A Phenomapping Tool and Clinical Score to Identify Low Diuretic Efficiency in Acute Decompensated Heart Failure.

Background: Individuals with acute decompensated heart failure (ADHF) have a varying response to diuretic therapy. Strategies for the early identification of low diuretic efficiency to inform decongestion therapies are lacking.
Objectives: The authors sought to develop and externally validate a machine learning-based phenomapping approach and integer-based diuresis score to identify patients with low diuretic efficiency.
Methods: Participants with ADHF from ROSE-AHF, CARRESS-HF, and ATHENA-HF were pooled in the derivation cohort (n = 794). Multivariable finite-mixture model-based phenomapping was performed to identify phenogroups based on diuretic efficiency (urine output over the first 72 hours per total intravenous furosemide equivalent loop diuretic dose). Phenogroups were externally validated in other pooled ADHF trials (DOSE/ESCAPE). An integer-based diuresis score (BAN-ADHF score: blood urea nitrogen, creatinine, natriuretic peptide levels, atrial fibrillation, diastolic blood pressure, hypertension and home diuretic, and heart failure hospitalization) was developed and validated based on predictors of the diuretic efficiency phenogroups to estimate the probability of low diuretic efficiency using the pooled ADHF trials described earlier. The associations of the BAN-ADHF score with markers and symptoms of congestion, length of stay, in-hospital mortality, and global well-being were assessed using adjusted regression models.
Results: Clustering identified 3 phenogroups based on diuretic efficiency: phenogroup 1 (n = 370; 47%) had lower diuretic efficiency (median: 13.1 mL/mg; Q1-Q3: 7.7-19.4 mL/mg) than phenogroups 2 (n = 290; 37%) and 3 (n = 134; 17%) (median: 17.8 mL/mg; Q1-Q3: 10.8-26.1 mL/mg and median: 35.3 mL/mg; Q1-Q3: 17.5-49.0 mL/mg, respectively) (P < 0.001). The median urine output difference in response to 80 mg intravenous twice-daily furosemide between the lowest and highest diuretic efficiency group (phenogroup 1 vs 3) was 3,520 mL/d. The BAN-ADHF score demonstrated good model performance for predicting the lowest diuretic efficiency phenogroup membership (C-index: 0.92 in DOSE/ESCAPE validation cohort) that was superior to measures of kidney function (creatinine or blood urea nitrogen), natriuretic peptide levels, or home diuretic dose (DeLong P < 0.001 for all). Net urine output in response to 80 mg intravenous twice-daily furosemide among patients with a low vs high (5 vs 20) BAN-ADHF score was 2,650 vs 660 mL per 24 hours, respectively. Participants with higher BAN-ADHF scores had significantly lower global well-being, higher natriuretic peptide levels on discharge, a longer in-hospital stay, and a higher risk of in-hospital mortality in both derivation and validation cohorts.
Conclusions: The authors developed and validated a phenomapping strategy and diuresis score for individuals with ADHF and differential response to diuretic therapy, which was associated with length of stay and mortality.
Competing Interests: Funding Support and Author Disclosures Dr Pandey has received research support from the National Institute of Health (5R01MD017529, R21HL169708) and grant funding from Applied Therapeutics and Gilead Sciences; has received honoraria outside of the present study as an advisor/consultant for Tricog Health Inc, Lilly USA, Rivus, Cytokinetics, Roche Diagnostics, Axon Therapies, Medtronic, Edward Lifesciences, Science37, Novo Nordisk, Bayer, Merck, Sarfez Pharmaceuticals, and Emmi Solutions; has received nonfinancial support from Pfizer and Merck; and is also a consultant for Palomarin Inc with stock compensation. Dr Segar has received honoraria from Merck. Dr Patel has served as a consultant to Novo Nordisk. Dr Fonarow has done consulting for Abbott, Amgen, AstraZeneca, Bayer, Cytokinetics, Janssen, Medtronic, Merck, and Novartis. Dr Mentz has received research support and honoraria from Abbott, American Regent, Amgen, AstraZeneca, Bayer, Boehringer Ingelheim/Eli Lilly, Boston Scientific, Cytokinetics, Fast BioMedical, Gilead, Innolife, Medtronic, Merck, Novartis, Relypsa, Respicardia, Roche, Sanofi, Vifor, Windtree Therapeutics, and Zoll. Dr Allen reports grant funding from American Heart Association, National Institutes of Health, and PCORI; and consulting fees from Amgen, Boston Scientific, Cytokinetics, Novartis, and WCG ACI Clinical. Dr Pandey has received grant funding from Applied Therapeutics and Gilead Sciences; has received honoraria outside of the present study as an advisor/consultant for Tricog Health Inc, Lilly USA, Rivus, Cytokinetics, Bayer, Edwards Lifesciences, Medtronic, Sarfez Pharmacuticals, Novo Nordisk, and Roche Diagnostics; has received support from Pfizer and Merck; and is a consultant for Palomarin Inc with stock compensation. Dr Khan serves as an advisory board member for Bayer. All other authors have reported that they have no relationships relevant to the contents of this paper to disclose.
(Copyright © 2024 American College of Cardiology Foundation. Published by Elsevier Inc. All rights reserved.)