# STUDYING INTRA-INDIVIDUAL PAIN VARIABILITY IN SICKLE CELL DISEASE AND RESOLUTION OF PAIN AFTER HEMATOPOIETIC CELL TRANSPLANT: A NOVEL MODEL SYSTEM TO ELUCIDATE MECHANISMS OF TRANSITION TO CHRONIC PAIN

> **NIH NIH K23** · EMORY UNIVERSITY · 2021 · $55,231

## Abstract

PROJECT SUMMARY (FROM PARENT K23 APPLICATION)
Sickle Cell Disease (SCD) affects over 100,000 individuals in the U.S, mostly from minority ethnicities, leading
to significant morbidity and poorer quality of life, often due to recurrent and chronic pain. The mechanisms of
transition from recurrent acute pain episodes to chronic pain are poorly understood. Although pain has been
measured with surrogate markers such as healthcare utilization for pain and as markers of disease severity,
these grossly underestimate the true burden and impact of pain on functioning and patient-reported outcomes
(PROs) in patients with SCD. The candidate and her mentor have developed and established content validity of
a web-based electronic pain diary which captures pain on a momentary level, in the patient’s natural
environment. Intra-individual variability in pain intensity, distinct from pain intensity, is becoming recognized as
an independent and important facet of the pain experience. Hematopoietic Cell Transplantation (HCT) provides
a unique model for the study of chronic pain where the underlying sickling and vasocclusion are removed by
HCT. Preliminary data suggest that not everyone experiences resolution of pain after cure. The candidate will
use the framework of two large NIH-sponsored multi-center clinical trials of HCT for SCD to address critical
knowledge gaps in SCD pain and the relevance of pain variability on PROs, and on identifying phenotypes of
pain (Aim 1). In Aim 2(a), she will study the role of pre- HCT pain attributes and post-HCT complications on pain
resolution after cure of SCD. Using advanced statistical models, she will define trajectories of pain resolution
following HCT with the goal to identify predictors of persistent pain after cure, and identify the patients whose
pain is most likely to benefit from HCT. In Aim 2b of this proposal, using Quantitative Sensory Testing (QST)
methods, she will investigate whether indicators of central sensitization persist in those with persistent pain after
cure of SCD, providing novel insights into chronic pain in SCD. In Aim 3, she will, for the first time, determine the
feasibility of a prospective, longitudinal study of development of chronic pain in SCD. The candidate has a unique
research niche at the crossroads of SCD, pain and health outcomes research and is seeking to use an innovative
approaches to advance the field of chronic pain. She is also uniquely qualified as one of the few SCD pain
researchers skilled in QST methodology. This proposal envisages that the candidate will acquire expertise in
advanced statistical methods through didactic courses and application in mentored research, gain experience
managing the acquisition of pain and PROMIS endpoints in large multicenter trials, and in prospective,
longitudinal observational studies. Her advisory committee consists of experts in SCD (Dr. Lakshmanan
Krishnamurti, Dr. Wally Smith, and Dr. Clinton Joiner), HCT for SCD (Dr. Lakshmanan Krishnamur...

## Key facts

- **NIH application ID:** 10272768
- **Project number:** 3K23HL140142-03S1
- **Recipient organization:** EMORY UNIVERSITY
- **Principal Investigator:** Nitya Bakshi
- **Activity code:** K23 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $55,231
- **Award type:** 3
- **Project period:** 2020-12-01 → 2022-07-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10272768

## Citation

> US National Institutes of Health, RePORTER application 10272768, STUDYING INTRA-INDIVIDUAL PAIN VARIABILITY IN SICKLE CELL DISEASE AND RESOLUTION OF PAIN AFTER HEMATOPOIETIC CELL TRANSPLANT: A NOVEL MODEL SYSTEM TO ELUCIDATE MECHANISMS OF TRANSITION TO CHRONIC PAIN (3K23HL140142-03S1). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10272768. Licensed CC0.

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