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
Date Issued
02 August 2021
Access level
open access
Resource Type
journal article
Author(s)
Zucker J.
Gomez-Simmonds A.
Purpura L.J.
Shoucri S.
Lasota E.
Morley N.E.
Sovic B.W.
Castellon M.A.
Theodore D.A.
Bartram L.L.
Miko B.A.
Scherer M.L.
Meyers K.A.
Turner W.C.
Kelly M.
Pavlicova M.
Basaraba C.N.
Brodie D.
Burkart K.M.
Bathon J.
Uhlemann A.C.
Yin M.T.
Castor D.
Sobieszczyk M.E.
Columbia University Irving Medical Center
Publisher(s)
MDPI
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.
Volume
10
Issue
16
Language
English
OCDE Knowledge area
Enfermedades infecciosas
Ciencias médicas, Ciencias de la salud
Subjects
Scopus EID
2-s2.0-85112185474
Source
Journal of Clinical Medicine
ISSN of the container
20770383
Sponsor(s)
Funding: Research reported in this publication was supported by the National Institute of Allergy and Infectious Diseases of the National Institutes of Health under award number 5UM1AI069470-14 and supplement to the award (M.E.S., J.Z.), K23AI150378 (J.Z.) and L30AI133789 (J.Z.). The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.
Sources of information:
Directorio de Producción Científica
Scopus