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326-OR: A Novel Machine Learning Analysis of Brain Multimodal Magnetic Resonance Imaging Classifies Painful Diabetic Neuropathic Pain Severity with High Accuracy

Authors :
Solomon Tesfaye
Gordon Sloan
Iain D. Wilkinson
Pallai Shillo
Kevin Teh
Dinesh Selvarajah
Source :
Diabetes. 68
Publication Year :
2019
Publisher :
American Diabetes Association, 2019.

Abstract

Using advanced MR neuroimaging we have demonstrated altered brain structure and functional connectivity that could serve as a potential Central Pain Signature (CPS) for painful diabetic neuropathy (DN). However, the key challenge is how to apply this potential biomarker for routine diagnostic purposes. The aim of this study was to examine if the CPS can accurately classify painful DN patients based on a patient’s pain severity. Methods: 53 painful DN patients underwent detailed clinical and neurophysiological assessments. The NTSS-6, a validated questionnaire that measures both the frequency and the intensity of neuropathic pain was used to assess pain severity. Patients were divided into high pain (NTSS-6 score >7) and low pain ( Results: There was no age or gender difference (p > 0.05) between the high and low pain groups. The CPS classified painful DN patients based on their pain intensity with 94% accuracy (AUC 0.98). The positive and negative predictive values were 0.80 and 1.00 respectively. The F1 scores for predicting high pain and low pain were 0.89 and 0.96 respectively. Brain regions identified as the best classifier were the left and right postcentral gyri, thalami, and anterior and posterior cingulate cortices. Conclusions: This novel study demonstrates that a simple, 15-minute MR brain scan can accurately classify painful DN patients according to pain intensities with high accuracy. This assessment tool has a great potential as a biomarker of diabetic neuropathic pain and may serve as a target for future trials of analgesic compounds for painful DN. Disclosure D. Selvarajah: None. K. Teh: None. G.P. Sloan: None. P. Shillo: None. I.D. Wilkinson: None. S. Tesfaye: Advisory Panel; Self; Mitsubishi Tanabe Pharma Corporation, WÖRWAG Pharma. Speaker's Bureau; Self; AstraZeneca, Merck & Co., Inc., NAPP Pharmaceuticals Limited, Neurometrix, Novo Nordisk Inc., Pfizer Inc. Other Relationship; Self; Impeto Medical. Funding European Foundation for the Study of Diabetes

Details

ISSN :
1939327X and 00121797
Volume :
68
Database :
OpenAIRE
Journal :
Diabetes
Accession number :
edsair.doi...........05b327aa63f732c4f24b3b6b4cc33ade
Full Text :
https://doi.org/10.2337/db19-326-or