NCT07099885

Simple Urine Composition-based Personalized Algorithm for Effective Congestion Relief in Decompensated Heart Failure

• Status: Recruiting
• Phase: N/A
• Study type: Interventional
• Enrollment: 90 (estimated)
• Sponsor: Wroclaw Medical University · Academic / Other
• Sex: All
• Age: 18 Years
• Healthy volunteers: Not accepted

Summary

The aim of this study is to evaluate the effectiveness of loop diuretic adaptative algorithm that is based on machine learning, urine output prediction tool, in decongestion of acute heart failure patients. A total of 90 patients will be enrolled in the study. Of these, 45 will be assigned to the algorithm-based intervention group, while the remaining 45 will serve as the control group. In the control group, all decisions regarding diuretic therapy will be made solely by the attending physician, without the use of the algorithm. Patients will receive intravenous furosemide, with the initial dose determined by the attending physician. Two hours after administration of the diuretic, a spot urine sample will be collected to measure sodium and creatinine concentrations. Based on these values, the 6-hour urine output will be estimated using the machine learning, urine output prediction tool (http://diuresis.umw.edu.pl). This estimate will guide the diuretic therapy plan for the first 24 hours of hospitalization. On the second day, the procedure will be repeated using the same methodology.

Conditions

• Acute Heart Failure (AHF)
• Congestion, Venous

Interventions

Type	Name	Description
DIAGNOSTIC_TEST	Standard of Care (SOC)	Diuretic treatment per attending physician.
DIAGNOSTIC_TEST	Algorithhm-based decongestion	Patient will receive standarized furosemide dosing based on the result of the algorithm-estimated 6h urine output (profiles of diuretic response).

Timeline

• Start date: 2025-08-01
• Primary completion: 2026-12-31
• Completion: 2027-01-31
• First posted: 2025-08-01
• Last updated: 2025-08-12

Locations

2 sites across 1 country: Poland