Prognostic scoring system for metastatic spine tumors derived from hepatocellular carcinoma

Analysis of Short-Term versus Long-Term Readmission-Free Survival After Metastatic Spine Tumor Surgery

BACKGROUND

Readmission-free survival (ReAFS) is a novel clinical and quality metric after metastatic spine tumor surgery (MSTS). We believe that factors influencing ReAFS after index MSTS vary based on time. We considered 2 time frames and defined short-term ReAFS as survival without an unplanned hospital readmission up to 90 days and long-term ReAFS as survival without unplanned hospital readmission up to 1 year after MSTS.

METHODS

We retrospectively analyzed 266 patients who underwent MSTS between 2005 and 2016. All relevant oncologic, surgical and follow-up data were collected. Multivariate logistic regression analysis was used to analyze prognostic factors associated with higher probability of short-term ReAFS and long-term ReAFS.

RESULTS

Multivariate analysis showed that Eastern Cooperative Oncology Group score ≤2 (P = 0.011), preoperative hemoglobin (Hb) level >12 g/dL (P = 0.008), ≤3 comorbidities (P = 0.052), shorter index length of stay ≤10 days (P = 0.007), and absence of neurologic/hematologic complications during index stay (P = 0.015) significantly increased the probability of short-term ReAFS, whereas preoperative Hb level >12 g/dL (P = 0.003) or tumor primaries with advanced treatment modalities such as breast (P = 0.012), hematologic (P = 0.006), prostate (P = 0.004), and renal/thyroid (P = 0.038) as opposed to aggressive lung tumor primaries were associated with significantly higher probability of long-term ReAFS.

CONCLUSIONS

Patient and treatment factors predominantly influence ReAFS up to 90 days, whereas primary tumor-related factors alongside general health influence ReAFS beyond 90 days after index MSTS. Awareness of these factors may help oncologists and surgeons optimize treatment planning. The clinical significance of this study will continue to evolve, because we have been witnessing over the past decade that patients are becoming more involved in both their general health and understanding the natural history of the diseases that affect them.

Natural disease progression and novel survival prediction model for hepatocellular carcinoma with spinal metastases: a 10-year single-center study

BACKGROUND

Individual prediction of life expectancy in patients with spinal metastases from hepatocellular carcinoma (HCC) is key for optimal treatment selection, especially when identifying potential candidates for surgery. Most reported prognostic tools provide categorical predictions, and only a few include HCC-related factors. This study aimed to investigate the natural progression of the disease and develop a prognostic tool that is capable of providing individualized predictions.

METHODS

Patients with HCC-derived metastatic spinal disease were identified from a retrospective cohort of patients with spinal metastases who were diagnosed at Chiang Mai University Hospital between 2006 and 2015. Kaplain-Meier methods and log-rank tests were used to statistically evaluate potential factors. Significant predictors from the univariable analysis were included in the flexible parametric survival regression for the development of a prognostic prediction model.

RESULTS

Of the 1143 patients diagnosed with HCC, 69 (6%) had spinal metastases. The median survival time of patients with HCC after spinal metastases was 79 days. In the multivariable analysis, a total of 11 potential clinical predictors were included. After backward elimination, four final predictors remained: patients aged > 60 years, Karnofsky Performance Status, total bilirubin level, and multifocality of HCC. The model showed an acceptable discrimination at C-statistics 0.73 (95% confidence interval 0.68-0.79) and fair calibration.

CONCLUSION

Four clinical parameters were used in the development of the individual survival prediction model for patients with HCC-derived spinal metastases of Chiang Mai University or HCC-SM CMU model. Prospective external validation studies in a larger population are required prior to the clinical implication of the model.