{"doi":"10.3390/jcm10163523","title":"Supervised Machine Learning Approach to Identify Early Predictors of Poor Outcome in Patients with COVID-19 Presenting to a Large Quaternary Care Hospital in New York City","abstract":"BACKGROUND: The progression of clinical manifestations in patients with coronavirus disease 2019 (COVID-19) highlights the need to account for symptom duration at the time of hospital presentation in decision-making algorithms. METHODS: We performed a nested case-control analysis of 4103 adult patients with COVID-19 and at least 28 days of follow-up who presented to a New York City medical center. Multivariable logistic regression and classification and regression tree (CART) analysis were used to identify predictors of poor outcome. RESULTS: Patients presenting to the hospital earlier in their disease course were older, had more comorbidities, and a greater proportion decompensated (<4 days, 41%; 4-8 days, 31%; >8 days, 26%). The first recorded oxygen delivery method was the most important predictor of decompensation overall in CART analysis. In patients with symptoms for <4, 4-8, and >8 days, requiring at least non-rebreather, age ≥ 63 years, and neutrophil/lymphocyte ratio ≥ 5.1; requiring at least non-rebreather, IL-6 ≥ 24.7 pg/mL, and D-dimer ≥ 2.4 µg/mL; and IL-6 ≥ 64.3 pg/mL, requiring non-rebreather, and CRP ≥ 152.5 mg/mL in predictive models were independently associated with poor outcome, respectively. CONCLUSION: Symptom duration in tandem with initial clinical and laboratory markers can be used to identify patients with COVID-19 at increased risk for poor outcomes.","journal":"Journal of Clinical Medicine","year":2021,"id":205017,"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":0.8629,"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":281544,"name":"Angela Gomez‐Simmonds","orcid":"0000-0001-5078-7137","position":1,"is_corresponding":false},{"id":656408,"name":"Lawrence J. 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Miko","orcid":"0000-0002-6115-3014","position":10,"is_corresponding":false},{"id":433103,"name":"Matthew Scherer","orcid":null,"position":11,"is_corresponding":false},{"id":443750,"name":"Kathrine Meyers","orcid":"0000-0002-8573-1958","position":12,"is_corresponding":false},{"id":239214,"name":"William C. Turner","orcid":null,"position":13,"is_corresponding":false},{"id":788412,"name":"Maureen Kelly","orcid":null,"position":14,"is_corresponding":false},{"id":366308,"name":"Martina Pavlicová","orcid":"0000-0003-4400-4102","position":15,"is_corresponding":false},{"id":787895,"name":"Cale Basaraba","orcid":"0000-0002-9240-6344","position":16,"is_corresponding":false},{"id":104625,"name":"Matthew R. Baldwin","orcid":"0000-0003-4670-3433","position":17,"is_corresponding":false},{"id":104637,"name":"Daniel Brodie","orcid":"0000-0002-0813-3145","position":18,"is_corresponding":false},{"id":363976,"name":"Kristin M. 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