Predicting Skeletal-related Events Using SINS.

STUDY

DESIGN

Predictive study utilized retrospectively collected data.

OBJECTIVE

The primary objective was to evaluate the predictive association between the Spine Instability Neoplastic Score (SINS) and Skeletal-related events (SREs). Secondary objectives included examining characteristics of cases with SINS ≤ 6 among those who developed SRE and evaluating the impact of additional predictors on prediction accuracy.

SUMMARY OF BACKGROUND DATA: Advances in cancer treatment have prolonged the lives of cancer patients, emphasizing the importance of maintaining quality of life. SREs from metastatic spinal tumors significantly impact the quality of life. However, currently, there is no scientifically established method to predict the occurrence of SRE. SINS, developed by the Spine Oncology Study Group, assesses spinal instability using six categories. Therefore, the predictive performance of SINS for SRE occurrence is of considerable interest to clinicians.

METHODS

This predictive study utilized retrospectively collected data from a single-center registry comprising over 1000 patients with metastatic spinal tumors. SINS and clinical data were collected. Logistic regression was used to create a prediction equation for SRE using SINS. Additional analyses explored factors associated with SRE in patients with SINS ≤ 6.

RESULTS

The study included 1041 patients with metastatic spinal tumors. SRE occurred in 121 cases (12%). The prediction model for SRE using SINS demonstrated an area under the curve (AUC) of 0.832. Characteristics associated with SRE included lower female prevalence, surgeries to primary sites, bone metastases to nonspinal sites, and metastases to other organs. A post hoc analysis incorporating additional predictors improved the AUC to 0.865.

CONCLUSIONS

The SINS demonstrated reasonable predictive performance for SRE within one month of the initial visit. Incorporating additional factors improved prediction accuracy. The study emphasizes the need for a comprehensive clinical prediction model for SRE in metastatic spinal tumors.