Receive a weekly summary and discussion of the top papers of the week by leading researchers in the field.

In The spine journal : official journal of the North American Spine Society

BACKGROUND CONTEXT : Accurately predicting the survival of patients with spinal metastases is important for guiding surgical intervention. The SORG machine-learning (ML) algorithm for the 90-day and 1-year mortality of patients with metastatic cancer to the spine has been multiply validated, with a high degree of accuracy in both internal and external validation studies. However, prior external validations were conducted using patient groups located on the east coast of the United States, representing a generally homogeneous population. The aim of this study was to externally validate the SORG algorithms with a Taiwanese population.

STUDY DESIGN/SETTING : Retrospective study at a single tertiary care center in Taiwan PATIENT SAMPLE: 427 patients who underwent surgery for metastatic spine disease from November 1, 2010 to December 31, 2018 OUTCOME MEASURES: 90-Day and 1-Year Mortality METHODS: The baseline characteristics of our validation cohort were compared with those of the previously published developmental and external validation cohorts. Discrimination (c-statistic and receiver operating curve), calibration (calibration plot, intercept, and slope), overall performance (Brier score), and decision curve analysis were used to assess the performance of the SORG ML algorithms in this cohort.

RESULTS : Ninety-day and 1-year mortality rates were 110 / 427 (26%) and 256 / 427 (60%), respectively. The external validation cohort and the developmental cohort differed in body mass index (BMI), preoperative performance status, American Spinal Injury Association impairment scale, primary tumor histology and in several laboratory measurements. The SORG ML algorithm for 90-day and 1-year mortality demonstrated a high level of discriminative ability (c-statistics of 0.73 [95% confidence interval [CI], 0.67-0.78] and 0.74 [95% CI, 0.69-0.79]), overall performance, and had a positive net benefit throughout the range of threshold probabilities in decision curve analysis. The algorithm for 1-year mortality had a calibration intercept of 0.08, representing a good calibration. However, the 90-day mortality algorithm underestimated mortality for the lowest predicted probabilities, with an overall intercept of 0.81.

CONCLUSIONS : The SORG algorithms for predicting 90-day and 1-year mortality in patients with spinal metastatic disease generally performed well on international external validation in a predominately Taiwanese population. However, 90-day mortality was underestimated in this group. Whether this inconsistency was due to different primary tumor characteristics, BMI, selection bias or other factors remains unclear, and may be better understood with further validative works that utilize international/diverse populations.

Yang Jiun-Jen, Chen Chih-Wei, Fourman Mitchell S, Bongers Michiel E R, Karhade Aditya V, Groot Olivier Q, Lin Wei-Hsin, Yen Hung-Kuan, Huang Po-Hao, Yang Shu-Hua, Schwab Joseph H, Hu Ming-Hsiao


Body mass index, Neoplasm staging, Spine metastases, Surgical oncology, Survival, Taiwanese