{"doi":"10.1093/infdis/jiaa663","title":"CoVA: An Acuity Score for Outpatient Screening that Predicts Coronavirus Disease 2019 Prognosis","abstract":"BACKGROUND: We sought to develop an automatable score to predict hospitalization, critical illness, or death for patients at risk for coronavirus disease 2019 (COVID-19) presenting for urgent care. METHODS: We developed the COVID-19 Acuity Score (CoVA) based on a single-center study of adult outpatients seen in respiratory illness clinics or the emergency department. Data were extracted from the Partners Enterprise Data Warehouse, and split into development (n = 9381, 7 March-2 May) and prospective (n = 2205, 3-14 May) cohorts. Outcomes were hospitalization, critical illness (intensive care unit or ventilation), or death within 7 days. Calibration was assessed using the expected-to-observed event ratio (E/O). Discrimination was assessed by area under the receiver operating curve (AUC). RESULTS: In the prospective cohort, 26.1%, 6.3%, and 0.5% of patients experienced hospitalization, critical illness, or death, respectively. CoVA showed excellent performance in prospective validation for hospitalization (expected-to-observed ratio [E/O]: 1.01; AUC: 0.76), for critical illness (E/O: 1.03; AUC: 0.79), and for death (E/O: 1.63; AUC: 0.93). Among 30 predictors, the top 5 were age, diastolic blood pressure, blood oxygen saturation, COVID-19 testing status, and respiratory rate. CONCLUSIONS: CoVA is a prospectively validated automatable score for the outpatient setting to predict adverse events related to COVID-19 infection.","journal":"The Journal of Infectious Diseases","year":2020,"id":63514,"datarank":0.5709993734655481,"base_score":3.8066624897703196,"endowment":3.8066624897703196,"self_citation_contribution":0.5709993734655481,"citation_network_contribution":0.0,"self_endowment_contribution":0.5709993734655481,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":44,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.5948,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2020-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":336247,"name":"Aayushee Jain","orcid":"0000-0002-5018-3234","position":1,"is_corresponding":false},{"id":336248,"name":"Michael J. 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Chu","orcid":null,"position":18,"is_corresponding":false},{"id":50969,"name":"Michael Dougan","orcid":"0000-0001-9266-2009","position":19,"is_corresponding":false},{"id":338117,"name":"Lawrence W. Stratton","orcid":null,"position":20,"is_corresponding":false},{"id":268233,"name":"Jonathan Rosand","orcid":"0000-0002-1014-9138","position":21,"is_corresponding":false},{"id":19604,"name":"Bruce Fischl","orcid":"0000-0002-2413-1115","position":22,"is_corresponding":false},{"id":45250,"name":"Sudeshna Das","orcid":"0000-0002-9486-6811","position":23,"is_corresponding":false},{"id":311025,"name":"Shibani S. Mukerji","orcid":"0000-0002-5677-6954","position":24,"is_corresponding":false},{"id":311026,"name":"Gregory K. Robbins","orcid":"0000-0001-7545-5817","position":25,"is_corresponding":false},{"id":280809,"name":"M. Brandon Westover","orcid":"0000-0003-4803-312X","position":26,"is_corresponding":false},{"id":305327,"name":"Haoqi Sun","orcid":"0000-0002-5041-8312","position":0,"is_corresponding":true}],"reference_count":29,"raw_metadata":{"citation_network_status":"fetched"},"created_at":"2026-07-18T21:11:24.000992Z","pmid":"33098643","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":[]}