Prediction of Survival Prognosis for Spinal Metastasis From Cancer of Unknown Primary: Derivation and Validation of a Nomogram Model.

Abstract

STUDY DESIGN:

Retrospective and prospective cohort study.

OBJECTIVES:

Survival estimation is necessary in the decision-making process for treatment in patients with spinal metastasis from cancer of unknown primary (SMCUP). We aimed to develop a novel survival prediction system and compare its accuracy with that of existing survival models.

METHODS:

A retrospective derivation cohort of 268 patients and a prospective validation cohort of 105 patients with SMCUP were performed. Univariate and multivariable survival analysis were used to generate independently prognostic variables. A nomogram model for survival prediction was established by integrating these independent predictors based on the size of the significant variables' β regression coefficient. Then, the model was subjected to bootstrap validation with calibration curves and concordance index (C-index). Finally, predictive accuracy was compared with Tomita, revised Tokuhashi and SORG score by the receiver-operating characteristic (ROC) curve.

RESULTS:

The survival prediction model included six independent prognostic factors, including pathology (P < .001), visceral metastases (P < .001), Frankel score (P < .001), weight loss (P = .005), hemoglobin (P = .001) and serum tumor markers (P < .001). Calibration curve of the model showed good agreement between predicted and actual mortality risk in 6-, 12-, and 24-month estimation in derivation and validation cohorts. The C-index was .775 in the derivation cohort and .771 in the validation cohort. ROC curve analysis showed that the current model had the best accuracy for SMCUP survival estimation amongst 4 models.

CONCLUSIONS:

The novel nomogram system can be applied in survival prediction for SMCUP patients, and furtherly be used to give individualized therapeutic suggestions based on patients' prognosis.

• Authors: Minglei Yang, Xiaoyu Ma, Pengru Wang, Jiaxiang Yang, Nanzhe Zhong, Yujie Liu, Jun Shen, Wei Wan, Jian Jiao, Wei Xu, Jianru Xiao
• Journal: Global spine journal (Global Spine J) Vol. 14 Issue 1 Pg. 283-294 (Jan 2024) ISSN: 2192-5682 [Print] England
• PMID: 35615968 (Publication Type: Journal Article)