NCT03734484

Gram Type Infection-Specific Sepsis Identification Using Machine Learning

• Status: Withdrawn
• Phase: Phase 2
• Study type: Interventional
• Enrollment: 0 (actual)
• Sponsor: Dascena · Industry
• Sex: All
• Age: 18 Years
• Healthy volunteers: Accepted

Summary

The focus of this study will be to conduct a prospective, randomized controlled trial (RCT) at Cape Regional Medical Center (CRMC), Oroville Hospital (OH), and UCSF Medical Center (UCSF) in which a Gram type infection-specific algorithm will be applied to EHR data for the detection of severe sepsis. For patients determined to have a high risk of severe sepsis, the algorithm will generate automated voice, telephone notification to nursing staff at CRMC, OH, and UCSF. The algorithm's performance will be measured by analysis of the primary endpoint, time to antibiotic administration. The secondary endpoint will be reduction in the administration of unnecessary antibiotics, which includes reductions in secondary antibiotics and reductions in total time on antibiotics.

Conditions

• Sepsis
• Severe Sepsis
• Septic Shock

Interventions

Type	Name	Description
DIAGNOSTIC_TEST	InSight	The InSight algorithm which draws information from a patient's electronic health record (EHR) to predict the onset of severe sepsis, and in this study will be customized to differentiate between various Gram-type infections.

Timeline

• Start date: 2022-05-01
• Primary completion: 2022-11-30
• Completion: 2023-03-01
• First posted: 2018-11-08
• Last updated: 2021-09-23