Predicting Favorable Outcomes in Hospitalized Covid-19 Patients
- Conditions
- COVIDCorona Virus InfectionAdverse Event
- Interventions
- Other: EPIC risk score display
- Registration Number
- NCT04570488
- Lead Sponsor
- NYU Langone Health
- Brief Summary
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
- Detailed Description
To assess if display of low risk of adverse event in EPIC can safely reduce length of stay and plan for discharge.
Recruitment & Eligibility
- Status
- COMPLETED
- Sex
- All
- Target Recruitment
- 1415
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
Age < 18 years not hospitalized for COVID19+.
Study & Design
- Study Type
- OBSERVATIONAL
- Study Design
- Not specified
- Arm && Interventions
Group Intervention Description Quality improvement - Display EPIC risk score display Display of risk score/ colored flag in Epic patient list column; will be viewable to all frontline workers
- Primary Outcome Measures
Name Time Method Reduction in days from first low-risk score to discharge 96 Hours Reduction in days from first low-risk score to discharge
- Secondary Outcome Measures
Name Time Method Reduction in length of stay (LOS) 96 hours Reduction in LOS for green patients that have not been in the ICU
Reduction in GTD vs. LOS 96 hours Reduction in GTD vs. LOS for all green patients discharged alive vs all patients discharged alive
No change in 30 day re-ED presentation or hospital admission rate for cohort 96 hours No change in 30 day re-ED presentation or hospital admission rate for cohort
Trial Locations
- Locations (1)
NYU Langone Health
🇺🇸New York, New York, United States