دورية أكاديمية
Generalisable long COVID subtypes: findings from the NIH N3C and RECOVER programmes.
| العنوان: | Generalisable long COVID subtypes: findings from the NIH N3C and RECOVER programmes. |
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| المؤلفون: | Reese JT; Environmental Genomics and Systems Biology Division, Lawrence Berkeley National Laboratory, Berkeley, CA, USA., Blau H; The Jackson Laboratory for Genomic Medicine, 10 Discovery Drive, Farmington, CT, USA., Casiraghi E; Environmental Genomics and Systems Biology Division, Lawrence Berkeley National Laboratory, Berkeley, CA, USA; AnacletoLab, Dipartimento di Informatica, Università Degli Studi di Milano, Milan, Italy., Bergquist T; Sage Bionetworks, Seattle, WA, USA., Loomba JJ; The Integrated Translational Health Research Institute of Virginia (iTHRIV), University of Virginia, Charlottesville, VA, USA., Callahan TJ; Department of Biomedical Informatics, Columbia University Irving Medical Center, New York, NY, USA., Laraway B; Departments of Biomedical Informatics and Pediatrics, University of Colorado Anschutz Medical Campus, Aurora, CO, USA., Antonescu C; University of Arizona - Banner Health, Phoenix, AZ, USA., Coleman B; The Jackson Laboratory for Genomic Medicine, 10 Discovery Drive, Farmington, CT, USA., Gargano M; The Jackson Laboratory for Genomic Medicine, 10 Discovery Drive, Farmington, CT, USA., Wilkins KJ; Biostatistics Program, National Institute of Diabetes and Digestive and Kidney Diseases, National Institutes of Health, Bethesda, MD, USA., Cappelletti L; AnacletoLab, Dipartimento di Informatica, Università Degli Studi di Milano, Milan, Italy., Fontana T; AnacletoLab, Dipartimento di Informatica, Università Degli Studi di Milano, Milan, Italy., Ammar N; Health Science Center, University of Tennessee, Memphis, TN, USA., Antony B; Department of Computer Science, Virginia Tech, Blacksburg, VA, USA., Murali TM; Department of Computer Science, Virginia Tech, Blacksburg, VA, USA., Caufield JH; Environmental Genomics and Systems Biology Division, Lawrence Berkeley National Laboratory, Berkeley, CA, USA., Karlebach G; The Jackson Laboratory for Genomic Medicine, 10 Discovery Drive, Farmington, CT, USA., McMurry JA; Departments of Biomedical Informatics and Pediatrics, University of Colorado Anschutz Medical Campus, Aurora, CO, USA., Williams A; Tufts Medical Center Clinical and Translational Science Institute, Tufts Medical Center, Boston, MA, USA; Tufts University School of Medicine, Institute for Clinical Research and Health Policy Studies, Boston, MA, USA; Northeastern University, OHDSI Center at the Roux Institute, Boston, MA, USA., Moffitt R; Department of Biomedical Informatics and Stony Brook Cancer Center, Stony Brook University, Stony Brook, NY, USA., Banerjee J; Sage Bionetworks, Seattle, WA, USA., Solomonides AE; HealthSystem Research Institute, NorthShore University, Evanston, IL, USA., Davis H; Patient-Led Research Collaborative, NY, USA., Kostka K; Northeastern University, OHDSI Center at the Roux Institute, Boston, MA, USA., Valentini G; AnacletoLab, Dipartimento di Informatica, Università Degli Studi di Milano, Milan, Italy., Sahner D; Axle Informatics, Rockville, MD, USA., Chute CG; Schools of Medicine, Public Health and Nursing, Johns Hopkins University, Baltimore, MD, USA., Madlock-Brown C; Health Science Center, University of Tennessee, Memphis, TN, USA., Haendel MA; Departments of Biomedical Informatics and Pediatrics, University of Colorado Anschutz Medical Campus, Aurora, CO, USA., Robinson PN; The Jackson Laboratory for Genomic Medicine, 10 Discovery Drive, Farmington, CT, USA; Institute for Systems Genomics, University of Connecticut, Farmington, CT, USA. Electronic address: Peter.Robinson@jax.org. |
| مؤلفون مشاركون: | N3C Consortium, RECOVER Consortium |
| المصدر: | EBioMedicine [EBioMedicine] 2023 Jan; Vol. 87, pp. 104413. Date of Electronic Publication: 2022 Dec 21. |
