Commentary: Sarcopenia as a Prognostic Factor for 90-Day and Overall Mortality in Patients Undergoing Spine Surgery for Metastatic Tumors: A Multi-Center Retrospective Cohort Study

Sarcopenia in Patients With Spinal Metastasis: A Systematic Review and Meta-Analysis of Retrospective Cohort Studies

Background

As a metastasis cancer that happens up to 70% of the cancer patients, spinal metastasis is drawing attention for its significant impairment to health. There exist several predictive models designed to estimate mortality in spinal metastasis patients but they are reported with limited accuracy. In recent years, some retrospective cohort studies have been carried out to associate sarcopenia with mortality in spinal metastasis.

Introduction

As a risk factor leading to adverse events in many diseases, sarcopenia was considered to significantly impact on patients with spinal metastasis in mortality by some scientists. We aimed to look through the current evidence and use statistic measures to value the role of sarcopenia in spinal metastasis. In this study, we are going to perform a systematic review and meta-analysis of available retrospective cohort studies where sarcopenia is assessed for outcomes in spinal metastasis patients.

Methods

On October 7, 2021, we performed a search in PubMed, Embase, and the Cochrane Library. We set no restrictions on language, date or areas. Results were expressed as hazard ratio (HR) or odds ratio (OR) with 95% CI by random effects model. Sensitivity analyses were performed to explore sources of heterogeneity and stability of results.

Results

Of the 4,196 papers screened, 10 retrospective cohort studies were included, with a total of 1,674 patients. Results showed that sarcopenia was associated with higher overall mortality (OR, 1.60; 95% CI 1.35–1.90) and lower overall survival (HR, 2.08; 95% CI 1.55–2.80). The sensitivity analysis proved the stability of results in terms of publication years, region, time of diagnosis, sample size, female rate, measurement and follow up period.

Conclusions

Sarcopenia is a robust indicator of mortality in spinal metastasis patients and it might be applied to decision-making tools to assess survival probability and adjust the extent of treatment, while a lack of higher level of evidence is existing.

Systematic Review Registration

PROSPERO CRD42021283348.

Decreased psoas muscle area is a prognosticator for 90-day and 1-year survival in patients undergoing surgical treatment for spinal metastasis

BACKGROUND AND AIMS

Survival estimation for patients with spinal metastasis is crucial to treatment decisions. Psoas muscle area (PMA), a surrogate for total muscle mass, has been proposed as a useful survival prognosticator. However, few studies have validated the predictive value of decreased PMA in an Asian cohort or its predictive value after controlling for existing preoperative scoring systems (PSSs). In this study, we aim to answer: (1) Is PMA associated with survival in Han Chinese patients with spinal metastasis? (2) Is PMA a good prognosticator according to concordance index (c-index) and decision curve analysis (DCA) after controlling for six existing and commonly used PSSs?

METHODS

This study included 180 adult (≥18 years old) Taiwanese patients with a mean age of 58.3 years (range: 22-85) undergoing surgical treatment for spinal metastasis. A patient's PMA was classified into decreased, medium, and large if it fell into the lower (0-33%), middle (33-67%), and upper (67-100%) 1/3 in the study cohort, respectively. We used logistic and cox proportional-hazard regressions to assess whether PMA was associated with 90-day, 1-year, and overall survival. The model performance before and after addition of PMA to six commonly used PSSs, including Tomita score, original Tokuhashi score, revised Tokuhashi score, modified Bauer score, New England Spinal Metastasis Score, and Skeletal Oncology Research Group machine learning algorithms (SORG-MLAs), was compared by c-index and DCA to determine if PMA was a useful survival prognosticator.

RESULTS

Patients with a larger PMA is associated with better 90-day, but not 1-year, survival. The model performance of 90-day survival prediction improved after PMA was incorporated into all PSSs except SORG-MLAs. PMA barely improved the discriminatory ability (c-index, 0.74; 95% confidence interval [CI], 0.67-0.82 vs. c-index, 0.74; 95% CI, 0.66-0.81) and provided little gain of clinical net benefit on DCA for SORG-MLAs' 90-day survival prediction.

CONCLUSIONS

PMA is a prognosticator for 90-day survival and improves the discriminatory ability of earlier-proposed PSSs in our Asian cohort. However, incorporating PMA into more modern PSSs such as SORG-MLAs did not significantly improve its prediction performance.

