Does the Pathologic Fracture Predict Severe Paralysis in Patients with Metastatic Epidural Spinal Cord Compression (MESCC)?—A Retrospective, Single-Center Cohort Analysis

Abstract

Currently, there is uncertainty about the predictive factors for metastatic epidural spinal cord compression (MESCC) and consecutive symptomatology in tumor patients. Prognostic algorithms for identifying patients at risk for paralysis are missing. The influence of the pathologic fracture on the patient’s symptoms is widely discussed in the literature and we hypothesize that pathologic fractures contribute to spinal cord compression and are therefore predictive of severe paralysis. We tested this hypothesis in 136 patients who underwent surgery for spinal metastases. The most common primary cancers were prostate (24.3%, n = 33), breast (11.0%, n = 15), lung (10.3%, n = 14), and cancer of unknown primary (10.3%, n = 14). MESCC primarily affected the thoracic (77.2%, n = 105), followed by the lumbar (13.2%, n = 18) and cervical (9.6%, n = 13) spine. Pathologic fractures occurred in 63.2% (n = 86) of patients, mainly in osteolytic metastases. On the American spinal injury association (ASIA) impairment scale (AIS), 63.2% (n = 86) of patients exhibited AIS grade D and 36.8% (n = 50) AIS grade C-A preoperatively. The presence of a pathologic fracture alone did not predict severe paralysis (AIS C-A, p = 0.583). However, the duration of sensorimotor impairments, patient age, spinal instability neoplastic score (SINS), and the epidural spinal cord compression (ESCC) grade together predicted severe paralysis (p = 0.006) as did the ESCC grade 3 alone (p = 0.028). This is in contrast to previous studies that stated no correlation between the degree of spinal cord compression and the severity of neurologic impairments. Furthermore, the high percentage of pathologic fractures found in this study is above previously reported incidences. The risk factors identified can help to predict the development of paralysis and assist in the improvement of follow-up algorithms and the timing of therapeutic interventions.