PropertyValue
?:abstract
  • Timely and effective clinical decision-making for COVID-19 requires rapid identification of risk factors for disease outcomes. Our objective was to identify characteristics available immediately upon first clinical evaluation related COVID-19 mortality. We conducted a retrospective study of 8770 laboratory-confirmed cases of SARS-CoV-2 from a network of 53 facilities in New-York City. We analysed 3 classes of variables; demographic, clinical, and comorbid factors, in a two-tiered analysis that included traditional regression strategies and machine learning. COVID-19 mortality was 12.7%. Logistic regression identified older age (OR, 1.69 [95% CI 1.66–1.92]), male sex (OR, 1.57 [95% CI 1.30–1.90]), higher BMI (OR, 1.03 [95% CI 1.102–1.05]), higher heart rate (OR, 1.01 [95% CI 1.00–1.01]), higher respiratory rate (OR, 1.05 [95% CI 1.03–1.07]), lower oxygen saturation (OR, 0.94 [95% CI 0.93–0.96]), and chronic kidney disease (OR, 1.53 [95% CI 1.20–1.95]) were associated with COVID-19 mortality. Using gradient-boosting machine learning, these factors predicted COVID-19 related mortality (AUC = 0.86) following cross-validation in a training set. Immediate, objective and culturally generalizable measures accessible upon clinical presentation are effective predictors of COVID-19 outcome. These findings may inform rapid response strategies to optimize health care delivery in parts of the world who have not yet confronted this epidemic, as well as in those forecasting a possible second outbreak.
is ?:annotates of
?:creator
?:doi
?:doi
  • 10.1038/s41598-020-78392-1
?:journal
  • Sci_Rep
?:license
  • cc-by
?:pdf_json_files
  • document_parses/pdf_json/1ff3685c3b2ee07737d76f3dc087c38ddb412e30.json
?:pmc_json_files
  • document_parses/pmc_json/PMC7726000.xml.json
?:pmcid
?:pmid
?:pmid
  • 33298991.0
?:publication_isRelatedTo_Disease
?:sha_id
?:source
  • Medline; PMC
?:title
  • Vital signs assessed in initial clinical encounters predict COVID-19 mortality in an NYC hospital system
?:type
?:year
  • 2020-12-09

Metadata

Anon_0  
expand all