Estimating survival and choosing treatment for spinal metastases: Do spine surgeons agree with each other?

Predictive Modeling for Spinal Metastatic Disease

Spinal metastasis is exceedingly common in patients with cancer and its prevalence is expected to increase. Surgical management of symptomatic spinal metastasis is indicated for pain relief, preservation or restoration of neurologic function, and mechanical stability. The overall prognosis is a major driver of treatment decisions; however, clinicians' ability to accurately predict survival is limited. In this narrative review, we first discuss the NOMS decision framework used to guide decision making in the treatment of patients with spinal metastasis. Given that decision making hinges on prognosis, multiple scoring systems have been developed over the last three decades to predict survival in patients with spinal metastasis; these systems have largely been developed using expert opinions or regression modeling. Although these tools have provided significant advances in our ability to predict prognosis, their utility is limited by the relative lack of patient-specific survival probability. Machine learning models have been developed in recent years to close this gap. Employing a greater number of features compared to models developed with conventional statistics, machine learning algorithms have been reported to predict 30-day, 6-week, 90-day, and 1-year mortality in spinal metastatic disease with excellent discrimination. These models are well calibrated and have been externally validated with domestic and international independent cohorts. Despite hypothesized and realized limitations, the role of machine learning methodology in predicting outcomes in spinal metastatic disease is likely to grow.

Preoperative Performance Status Threshold for Favorable Surgical Outcome in Metastatic Spine Disease

BACKGROUND AND OBJECTIVES

Surgical treatment is an integral component of multimodality management of metastatic spine disease but must be balanced against the risk of surgery-related morbidity and mortality, making tailored surgical counseling a clinical challenge. The aim of this study was to investigate the potential predictive value of the preoperative performance status for surgical outcome in patients with spinal metastases.

METHODS

Performance status was determined using the Karnofsky Performance Scale (KPS), and surgical outcome was classified as "favorable" or "unfavorable" based on postoperative changes in neurological function and perioperative complications. The correlation between preoperative performance status and surgical outcome was assessed to determine a KPS-related performance threshold.

RESULTS

A total of 463 patients were included. The mean age was 63 years (range: 22-87), and the mean preoperative KPS was 70 (range: 30-100). Analysis of clinical outcome in relation to the preoperative performance status revealed a KPS threshold between 40% and 50% with a relative risk of an unfavorable outcome of 65.7% in KPS ≤40% compared with the relative chance for a favorable outcome of 77.1% in KPS ≥50%. Accordingly, we found significantly higher rates of preserved or restored ambulatory function in KPS ≥50% (85.7%) than in KPS ≤40% (48.6%; P < .001) as opposed to a significantly higher risk of perioperative mortality in KPS ≤40% (11.4%) than in KPS ≥50% (2.1%, P = .012).

CONCLUSION

Our results underline the predictive value of the KPS in metastatic spine patients for counseling and decision-making. The study suggests an overall clinical benefit of surgical treatment of spinal metastases in patients with a preoperative KPS score ≥50%, while a high risk of unfavorable outcome outweighing the potential clinical benefit from surgery is encountered in patients with a KPS score ≤40%.

Determining patients with spinal metastases suitable for surgical intervention: A cost-effective analysis

BACKGROUND

Both nonoperative and operative treatments for spinal metastasis are expensive interventions. Patients' expected 3-month survival is believed to be a key factor to determine the most suitable treatment. However, to the best of our knowledge, no previous study lends support to the hypothesis. We sought to determine the cost-effectiveness of operative and nonoperative interventions, stratified by patients' predicted probability of 3-month survival.

METHODS

A Markov model with four defined health states was used to estimate the quality-adjusted life years (QALYs) and costs for operative intervention with postoperative radiotherapy and radiotherapy alone (palliative low-dose external beam radiotherapy) of spine metastases. Transition probabilities for the model, including the risks of mortality and functional deterioration, were obtained from secondary and our institutional data. Willingness to pay thresholds were prespecified at $100,000 and $150,000. The analyses were censored after 5-year simulation from a health system perspective and discounted outcomes at 3% per year. Sensitivity analyses were conducted to test the robustness of the study design.

RESULTS

The incremental cost-effectiveness ratios were $140,907 per QALY for patients with a 3-month survival probability >50%, $3,178,510 per QALY for patients with a 3-month survival probability <50%, and $168,385 per QALY for patients with independent ambulatory and 3-month survival probability >50%.

CONCLUSIONS

This study emphasizes the need to choose patients carefully and estimate preoperative survival for those with spinal metastases. In addition to reaffirming previous research regarding the influence of ambulatory status on cost-effectiveness, our study goes a step further by highlighting that operative intervention with postoperative radiotherapy could be more cost-effective than radiotherapy alone for patients with a better survival outlook. Accurate survival prediction tools and larger future studies could offer more detailed insights for clinical decisions.

Ten Years After SINS: Role of Surgery and Radiotherapy in the Management of Patients With Vertebral Metastases

The objective of the different types of treatments for a spinal metastasis is to provide the best oncological and functional result with the least aggressive side effects. Initially created in 2010 to help clinicians in the management of vertebral metastases, the Spine Instability Neoplastic Score (SINS) has quickly found its place in the decision making and the treatment of patients with metastatic spinal disease. Here we conduct a review of the literature describing the different changes that occurred with the SINS score in the last ten years. After a brief presentation of the spinal metastases’ distribution, with or without spinal cord compression, we present the utility of SINS in the radiological diagnosis and extension of the disease, in addition to its limits, especially for scores ranging between 7 and 12. We take this opportunity to expose the latest advances in surgery and radiotherapy concerning spinal metastases, as well as in palliative care and pain control. We also discuss the reliability of SINS amongst radiologists, radiation oncologists, spine surgeons and spine surgery trainees. Finally, we will present the new SINS-derived predictive scores, biomarkers and artificial intelligence algorithms that allow a multidisciplinary approach for the management of spinal metastases.