Open AccessCan a power law improve prediction of pain recovery trajectory?
Author(s) -
G. C. Hartmann,
Steven Z. George
Publication year - 2018
Publication title -
pain reports
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.113
H-Index - 15
ISSN - 2471-2531
DOI - 10.1097/pr9.0000000000000657
Subject(s) - complex regional pain syndrome , chronic pain , power (physics) , trajectory , computer science , power law , cognitive psychology , psychology , artificial intelligence , physical medicine and rehabilitation , law , physical therapy , medicine , mathematics , statistics , political science , physics , quantum mechanics , astronomy
Chronic pain results from complex interactions of different body systems. Time-dependent power laws have been used in physics, biology, and social sciences to identify when predictable output arises from complex systems. Power laws have been used successfully to study nervous system processing for memory, but there has been limited application of a power law describing pain recovery. Objective: We investigated whether power laws can be used to characterize pain recovery trajectories. Methods: This review consists of empirical examples for an individual with complex regional pain syndrome and prediction of 12-month pain recovery outcomes in a cohort of patients seeking physical therapy for musculoskeletal pain. For each example, mathematical power-law models were fitted to the data. Results: This review demonstrated how a time-dependent power law could be used to refine outcome prediction, offer alternate ways to define chronicity, and improve methods for imputing missing data. Conclusion: The overall goal of this review was to introduce new conceptual direction to improve understanding of chronic pain development using mathematical approaches successful for other complex systems. Therefore, the primary conclusions are meant to be hypothesis generating only. Future research will determine whether time-dependent power laws have a meaningful role in improving strategies for predicting pain outcomes.