Improving Risk Stratification of Emergency Department Patients With Acute Heart Failure: Building and Testing a Machine-learning Platform for Personalized, Accurate, Real-time Risk Prediction
Overview
- Phase
- Not Applicable
- Intervention
- Not specified
- Conditions
- Heart Failure
- Sponsor
- Kaiser Permanente
- Enrollment
- 17459
- Locations
- 21
- Primary Endpoint
- Number of patients with serious adverse events
- Status
- Not yet recruiting
- Last Updated
- 2 years ago
Overview
Brief Summary
The primary goal is to build and test a previously developed and validated risk model and clinical decision support tool embedded within the electronic health record to improve risk stratification of emergency department (ED) patients with acute heart failure (AHF).
Detailed Description
The study team will build an electronic health record-embedded clinical decision support tool using a recently developed risk prediction model that curates patient-specific data in real-time, accurately estimates short-term patient risk, and presents tailored clinical recommendations. This will be a regional implementation study in which the tool is turned on at 21 emergency departments (ED) across Kaiser Permanente Northern California (KPNC). The study team will validate risk predictions and study key clinical outcomes as part of this trial.
Investigators
Eligibility Criteria
Inclusion Criteria
- •Adult (≥18y) KPNC members treated in the ED for presumed acute heart failure
Exclusion Criteria
- •Children (\<18y).
- •Patients who left against medical advice or eloped prior to ED physician evaluation.
Outcomes
Primary Outcomes
Number of patients with serious adverse events
Time Frame: 30 days from index ED visit
Serious adverse events include mortality, cardiopulmonary resuscitation (CPR), intubation/ventilation, new end-stage renal disease (ESRD), balloon pump placement, or percutaneous coronary intervention (PCI)/coronary artery bypass grafting (CABG)
Number of patients with all-cause mortality
Time Frame: 30 days from index ED visit