Machine learning models using non-linear techniques improve the prediction of resting energy expenditure in individuals receiving hemodialysis

Purpose Approximately 700,000 people in the USA have chronic kidney disease requiring dialysis. Protein-energy wasting (PEW), a condition of advanced catabolism, contributes to three-year survival rates of 50%. PEW occurs at all levels of Body Mass Index (BMI) but is devastating for those people at the extremes. Treatment for PEW depends on an accurate understanding of energy expenditure. Previous research established that current methods of identifying PEW and assessing adequate treatments are imprecise. This includes disease-specific equations for estimated resting energy expenditure (eREE). In this study, we applied machine learning (ML) modelling techniques to a clinical database of dialysis patients. We assessed the precision of the ML algorithms relative to the best-performing traditional equation, the MHDE. Methods This was a secondary analysis of the Rutgers Nutrition and Kidney Database. To build the ML models we divided the population into test and validation sets. Eleven ML models were run and optimized, with the best three selected by the lowest root mean squared error (RMSE) from measured REE. Values for eREE were generated for each ML model and for the MHDE. We compared precision using Bland-Altman plots. Results Individuals were 41.4% female and 82.0% African American. The mean age was 56.4 & PLUSMN; 11.1 years, and the median BMI was 28.8 (IQR = 24.8 - 34.0) kg/m(2). The best ML models were SVR, Linear Regression and Elastic net with RMSE of 103.6 kcal, 119.0 kcal and 121.1 kcal respectively. The SVR demonstrated the greatest precision, with 91.2% of values falling within acceptable limits. This compared to 47.1% for the MHDE. The models using non-linear techniques were precise across extremes of BMI. Conclusion ML improves precision in calculating eREE for dialysis patients, including those most vulnerable for PEW. Further development for clinical use is a priority. KEY MESSAGES Potentially impacting millions of patients worldwide, our continuing goal is to understand energy expenditure (EE) across the spectrum of CKD (stages 1-5) in adults and children being treated with dialysis or transplantation, with the intent of providing tools for the health professional that will improve the delivery of quality care. In past research, we have identified and focused on disease-specific variables which account for 60% of the variance in predicting EE in individuals receiving dialysis, but many questions remain unanswered. Our hypotheses are that (1) there are determinants of EE specific to CKD and, (2) predicting EE for individuals may be greatly advanced using sophisticated models that combine these determinants. In this study, we applied machine learning (ML) with linear and non-linear techniques to our existing dataset. The best models demonstrated improved precision in predicting EE for all individuals in the validation group.