A second-generation artificial intelligence-based therapeutic regimen improves diuretic resistance in heart failure: Results of a feasibility open-labeled clinical trial

Ram Gelman; Noa Hurvitz; Rima Nesserat; Yotam Kolben; Dean Nachman; Khurram Jamil; Samuel Agus; Rabea Asleh; Offer Amir; Marc Berg; Yaron Ilan

doi:10.1016/j.biopha.2023.114334

A second-generation artificial intelligence-based therapeutic regimen improves diuretic resistance in heart failure: Results of a feasibility open-labeled clinical trial

Biomed Pharmacother. 2023 May:161:114334. doi: 10.1016/j.biopha.2023.114334. Epub 2023 Mar 9.

Authors

Affiliations

¹ Departments of Medicine, Hadassah Medical Center, and Faculty of Medicine, Hebrew University, Jerusalem, Israel.
² Departments of Cardiology, Hadassah Medical Center, and Faculty of Medicine, Hebrew University, Jerusalem, Israel.
³ Oberon Sciences and Area 9 Innovation, Stanford University, Palo Alto, CA, USA.
⁴ Departments of Medicine, Hadassah Medical Center, and Faculty of Medicine, Hebrew University, Jerusalem, Israel. Electronic address: ilan@hadassah.org.il.

PMID: 36905809
DOI: 10.1016/j.biopha.2023.114334

Abstract

Introduction: Diuretics are a mainstay therapy for congestive heart failure (CHF); however, over one-third of patients develop diuretic resistance. Second-generation artificial intelligence (AI) systems introduce variability into treatment regimens to overcome the compensatory mechanisms underlying the loss of effectiveness of diuretics. This open-labeled, proof-of-concept clinical trial sought to investigate the ability to improve diuretic resistance by implementing algorithm-controlled therapeutic regimens.

Methods: Ten CHF patients with diuretic resistance were enrolled in an open-labeled trial where the Altus Care™ app managed diuretics' dosage and administration times. The app provides a personalized therapeutic regimen creating variability in dosages and administration times within pre-defined ranges. Response to therapy was measured by the Kansas City Cardiomyopathy Questionnaire (KCCQ) score, 6-minute walk test (SMW), N-terminal pro-brain natriuretic peptide (NT-proBNP) levels, and renal function.

Results: The second-generation, AI-based, personalized regimen alleviated diuretic resistance. All evaluable patients demonstrated clinical improvement within ten weeks of intervention. A dose reduction (based on a three-week average before and last three weeks of intervention) was achieved in 7/10 patients (70 %, p = 0.042). The KCCQ score improved in 9/10 (90 %, p = 0.002), the SMW improved in 9/9 (100 %, p = 0.006), NT-proBNP was decreased in 7/10 (70 %, p = 0.02), and serum creatinine was decreased in 6/10 (60 %, p = 0.05). The intervention was associated with reduced number of emergency room visits and the number of CHF-associated hospitalizations.

Summary: The results support that the randomization of diuretic regimens guided by a second-generation personalized AI algorithm improves the response to diuretic therapy. Prospective controlled studies are needed to confirm these findings.

Keywords: Artificial intelligence; Congestive heart failure; Diuretic resistance.

Publication types

Clinical Trial

MeSH terms

Artificial Intelligence
Diuretics* / therapeutic use
Feasibility Studies
Heart Failure*
Humans
Peptide Fragments / therapeutic use
Prospective Studies

Substances

Diuretics
Peptide Fragments