{"doi":"10.1093/infdis/jiab568","title":"Gene Expression Risk Scores for COVID-19 Illness Severity","abstract":"BACKGROUND: The correlates of coronavirus disease 2019 (COVID-19) illness severity following infection with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) are incompletely understood. METHODS: We assessed peripheral blood gene expression in 53 adults with confirmed SARS-CoV-2 infection clinically adjudicated as having mild, moderate, or severe disease. Supervised principal components analysis was used to build a weighted gene expression risk score (WGERS) to discriminate between severe and nonsevere COVID-19. RESULTS: Gene expression patterns in participants with mild and moderate illness were similar, but significantly different from severe illness. When comparing severe versus nonsevere illness, we identified >4000 genes differentially expressed (false discovery rate < 0.05). Biological pathways increased in severe COVID-19 were associated with platelet activation and coagulation, and those significantly decreased with T-cell signaling and differentiation. A WGERS based on 18 genes distinguished severe illness in our training cohort (cross-validated receiver operating characteristic-area under the curve [ROC-AUC] = 0.98), and need for intensive care in an independent cohort (ROC-AUC = 0.85). Dichotomizing the WGERS yielded 100% sensitivity and 85% specificity for classifying severe illness in our training cohort, and 84% sensitivity and 74% specificity for defining the need for intensive care in the validation cohort. CONCLUSIONS: These data suggest that gene expression classifiers may provide clinical utility as predictors of COVID-19 illness severity.","journal":"The Journal of Infectious Diseases","year":2021,"id":183681,"datarank":0.518936375532685,"base_score":2.639057329615259,"endowment":2.639057329615259,"self_citation_contribution":0.3958585994422889,"citation_network_contribution":0.12307777609039616,"self_endowment_contribution":0.3958585994422889,"citer_contribution":0.12307777609039616,"corpus_percentile":null,"corpus_rank":null,"citation_count":13,"citer_count":10,"citers_with_citation_signal":6,"citers_with_endowment":6,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9432,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2021-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":565671,"name":"Andrea Baran","orcid":"0000-0002-7975-8919","position":1,"is_corresponding":false},{"id":561598,"name":"Soumyaroop Bhattacharya","orcid":"0000-0003-1140-7845","position":2,"is_corresponding":false},{"id":104968,"name":"Angela R Branche","orcid":"0000-0002-7742-5352","position":3,"is_corresponding":false},{"id":326717,"name":"Daniel P. Croft","orcid":"0000-0002-1990-5542","position":4,"is_corresponding":false},{"id":436585,"name":"Anthony Corbett","orcid":"0000-0001-9545-0853","position":5,"is_corresponding":false},{"id":38189,"name":"Edward E. Walsh","orcid":"0000-0002-8792-8877","position":6,"is_corresponding":false},{"id":38191,"name":"Ann R. Falsey","orcid":"0000-0002-7141-9701","position":7,"is_corresponding":false},{"id":454635,"name":"Thomas J. Mariani","orcid":"0000-0001-5944-1298","position":8,"is_corresponding":false},{"id":565672,"name":"Derick R. Peterson","orcid":"0000-0001-8603-660X","position":0,"is_corresponding":true}],"reference_count":33,"raw_metadata":{"citation_network_status":"fetched"},"created_at":"2026-07-18T23:48:22.008011Z","pmid":"34850892","pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}