دورية أكاديمية
Data-driven clustering identifies features distinguishing multisystem inflammatory syndrome from acute COVID-19 in children and adolescents.
| العنوان: | Data-driven clustering identifies features distinguishing multisystem inflammatory syndrome from acute COVID-19 in children and adolescents. |
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| المؤلفون: | Geva, Alon, Patel, Manish M, Newhams, Margaret M, Young, Cameron C, Son, Mary Beth F, Kong, Michele, Maddux, Aline B, Hall, Mark W, Riggs, Becky J, Singh, Aalok R, Giuliano, John S, Hobbs, Charlotte V, Loftis, Laura L, McLaughlin, Gwenn E, Schwartz, Stephanie P, Schuster, Jennifer E, Babbitt, Christopher J, Halasa, Natasha B, Gertz, Shira J, Doymaz, Sule, Hume, Janet R, Bradford, Tamara T, Irby, Katherine, Carroll, Christopher L, McGuire, John K, Tarquinio, Keiko M, Rowan, Courtney M, Mack, Elizabeth H, Cvijanovich, Natalie Z, Fitzgerald, Julie C, Spinella, Philip C, Staat, Mary A, Clouser, Katharine N, Soma, Vijaya L, Dapul, Heda, Maamari, Mia, Bowens, Cindy, Havlin, Kevin M, Mourani, Peter M, Heidemann, Sabrina M, Horwitz, Steven M, Feldstein, Leora R, Tenforde, Mark W, Newburger, Jane W, Mandl, Kenneth D, Randolph, Adrienne G |
| المصدر: | EClinicalMedicine ; 40 ; 101112 ; United States ; England |
| سنة النشر: | 2021 |
| مصطلحات موضوعية: | COVID-19, Clustering, Critical care medicine, Multisystem inflammatory syndrome, Pediatrics |
| الوصف: | Background: Multisystem inflammatory syndrome in children (MIS-C) consensus criteria were designed for maximal sensitivity and therefore capture patients with acute COVID-19 pneumonia. Methods: We performed unsupervised clustering on data from 1,526 patients (684 labeled MIS-C by clinicians) <21 years old hospitalized with COVID-19-related illness admitted between 15 March 2020 and 31 December 2020. We compared prevalence of assigned MIS-C labels and clinical features among clusters, followed by recursive feature elimination to identify characteristics of potentially misclassified MIS-C-labeled patients. Findings: Of 94 clinical features tested, 46 were retained for clustering. Cluster 1 patients (N = 498; 92% labeled MIS-C) were mostly previously healthy (71%), with mean age 7·2 ± 0·4 years, predominant cardiovascular (77%) and/or mucocutaneous (82%) involvement, high inflammatory biomarkers, and mostly SARS-CoV-2 PCR negative (60%). Cluster 2 patients (N = 445; 27% labeled MIS-C) frequently had pre-existing conditions (79%, with 39% respiratory), were similarly 7·4 ± 2·1 years old, and commonly had chest radiograph infiltrates (79%) and positive PCR testing (90%). Cluster 3 patients (N = 583; 19% labeled MIS-C) were younger (2·8 ± 2·0 y), PCR positive (86%), with less inflammation. Radiographic findings of pulmonary infiltrates and positive SARS-CoV-2 PCR accurately distinguished cluster 2 MIS-C labeled patients from cluster 1 patients. Interpretation: Using a data driven, unsupervised approach, we identified features that cluster patients into a group with high likelihood of having MIS-C. Other features identified a cluster of patients more likely to have acute severe COVID-19 pulmonary disease, and patients in this cluster labeled by clinicians as MIS-C may be misclassified. These data driven phenotypes may help refine the diagnosis of MIS-C. ; VoR ; SUNY Downstate ; Pediatrics ; N/A |
| نوع الوثيقة: | article in journal/newspaper |
| اللغة: | English |
| تدمد: | 2589-5370 |
| العلاقة: | https://www.sciencedirect.com/science/article/pii/S2589537021003928?via%3DihubTest; Geva A, Patel MM, Newhams MM, Young CC, Son MBF, Kong M, Maddux AB, Hall MW, Riggs BJ, Singh AR, Giuliano JS, Hobbs CV, Loftis LL, McLaughlin GE, Schwartz SP, Schuster JE, Babbitt CJ, Halasa NB, Gertz SJ, Doymaz S, Hume JR, Bradford TT, Irby K, Carroll CL, McGuire JK, Tarquinio KM, Rowan CM, Mack EH, Cvijanovich NZ, Fitzgerald JC, Spinella PC, Staat MA, Clouser KN, Soma VL, Dapul H, Maamari M, Bowens C, Havlin KM, Mourani PM, Heidemann SM, Horwitz SM, Feldstein LR, Tenforde MW, Newburger JW, Mandl KD, Randolph AG; Overcoming COVID-19 Investigators. Data-driven clustering identifies features distinguishing multisystem inflammatory syndrome from acute COVID-19 in children and adolescents. EClinicalMedicine. 2021 Oct;40:101112. doi:10.1016/j.eclinm.2021.101112. Epub 2021 Aug 31. PMID: 34485878; PMCID: PMC8405351.; http://hdl.handle.net/20.500.12648/7599Test; EClinicalMedicine |
| DOI: | 10.1016/j.eclinm.2021.101112 |
| الإتاحة: | https://doi.org/20.500.12648/7599Test https://doi.org/10.1016/j.eclinm.2021.101112Test https://hdl.handle.net/20.500.12648/7599Test |
| حقوق: | © 2021 The Authors. ; Attribution-NonCommercial-NoDerivatives 4.0 International ; http://creativecommons.org/licenses/by-nc-nd/4.0Test/ |
| رقم الانضمام: | edsbas.4B166E7E |
| قاعدة البيانات: | BASE |
Full Text Finder | تدمد: | 25895370 |
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| DOI: | 10.1016/j.eclinm.2021.101112 |