The spine journal : official journal of the North American Spine Society · 2025
Radiographic phenotype-driven clustering in lumbar decompression: comparative study of outcome and reoperation risk.
СтатьяСравнительное исследованиеПоддержка NIH: внешняя программаНазвание и аннотация: Перевод в очереди
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Asada Tomoyuki, Halayqeh Sereen, Lui Adrian, Pezzi Andrea, Zhao Eric R, Ehrlich Adin M, Tuma Olivia C, Araghi Kasra, Harhash Tarek, Patel Rujvee, Morse Kyle, Dowdell James E, Qureshi Sheeraz A, Iyer Sravisht
Аннотация
BACKGROUND CONTEXT: Lumbar spinal canal stenosis (LSCS) presents with various radiographic findings, often including concurrent degenerative changes. Prior studies have investigated the effects of individual radiographic findings and parameters separately using conventional methods such as logistic regression. However, applying these independent effects to real-world patients remains challenging due to an unknown interaction effect among multiple degenerative radiographic findings.
PURPOSE: To identify distinct patient phenotypes based on preoperative radiographic findings using unsupervised clustering and to evaluate their associations with postoperative patient-reported outcomes and reoperation rates.
STUDY DESIGN: Retrospective cohort study.
PATIENT SAMPLE: Patients undergoing single-level lumbar decompression.
OUTCOME MEASURES: Oswestry Disability Index (ODI), Short Form-12 physical component scale (SF-12 PCS), reoperation rates.
METHODS: Unsupervised clustering was performed using preoperative radiographic data from standing X-ray imaging and magnetic resonance imaging (MRI). Variable selection was optimized through preliminary correlation analysis, causal assessment using a directed acyclic graph, and expert review. A multivariable mixed-effects model was used to assess the impact of cluster membership on postoperative outcomes. Reoperation rates were compared using Kaplan-Meier survival analysis and Cox proportional hazards models.
RESULTS: Unsupervised clustering identified 4 distinct clusters base on 10 radiographic variables: cluster 1 as "Young and Less Degenerative Spine" (cluster Y), cluster 2 as "Combined Coronal and Sagittal Spondylosis" (cluster CS), cluster 3 as "Coronal Spondylosis Characterized by Laterolisthesis" (cluster C), and cluster 4 as "Sagittal Spondylosis Characterized by Degenerative Spondylolisthesis" (cluster S). Multivariable regression analysis, adjusting for comorbidity, sex, and body mass index have revealed cluster C demonstrated slower improvement in ODI (β = 5.4, SE = 2.7, p=.043) and SF-12 PCS (β=-2.9, SE=1.4, p=.045) compared to cluster Y. Regarding reoperation, cluster CS showed the highest hazard ratio (24.3%, HR=4.18, 95% CI: 1.48-13.07, p=.007) compared to cluster S with the lowest reoperation rate (6.8%).
CONCLUSION: Unsupervised clustering based on preoperative radiographic findings identified 4 distinct degenerative phenotypes in LSCS. Patients with coronal spondylosis was associated with slower improvements in disability and function compared to those with minimal degeneration. Additionally, patients with combined sagittal and coronal degeneration exhibited the highest reoperation rates. These findings highlight the clinical relevance of coronal and sagittal degeneration in surgical decision-making.
PURPOSE: To identify distinct patient phenotypes based on preoperative radiographic findings using unsupervised clustering and to evaluate their associations with postoperative patient-reported outcomes and reoperation rates.
STUDY DESIGN: Retrospective cohort study.
PATIENT SAMPLE: Patients undergoing single-level lumbar decompression.
OUTCOME MEASURES: Oswestry Disability Index (ODI), Short Form-12 physical component scale (SF-12 PCS), reoperation rates.
METHODS: Unsupervised clustering was performed using preoperative radiographic data from standing X-ray imaging and magnetic resonance imaging (MRI). Variable selection was optimized through preliminary correlation analysis, causal assessment using a directed acyclic graph, and expert review. A multivariable mixed-effects model was used to assess the impact of cluster membership on postoperative outcomes. Reoperation rates were compared using Kaplan-Meier survival analysis and Cox proportional hazards models.
RESULTS: Unsupervised clustering identified 4 distinct clusters base on 10 radiographic variables: cluster 1 as "Young and Less Degenerative Spine" (cluster Y), cluster 2 as "Combined Coronal and Sagittal Spondylosis" (cluster CS), cluster 3 as "Coronal Spondylosis Characterized by Laterolisthesis" (cluster C), and cluster 4 as "Sagittal Spondylosis Characterized by Degenerative Spondylolisthesis" (cluster S). Multivariable regression analysis, adjusting for comorbidity, sex, and body mass index have revealed cluster C demonstrated slower improvement in ODI (β = 5.4, SE = 2.7, p=.043) and SF-12 PCS (β=-2.9, SE=1.4, p=.045) compared to cluster Y. Regarding reoperation, cluster CS showed the highest hazard ratio (24.3%, HR=4.18, 95% CI: 1.48-13.07, p=.007) compared to cluster S with the lowest reoperation rate (6.8%).
CONCLUSION: Unsupervised clustering based on preoperative radiographic findings identified 4 distinct degenerative phenotypes in LSCS. Patients with coronal spondylosis was associated with slower improvements in disability and function compared to those with minimal degeneration. Additionally, patients with combined sagittal and coronal degeneration exhibited the highest reoperation rates. These findings highlight the clinical relevance of coronal and sagittal degeneration in surgical decision-making.