Predicting Favorable Outcomes in Hospitalized Covid-19 Patients

NYU Langone Health

Status: Completed

Conditions: Adverse Event; Corona Virus Infection; COVID

Treatments: Other: EPIC risk score display

Study type: Observational

Funder types: Other

Identifiers

NCT04570488

PAU COVID19

Details and patient eligibility

About

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

Full description

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

Enrollment: 1,415 patients

Sex: All

Ages: 18 to 100 years old

Volunteers: No Healthy Volunteers

Inclusion and exclusion criteria

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+.

Trial design

1,415 participants in 2 patient groups

Quality improvement - Display

Description:

Display of risk score/ colored flag in Epic patient list column; will be viewable to all frontline workers

Treatment:

Other: EPIC risk score display

No Display

Description:

No display ("hidden") of risk score/ colored flag in Epic patient list column; not viewable to all frontline workers