Machine learning clustering of adult spinal deformity patients identifies four prognostic phenotypes: a multicenter prospective cohort analysis with single surgeon external validation.

Abstract

Background context

Among adult spinal deformity (ASD) patients, heterogeneity in patient pathology, surgical expectations, baseline impairments, and frailty complicates comparisons in clinical outcomes and research. This study aims to qualitatively segment ASD patients using machine learning-based clustering on a large, multicenter, prospectively gathered ASD cohort.

Purpose

To qualitatively segment adult spinal deformity patients using machine learning-based clustering on a large, multicenter, prospectively gathered cohort.

Study design/setting

Machine learning algorithm using patients from a prospective multicenter study and a validation cohort from a retrospective single center, single surgeon cohort with complete 2-year follow up.

Patient sample

About 805 ASD patients; 563 patients from a prospective multicenter study and 242 from a single center to be used as a validation cohort.

Outcome measures

To validate and extend the Ames-ISSG/ESSG classification using machine learning-based clustering analysis on a large, complex, multicenter, prospectively gathered ASD cohort.

Methods

We analyzed a training cohort of 563 ASD patients from a prospective multicenter study and a validation cohort of 242 ASD patients from a retrospective single center/surgeon cohort with complete two-year patient-reported outcomes (PROs) and clinical/radiographic follow-up. Using k-means clustering, a machine learning algorithm, we clustered patients based on baseline PROs, Edmonton frailty, age, surgical history, and overall health. Baseline differences in clusters identified using the training cohort were assessed using Chi-Squared and ANOVA with pairwise comparisons. To evaluate the classification system's ability to discern postoperative trajectories, a second machine learning algorithm assigned the single-center/surgeon patients to the same 4 clusters, and we compared the clusters' two-year PROs and clinical outcomes.

Results

K-means clustering revealed four distinct phenotypes from the multicenter training cohort based on age, frailty, and mental health: Old/Frail/Content (OFC, 27.7%), Old/Frail/Distressed (OFD, 33.2%), Old/Resilient/Content (ORC, 27.2%), and Young/Resilient/Content (YRC, 11.9%). OFC and OFD clusters had the highest frailty scores (OFC: 3.76, OFD: 4.72) and a higher proportion of patients with prior thoracolumbar fusion (OFC: 47.4%, OFD: 49.2%). ORC and YRC clusters exhibited lower frailty scores and fewest patients with prior thoracolumbar procedures (ORC: 2.10, 36.6%; YRC: 0.84, 19.4%). OFC had 69.9% of patients with global sagittal deformity and the highest T1PA (29.0), while YRC had 70.2% exhibiting coronal deformity, the highest mean coronal Cobb Angle (54.0), and the lowest T1PA (11.9). OFD and ORC had similar alignment phenotypes with intermediate values for Coronal Cobb Angle (OFD: 33.7; ORC: 40.0) and T1PA (OFD: 24.9; ORC: 24.6) between OFC (worst sagittal alignment) and YRC (worst coronal alignment). In the single surgeon validation cohort, the OFC cluster experienced the greatest increase in SRS Function scores (1.34 points, 95%CI 1.01-1.67) compared to OFD (0.5 points, 95%CI 0.245-0.755), ORC (0.7 points, 95%CI 0.415-0.985), and YRC (0.24 points, 95%CI -0.024-0.504) clusters. OFD cluster patients improved the least over 2 years. Multivariable Cox regression analysis demonstrated that the OFD cohort had significantly worse reoperation outcomes compared to other clusters (HR: 3.303, 95%CI: 1.085-8.390).

Conclusion

Machine-learning clustering found four different ASD patient qualitative phenotypes, defined by their age, frailty, physical functioning, and mental health upon presentation, which primarily determines their ability to improve their PROs following surgery. This reaffirms that these qualitative measures must be assessed in addition to the radiographic variables when counseling ASD patients regarding their expected surgical outcomes.

Department

Description

Provenance

Citation

Published Version (Please cite this version)

10.1016/j.spinee.2024.02.010

Publication Info

Mohanty, Sarthak, Fthimnir M Hassan, Lawrence G Lenke, Erik Lewerenz, Peter G Passias, Eric O Klineberg, Virginie Lafage, Justin S Smith, et al. (2024). Machine learning clustering of adult spinal deformity patients identifies four prognostic phenotypes: a multicenter prospective cohort analysis with single surgeon external validation. The spine journal : official journal of the North American Spine Society. p. S1529-9430(24)00073-1. 10.1016/j.spinee.2024.02.010 Retrieved from https://hdl.handle.net/10161/30195.

This is constructed from limited available data and may be imprecise. To cite this article, please review & use the official citation provided by the journal.

Scholars@Duke

Shaffrey

Christopher Ignatius Shaffrey

Professor of Orthopaedic Surgery

I have more than 25 years of experience treating patients of all ages with spinal disorders. I have had an interest in the management of spinal disorders since starting my medical education. I performed residencies in both orthopaedic surgery and neurosurgery to gain a comprehensive understanding of the entire range of spinal disorders. My goal has been to find innovative ways to manage the range of spinal conditions, straightforward to complex. I have a focus on managing patients with complex spinal disorders. My patient evaluation and management philosophy is to provide engaged, compassionate care that focuses on providing the simplest and least aggressive treatment option for a particular condition. In many cases, non-operative treatment options exist to improve a patient’s symptoms. I have been actively engaged in clinical research to find the best ways to manage spinal disorders in order to achieve better results with fewer complications.


Unless otherwise indicated, scholarly articles published by Duke faculty members are made available here with a CC-BY-NC (Creative Commons Attribution Non-Commercial) license, as enabled by the Duke Open Access Policy. If you wish to use the materials in ways not already permitted under CC-BY-NC, please consult the copyright owner. Other materials are made available here through the author’s grant of a non-exclusive license to make their work openly accessible.