Abstract A026: Assessing the impact of obesity on postoperative outcomes in RCC using machine learning
Authors: Khosla Atulya A. , Pustake Manas , Chhabra Sufal , Zhang Yanjia , Roy Mukesh , Rubens Muni , Ramamoorthy Venkataraghavan , Saxena Anshul , Jaiyesimi Ishmael A. 
Published: November 2024

Introduction Obesity is associated with an increased risk of developing renal cell carcinoma (RCC) but paradoxically correlates with improved outcomes in metastatic cases. Moreover, the data on the relationship between obesity and postoperative outcomes following nephrectomy are inconsistent. Accurate prediction of postoperative outcomes, including complications, 30-day readmissions, and mortality, is essential for improving patient outcomes in surgical procedures. In this study, we utilized ML models to predict such outcomes in RCC patients undergoing nephroureterectomy, radical nephrectomy, partial nephrectomy, and other excision procedures on the kidney using data from the National Surgical Quality Improvement Program (NSQIP; 2016- 2021). Methods A gradient-boosted tree (GBT) ML model was developed and trained to predict the primary outcomes of interest: major complications, minor complications, 30-day readmission, and mortality. Patients with a BMI ≥30 kg/m2 were considered obese, and patients with a BMI &lt;30 kg/m2 were used as controls. The model's performance was rigorously evaluated using various measures, such as AUROC, generalized R-square, and misclassification rate. The model's predictions were validated using separate training (70%) and validation (30%) cohorts to ensure generalizability and applicability in diverse patient populations. Results In the analysis, 36,284 cases were included. Bivariate analysis revealed that obesity was associated with an increased rate of minor complications (5.48% vs. 4.41%, p&lt;0.0001) compared to non-obese individuals with similar major complications (3.33 vs. 3.21, p=0.49), mortality (0.54 vs. 0.64, p=0.22) and 30-day readmission rates (5.74 vs. 5.83, p=0.70). The GBT model demonstrated substantial predictive power across all four outcomes. In predicting mortality, the model achieved an AUROC of 0.80 in the validation set, with a misclassification rate of 0.68%. The prediction of major complications was also robust, with an AUROC of 0.91 in the validation set and a misclassification rate of 2.2%. Similarly, the model's performance in predicting minor complications and 30-day readmissions was strong, with AUROC values of 0.70 and 0.66, respectively, in the validation sets. Conclusions Obesity was linked to a higher rate of minor complications but did not affect other postoperative outcomes in RCC patients undergoing nephrectomy. By integrating a wide range of patient-specific variables, ML models are powerful tools for clinicians to identify high-risk patients and tailor perioperative care accordingly. The interaction between RCC and the surrounding adipose tissue microenvironment may have clinical relevance, warranting further research to elucidate the role of BMI in postoperative complications. Our findings advocate for the expanded use of ML techniques in surgical decision-making, which could enhance patient safety, optimize resource use, and improve overall surgical outcomes.

Citation Format: Atulya A. Khosla, Manas Pustake, Sufal Chhabra, Yanjia Zhang, Mukesh Roy, Muni Rubens, Venkataraghavan Ramamoorthy, Anshul Saxena, Ishmael A. Jaiyesimi. Assessing the impact of obesity on postoperative outcomes in RCC using machine learning [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Tumor-body Interactions: The Roles of Micro- and Macroenvironment in Cancer; 2024 Nov 17-20; Boston, MA. Philadelphia (PA): AACR; Cancer Res 2024;84(22_Suppl):Abstract nr A026.