis (aHR 3.10 [95% CI 1.94–4.96]) (Figures 1 and 2), and cough and cold preparations (aHR 1.52 [95% CI 1.18–1.96]). The incidence of at least one systemic/syndromic/medication feature of PASC was 42.0% among PCR-positive children versus 38.2% among PCR-negative children, with an incidence proportion difference of 3.8% (95% CI 3.3–4.3%). A higher strength of association for PASC was identified in those cared for in the ICU during the acute illness phase, children less than 5 years-old, and individuals with complex chronic conditions.
Adjusted hazard ratios (aHR) with associated 95% CI among patients who tested positive for SARS-CoV-2 infection versus those who tested negative for the risk of each syndromic feature (symptom) using Cox proportional hazards models. Models were adjusted for age at cohort entrance, sex, race/ethnicity, institution, testing place location, presence of a complex medical condition and date of cohort entrance.
Adjusted hazard ratios (aHR) with associated 95% CI among patients who tested positive for SARS-CoV-2 infection versus those who tested negative for the risk of each systemic feature using Cox proportional hazards models. Models were adjusted for age at cohort entrance, sex, race/ethnicity, institution, testing place location, and date of cohort entrance. For each health condition evaluated, patients with evidence of that condition 18 months before cohort entrance were excluded from the denominator in order to identify incident cases. Each ratio compares the risk of the outcome in children who tested positive for SARS-CoV-2 infection versus those who tested negative. Footnote: The diagnostic cluster for COVID-19 indicates children receiving care for the illness in the post-acute period.
Conclusion
In this large-scale, exploratory study, the burden of PASC in children appeared to be lower than earlier reports. Acute illness severity, young age, and comorbid complex chronic disease increased the risk of PASC.
Disclosures
Grace Lee, MD, MPH, United Health Group PASC Advisory Council: Advisor/Consultant Asuncion Mejias, MD, PhD, MsCS, Janssen: Advisor/Consultant|Janssen: Grant/Research Support|Merck: Advisor/Consultant|Merck: Grant/Research Support|Roche: Advisor/Consultant|Sanofi-Pasteur: Advisor/Consultant|Sanofi-Pasteur: Honoraria Ravi Jhaveri, MD, AstraZeneca: Advisor/Consultant|Dynavax: Advisor/Consultant|Elsevier: editorial stipend|Sequiris: Advisor/Consultant.
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Affiliation(s)
- Suchitra Rao
- University of Colorado School of Medicine , Aurora, CO
| | - Grace Lee
- Stanford University School of Medicine , Stanford, California
| | - Vitaly Lorman
- Children’s Hospital of Philadelphia , Philadelphia, Pennsylvania
| | - Hanieh Razzaghi
- Children’s Hospital of Philadelphia , Philadelphia, Pennsylvania
| | | | - Nathan Pajor
- Medicine, Cincinnati Children’s Hospital Medical Center and University of Cincinnati College of Medicine , Cincinnati, Ohio
| | | | - Ravi Jhaveri
- Ann & Robert H. Lurie Children’s Hospital of Chicago , Chicago, Illinois
| | | | - Ryan Webb
- Children’s Hospital of Philadelphia , Philadelphia, Pennsylvania
| | | | - Charles Bailey
- Children’s Hospital of Philadelphia , Philadelphia, Pennsylvania
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Rao S, Lee GM, Razzaghi H, Lorman V, Mejias A, Pajor NM, Thacker D, Webb R, Dickinson K, Bailey LC, Jhaveri R, Christakis DA, Bennett TD, Chen Y, Forrest CB. Clinical Features and Burden of Postacute Sequelae of SARS-CoV-2 Infection in Children and Adolescents. JAMA Pediatr 2022; 176:1000-1009. [PMID: 35994282 PMCID: PMC9396470 DOI: 10.1001/jamapediatrics.2022.2800] [Citation(s) in RCA: 47] [Impact Index Per Article: 23.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/07/2022] [Accepted: 06/08/2022] [Indexed: 01/20/2023]
Abstract
Importance The postacute sequelae of SARS-CoV-2 infection (PASC) has emerged as a long-term complication in adults, but current understanding of the clinical presentation of PASC in children is limited. Objective To identify diagnosed symptoms, diagnosed health conditions, and medications associated with PASC in children. Design, Setting and Participants This retrospective cohort study used electronic health records from 9 US children's hospitals for individuals younger than 21 years who underwent antigen or reverse transcriptase-polymerase chain reaction (RT-PCR) testing for SARS-CoV-2 between March 1, 2020, and October 31, 2021, and had at least 1 encounter in the 3 years before testing. Exposures SARS-CoV-2 positivity by viral test (antigen or RT-PCR). Main Outcomes