# Deriving high-quality evidence from national healthcare databases to improve suicidality detection and treatment outcomes in PTSD and TBI

> **NIH NIH R56** · UNIVERSITY OF NEW MEXICO HEALTH SCIS CTR · 2020 · $776,198

## Abstract

PROJECT SUMMARY
Post-traumatic stress disorder (PTSD) has complex profiles of co-occurring medical conditions (comorbidities)
and is associated with high risk of suicide, particularly among Veterans, in which it is a leading cause of death.
There is a critical lack of advancement in PTSD pharmacotherapy, as illustrated by increased use of off-label
medications and polypharmacy (multiple drugs used simultaneously). The consequent limited evidence on the
relative risks and benefits of treatments creates a crisis in PTSD management. Moreover, PTSD and its major
comorbidities [traumatic brain injury (TBI) and suicidality] often remain undocumented in electronic health
records (EHR). There is also poor predictability of disease outcomes since there are frequent changes in
pharmacological treatment and multiple modifying comorbidities. Our long-term goal is to improve diagnostics,
secondary/tertiary prevention, and treatment outcomes of PTSD and its comorbidities via enhanced EHR
utilization. To achieve our objectives, we will analyze EHR and administrative claims data from Veterans
Administration (VA) and non-VA databases, collectively covering >2M PTSD and >2M TBI patients.
Specifically, we aim to: (1) Identify undetected PTSD, TBI, and self-harm from EHRs (using machine learning
with and without natural language language processing) to guide health service improvements. (2) Predict
PTSD clinical course in the VA population through novel modeling of disease trajectories that account for
time-varying treatments and biases (3) Compare the effectiveness of PTSD psychotropic monotherapies,
polypharmacy, and psychotherapy to guide the choice of treatment for improved patient outcomes. By
enhancing and validating a machine learning approach developed by our team, we will impute unrecorded
PTSD, TBI, and self-harm from both datasets, and characterize factors associated with documentation
disparities. We will model diseases trajectories with enhanced latent class analysis, focusing on self-harm,
substance misuse, and psychiatric hospitalization in PTSD. With Local Control methodology innovations, we
will compare the risk of PTSD in veterans with and without comorbid TBI. Finally, we will perform the largest
comparative effectiveness studies (to date) of PTSD treatments on >100 monotherapy and polypharmacy
regimens plus psychotherapy interventions. These studies will provide high-quality evidence on the risk of
hospitalizations, substance misuse, and suicidal acts/self-harm. Successful completion of these investigations
will improve the quality of decision making for providers and patients, and guide improved service delivery to
the population of veterans and non-veterans with PTSD/TBI, and/or high risk of suicide.

## Key facts

- **NIH application ID:** 10088135
- **Project number:** 1R56MH120826-01A1
- **Recipient organization:** UNIVERSITY OF NEW MEXICO HEALTH SCIS CTR
- **Principal Investigator:** Christophe G. Lambert
- **Activity code:** R56 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $776,198
- **Award type:** 1
- **Project period:** 2020-06-01 → 2022-05-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10088135

## Citation

> US National Institutes of Health, RePORTER application 10088135, Deriving high-quality evidence from national healthcare databases to improve suicidality detection and treatment outcomes in PTSD and TBI (1R56MH120826-01A1). Retrieved via AI Analytics 2026-05-26 from https://api.ai-analytics.org/grant/nih/10088135. Licensed CC0.

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