International Validation of the SORG Machine-learning Algorithm for Predicting the Survival of Patients with Extremity Metastases Undergoing Surgical Treatment

BACKGROUND

The Skeletal Oncology Research Group machine-learning algorithms (SORG-MLAs) estimate 90-day and 1-year survival in patients with long-bone metastases undergoing surgical treatment and have demonstrated good discriminatory ability on internal validation. However, the performance of a prediction model could potentially vary by race or region, and the SORG-MLA must be externally validated in an Asian cohort. Furthermore, the authors of the original developmental study did not consider the Eastern Cooperative Oncology Group (ECOG) performance status, a survival prognosticator repeatedly validated in other studies, in their algorithms because of missing data.

QUESTIONS/PURPOSES

(1) Is the SORG-MLA generalizable to Taiwanese patients for predicting 90-day and 1-year mortality? (2) Is the ECOG score an independent factor associated with 90-day and 1-year mortality while controlling for SORG-MLA predictions?

METHODS

All 356 patients who underwent surgery for long-bone metastases between 2014 and 2019 at one tertiary care center in Taiwan were included. Ninety-eight percent (349 of 356) of patients were of Han Chinese descent. The median (range) patient age was 61 years (25 to 95), 52% (184 of 356) were women, and the median BMI was 23 kg/m2 (13 to 39 kg/m2). The most common primary tumors were lung cancer (33% [116 of 356]) and breast cancer (16% [58 of 356]). Fifty-five percent (195 of 356) of patients presented with a complete pathologic fracture. Intramedullary nailing was the most commonly performed type of surgery (59% [210 of 356]), followed by plate screw fixation (23% [81 of 356]) and endoprosthetic reconstruction (18% [65 of 356]). Six patients were lost to follow-up within 90 days; 30 were lost to follow-up within 1 year. Eighty-five percent (301 of 356) of patients were followed until death or for at least 2 years. Survival was 82% (287 of 350) at 90 days and 49% (159 of 326) at 1 year. The model's performance metrics included discrimination (concordance index [c-index]), calibration (intercept and slope), and Brier score. In general, a c-index of 0.5 indicates random guess and a c-index of 0.8 denotes excellent discrimination. Calibration refers to the agreement between the predicted outcomes and the actual outcomes, with a perfect calibration having an intercept of 0 and a slope of 1. The Brier score of a prediction model must be compared with and ideally should be smaller than the score of the null model. A decision curve analysis was then performed for the 90-day and 1-year prediction models to evaluate their net benefit across a range of different threshold probabilities. A multivariate logistic regression analysis was used to evaluate whether the ECOG score was an independent prognosticator while controlling for the SORG-MLA's predictions. We did not perform retraining/recalibration because we were not trying to update the SORG-MLA algorithm in this study.

RESULTS

The SORG-MLA had good discriminatory ability at both timepoints, with a c-index of 0.80 (95% confidence interval 0.74 to 0.86) for 90-day survival prediction and a c-index of 0.84 (95% CI 0.80 to 0.89) for 1-year survival prediction. However, the calibration analysis showed that the SORG-MLAs tended to underestimate Taiwanese patients' survival (90-day survival prediction: calibration intercept 0.78 [95% CI 0.46 to 1.10], calibration slope 0.74 [95% CI 0.53 to 0.96]; 1-year survival prediction: calibration intercept 0.75 [95% CI 0.49 to 1.00], calibration slope 1.22 [95% CI 0.95 to 1.49]). The Brier score of the 90-day and 1-year SORG-MLA prediction models was lower than their respective null model (0.12 versus 0.16 for 90-day prediction; 0.16 versus 0.25 for 1-year prediction), indicating good overall performance of SORG-MLAs at these two timepoints. Decision curve analysis showed SORG-MLAs provided net benefits when threshold probabilities ranged from 0.40 to 0.95 for 90-day survival prediction and from 0.15 to 1.0 for 1-year prediction. The ECOG score was an independent factor associated with 90-day mortality (odds ratio 1.94 [95% CI 1.01 to 3.73]) but not 1-year mortality (OR 1.07 [95% CI 0.53 to 2.17]) after controlling for SORG-MLA predictions for 90-day and 1-year survival, respectively.

CONCLUSION

SORG-MLAs retained good discriminatory ability in Taiwanese patients with long-bone metastases, although their actual survival time was slightly underestimated. More international validation and incremental value studies that address factors such as the ECOG score are warranted to refine the algorithms, which can be freely accessed online at https://sorg-apps.shinyapps.io/extremitymetssurvival/.

LEVEL OF EVIDENCE

Level III, therapeutic study.