and Measures Syndromic (symptoms), systemic (conditions), and medication PASC features were identified in the 28 to 179 days following the initial test date. Adjusted hazard ratios (aHRs) were obtained for 151 clinically predicted PASC features by contrasting viral test-positive groups with viral test-negative groups using proportional hazards models, adjusting for site, age, sex, testing location, race and ethnicity, and time period of cohort entrance. The incidence proportion for any syndromic, systemic, or medication PASC feature was estimated in the 2 groups to obtain a burden of PASC estimate. Results Among 659 286 children in the study sample, 348 091 (52.8%) were male, and the mean (SD) age was 8.1 (5.7) years. A total of 59 893 (9.1%) tested positive by viral test for SARS-CoV-2, and 599 393 (90.9%) tested negative. Most were tested in outpatient testing facility settings (322 813 [50.3%]) or office settings (162 138 [24.6%]). The most common syndromic, systemic, and medication features were loss of taste or smell (aHR, 1.96; 95% CI, 1.16-3.32), myocarditis (aHR, 3.10; 95% CI, 1.94-4.96), and cough and cold preparations (aHR, 1.52; 95% CI, 1.18-1.96), respectively. The incidence of at least 1 systemic, syndromic, or medication feature of PASC was 41.9% (95% CI, 41.4-42.4) among viral test-positive children vs 38.2% (95% CI, 38.1-38.4) among viral test-negative children, with an incidence proportion difference of 3.7% (95% CI, 3.2-4.2). A higher strength of association for PASC was identified in those cared for in the intensive care unit during the acute illness phase, children younger than 5 years, and individuals with complex chronic conditions. Conclusions and Relevance In this large-scale, exploratory study, the burden of pediatric PASC that presented to health systems was low. Myocarditis was the most commonly diagnosed PASC-associated condition. Acute illness severity, young age, and comorbid complex chronic disease increased the risk of PASC.
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Affiliation(s)
- Suchitra Rao
- Department of Pediatrics, University of Colorado School of Medicine and Children’s Hospital Colorado, Aurora
| | - Grace M. Lee
- Department of Pediatrics (Infectious Diseases), Stanford University School of Medicine, Stanford, California
| | - Hanieh Razzaghi
- Applied Clinical Research Center, Children’s Hospital of Philadelphia, Philadelphia, Pennsylvania
| | - Vitaly Lorman
- Applied Clinical Research Center, Children’s Hospital of Philadelphia, Philadelphia, Pennsylvania
| | - Asuncion Mejias
- Division of Infectious Diseases, Department of Pediatrics, Nationwide Children’s Hospital and The Ohio State University, Columbus
| | - Nathan M. Pajor
- Division of Pulmonary Medicine, Cincinnati Children’s Hospital Medical Center and University of Cincinnati College of Medicine, Cincinnati, Ohio
| | - Deepika Thacker
- Division of Cardiology, Nemours Children’s Health, Wilmington, Delaware
| | - Ryan Webb
- Applied Clinical Research Center, Children’s Hospital of Philadelphia, Philadelphia, Pennsylvania
| | - Kimberley Dickinson
- Applied Clinical Research Center, Children’s Hospital of Philadelphia, Philadelphia, Pennsylvania
| | - L. Charles Bailey
- Applied Clinical Research Center, Children’s Hospital of Philadelphia, Philadelphia, Pennsylvania
| | - Ravi Jhaveri
- Division of Infectious Diseases, Ann & Robert H. Lurie Children’s Hospital of Chicago, Chicago, Illinois
| | - Dimitri A. Christakis
- Center for Child Health, Behavior and Development, Seattle Children’s Hospital, Seattle, Washington
- Editor, JAMA Pediatrics
| | - Tellen D. Bennett
- Department of Pediatrics, University of Colorado School of Medicine and Children’s Hospital Colorado, Aurora
| | - Yong Chen
- Department of Biostatistics, Epidemiology and Informatics, the Perelman School of Medicine, University of Pennsylvania, Pennsylvania
| | - Christopher B. Forrest
- Applied Clinical Research Center, Children’s Hospital of Philadelphia, Philadelphia, Pennsylvania
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Rao S, Jing N, Liu X, Lorman V, Maltenfort M, Schuchard J, Wu Q, Tong J, Razzaghi H, Mejias A, Lee GM, Pajor NM, Schulert GS, Thacker D, Jhaveri R, Christakis DA, Bailey LC, Forrest CB, Chen Y. Clinical Subphenotypes of Multisystem Inflammatory Syndrome in Children: An EHR-based cohort study from the RECOVER program. medRxiv 2022:2022.09.26.22280364. [PMID: 36203555 PMCID: PMC9536089 DOI: 10.1101/2022.09.26.22280364] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/16/2023]
Abstract
