Are Machine Learning Models better than the current equations used to estimate glomerular filtration rate?

• Grant number: 23/04903-7
• Support Opportunities: Scholarships in Brazil - Scientific Initiation
• Start date: July 01, 2023
• End date: June 30, 2024
• Field of knowledge: Health Sciences - Medicine - Medical Clinics
• Principal Investigator: Luís Gustavo Modelli de Andrade
• Grantee: Bruna Ferraz Deorio
• Host Institution: Hospital das Clínicas da Faculdade de Medicina de Botucatu. Universidade Estadual Paulista (UNESP). Campus de Botucatu. Botucatu , SP, Brazil

Abstract

An accurate prediction of glomerular filtration rate (GFR) is extremely important to classify and define chronic kidney disease stages. The estimation of GFR is based on equations derived from regression models, mostly common are CKD-EPI, MDRD and Schwartz. The aim is to evaluate the performance of machine learning models compared to the standard equations to predict the measured glomerular filtration rate.Considering the importance of GFR for the diagnosis, classification and treatment of Chronic Kidney Disease and the failures and limitations of the current sets to estimate the GFR measurement with accuracy and precision, the present study aims to elaborate a new proposal for a nephrology equation, aiming to improve the evaluation of renal function and improve care for patients in need.The study inclues a cross-section retrospective sample of 10,610 participants referred to a Hospital in Lyon, France to undergo GFR measures for suspected kidney dysfunction or kidney donation. The GFR will be measured by urinary inulin clearance. We will split the data into derivation (training) and validation (test) datasets. The machine learning models are tree-based model (gradient-boosting decision trees (xgBoost), and LightGBM), Lasso regression, and cubist regression. To compare the accuracy of the models in the test set we will use Root mean square error (RMSE) and Bias (median of the difference between measured GFR and estimated GFR).