NCT03752489

Unsupervised Machine Learning for Clustering of Septic Patients to Determine Optimal Treatment

• Status: Unknown
• Phase: Phase 2
• Study type: Interventional
• Enrollment: 51,645 (estimated)
• 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 fluid treatment-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, reductions in in-hospital mortality.

Conditions

• Sepsis
• Severe Sepsis
• Septic Shock

Interventions

Type	Name	Description
DIAGNOSTIC_TEST	Treatment-specific 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 clusters of patients who respond similarly to fluids treatment according to the nature of their disease progression.
DIAGNOSTIC_TEST	InSight	The non-customized InSight algorithm which draws information from a patient's electronic health record (EHR) to predict the onset of severe sepsis.

Timeline

• Start date: 2022-04-01
• Primary completion: 2024-03-31
• Completion: 2024-03-31
• First posted: 2018-11-26
• Last updated: 2021-09-23