Preliminary Construction of Prognostic Risk Early Warning Model for Renal Cell Carcinoma with Type 2 Diabetes Mellitus Based on SMOTE Algorithm

Abstract

157 patients with renal cell carcinoma who underwent surgical treatment in our hospital from January 2019 to December 2020 were selected as the study subjects. Patients were divided into good prognosis group and poor prognosis group according to whether there were end-point events (death or distant metastasis or recurrence of tumor) during the follow-up period. The clinical data of the two groups of patients were collected, the independent risk factors for poor prognosis were screened by univariate and binary logistic regression analysis and the logistic regression model (P1) were established. At the same time, the data set was improved based on synthetic minority oversampling technique algorithm and the early warning model (P2) of the improved data set was constructed and the prediction efficiency of the model was compared and verified. There were 27 patients with end-point events during the follow-up period. The increased course of type 2 diabetes mellitus, high preoperative haemoglobin A1C, high body mass index and tumor–node–metastasis stage T3/T4 were independent risk factors for poor prognosis in patients with type 2 diabetes mellitus and renal cell carcinoma (p<0.05), while metformin was an independent protective factor for poor prognosis (p<0.05). Early warning model based on synthetic minority oversampling technique, oversampling algorithm P2=1/[1+e-(-13.084-0.438*X1+0.446*X2+0.096*X3-0.781*X4+1.155*X5)], determination coefficient and receiver operating characteristic of P2 model, the areas under the curve were significantly higher than those of the P1 model. The course of type 2 diabetes mellitus, preoperative, body mass index level, tumor–node–metastasis stage and whether to receive metformin hypoglycemic therapy in patients with type 2 diabetes mellitus combined with renal cell carcinoma are closely related to poor prognosis. Based on this, the individualized early warning model established by synthetic minority oversampling technique oversampling algorithm is beneficial to patients with high risk of poor prognosis early identification.