Background Multi-system inflammatory syndrome in children (MIS-C) represents one of the most severe post-acute sequelae of SARS-CoV-2 infection in children, and there is a critical need to characterize its disease patterns for improved recognition and management. Our objective was to characterize subphenotypes of MIS-C based on presentation, demographics and laboratory parameters. Methods We conducted a retrospective cohort study of children with MIS-C from March 1, 2020 - April 30, 2022 and cared for in 8 pediatric medical centers that participate in PEDSnet. We included demographics, symptoms, conditions, laboratory values, medications and outcomes (ICU admission, death), and grouped variables into eight categories according to organ system involvement. We used a heterogeneity-adaptive latent class analysis model to identify three clinically-relevant subphenotypes. We further characterized the sociodemographic and clinical characteristics of each subphenotype, and evaluated their temporal patterns. Findings We identified 1186 children hospitalized with MIS-C. The highest proportion of children (44·4%) were aged between 5-11 years, with a male predominance (61.0%), and non- Hispanic white ethnicity (40·2%). Most (67·8%) children did not have a chronic condition. Class 1 represented children with a severe clinical phenotype, with 72·5% admitted to the ICU, higher inflammatory markers, hypotension/shock/dehydration, cardiac involvement, acute kidney injury and respiratory involvement. Class 2 represented a moderate presentation, with 4-6 organ systems involved, and some overlapping features with acute COVID-19. Class 3 represented a mild presentation, with fewer organ systems involved, lower CRP, troponin values and less cardiac involvement. Class 1 initially represented 51·1% of children early in the pandemic, which decreased to 33·9% from the pre-delta period to the omicron period. Interpretation MIS-C has a spectrum of clinical severity, with degree of laboratory abnormalities rather than the number of organ systems involved providing more useful indicators of severity. The proportion of severe/critical MIS-C decreased over time. Research in context Evidence before this study: We searched PubMed and preprint articles from December 2019, to July 2022, for studies published in English that investigated the clinical subphenotypes of MIS-C using the terms "multi-system inflammatory syndrome in children" or "pediatric inflammatory multisystem syndrome" and "phenotypes". Most previous research described the symptoms, clinical characteristics and risk factors associated with MIS-C and how these differ from acute COVID-19, Kawasaki Disease and Toxic Shock Syndrome. One single-center study of 63 patients conducted in 2020 divided patients into Kawasaki and non-Kawasaki disease subphenotypes. Another CDC study evaluated 3 subclasses of MIS-C in 570 children, with one class representing the highest number of organ systems, a second class with predominant respiratory system involvement, and a third class with features overlapping with Kawasaki Disease. However, this study evaluated cases from March to July 2020, during the early phase of the pandemic when misclassification of cases as Kawasaki disease or acute COVID-19 may have occurred. Therefore, it is not known from the existing literature whether the presentation of MIS-C has changed with newer variants such as delta and omicron.Added value of this study: PEDSnet provides one of the largest MIS-C cohorts described so far, providing sufficient power for detailed analyses on MIS-C subphenotypes. Our analyses span the entire length of the pandemic, including the more recent omicron wave, and provide an update on the presentations of MIS-C and its temporal dynamics. We found that children have a spectrum of illness that can be characterized as mild (lower inflammatory markers, fewer organ systems involved), moderate (4-6 organ involvement with clinical overlap with acute COVID-19) and severe (higher inflammatory markers, critically ill, more likely to have cardiac involvement, with hypotension/shock and need for vasopressors).Implications of all the available evidence: These results provide an update to the subphenotypes of MIS-C including the more recent delta and omicron periods and aid in the understanding of the various presentations of MIS-C. These and other findings provide a useful framework for clinicians in the recognition of MIS-C, identify factors associated with children at risk for increased severity, including the importance of laboratory parameters, for risk stratification, and to facilitate early evaluation, diagnosis and treatment.
