A Prognostic Index Derived from LASSO-Selected Preoperative Inflammation and Nutritional Markers for Non-Muscle-Invasive Bladder Cancer

Background There is an urgent need to identify a robust predictor for Bacillus Calmette-Guerin (BCG) response in patients with non-muscle-invasive bladder cancer (NMIBC).We aimed to employ the Lasso regression model for the selection and construction of an index (BCGI) utilizing inflammation and nutrition indicators to predict the response to BCG therapy. Methods After acquiring the ethic approval, we searched the electric medical records in our institution and performed data screening. Then, we developed the BCGI using a Lasso regression model and subsequently evaluated its performance in both the train and internal test datasets through Kaplan-Meier survival curves and Cox regression analysis. Then, we also evaluated the prognostic value of BCGI alongside the EAU2021 model.Results: Train dataset and internal test dataset contained 295 and 196 patients, respectively. Referring to the Lasso results, BCGI consisted of hemoglobin, albumin, and platelet count, which could significantly predict the recurrence of NMIBC patients who accepted BCG in train (P=0.012) and test (P=0.004) datasets. The BCGI also exhibited statistically prognostic value in no smoking history, WHO high grade, and T1 subgroups, both in train and test datasets. In multivariable analysis, BCGI exhibited independently prognostic value in train (P=0.012) and test (P=0.012) datasets. Finally, we constructed a nomogram which consisted of smoking history, T stage, WHO grade, tumor size, and BCGI. Then, BCGI demonstrated significant independent prognostic value in NMIBC patients treated with BCG, a result not observed with EAU2021 score or classification. Conclusion Based on the results, we reasonable suggest that BCGI may be a useful predictor for NMIBC patients who accepted BCG. Furthermore, we have demonstrated the efficacy of constructing a prognostic index using clinical factors and a Lasso regression model, a versatile approach applicable to various medical conditions.Micro abstract: In order to develop a model for predicting the response to BCG treatment in patients with non-muscle invasive bladder cancer, we included inflammatory and nutritional indicators from a cohort of 491 patients. We constructed a prognostic index using a Lasso regression model and validated its effectiveness, demonstrating its potential for clinical application.