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. 2024 May 23;14(1):11781.
doi: 10.1038/s41598-024-62812-7.

Utilizing machine learning to predict post-treatment outcomes in chronic non-specific neck pain patients undergoing cervical extension traction

Ibrahim M Moustafa  1   2   3 , Dilber Uzun Ozsahin  4   5   6 , Mubarak Taiwo Mustapha  5   6   7 , Amal Ahbouch  1   2 , Paul A Oakley  8   9   10 , Deed E Harrison  11
Affiliations

Utilizing machine learning to predict post-treatment outcomes in chronic non-specific neck pain patients undergoing cervical extension traction

Ibrahim M Moustafa et al. Sci Rep. .

Abstract

This study explored the application of machine learning in predicting post-treatment outcomes for chronic neck pain patients undergoing a multimodal program featuring cervical extension traction (CET). Pre-treatment demographic and clinical variables were used to develop predictive models capable of anticipating modifications in cervical lordotic angle (CLA), pain and disability of 570 patients treated between 2014 and 2020. Linear regression models used pre-treatment variables of age, body mass index, CLA, anterior head translation, disability index, pain score, treatment frequency, duration and compliance. These models used the sci-kit-learn machine learning library within Python for implementing linear regression algorithms. The linear regression models demonstrated high precision and accuracy, and effectively explained 30-55% of the variability in post-treatment outcomes, the highest for the CLA. This pioneering study integrates machine learning into spinal rehabilitation. The developed models offer valuable information to customize interventions, set realistic expectations, and optimize treatment strategies based on individual patient characteristics as treated conservatively with rehabilitation programs using CET as part of multimodal care.

Keywords: Cervical spine; Disability; Lordosis; Machine learning; Neck pain; Prediction; Traction.

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Conflict of interest statement

PAO is a paid consultant for CBP NonProfit, Inc. DEH teaches rehabilitation methods and is the CEO of a company that distributes the DCTO product to physicians in the U.S.A. used in this manuscript. All the other authors declare that they have no competing interests.

Figures

Figure 1
Figure 1
Box and whisker plots for ARA C2–C7 (°) at baseline and final evaluation.
Figure 2
Figure 2
Box and whisker plots for neck disability index (NDI) at baseline and final evaluation. Presented as raw score out of 50 and percentage disability is multiplied by 2.
Figure 3
Figure 3
Box and whisker plots for neck pain rating score (NPRS) intensity score from 0 to 10 at baseline and final evaluation.
Figure 4
Figure 4
Scatter plot of the predicted vs. actual outcomes for cervical lordotic angle (ARA C2–C7), disability index (NDI), and pain score (NRS). The x-axis is the actual participant’s score for ARA, NDI, NPRS, while the y-axis shows the model predictions.
Figure 5
Figure 5
Distribution plot of the predicted vs. actual outcomes of ARA cervical lordotic angle, NDI as a disability index, and the NPRS pain score. In each plot, the blue is the actual patient response to care and the orange is the model predictions. The shaded areas represent the density of the actual and predicted values, showcasing the model's accuracy in capturing the underlying distribution of the data.
Figure 6
Figure 6
Radiographic measures: Absolute rotation angle (ARA) measurement of cervical lordosis from C2 to C7. Measurement of anterior head translation (AHT) using the horizontal offset of C2 relative to a vertical line originating at the posterior inferior body of C7,.
Figure 7
Figure 7
The Denneroll cervical traction orthotic (DCTO) for cervical extension traction (CET). Copyright CBP Seminars and reprinted with permission by author owner (DEH).
See this image and copyright information in PMC

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