# Deprescribing Decision-Making using Machine Learning Individualized Treatment Rules to Improve CNS Polypharmacy

> **NIH NIH R01** · DUKE UNIVERSITY · 2024 · $607,128

## Abstract

ABSTRACT
 Central Nervous System (CNS) polypharmacy is a cumulative exposure to 3 or more medications
with CNS-acting properties. CNS medications include anticholinergics, antidepressants, antipsychotics,
benzodiazepines, gabapentinoids, hypnotics, opioids, and muscle relaxants. A growing number of older
Americans are prescribed multiple CNS-acting medications and are affected by CNS polypharmacy,
which is rapidly becoming a wide-spread geriatric syndrome. CNS polypharmacy leads to adverse
outcomes such as physical and cognitive impairment and medication-related falls, serious injuries,
hospitalization, diminished quality of life, and even death. Deprescribing, the systematic process of
tapering or stopping medications, offers potential to reduce the burden of CNS medications. However,
deprescribing may also result in the exacerbation of underlying symptoms for which CNS medications
were originally prescribed. Existing guidelines, such as Beers or STOPP, do not take into consideration
the complex interplay of multiple medications and individual patient’s unique clinical characteristics, which
are necessary for individualized, patient-centered care. Essential are individualized deprescribing
solutions that account for these complexities.
 This project will identify varying benefits and harms of deprescribing across individuals and
medications classes. We will then subsequently develop individualized treatment rules (ITRs) for
personalized medication discontinuation recommendations based on patients’ unique clinical
characteristics to support clinical practice. First, using causal inference methods, we will analyze
PCORNet multi-site electronic health record (EHR) data with linked Medicare claims data to quantify the
probability of medication discontinuation based on patients’ observed clinical characteristics. Then we
will employ supervised and unsupervised machine learning (ML) methods, such as decision trees and
clustering techniques, to accurately predict patient outcomes after medication class discontinuation
based on that patient’s unique clinical characteristics.
 Our team will leverage prior experience in causal inference and machine learning applied to EHR
data to estimate deprescribing's clinical benefits and harms. We'll ultimately integrate these results into
a user-friendly clinical interface, ensuring trustworthy ML and transparent model decision-making
processes. As an initial step, we'll engage clinicians, patients, and community partners early in the design
phase to assess understanding and trust in ML-based deprescribing guidance. This will prepare us for a
future large-scale clinical trial to test the efficacy in achieving deprescribing goals.

## Key facts

- **NIH application ID:** 10944470
- **Project number:** 1R01AG088214-01
- **Recipient organization:** DUKE UNIVERSITY
- **Principal Investigator:** JULIESSA M PAVON
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $607,128
- **Award type:** 1
- **Project period:** 2024-09-01 → 2029-05-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10944470

## Citation

> US National Institutes of Health, RePORTER application 10944470, Deprescribing Decision-Making using Machine Learning Individualized Treatment Rules to Improve CNS Polypharmacy (1R01AG088214-01). Retrieved via AI Analytics 2026-05-26 from https://api.ai-analytics.org/grant/nih/10944470. Licensed CC0.

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