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

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.

Spinal osteotomies for rigid deformities.

Various osteotomies are useful in making a rigid deformity flexible enough for realignment in coronal and sagittal plane. This article defines the osteotomies and their usefulness in treatment of specific rigid deformities. The pedicle subtraction osteotomy and vertebral column resection used in treating rigid deformities are described in detail.

Plain radiography of the lumbosacral spine in Marfan syndrome.

BACKGROUND CONTEXT: Marfan syndrome is a connective tissue disorder that results from a defect in the production of fibrillin. These patients tend to have several osseous anomalies of the lumbosacral spine. PURPOSE: This study examines the effectiveness of plain radiographic findings in predicting Marfan syndrome. STUDY DESIGN/SETTING: Case-control study. PATIENT SAMPLE: Fourteen height-matched controls and 33 patients with Marfan syndrome were obtained from our genetics clinic or through the National Marfan Foundation. OUTCOME MEASURES: Determined using measurements taken on plain radiographs. METHODS: Five measurements were acquired of the lumbosacral spine from the radiographs of both groups: interpedicular distance, scalloping value, sagittal canal diameter, vertebral body width and transverse process width. RESULTS: The following measurements were significantly larger in patients with Marfan syndrome: interpedicular distance at L1-L5 (p<.0001); sagittal diameters of the vertebral canal at L4-S2 (p<.01); transverse process to vertebral body width ratio at L2-L5 (p<.01). There was no significant difference in the scalloping values from L1-L5 between the patients with Marfan syndrome and the controls. A multivariate regression analysis generated the following criteria for plain film diagnosis of Marfan syndrome (two criteria need to be met for diagnosis): interpedicular distance at L5 greater than or equal to 36.0 mm, sagittal diameter at L5 greater than or equal to 13.5 mm or transverse process to vertebral width ratio at L3 greater than or equal to 2.25. CONCLUSION: Based on this criteria, patients can be diagnosed with Marfan syndrome with a high sensitivity (81.8%) but a low specificity (58.3%). Thus, plain radiography can be a useful means of screening patients with Marfan syndrome.