Prediction of Clinical Outcomes of Spinal Muscular Atrophy Using Motion Tracking Data and Elastic Net Regression

Abstract

Spinal muscular atrophy (SMA) is a common muscle disease that can lead to high rate of infant mortality. It is important to be able to quickly and accurately diagnose SMAs as well as track disease progression throughout the treatment process. This study introduced a framework for deriving movement features from motion tracking data, and applied a regularized regression method to predict the gold standard clinical measures for SMA, the CHOP INTEND Extremities Scores (CIES). Our results showed the CIES could be predicted with good accuracy using derived motion features and Elastic Net regression. An RMSE of 8.5 points on CIES was achieved in both cross-validation and prediction on the held-out set. A high ROC-AUC of 0.91 was achieved for discriminating SMA infants from Controls on both session and subject levels. It was concluded that motion tracking devices could potentially be used as a low-cost yet effective method to assess and monitor infants with SMA.