Can we use Artificial Intelligence Cluster Analysis to Identify Patients with Metastatic Breast Cancer to the Spine at Highest Risk of Post-Operative Adverse Events?


Objective:

Group patients who required open surgery for metastatic breast cancer to the spine by functional level and metastatic disease characteristics to identify factors that predispose to poor outcomes.


Methods:

A retrospective analysis included patients managed at two tertiary referral centers from 2008 to 2020. The primary outcome was a 90-day adverse event. A 2-step unsupervised cluster analysis stratified patients into cohorts using function at presentation, pre-operative spine radiation, structural instability, epidural spinal cord compression (ESCC), neural deficits and tumor location/hormone status. Comparisons were performed using chi-squared and one-way ANOVA.


Results:

Five patient “clusters” were identified. High Function (HIGH) had thoracic metastases and an ECOG of 1.0±0.8. Low Function/Irradiated (LOW+RADS) had pre-operative radiation and the lowest Karnofsky scores (56.0±10.6). ER/PR Positive patients (ER/PR) had >90% estrogen/progesterone positivity and moderate Karnofsky scores (74.0±11.5). Lumbar/Non-Compressive (NON-COMP) had the fewest patients with ESCC grade 2 or 3 epidural disease (42.1%, p<0.001). Low Function/Neuro Deficits (LOW+NEURO) had ESCC grade 2 or 3 disease and neurologic deficits. Adverse event rates were 25.0% in the HIGH group, 73.3% in LOW+RADS, 24.0% in ER/PR, 31.6% in NON-COMP and 60.0% in LOW+NEURO (p = 0.003).


Conclusions:

Function at presentation, tumor hormone signature, radiation history and epidural compression delineated post-operative trajectory. We believe our results can aid in expectation management and the identification of at-risk patients who may merit closer surveillance following surgical intervention.


Keywords:

breast cancer; complications; predictive analytics; spine fusion; spine metastasis; spine surgery.

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