Study On Glioblastoma Preoperative Outcome Scale

Objectives Glioblastoma is the most common and malignant primary brain tumor in adult. Median survival time is generally less than one year from the time of diagnosis, and even in the most favorable situation, most patients die within two year. In order to predict the prognosis of patients with glioblastoma, we did univariate and multivariate analysis of clinical factors associated with the prognosis, and we chose the significant clinical factors to propose the glioblastoma preoperative outcome scale (GPOS), which can evaluate the prognosis of patients with glioblastoma. So the doctor can optimize the therapeutic schedule to improve survival quality.Methods The author retrospectively analyzed205patients with histologically confirmed glioblastoma who underwent tumor resection at3hospitals in Shandong province between1999and2004. Univariate analysis of prognosis factors of survival time was performed using Kaplan-Meier method. The significant factors found in univariate analysis were tested in multivariate analysis using the COX regression method, then we confirmed the significant preoperative clinical factors. Based on the relative risk and discrete variable value(0-1) to establish a rating system, namely glioblastoma preoperative outcome scale (GPOS). Add the scores of each factor, we could get the final scores. To study the survival time of patients with different scores, log-rank regression method inspect survival rate, P<0.05stands for statistically difference.Results We made univariate analysis using Kaplan-Meier method. The result showed age, preoperative Karnofsky Performance Scale (KPS)(table1), tumor location, extent of resection, radiotherapy, chemotherapy and reoperation influence the prognosis, which had significant statistically differences(P<0.05). Multivariate analysis for the7variables was performed using the Cox proportional hazards regression model, the results showed that variables of age, preoperative KPS, tumor location, extent of resection, radiotherapy and chemotherapy had tight relationship with prognosis. We chose age, tumor location and preoperative KPS as the key preoperative factors. According to the GPOS, the eventually score was from0to3. We divided different groups with the same score, log-rank regression method examined the survival rate of different groups, the results showed statistically significant differences, the lower the score, the better prognosis (χ2=89.22, P<0.0001)Conclusion After statistical analysis, the age, tumor location and preoperative KPS score were the key preoperative clinical characteristics, based on that we established GPOS score. Using the system we can inspect clinical data and conclude the prognosis with different scores. Based on the data of glioblastoma patients, we quantified the preoperative clinical characteristics to establish GPOS, which is simple and practical. GPOS score was a reliable way to predict the prognosis preoperatively. Application of this rating system, can better guide the clinical doctor to predict the prognosis of patients with glioblastoma, and comprehensive other factors to set individualized treatment program. Ultimately we could make more benefit for patients and improve the quality of life.