Predicting Favorable Outcomes in Hospitalized Covid-19 Patients

NYU Langone Health1,415 enrolled

Overview

Testing use of predictive analytics to predict which COVID-19+ patients are at low risk for an adverse event (ICU transfer, intubation, mortality, hospice discharge, re-presentation to the ED, oxygen requirements exceeding nasal cannula at 6L/Min) in the next 96 hours

To assess if display of low risk of adverse event in EPIC can safely reduce length of stay and plan for discharge.

Study Type

OBSERVATIONAL

Enrollment

1,415

Conditions

COVID Corona Virus Infection Adverse Event

Interventions

EPIC risk score displayOTHER

Display of risk score/ colored flag in Epic patient list column

Eligibility

Sex: ALLMin age: 18 YearsMax age: 100 Years

Medical Language ↔ Plain English

Inclusion Criteria: Adult hospitalized COVID19+ patients predicted to have no adverse event at 96 events with a threshold at 90% PPV, with at least one low risk during their admission who are discharged alive and have not been in the ICU Exclusion Criteria: Age \< 18 years not hospitalized for COVID19+.

Locations (1)

NYU Langone Health

New York, New York, United States

Outcomes

Primary Outcomes

Reduction in days from first low-risk score to discharge

Time frame: 96 Hours

Secondary Outcomes

Reduction in length of stay (LOS)

Reduction in LOS for green patients that have not been in the ICU

Time frame: 96 hours

Reduction in GTD vs. LOS

Reduction in GTD vs. LOS for all green patients discharged alive vs all patients discharged alive

Time frame: 96 hours

No change in 30 day re-ED presentation or hospital admission rate for cohort

Time frame: 96 hours

Data from ClinicalTrials.gov

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