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Accepted for/Published in: JMIR Public Health and Surveillance

Date Submitted: Oct 16, 2020
Date Accepted: Feb 26, 2021
Date Submitted to PubMed: Mar 5, 2021

The final, peer-reviewed published version of this preprint can be found here:

Use of a Telemedicine Risk Assessment Tool to Predict the Risk of Hospitalization of 496 Outpatients With COVID-19: Retrospective Analysis

O'Keefe JB, Tong EJ, Taylor TH Jr, O’Keefe GAD, Tong DC

Use of a Telemedicine Risk Assessment Tool to Predict the Risk of Hospitalization of 496 Outpatients With COVID-19: Retrospective Analysis

JMIR Public Health Surveill 2021;7(4):e25075

DOI: 10.2196/25075

PMID: 33667174

PMCID: 8092025

Use of a Telemedicine Risk Assessment Tool to Predict the Risk of Hospitalization of 496 Outpatients with COVID-19: Retrospective Analysis

  • James Brendan O'Keefe; 
  • Elizabeth J. Tong; 
  • Thomas H. Taylor Jr; 
  • Ghazala A. Datoo O’Keefe; 
  • David C. Tong

ABSTRACT

Background:

Risk assessment of patients with acute coronavirus disease 2019 (COVID-19) in a telemedicine context is not well described. In the setting of large numbers of patients, a risk assessment tool may guide resource allocation not only for patient care but also for maximum healthcare and public health benefit.

Objective:

Determine whether a coronavirus disease 2019 outpatient risk assessment tool accurately predicts hospitalizations.

Methods:

Design: Retrospective cohort, enrollment from March 24 to May 26, 2020 (final call range March 27 to June 19, 2020) Setting: Telemedicine program at a large medical system in Atlanta, Georgia Participants: 496 patients with COVID-19. Exclusion criteria included: (1) hospitalization prior to enrollment, (2) immediate discharge with no follow-up calls. Exposure: Acute COVID-19 illness Main Outcome and Measures: Hospitalization was the outcome. Days to hospitalization was the metric. Survival analysis using Cox regression was used to determine factors associated with hospitalization.

Results:

Results. The risk-assessment rubric assigned 496 outpatients to tiers: Tier 1, 237 (47.8%); Tier 2, 185 (37.3%); Tier 3, 74 (14.9%). Subsequent hospitalizations numbered 3 (1%), 15 (7%), and 17 (23%) and for Tiers 1-3, respectively. From a Cox regression model with age ≥ 60, gender, and self-reported obesity as covariates, the adjusted hazard ratios using Tier 1 as reference were: Tier 2 HR=3.74 (95% CI, 1.06-13.27; P=0.041); Tier 3 HR=10.87 (95% CI, 3.09-38.27; P<0.001).

Conclusions:

A telemedicine risk assessment tool prospectively applied to an outpatient population with COVID-19 identified both low-risk and high-risk patients with better performance than individual risk factors.


 Citation

Please cite as:

O'Keefe JB, Tong EJ, Taylor TH Jr, O’Keefe GAD, Tong DC

Use of a Telemedicine Risk Assessment Tool to Predict the Risk of Hospitalization of 496 Outpatients With COVID-19: Retrospective Analysis

JMIR Public Health Surveill 2021;7(4):e25075

DOI: 10.2196/25075

PMID: 33667174

PMCID: 8092025

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© The authors. All rights reserved. This is a privileged document currently under peer-review/community review (or an accepted/rejected manuscript). Authors have provided JMIR Publications with an exclusive license to publish this preprint on it's website for review and ahead-of-print citation purposes only. While the final peer-reviewed paper may be licensed under a cc-by license on publication, at this stage authors and publisher expressively prohibit redistribution of this draft paper other than for review purposes.

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