Key result
Trial protocol evaluates feasibility of CURATE.AI-assisted dose titration for personalized anti-hypertensive therapy.
Why the study?
Artificial intelligence-driven platforms like CURATE.AI have potential to personalize anti-hypertensive dose titration, but feasibility of such interventions in clinical trials is unknown.
Does CURATE.AI-assisted dose titration improve feasibility and blood pressure control in participants with primary hypertension?
RCT (n=45)
Open-label
1:1:1 ratio
Does CURATE.AI-assisted dose titration improve feasibility and blood pressure control in participants with primary hypertension?
This study protocol outlines a pilot RCT to evaluate the feasibility and preliminary efficacy of an AI-driven platform for personalized anti-hypertensive dose titration.
Answers are grounded in this paper and cite their sources.
CURATE.AI titration feasibility tested in RCT; leaves open whether AI personalization improves hypertension control or safety in larger trials.
Aims: Artificial intelligence-driven small data platforms such as CURATE.AI hold potential for personalized hypertension care by assisting physicians in identifying personalized anti-hypertensive doses for titration. This trial aims to assess the feasibility of a larger randomized controlled trial (RCT), evaluating the efficacy of CURATE.AI-assisted dose titration intervention. We will also collect preliminary efficacy and safety data and explore stakeholder feedback in the early design process. Methods and results: In this open-label, randomized, pilot feasibility trial, we aim to recruit 45 participants with primary hypertension. Participants will be randomized in 1:1:1 ratio into control (no intervention), home blood pressure monitoring (active control; HBPM), or CURATE.AI arms (intervention; HBPM and CURATE.AI-assisted dose titration). The home treatments include 1 month of two-drug anti-hypertensive regimens. Primary endpoints assess the logistical (e.g. dose adherence) and scientific (e.g. percentage of participants for which CURATE.AI profiles can be generated) feasibility, and define the progression criteria for the RCT in a 'traffic light system'. Secondary endpoints assess preliminary efficacy [e.g. mean change in office blood pressures (BPs)] and safety (e.g. hospitalization events) associated with each treatment protocol. Participants with both baseline and post-treatment BP measurements will form the intent-to-treat analysis. Following their involvement with the CURATE.AI intervention, feedback from CURATE.AI participants and healthcare providers will be collected via exit survey and interviews. Conclusion: Findings from this study will inform about potential refinements of the current treatment protocols before proceeding with a larger RCT, or potential expansion to collect additional information. Positive results may suggest the potential efficacy of CURATE.AI to improve BP control. Trial registration number: NCT05376683.
No takes yet. Share an insight, caveat, or question.
Truong et al. (2023) conducted an RCT in Primary hypertension (n=45). CURATE.AI-assisted dose titration vs. Control (no intervention) or home blood pressure monitoring (active control) was evaluated on Logistical (e.g. dose adherence) and scientific (e.g. percentage of participants for which CURATE.AI profiles can be generated) feasibility. This study is a protocol for a pilot feasibility trial evaluating CURATE.AI-assisted dose titration for personalized anti-hypertensive therapy; no results are reported.
Synapse has enriched 10 related papers. Consider them for comparative context: