Introduction:

Surgery, either partial or radical nephrectomy, is the mainstay of treatment for renal masses. Partial nephrectomy has been advocated as the preferred approach, when feasible, to preserve renal function and improve long-term cardiovascular and renal outcomes without compromising oncologic outcomes. However, partial nephrectomy is associated with increased perioperative morbidity. A model estimating renal function after renal surgery could be used as a clinical decision support tool to guide the selection of partial or radical nephrectomy.  In this study, we develop and externally validate a machine learning model predicting renal function after nephrectomy (RFAN-ML). 

Methods:

We used electronic health records to identify patients undergoing a partial or radical nephrectomy at Massachusetts General Brigham (MGB). We extracted demographic, clinical, and disease specific features. We split the data into training and test samples, based on the hospital site. To develop a practical model, we performed feature selection to identify the final set of input features from the extracted set of candidate features. We used the selected features to train and compare various supervised machine learning (ML) regression models to estimate the new baseline estimated glomerular filtration rate (GFR), measured as the average of all GFR values between 3 and 12 months post-operatively. The primary performance metric was root mean squared error (RMSE). Secondary performance metrics included R squared and mean absolute errors (MAE). We externally validated the model at New York University Medical Center (NYUMC) and compared our model to previous benchmarks. 

Results:

The training sample comprised of 1518 patients and the final input features selected were age at nephrectomy, type of nephrectomy (partial vs. radical), pre-operative GFR, and body mass index. The best ML model predicting a new baseline GFR was Ridge regression. In the MGB test sample (n=416), this model (RFAN-ML) demonstrated an RMSE of 13.5 (95% confidence interval 12.5 - 14.5), R squared of 0.732 (95% CI 0.677 - 0.779), and MAE of 10.5 (95% CI 9.7 - 11.3). In the NYUMC external validation sample (n=891), RFAN-ML significantly outperformed previous benchmark models (RMSE RFAN-ML 16.5 (95% CI 15.6 - 17.3), benchmark 1 19.4 (95% CI 18.4 - 20.3), benchmark 2 19.1 (95% CI 18.0 - 20.2), p<0.01, Figure 1). 

Conclusion:

Estimating renal function after partial or radical nephrectomy can facilitate personalizing the treatment of renal masses. In this study, we developed and externally validated RFAN-ML, a robust machine learning model for predicting renal function after partial or radical nephrectomy that outperformed previous benchmarks.

Funding: N/A