Machine learning-augmented objective functional testing in the degenerative spine: quantifying impairment using patient-specific five-repetition sit-to-stand assessment

Publication

Machine learning-augmented objective functional testing in the degenerative spine: quantifying impairment using patient-specific five-repetition sit-to-stand assessment

Journal Paper/Review - Nov 1, 2021

Staartjes Victor E, Klukowska Anita M, Vieli Moira, Niftrik Christiaan H B van, Stienen Martin N., Serra Carlo, Regli Luca, Vandertop W Peter, Schröder Marc L

Citation

Staartjes V, Klukowska A, Vieli M, Niftrik C, Stienen M, Serra C, Regli L, Vandertop W, Schröder M. Machine learning-augmented objective functional testing in the degenerative spine: quantifying impairment using patient-specific five-repetition sit-to-stand assessment. Neurosurg Focus 2021; 51:E8.

Type

Journal Paper/Review (English)

Journal

Neurosurg Focus 2021; 51

Publication Date

Nov 1, 2021

Issn Electronic

1092-0684

Brief description/objective

OBJECTIVE
What is considered "abnormal" in clinical testing is typically defined by simple thresholds derived from normative data. For instance, when testing using the five-repetition sit-to-stand (5R-STS) test, the upper limit of normal (ULN) from a population of spine-healthy volunteers (10.5 seconds) is used to identify objective functional impairment (OFI), but this fails to consider different properties of individuals (e.g., taller and shorter, older and younger). Therefore, the authors developed a personalized testing strategy to quantify patient-specific OFI using machine learning.

METHODS
Patients with disc herniation, spinal stenosis, spondylolisthesis, or discogenic chronic low-back pain and a population of spine-healthy volunteers, from two prospective studies, were included. A machine learning model was trained on normative data to predict personalized "expected" test times and their confidence intervals and ULNs (99th percentiles) based on simple demographics. OFI was defined as a test time greater than the personalized ULN. OFI was categorized into types 1 to 3 based on a clustering algorithm. A web app was developed to deploy the model clinically.

RESULTS
Overall, 288 patients and 129 spine-healthy individuals were included. The model predicted "expected" test times with a mean absolute error of 1.18 (95% CI 1.13-1.21) seconds and R2 of 0.37 (95% CI 0.34-0.41). Based on the implemented personalized testing strategy, 191 patients (66.3%) exhibited OFI. Type 1, 2, and 3 impairments were seen in 64 (33.5%), 91 (47.6%), and 36 (18.8%) patients, respectively. Increasing detected levels of OFI were associated with statistically significant increases in subjective functional impairment, extreme anxiety and depression symptoms, being bedridden, extreme pain or discomfort, inability to carry out activities of daily living, and a limited ability to work.

CONCLUSIONS
In the era of "precision medicine," simple population-based thresholds may eventually not be adequate to monitor quality and safety in neurosurgery. Individualized assessment integrating machine learning techniques provides more detailed and objective clinical assessment. The personalized testing strategy demonstrated concurrent validity with quality-of-life measures, and the freely accessible web app (https://neurosurgery.shinyapps.io/5RSTS/) enabled clinical application.