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Rao S, Lee GM, Razzaghi H, Lorman V, Mejias A, Pajor NM, Thacker D, Webb R, Dickinson K, Bailey LC, Jhaveri R, Christakis DA, Bennett TD, Chen Y, Forrest CB. 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 2022:2022.05.24.22275544. [PMID: 35665016 PMCID: PMC9164455 DOI: 10.1101/2022.05.24.22275544] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/20/2023]
Abstract
Importance The post-acute sequelae of SARS-CoV-2 (PASC) has emerged as a long-term complication in adults, but current understanding of the clinical presentation of PASC in children is limited. Objective To identify diagnosed symptoms, diagnosed health conditions and medications associated with PASC in children. Design Setting and Participants Retrospective cohort study using electronic health records from 9 US children's hospitals for individuals <21 years-old who underwent reverse transcriptase polymerase chain reaction (RT-PCR) testing for SARS-CoV-2 between March 1, 2020 - October 31, 2021 and had at least 1 encounter in the 3 years before testing. Exposure SARS-CoV-2 PCR positivity. Main Outcomes and Measures We identified syndromic (symptoms), systemic (conditions), and medication PASC features in the 28-179 days following the initial test date. Adjusted hazard ratios (aHRs) were obtained for 151 clinically predicted PASC features by contrasting PCR-positive with PCR-negative groups using proportional hazards models, adjusting for site, age, sex, testing location, race/ethnicity, and time-period of cohort entrance. We estimated the incidence proportion for any syndromic, systemic or medication PASC feature in the two groups to obtain a burden of PASC estimate. Results Among 659,286 children in the study sample, 59,893 (9.1%) tested positive by PCR for SARS-CoV-2. Most were tested in outpatient testing facility (50.3%) or office (24.6%) settings. The most common syndromic, systemic, and medication features were loss of taste or smell (aHR 1.96 [95% CI 1.16-3.32), myocarditis (aHR 3.10 [95% CI 1.94-4.96]), and cough and cold preparations (aHR 1.52 [95% CI 1.18-1.96]). The incidence of at least one systemic/syndromic/medication feature of PASC was 41.9% among PCR-positive children versus 38.2% among PCR-negative children, with an incidence proportion difference of 3.7% (95% CI 3.2-4.2%). A higher strength of association for PASC was identified in those cared for in the ICU during the acute illness phase, children less than 5 years-old, and individuals with complex chronic conditions. Conclusions and Relevance In this large-scale, exploratory study, the burden of pediatric PASC that presented to health systems was low. Myocarditis was the most commonly diagnosed PASC-associated condition. Acute illness severity, young age, and comorbid complex chronic disease increased the risk of PASC. Key Points Question: What are the incidence and clinical features of post-acute sequelae of SARS-CoV-2 infection (PASC) in children?Findings: In this retrospective cohort study of 659,286 children tested for SARS-CoV-2 by polymerase chain reaction (PCR), the symptom, condition and medication with the strongest associations with SARS-CoV-2 infection were loss of taste/smell, myocarditis, and cough and cold preparations. The incidence proportion of non-MIS-C related PASC in the PCR-positive group exceeded the PCR-negative group by 3.7% (95% CI 3.2-4.2), with increased rates associated with acute illness severity, young age, and medical complexity.Meaning: PASC in children appears to be uncommon, with features that differ from adults.
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Walter JC, Dorignac J, Lorman V, Rech J, Bouet JY, Nollmann M, Palmeri J, Parmeggiani A, Geniet F. Surfing on Protein Waves: Proteophoresis as a Mechanism for Bacterial Genome Partitioning. Phys Rev Lett 2017; 119:028101. [PMID: 28753349 DOI: 10.1103/physrevlett.119.028101] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/22/2016] [Indexed: 05/11/2023]
Abstract
Efficient bacterial chromosome segregation typically requires the coordinated action of a three-component machinery, fueled by adenosine triphosphate, called the partition complex. We present a phenomenological model accounting for the dynamic activity of this system that is also relevant for the physics of catalytic particles in active environments. The model is obtained by coupling simple linear reaction-diffusion equations with a proteophoresis, or "volumetric" chemophoresis, force field that arises from protein-protein interactions and provides a physically viable mechanism for complex translocation. This minimal description captures most known experimental observations: dynamic oscillations of complex components, complex separation, and subsequent symmetrical positioning. The predictions of our model are in phenomenological agreement with and provide substantial insight into recent experiments. From a nonlinear physics view point, this system explores the active separation of matter at micrometric scales with a dynamical instability between static positioning and traveling wave regimes triggered by the dynamical spontaneous breaking of rotational symmetry.
