{"doi":"10.1093/ofid/ofab275","title":"Visceral Adiposity and Severe COVID-19 Disease: Application of an Artificial Intelligence Algorithm to Improve Clinical Risk Prediction","abstract":"Abstract Background Obesity has been linked to severe clinical outcomes among people who are hospitalized with coronavirus disease 2019 (COVID-19). We tested the hypothesis that visceral adipose tissue (VAT) is associated with severe outcomes in patients hospitalized with COVID-19, independent of body mass index (BMI). Methods We analyzed data from the Massachusetts General Hospital COVID-19 Data Registry, which included patients admitted with polymerase chain reaction–confirmed severe acute respiratory syndrome coronavirus 2 infection from March 11 to May 4, 2020. We used a validated, fully automated artificial intelligence (AI) algorithm to quantify VAT from computed tomography (CT) scans during or before the hospital admission. VAT quantification took an average of 2 ± 0.5 seconds per patient. We dichotomized VAT as high and low at a threshold of ≥100 cm2 and used Kaplan-Meier curves and Cox proportional hazards regression to assess the relationship between VAT and death or intubation over 28 days, adjusting for age, sex, race, BMI, and diabetes status. Results A total of 378 participants had CT imaging. Kaplan-Meier curves showed that participants with high VAT had a greater risk of the outcome compared with those with low VAT (P < .005), especially in those with BMI <30 kg/m2 (P < .005). In multivariable models, the adjusted hazard ratio (aHR) for high vs low VAT was unchanged (aHR, 1.97; 95% CI, 1.24–3.09), whereas BMI was no longer significant (aHR for obese vs normal BMI, 1.14; 95% CI, 0.71–1.82). Conclusions High VAT is associated with a greater risk of severe disease or death in COVID-19 and can offer more precise information to risk-stratify individuals beyond BMI. AI offers a promising approach to routinely ascertain VAT and improve clinical risk prediction in COVID-19.","journal":"Open Forum Infectious Diseases","year":2021,"id":179512,"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":18,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.92,"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":727075,"name":"Tzu-Ming Harry Hsu","orcid":"0000-0001-7198-7832","position":1,"is_corresponding":false},{"id":251940,"name":"Jacqueline A. Seiglie","orcid":"0000-0001-9278-4516","position":2,"is_corresponding":false},{"id":69650,"name":"Mark J. Siedner","orcid":"0000-0003-3506-842X","position":3,"is_corresponding":false},{"id":311870,"name":"Janet Lo","orcid":"0000-0002-5678-6140","position":4,"is_corresponding":false},{"id":263596,"name":"Virginia A. Triant","orcid":"0000-0001-5288-6067","position":5,"is_corresponding":false},{"id":263593,"name":"John Hsu","orcid":"0000-0001-8244-231X","position":6,"is_corresponding":false},{"id":263591,"name":"Andrea S. Foulkes","orcid":"0000-0002-9520-0501","position":7,"is_corresponding":false},{"id":263592,"name":"Ingrid V. Bassett","orcid":"0000-0001-6920-6080","position":8,"is_corresponding":false},{"id":727076,"name":"Ramin Khorasani","orcid":"0000-0002-2681-3574","position":9,"is_corresponding":false},{"id":251965,"name":"Deborah J. Wexler","orcid":"0000-0001-6979-402X","position":10,"is_corresponding":false},{"id":4665,"name":"Peter Szolovits","orcid":"0000-0001-8411-6403","position":11,"is_corresponding":false},{"id":1092,"name":"James B. Meigs","orcid":"0000-0002-2439-2657","position":12,"is_corresponding":false},{"id":251966,"name":"Jennifer Manne‐Goehler","orcid":"0000-0001-9295-0035","position":13,"is_corresponding":false},{"id":727074,"name":"Alexander Goehler","orcid":"0000-0002-6237-2032","position":0,"is_corresponding":true}],"reference_count":21,"raw_metadata":null,"created_at":"2026-07-18T23:47:48.974996Z","pmid":"34258315","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":[]}