{"doi":"10.1093/ofid/ofac423","title":"Mid-regional Proadrenomedullin Biomarker Predicts Coronavirus Disease 2019 Clinical Outcomes: A US-Based Cohort Study","abstract":"Background: Mid-regional proadrenomedullin (MR-proADM) is a biomarker released following endothelial damage. Studies have shown a correlation in predicting coronavirus disease 2019 (COVID-19) outcomes with MR-proADM levels. Our study aimed to investigate baseline MR-proADM as a predictor of a wider range of clinical outcomes of varying severity in patients admitted with COVID-19, and to compare to other biomarkers. Methods: Data from the Boston Area COVID-19 Consortium (BACC) Bay Tocilizumab Trial was used in this study. Patients with biomarker determinations, and not admitted to the intensive care unit (ICU) on admission, were included. MR-proADM cutoff of 0.87 nmol/L was assessed in predicting clinical outcomes. Results: < .001). The sensitivity, specificity, negative predictive value, and positive predictive value of MR-proADM cutoff of 0.87 nmol/L in predicting mechanical ventilation or death were 75%, 65%, 95%, and 21%, respectively, with an area under the receiver operating characteristic curve of 0.76. On multivariable logistic regression analysis, MR-proADM >0.87 nmol/L was independently associated with mechanical ventilation or death, ICU admission, prolonged hospitalization beyond day 4, and day 4 COVID-19 ordinal scale equal to or worse than day 1. Conclusions: MR-proADM functions as a valuable biomarker for the early risk stratification and detection of severe disease progression of patients with COVID-19. In the prediction of death, MR-proADM performed better compared to many other commonly used biomarkers.","journal":"Open Forum Infectious Diseases","year":2022,"id":287601,"datarank":0.0,"base_score":0.0,"endowment":0.0,"self_citation_contribution":0.0,"citation_network_contribution":0.0,"self_endowment_contribution":0.0,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":3,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9524,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2022-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":967637,"name":"Vahe S Panossian","orcid":"0000-0001-8085-1021","position":1,"is_corresponding":false},{"id":967638,"name":"Christine Atallah","orcid":"0000-0001-9393-4280","position":2,"is_corresponding":false},{"id":968042,"name":"Andrej Schwabe","orcid":null,"position":3,"is_corresponding":false},{"id":968043,"name":"Sascha Johannes","orcid":null,"position":4,"is_corresponding":false},{"id":113413,"name":"J. Wiemer","orcid":null,"position":5,"is_corresponding":false},{"id":89098,"name":"Michael K. Mansour","orcid":"0000-0001-8892-8695","position":6,"is_corresponding":false},{"id":426962,"name":"Natalie J. Atallah","orcid":"0000-0001-5086-8471","position":0,"is_corresponding":true}],"reference_count":50,"raw_metadata":null,"created_at":"2026-07-19T00:30:01.051685Z","pmid":"36072696","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":[]}