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Related Concept Videos

Heart Failure Drugs: Diuretics01:22

Heart Failure Drugs: Diuretics

Heart failure and kidney perfusion are interconnected in a complex way. Reduced renal perfusion and venous congestion are two significant factors that contribute to renal dysfunction in heart failure. The kidneys, primarily responsible for fluid balance in the body, are adversely affected due to compromised cardiac output and increased venous pressure. In response to reduced renal perfusion, the kidneys activate neurohumoral mechanisms to restore balance. However, these mechanisms can be...
Heart Failure V: Medical Management01:30

Heart Failure V: Medical Management

Medical Management of Acute Decompensated Heart Failure (ADHF)The primary goals of therapy for patients hospitalized with acute decompensated heart failure (ADHF) include:Relieving symptomsOptimizing volume statusSupporting oxygenation and ventilationMaintaining cardiac output (CO) and end-organ perfusionIdentifying and addressing the cause of ADHFPreventing complicationsProviding patient education on factors precipitating HF exacerbationPlanning for dischargeOngoing monitoring and assessment...
Drug Dosing in Renal Diseases: Estimation of Glomerular Filtration Rate Based on Serum Creatinine Concentration01:28

Drug Dosing in Renal Diseases: Estimation of Glomerular Filtration Rate Based on Serum Creatinine Concentration

Glomerular filtration rate (GFR) can be estimated from serum creatinine using the modification of diet in renal disease (MDRD) formula or the chronic kidney disease–epidemiology collaboration (CKD–EPI) equation. Both methods are widely used in clinical practice to assess kidney function and guide treatment decisions.The MDRD equation does not require weight or height measurements and is normalized to the body surface area of 1.73 m², considered the average adult surface area. This equation is...

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Related Experiment Videos

A Machine-Learning Model for Accurate Diuresis Prediction in Acute Heart Failure (DRC-AHF).

Gracjan Iwanek1,2, Mateusz Guzik1,2, Amadeusz Oleszczak1

  • 1Department of Cardiology, Clinical Department of Intensive Cardiac Care, Wroclaw Medical University, Faculty of Medicine, Institute of Heart Diseases, Wroclaw, Poland.

ESC Heart Failure
|June 26, 2026
PubMed
Summary
This summary is machine-generated.

A machine learning model accurately predicts diuretic response in acute heart failure (AHF) patients using eGFR, urine sodium, and creatinine. This tool improves prediction accuracy with more data and is freely available online.

Keywords:
acute heart failuredecongestiondiuresis predictiondiuretic response

Related Experiment Videos

Area of Science:

  • Cardiology
  • Nephrology
  • Artificial Intelligence in Medicine

Background:

  • Acute heart failure (AHF) management requires precise monitoring of diuretic response.
  • Predicting fluid and electrolyte balance is crucial for optimizing AHF treatment.

Purpose of the Study:

  • To develop and validate a machine learning tool for accurate quantitative prediction of diuretic response in AHF.
  • To assess the model's performance using clinical predictors like estimated glomerular filtration rate (eGFR), urine sodium (uNa), and urine creatinine (uCr).

Main Methods:

  • A prospective cohort of 296 AHF patients was used for model derivation and validation.
  • A Random Forest regression model was trained on eGFR, uNa, and uCr measured 2 hours post-furosemide.
  • External validation was performed on an independent cohort of 50 AHF patients.

Main Results:

  • The model demonstrated high accuracy in predicting 6-hour urine output in the validation cohort (r=0.90, p<0.0001).
  • It correctly classified 88% of patients into diuresis categories, outperforming the NRPE (58%).
  • Model accuracy improved with increasing training data size.

Conclusions:

  • A three-variable machine learning model effectively predicts diuretic response in AHF.
  • The tool shows potential for clinical application and adaptive improvement with more data.
  • A freely accessible online calculator is available for clinical use.