| نوع المنشور: | Journal Article |
| اللغة: | English |
| بيانات الدورية: | Publisher: Elsevier B.V Country of Publication: Netherlands NLM ID: 101647039 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 2352-3964 (Electronic) Linking ISSN: 23523964 NLM ISO Abbreviation: EBioMedicine Subsets: MEDLINE |
| أسماء مطبوعة: | Original Publication: [Amsterdam] : Elsevier B.V., [2014]- |
| مواضيع طبية MeSH: | COVID-19* , Post-Acute COVID-19 Syndrome*, Humans ; Disease Progression ; SARS-CoV-2 |
| مستخلص: | Background: Stratification of patients with post-acute sequelae of SARS-CoV-2 infection (PASC, or long COVID) would allow precision clinical management strategies. However, long COVID is incompletely understood and characterised by a wide range of manifestations that are difficult to analyse computationally. Additionally, the generalisability of machine learning classification of COVID-19 clinical outcomes has rarely been tested. Methods: We present a method for computationally modelling PASC phenotype data based on electronic healthcare records (EHRs) and for assessing pairwise phenotypic similarity between patients using semantic similarity. Our approach defines a nonlinear similarity function that maps from a feature space of phenotypic abnormalities to a matrix of pairwise patient similarity that can be clustered using unsupervised machine learning. Findings: We found six clusters of PASC patients, each with distinct profiles of phenotypic abnormalities, including clusters with distinct pulmonary, neuropsychiatric, and cardiovascular abnormalities, and a cluster associated with broad, severe manifestations and increased mortality. There was significant association of cluster membership with a range of pre-existing conditions and measures of severity during acute COVID-19. We assigned new patients from other healthcare centres to clusters by maximum semantic similarity to the original patients, and showed that the clusters were generalisable across different hospital systems. The increased mortality rate originally identified in one cluster was consistently observed in patients assigned to that cluster in other hospital systems. Interpretation: Semantic phenotypic clustering provides a foundation for assigning patients to stratified subgroups for natural history or therapy studies on PASC. Funding: NIH (TR002306/OT2HL161847-01/OD011883/HG010860), U.S.D.O.E. (DE-AC02-05CH11231), Donald A. Roux Family Fund at Jackson Laboratory, Marsico Family at CU Anschutz. Competing Interests: Declaration of interests T. Bergquist received other support from Bill and Melinda Gates Foundation, H. Davis received support from Balvi Foundation and is a cofounder of Patient Led Research Collaborative. The other authors declare that they have no other competing interests. (Copyright © 2022 The Authors. Published by Elsevier B.V. All rights reserved.) |
| التعليقات: | Update of: medRxiv. 2022 Jul 20;:. (PMID: 35665012) |
| معلومات مُعتمدة: | OT2 HL161847 United States HL NHLBI NIH HHS; K01 AG070329 United States AG NIA NIH HHS; R24 OD011883 United States OD NIH HHS; U24 HG011449 United States HG NHGRI NIH HHS; U24 TR002306 United States TR NCATS NIH HHS; RM1 HG010860 United States HG NHGRI NIH HHS |
| فهرسة مساهمة: | Investigator: H Spratt; S Visweswaran; JE Flack; YJ Yoo; D Gabriel; GC Alexander; HB Mehta; F Liu; RT Miller; R Wong; EL Hill; LE Thorpe; J Divers Keywords: COVID-19; Human Phenotype Ontology; Long COVID; Machine learning; Precision medicine; Semantic similarity |
| تواريخ الأحداث: | Date Created: 20221223 Date Completed: 20230419 Latest Revision: 20240310 |
| رمز التحديث: | 20240310 |
| مُعرف محوري في PubMed: | PMC9769411 |
| DOI: | 10.1016/j.ebiom.2022.104413 |
| PMID: | 36563487 |
| قاعدة البيانات: | MEDLINE |
Full Text via ClinicalKey 
Full Text Finder | تدمد: | 2352-3964 |
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| DOI: | 10.1016/j.ebiom.2022.104413 |