Simple Urine Composition-based Personalized Algorithm for Effective Congestion Relief in Decompensated Heart Failure (SAFER-DHF)

Wroclaw Medical University

Status: Enrolling

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

Treatments: Diagnostic Test: Algorithhm-based decongestion; Diagnostic Test: Standard of Care (SOC)

Study type: Interventional

Funder types: Other

Identifiers

NCT07099885

226/2025

Details and patient eligibility

About

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.

Enrollment: 90 estimated patients

Sex: All

Ages: 18+ years old

Volunteers: No Healthy Volunteers

Inclusion criteria

• Adult patients over 18 years of age who provide informed consent
• Ability to enroll in the study within the first 24 hours of hospitalization
• Primary reason for hospitalization is acute heart failure with signs of congestion (at least moderate lower extremity edema)
• NT-proBNP > 1500 pg/ml
• Anticipated need for diuretic therapy for at least 48 hours from the time of study enrollment

Exclusion criteria

• End-stage kidney disease requiring renal replacement therapy
• Hemodynamic instability requiring inotropic support
• Active infection requiring intravenous antibiotic therapy

Trial design

Primary purpose: Treatment

Allocation: Randomized

Interventional model: Parallel Assignment

Masking: Single Blind

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90 participants in 2 patient groups

Algorithm based decongestion

Experimental group

Description:

The patient will receive first dose of intravenous loop diuretic determined per the attending physician. Two hours after diuretic administration, a urine sample will be collected to measure sodium and creatinine concentrations. Using these values, the 6-hour urine output will be estimated with the aid of the machine learning, urine output prediction tool. This estimated diuresis will guide the diuretic therapy plan for the first 24 hours using the adaptative furosemide dosing. On the second day, the procedure will be repeated.

Treatment:

Diagnostic Test: Algorithhm-based decongestion

Standard of care

Active Comparator group

Treatment:

Diagnostic Test: Standard of Care (SOC)