# PROTECT: optima4BP 2.0: prediction of Optimal Treatment and Route to achieve and maintain BP Target

> **NIH NIH R44** · OPTIMA INTEGRATED HEALTH, INC. · 2021 · $715,983

## Abstract

Need. In the US, 40 million patients with hypertension (HTN) have their blood pressure (BP) uncontrolled.
BP above clinical Target even for a few months increases the risk for stroke (35-40%), heart failure (HF) (up to
64%), myocardial infarction (MI) (15-25%). Physician-nurse-pharmacist resource-intensive demonstrations in
achieving & maintaining BP Target have shown promising results, but their real-life deployment was found
unsustainable long-term. As a result, a process-standardized and sustainable solution is acutely needed.
 Solution. In response to this need, Optima Integrated Health developed optima4BP 1.0. It is a first-in-class
artificial intelligence (AI) that simulates the process of clinical reasoning undertaken by the treating physician in
optimizing the anti-HTN treatment towards BP Target. Just like the physician, optima4BP 1.0 cannot determine
upfront the needed Optimal Treatment (OT) to achieve & maintain BP Target for 1-2 years. PROTECT
[optima4BP 2.0: prediction of Optimal Treatment and route to achieve and maintain BP Target] proposes to
establish upfront the personalized OT. The OT can then be used to select the shortest and safest treatment
modification route needed to achieve & maintain BP Target. Phase II Goal. Build optima4BP 2.0.
 Phase I. Phase I Prior Work demonstrated that k-Nearest Neighbor (kNN), an AI model, can predict with ≥
80% confidence the correct anti-HTN treatment, when compared to physician decision.
 Phase II. optima4BP 2.0 will predict the Optimal Treatment and route to achieve & maintain BP Target.
 Optimal Treatment data-mining source. PROTECT will use the SPRINT (Systolic Blood Pressure
Intervention Trial, 2015) and ACCORD (Action to Control Cardiovascular Risk in Diabetes, 2010) clinical trial
data. They represent the foundation of the most current anti-HTN treatment management national guidelines.
 Aim 1. Build kNN. Hypothesis. kNN can predict the proximity (clinical relevance) of a patient to an Optimal
Treatment (OT). Milestone. Achieve ≥ 90% accuracy of prediction to physician decision. Phase I Data
Preparation protocol will be applied to the SPRINT & ACCORD data. Then, the kNN Ensemble Learning
function will be built to select the Optimal Treatment with the highest demonstrated efficacy by comparing the
choice from 3 computational approaches developed and tested during Phase I.
 Aim 2. Build the Optimal Treatment Route (OTR). Hypothesis. Knowing the Current and Optimal
Treatment (OT), an OTR can be built. Milestone. Safest Route: Achieve 100% exclusion of treatments that led
to an adverse event in similar patient populations. Shortest Route: Achieve ≥30% reduction in number of
treatment changes compared to physician route. The OTR will be built by comparing at each Step on the
Route how similar each Candidate Treatment is to the OT through a computed similarity assessment.
 optima4BP 2.0 aims to establish a process-standardized & sustainable solution with the goal of
reducing the incidence of s...

## Key facts

- **NIH application ID:** 10159301
- **Project number:** 5R44HL140624-03
- **Recipient organization:** OPTIMA INTEGRATED HEALTH, INC.
- **Principal Investigator:** Gabriela Voskerician
- **Activity code:** R44 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $715,983
- **Award type:** 5
- **Project period:** 2018-05-01 → 2022-04-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10159301

## Citation

> US National Institutes of Health, RePORTER application 10159301, PROTECT: optima4BP 2.0: prediction of Optimal Treatment and Route to achieve and maintain BP Target (5R44HL140624-03). Retrieved via AI Analytics 2026-05-25 from https://api.ai-analytics.org/grant/nih/10159301. Licensed CC0.

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