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

Recruiting now · Not applicable

Conditions studied: Acute Heart Failure (AHF), Congestion, Venous

In brief

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.

Key facts

Study ID
NCT07099885
Run by
Wroclaw Medical University
People needed
90
Starts
2025-08-01
Expected to finish
2027-01-31
Last updated by the study team
2025-08-12

Who can join

Age: 18 and older. Sex: any. Healthy volunteers: not accepted.

You may qualify if…

You may not qualify if…

Where it is running

Full record on ClinicalTrials.gov

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