# Optimization and Validation of tools and algorithms that enable personalized care for patients with Chronic Low Back Pain

> **NIH NIH U19** · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · 2023 · $148,749

## Abstract

While there is good evidence for the role of biological, psychological, and social factors in the etiology and
prognosis of back pain, the synthesis of the 3 in research and clinical practice is suboptimal. This precludes a
personalized approach to cLPB treatment that would support improved clinical outcomes. The primary
objective of this research project is to address the critical need for new diagnostic and prognostic
markers and associated patient classification protocols for cLBP treatment. To achieve our objectives, we
propose three aims to prioritize and/or validate novel instruments that assess critical domains of the
biopsychosocial model, validate patient-centered outcome measures, and investigate their clinical utility using
the UCSF REACH cLBP Clinical and Digital Cohorts.
In Aim 1, we propose to validate common data elements (CDEs) that characterize important phenotypic traits in
cLBP patients. These data elements will be aligned with domains of the biopsychosocial model (Aim 1a, bio-
behavioral; Aim 1b, pathophysiological; and Aim 1c, functional/biomechanical). Before CDE's are introduced into
the REACH clinical cohort, they will be prioritized into three categories by measures of reproducibility, diagnostic
accuracy, and clinical validity: basic, supplemental, and emerging. Through this work, we will validate an imaging
suite that researchers can use to study the spine pathologies in clinical cohorts, and clinicians can use to improve
their care of cLBP patients.
In Aim 2, we will define personalized outcome measures that constitute a clinically meaningful treatment effect
for individual patients. These measures will be derived from the Patient-Reported Outcomes Measurement
Information System (PROMIS), and will objectively determine 'what is acceptable' to the patient.
In Aim 3 we will analyze phenotypic traits, using a combination of traditional data analyses and deep learning
methods, to define clinically useful cLBP phenotypes.
In both Aims 2 and 3, we will utilize both traditional statistical approaches and complex machine learning
techniques. If we show that our machine learning models outperform the clinicians (who are currently inundated
with data), these tools can prove to be beneficial clinical decision support systems in the setting of patient-centric
treatment planning.
Throughout, we plan dynamic interactions with the BACPAC consortium. BACPAC/REACH collaborations will
enhance our abilities to successfully attain our ultimate goal of developing algorithms for personalized cLBP
treatments that lead to improved clinical outcomes.

## Key facts

- **NIH application ID:** 10765796
- **Project number:** 4U19AR076737-02
- **Recipient organization:** UNIVERSITY OF CALIFORNIA, SAN FRANCISCO
- **Principal Investigator:** JEFFREY C. LOTZ
- **Activity code:** U19 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2023
- **Award amount:** $148,749
- **Award type:** 4N
- **Project period:** 2019-09-25 → 2025-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10765796

## Citation

> US National Institutes of Health, RePORTER application 10765796, Optimization and Validation of tools and algorithms that enable personalized care for patients with Chronic Low Back Pain (4U19AR076737-02). Retrieved via AI Analytics 2026-05-25 from https://api.ai-analytics.org/grant/nih/10765796. Licensed CC0.

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