Machine Learning Approach to Predict Pain Outcomes Following Primary and Secondary Targeted Muscle Reinnervation in Amputees
This novel custom ML model achieved strong predictive capability for TMR outcomes, demonstrating proof of concept of a practical tool for surgical planning and patient selection. The identification of several key modifiable risk factors suggests opportunities for pre-operative optimization to improve surgical outcomes.
Authors
- Ian L. Valerio (ORCID: https://orcid.org/0000-0001-8573-8810)
- Bárbara Gómez-Eslava (ORCID: https://orcid.org/0000-0002-1597-5519)
- William Renthal (ORCID: https://orcid.org/0000-0003-0206-2101)
- Zihe Zhang (ORCID: https://orcid.org/0000-0002-7768-8101)
- Anna Luan (ORCID: https://orcid.org/0000-0001-5470-2663)
- Floris V. Raasveld (ORCID: https://orcid.org/0000-0002-7566-1159)
- Arya S. Rao
- The NIH PRECISION Human Pain Network
- Clifford J. Woolf
- Kyle R. Eberlin
- Benjamin R. Johnston
Institutions
- Brigham and Women's Hospital (US)
- Boston Children's Hospital (US)
- Harvard University (US)
- Erasmus MC (NL)
- Massachusetts General Hospital (US)
- Institute of Catalysis and Petrochemistry (RU)
- Stanford Medicine (US)
- Erasmus University Rotterdam (NL)
- Stanford University (US)
Publication Details
- Journal
- Plastic & Reconstructive Surgery
- Published
- 2026-02-02
- DOI
- https://doi.org/10.1097/prs.0000000000012869
- Primary Topic
- Pain Management and Treatment
- Type
- article
- Field-Weighted Citation Impact
- 0.00