Machine Learning Prediction of Possible Central Line Associated Blood Stream Infections and Rate of Reduction

Conditions

• Central Line Associated Blood Stream Infections (CLABSI)

Eligibility Criteria

Sex

ALL

Age

18 Years - N/A

Healthy Volunteers

Not accepted

Interventions

• Infection Preventionist Led Best Practices Reminders — BEHAVIORAL
  Infection preventionists at each study hospital review a dashboard on a daily basis that contains predictions for the infection preventionist's hospital. If a patient is predicted to have a possible CLABSI by the ML model, the infection preventionist reviews the case and recommends next steps to the care team based on Providence's CLABSI prevention best-practices bundle, which include reviewing the line for necessity and recommending alternate IV access when appropriate. If line-removal isn't possible, the infection preventionist collaborates with the direct care team to ensure that the line maintenance best practices are observed, including maintaining a clean, dry and intact dressing and using daily chlorhexidine baths.

Study Details

Prospective, multi-center, cluster-randomized trial of a hospital Infection Preventionist (IP)-led quality improvement study to provide clinical teams with just-in-time clinical education and reinforcement of existing best practices recommendations based on the output of a possible Central Line Associated Blood Stream Infection (CLABSI) Machine Learning (ML) prediction model. The objective is to determine whether providing this model to Infection Preventionists will decrease the CLABSI rates versus routine clinical practice.

Key Dates

Start date

Jul 1, 2025

Status verified

Aug 2025

Primary completion

Nov 30, 2025

Completion

Dec 31, 2027

Study Design

Enrollment

17,800 participants (estimated)

Allocation

RANDOMIZED

Intervention model

PARALLEL

Primary purpose

HEALTH_SERVICES_RESEARCH

Arms

• Experimental: Hospitals receiving "EARLY" access to the prediction model.
  During the study period, the "EARLY" hospitals receive access to the Possible CLABSI ML model.
• No Intervention: Hospitals receiving "LATE" access to the prediction model.
  During the comparison period, the "LATE" hospitals do not receive access to the Possible CLABSI ML model.

Primary Outcome Measure

CLABSI Rate [ Time Frame: Day 1 of Hospitalization thru Discharge ]

Central Contacts

• Chris Dale, MD, MPH
  425-747-5822
  [email protected]
• Evan Sylvester, MPH
  425-681-6961
  [email protected]

Locations (19)

Find similar trials in Anchorage, AK

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