tetano
Editor, Senior Moderator
JMIR Public Health Surveill
. 2021 Feb 26.
doi: 10.2196/25075. Online ahead of print.
Use of a Telemedicine Risk Assessment Tool to Predict the Risk of Hospitalization of 496 Outpatients with COVID-19: Retrospective Analysis
James B O'Keefe[SUP] 1 [/SUP], Elizabeth J Tong[SUP] 2 [/SUP], Thomas H Taylor Jr[SUP] 3 [/SUP], Ghazala A Datoo O'Keefe[SUP] 4 [/SUP], David C Tong[SUP] 5 [/SUP]
Affiliations
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 COVID-19 telemedicine risk assessment tool accurately predicts hospitalizations.
Methods: We conducted a retrospective study of a COVID-19 telemedicine home monitoring program serving healthcare workers and the community in Atlanta, Georgia, enrollment from March 24 to May 26, 2020 (final call range March 27 to June 19, 2020). All patients were assessed by medical providers using an institutional COVID-19 risk assessment tool designating patients as "Tier 1" (low risk for hospitalization), "Tier 2" (intermediate risk), or "Tier 3" (high risk). Patients were followed with regular telephone calls to an endpoint of improvement or hospitalization. Using survival analysis by Cox regression with days to hospitalization as metric, we analyzed the performance of the risk tiers and explored individual patient factors associated with risk of hospitalization.
Results: Providers using 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%) for Tier 1, 15 (7%) for Tier 2, and 17 (23%) for Tier 3. From a Cox regression model with age ≥ 60, gender, and reported obesity as covariates, the adjusted hazard ratios for hospitalization using Tier 1 as reference were: Tier 2 HR=3.74 (95% CI, 1.06-13.27; p=.041); Tier 3 HR=10.87 (95% CI, 3.09-38.27; p<.001).
Conclusions: A telemedicine risk assessment tool prospectively applied to an outpatient population with COVID-19 identified populations with low, intermediate, and high risk of hospitalization.
. 2021 Feb 26.
doi: 10.2196/25075. Online ahead of print.
Use of a Telemedicine Risk Assessment Tool to Predict the Risk of Hospitalization of 496 Outpatients with COVID-19: Retrospective Analysis
James B O'Keefe[SUP] 1 [/SUP], Elizabeth J Tong[SUP] 2 [/SUP], Thomas H Taylor Jr[SUP] 3 [/SUP], Ghazala A Datoo O'Keefe[SUP] 4 [/SUP], David C Tong[SUP] 5 [/SUP]
Affiliations
- PMID: 33667174
- DOI: 10.2196/25075
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 COVID-19 telemedicine risk assessment tool accurately predicts hospitalizations.
Methods: We conducted a retrospective study of a COVID-19 telemedicine home monitoring program serving healthcare workers and the community in Atlanta, Georgia, enrollment from March 24 to May 26, 2020 (final call range March 27 to June 19, 2020). All patients were assessed by medical providers using an institutional COVID-19 risk assessment tool designating patients as "Tier 1" (low risk for hospitalization), "Tier 2" (intermediate risk), or "Tier 3" (high risk). Patients were followed with regular telephone calls to an endpoint of improvement or hospitalization. Using survival analysis by Cox regression with days to hospitalization as metric, we analyzed the performance of the risk tiers and explored individual patient factors associated with risk of hospitalization.
Results: Providers using 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%) for Tier 1, 15 (7%) for Tier 2, and 17 (23%) for Tier 3. From a Cox regression model with age ≥ 60, gender, and reported obesity as covariates, the adjusted hazard ratios for hospitalization using Tier 1 as reference were: Tier 2 HR=3.74 (95% CI, 1.06-13.27; p=.041); Tier 3 HR=10.87 (95% CI, 3.09-38.27; p<.001).
Conclusions: A telemedicine risk assessment tool prospectively applied to an outpatient population with COVID-19 identified populations with low, intermediate, and high risk of hospitalization.