Objectives: Clinicians often face uncertainty when interpreting whether a decline in estimated glomerular filtration rate is within the patient's expected range of fluctuation or if the decline signals a substantial deviation. Thus, accurate predictions of glomerular filtration rate can be an early warning system, prompting timely interventions, such as biopsies to preclude early graft rejection and adjustments in immunosuppression. Traditional models, encompassing linear and conventional methods, typically struggle with variabilities and complexities in posttransplant data.
Materials and methods: We evaluated the efficacy of a gradient boosting model in predicting posttransplant glomerular filtration rate, to potentially enhance accuracy over traditional prediction approaches. Our patient dataset included 68 pediatric patients aged 1 to 18 years who underwent kidney transplant between 2017 and 2023 at Baskent University Hospital (Ankara, Turkey). The dataset comprised 2285 glomerular filtration rate measurements, along with patient demographics and transplant-related data. For our model, we included "days to transplant" (glomerular filtration rate values pretransplant), "days from transplant" (glomerular filtration rate values up to 7 days posttransplant), patient age, sex, and donor types. We divided the dataset into a training set (70%) and a test set (30%). To evaluate model performance, we used mean absolute error and root mean squared error, with a focus on the accuracy of glomerular filtration rate predictions at various posttransplant stages.
Results: In the training set, the gradient boosting model demonstrated a significant improvement in prediction accuracy, achieving an mean absolute error of ~5.64 mL/min/1.73 m².
Conclusions: Our model underscored the promise of advanced machine learning techniques in refining prediction of glomerular filtration rate after kidney transplant. With its augmented precision, the model can support clinicians in making informed decisions regarding early biopsies and interventions, thus highlighting the vital role of sophisticated analytical methods in medical prognosis and the monitoring of pediatric patient care.