# Project 1

> **NIH NIH P01** · UNIVERSITY OF MARYLAND BALTIMORE · 2024 · $255,542

## Abstract

Clinical Practice Data Research Project - Abstract
Numerous studies have demonstrated the effectiveness of coordinated specialty care (CSC) for the treatment of people
with a first episode of psychosis (FEP). However, there is a major problem of patient disengagement, which has
adversely affected the impact of CSC on the long-term course of FEP. The lack of an individualized, empirically
developed approach to assess risk of disengagement for a given patient has compromised the ability of the field to
address this issue. Consistent with the transformative movement towards precision medicine, individualized risk
calculators for many medical conditions have been developed, validated and applied in clinical settings. On the heels of
these successes, the mental health field has seen a proliferation of research aiming to develop and validate risk-
prediction models for various mental health conditions, however research that fosters ethical, practical and clinically
useful implementation of risk prediction in applied settings has lagged behind. Our primary objective is to develop,
validate, and lay the groundwork for implementing a risk calculator for CSC program outcomes. A precision psychiatry
approach to enhancing retention in CSC programs is a logical and necessary next step to advance this field and optimize
CSC client outcomes. Our central hypothesis is that a calculator to predict personalized CSC program outcome will
allow us to identify participants most likely to disengage from care. Our approach rests on our extensive experience: (a)
implementing and analyzing the hub-wide and national Core Assessment Battery (CAB), (b) developing and validating
risk-prediction models in related contexts, (c) demonstrating feasibility via the preliminary analyses of our admission
CAB data that provide support for risk prediction with moderate accuracy, and (d) weighing ethical and practical issues
related to risk prediction. Our long-term goal is to develop a personalized medicine approach to identify and rapidly
address risk for disengagement and retain individuals in CSC to optimize its benefit.
 This Prospective Practice-Oriented Research Project entitled Developing and Validating Models to Predict Risk
of CSC Disengagement proposes to use CLHS CAB data to develop and validate longitudinal models for predicting risk
of CSC disengagement and to integrate stakeholder input on feasibility, acceptability, utility, facilitators, and barriers to
using risk information in clinical practice. We will develop several versions of a risk calculator predicting length of time in
program, program completion, and disengagement (Aim 1) and establish longitudinal validity (2-3 years) to compare
different calculators’ predictive accuracy (Aim 2). We will leverage the AC’s participatory research resources to develop
implementation strategies for using risk information in practice (Aim 3).

## Key facts

- **NIH application ID:** 11074215
- **Project number:** 1P01MH139228-01
- **Recipient organization:** UNIVERSITY OF MARYLAND BALTIMORE
- **Principal Investigator:** MELANIE E. BENNETT
- **Activity code:** P01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $255,542
- **Award type:** 1
- **Project period:** 2024-09-10 → 2029-07-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/11074215

## Citation

> US National Institutes of Health, RePORTER application 11074215, Project 1 (1P01MH139228-01). Retrieved via AI Analytics 2026-05-26 from https://api.ai-analytics.org/grant/nih/11074215. Licensed CC0.

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