Heart failure & cardiomyopathies
Prognostic Significance of Pulmonary Arterial Wedge Pressure Estimated by Deep Learning in Acute Heart Failure
Authors' conclusion
Our study suggests that ePAWP calculated by a deep learning approach may be useful for identifying and monitoring pulmonary congestion during hospitalization for ADHF.
Study context
- Population
- Patients admitted for acute decompensated heart failure (ADHF), January 2018–December 2019
- Participants
- 534 patients
- Comparison
- ePAWP tertiles at discharge
- First tertilen = 178reference
- Second tertilen = 170
- Third tertilen = 186
- Primary endpoint
- Composite of:
- All-cause mortality
- Rehospitalization for HF
- Follow-up
- Median 289 days
Methodological detailssingle-centre
- Single-centre
Primary result
As reported in the abstract
Composite of:
- All-cause mortality
- HF rehospitalization
Hazard ratio (95% CI)P value
ePAWP at discharge
1.10(1.02–1.19)
P = 0.010Size of ePAWP decrease
0.94(0.89–0.99)
P = 0.038- Significantly higher composite event rate in the third tertile (Kaplan-Meier)
- P = 0.006
- Primary endpoint reached
- 223 (41.7%)
Original publication
Prognostic Significance of Pulmonary Arterial Wedge Pressure Estimated by Deep Learning in Acute Heart Failure. ESC Heart Fail. Published 29 December 2022. doi:10.
Composed by AI from the published abstract and checked against it. How Briefs are made →