Costs Associated With Timely and Delayed Surgical Treatment of Spinal Metastases

Patients with progression of spinal metastases who present to the clinic have better outcomes compared to those who present to the emergency department

BACKGROUND

As cancer therapies have improved, spinal metastases are increasingly common. Resulting complications have a significant impact on patient's quality of life. Optimal methods of surveillance and avoidance of neurologic deficits are understudied. This study compares the clinical course of patients who initially presented to the emergency department (ED) versus a multidisciplinary spine oncology clinic and who underwent stereotactic body radiation therapy (SBRT) secondary to progression/presentation of metastatic spine disease.

METHODS

We performed a retrospective analysis of a prospectively maintained database of adult oncologic patients who underwent spinal SBRT at a single hospital from 2010 to 2021. Descriptive statistics and survival analyses were performed.

RESULTS

We identified 498 spinal radiographic treatment sites in 390 patients. Of these patients, 118 (30.3%) presented to the ED. Patients presenting to the ED compared to the clinic had significantly more severe spinal compression (52.5% vs. 11.7%; p < 0.0001), severe pain (28.8% vs. 10.3%; p < 0.0001), weakness (24.5% vs. 4.5%; p < 0.0001), and difficulty walking (24.5% vs. 4.5%; p < 0.0001). Patients who presented to the ED compared to the clinic were significantly more likely to have surgical intervention followed by SBRT (55.4% vs. 15.3%; p < 0.0001) compared to SBRT alone. Patients who presented to the ED compared to the clinic had a significantly quicker interval to distant spine progression (5.1 ± 6.5 vs. 9.1 ± 10.2 months; p = 0.004), systemic progression (5.1 ± 7.2 vs. 9.2 ± 10.7 months; p < 0.0001), and worse overall survival (9.3 ± 10.0 vs. 14.3 ± 13.7 months; p = 0.002).

CONCLUSION

The establishment of multidisciplinary spine oncology clinics is an opportunity to potentially allow for earlier, more data-driven treatment of their spinal metastatic disease.

Impact of COVID-19 on Spinal Diagnosis and Procedural Volume in the United States

STUDY DESIGN

Retrospective analysis of a national database.

OBJECTIVES

COVID-19 resulted in the widespread shifting of hospital resources to handle surging COVID-19 cases resulting in the postponement of surgeries, including numerous spine procedures. This study aimed to quantify the impact that COVID-19 had on the number of treated spinal conditions and diagnoses during the pandemic.

METHODS

Using CPT and ICD-10 codes, TriNetX, a national database, was utilized to quantify spine procedures and diagnoses in patients >18 years of age. The period of March 2020-May 2021 was compared to a reference pre-pandemic period of March 2018-May 2019. Each time period was then stratified into four seasons of the year, and the mean average number of procedures per healthcare organization was compared.

RESULTS

In total, 524,394 patient encounters from 53 healthcare organizations were included in the analysis. There were significant decreases in spine procedures and diagnoses during March-May 2020 compared to pre-pandemic levels. Measurable differences were noted for spine procedures during the winter of 2020-2021, including a decrease in lumbar laminectomy and anterior cervical arthrodesis. Comparing the pandemic period to the pre-pandemic period showed significant reductions in most spine procedures and treated diagnoses; however, there was an increase in open repair of thoracic fractures during this period.

CONCLUSIONS

COVID-19 resulted in a widespread decrease in spinal diagnosis and treated conditions. An inverse relationship was observed between new COVID-19 cases and spine procedural volume. Recent increases in procedural volume from pre-pandemic levels are promising signs that the spine surgery community has narrowed the gap in unmet care produced by the pandemic.

Deep Learning Model for Classifying Metastatic Epidural Spinal Cord Compression on MRI

Background

Metastatic epidural spinal cord compression (MESCC) is a devastating complication of advanced cancer. A deep learning (DL) model for automated MESCC classification on MRI could aid earlier diagnosis and referral.

Purpose

To develop a DL model for automated classification of MESCC on MRI.

Materials and Methods

Patients with known MESCC diagnosed on MRI between September 2007 and September 2017 were eligible. MRI studies with instrumentation, suboptimal image quality, and non-thoracic regions were excluded. Axial T2-weighted images were utilized. The internal dataset split was 82% and 18% for training/validation and test sets, respectively. External testing was also performed. Internal training/validation data were labeled using the Bilsky MESCC classification by a musculoskeletal radiologist (10-year experience) and a neuroradiologist (5-year experience). These labels were used to train a DL model utilizing a prototypical convolutional neural network. Internal and external test sets were labeled by the musculoskeletal radiologist as the reference standard. For assessment of DL model performance and interobserver variability, test sets were labeled independently by the neuroradiologist (5-year experience), a spine surgeon (5-year experience), and a radiation oncologist (11-year experience). Inter-rater agreement (Gwet’s kappa) and sensitivity/specificity were calculated.

Results

Overall, 215 MRI spine studies were analyzed [164 patients, mean age = 62 ± 12(SD)] with 177 (82%) for training/validation and 38 (18%) for internal testing. For internal testing, the DL model and specialists all showed almost perfect agreement (kappas = 0.92–0.98, p < 0.001) for dichotomous Bilsky classification (low versus high grade) compared to the reference standard. Similar performance was seen for external testing on a set of 32 MRI spines with the DL model and specialists all showing almost perfect agreement (kappas = 0.94–0.95, p < 0.001) compared to the reference standard.

Conclusion

A DL model showed comparable agreement to a subspecialist radiologist and clinical specialists for the classification of malignant epidural spinal cord compression and could optimize earlier diagnosis and surgical referral.