Identifying clusters of objective functional impairment in patients with degenerative lumbar spinal disease using unsupervised learning

Victor E. Staartjes, Anita M. Klukowska, Vittorio Stumpo, W. Peter Vandertop, Marc L. Schröder

Research output: Contribution to journalArticleAcademicpeer-review

1 Citation (Scopus)

Abstract

Objectives: The five-repetition sit-to-stand (5R-STS) test was designed to capture objective functional impairment (OFI), and thus provides an adjunctive dimension in patient assessment. It is conceivable that there are different subsets of patients with OFI and degenerative lumbar disease. We aim to identify clusters of objectively functionally impaired individuals based on 5R-STS and unsupervised machine learning (ML). Methods: Data from two prospective cohort studies on patients with surgery for degenerative lumbar disease and 5R-STS times of ≥ 10.5 s—indicating presence of OFI. K-means clustering—an unsupervised ML algorithm—was applied to identify clusters of OFI. Cluster hallmarks were then identified using descriptive and inferential statistical analyses. Results: We included 173 patients (mean age [standard deviation]: 46.7 [12.7] years, 45% male) and identified three types of OFI. OFI Type 1 (57 pts., 32.9%), Type 2 (81 pts., 46.8%), and Type 3 (35 pts., 20.2%) exhibited mean 5R-STS test times of 14.0 (3.2), 14.5 (3.3), and 27.1 (4.4) seconds, respectively. The grades of OFI according to the validated baseline severity stratification of the 5R-STS increased significantly with each OFI type, as did extreme anxiety and depression symptoms, issues with mobility and daily activities. Types 1 and 2 are characterized by mild to moderate OFI—with female gender, lower body mass index, and less smokers as Type I hallmarks. Conclusions: Unsupervised learning techniques identified three distinct clusters of patients with OFI that may represent a more holistic clinical classification of patients with OFI than test-time stratifications alone, by accounting for individual patient characteristics.
Original languageEnglish
JournalEuropean Spine Journal
Early online date2023
DOIs
Publication statusE-pub ahead of print - 2023

Keywords

  • Classification
  • Diagnostics
  • Functional impairment
  • Machine learning
  • Objective functional testing
  • Unsupervised machine learning

Cite this