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Affiliation(s)
- J-C Walter
- Laboratoire Charles Coulomb (L2C), Université Montpellier, CNRS, F-34095 Montpellier, France
| | - J Dorignac
- Laboratoire Charles Coulomb (L2C), Université Montpellier, CNRS, F-34095 Montpellier, France
| | - V Lorman
- Laboratoire Charles Coulomb (L2C), Université Montpellier, CNRS, F-34095 Montpellier, France
| | - J Rech
- LMGM, CBI, CNRS, Université Toulouse, UPS, F-31000 Toulouse, France
| | - J-Y Bouet
- LMGM, CBI, CNRS, Université Toulouse, UPS, F-31000 Toulouse, France
| | - M Nollmann
- CBS, CNRS, INSERM, Université Montpellier, F-34090 Montpellier, France
| | - J Palmeri
- Laboratoire Charles Coulomb (L2C), Université Montpellier, CNRS, F-34095 Montpellier, France
| | - A Parmeggiani
- Laboratoire Charles Coulomb (L2C), Université Montpellier, CNRS, F-34095 Montpellier, France
- DIMNP, CNRS, Université Montpellier, F-34095 Montpellier, France
| | - F Geniet
- Laboratoire Charles Coulomb (L2C), Université Montpellier, CNRS, F-34095 Montpellier, France
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Panov VP, Vij JK, Panarin YP, Blanc C, Lorman V, Goodby JW. Discontinuous change in the smectic layer thickness in ferrielectric liquid crystals. Phys Rev E Stat Nonlin Soft Matter Phys 2007; 75:042701. [PMID: 17500942 DOI: 10.1103/physreve.75.042701] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/10/2006] [Revised: 01/05/2007] [Indexed: 05/15/2023]
Abstract
The temperature dependence of the thickness of thick free-standing films is studied using a high-resolution film thickness measurement technique. A small discontinuity in the temperature dependence of the smectic layer thickness at every phase transition between ferro-, ferri-, and antiferroelectric phases is observed. We show that the major contribution to it arises from a change in the smectic tilt angle.
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Affiliation(s)
- V P Panov
- Department of Electronic and Electrical Engineering, Trinity College, University of Dublin, Dublin 2, Ireland
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Manna F, Lorman V, Podgornik R, Zeks B. Screwlike order, macroscopic chirality, and elastic distortions in high-density DNA mesophases. Phys Rev E Stat Nonlin Soft Matter Phys 2007; 75:030901. [PMID: 17500660 DOI: 10.1103/physreve.75.030901] [Citation(s) in RCA: 12] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/29/2006] [Indexed: 05/15/2023]
Abstract
We investigate a new screwlike liquid-crystalline ordering in solutions of helical biopolymers and its influence on the state of individual molecules. In the resulting mesophase translational and rotational motions of molecules are coupled in screw fluctuations. We show that in contrast to the case of conventional chiral liquid crystals the elastic distortion does not twist the screw order but leads to overwinding of individual helical molecules. This explains the peculiarities of high-density DNA mesophases.
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Affiliation(s)
- F Manna
- Laboratoire de Physique Mathematique et Theorique, Universite Montpellier II, F-34095 Montpellier, France
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Abstract
We investigate the orientational order of transverse polarization vectors of long, stiff polymer molecules and their coupling to bond orientational and positional order in high density mesophases. Homogeneous ordering of transverse polarization vector promotes distortions in the hexatic phase, whereas inhomogeneous ordering precipitates crystallization of the 2D sections with different orientations of the transverse polarization vector on each molecule in the unit cell. We propose possible scenarios for going from the hexatic phase, through the distorted hexatic phase, to the crystalline phase with an orthorhombic unit cell observed experimentally for the case of DNA.
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Affiliation(s)
- V Lorman
- Laboratoire de Physique Mathematique et Theorique, Universite Montpellier II, F-34095 Montpellier, France
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Tolédano P, Lorman V, Mettout B, Dmitriev V. Theory of the phase diagrams of lyotropic nematic and lyotropic cholesteric systems. Phys Rev E Stat Phys Plasmas Fluids Relat Interdiscip Topics 1995; 52:5040-5052. [PMID: 9964000 DOI: 10.1103/physreve.52.5040] [Citation(s) in RCA: 11] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/12/2023]
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Gufan YM, Vereshkov GM, Tolédano P, Mettout B, Bouzerar R, Lorman V. Order-parameter symmetries, phase diagrams, and physical properties of two-dimensional unconventional superconductors. I. d-wave-pairing superconductivity. Phys Rev B Condens Matter 1995; 51:9219-9227. [PMID: 9977564 DOI: 10.1103/physrevb.51.9219] [Citation(s) in RCA: 9] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/12/2023]
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Gufan YM, Vereshkov GM, Tolédano P, Mettout B, Bouzerar R, Lorman V. Order-parameter symmetries, phase diagrams, and physical properties of two-dimensional unconventional superconductors. II. p-wave-pairing superconductivity. Phys Rev B Condens Matter 1995; 51:9228-9244. [PMID: 9977565 DOI: 10.1103/physrevb.51.9228] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/12/2023]
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