Prediction of persistent acute renal failure

Severe Acute Kidney Injury (S-AKI) is a serious complication that can lead to the progression of chronic kidney disease, requiring in some cases renal replacement therapy such as dialysis. Early identification of patients at higher risk of kidney deterioration would allow the implementation of personalized preventive and follow-up strategies, optimizing clinical management and reducing the burden of disease.

This study aims to develop a predictive model to assess the risk of a patient with AKI-S experiencing the need for dialysis or a decrease of at least 25% of the Estimated Glomerular Filtration Rate at 60 days.

The model will be based on the analysis of clinical, laboratory and demographic factors , integrating statistical and machine learning techniques to generate an accurate prediction tool. Validation of the model will allow its application in clinical practice, facilitating risk stratification and guiding therapeutic decisions that can improve long-term renal outcomes.

This study will contribute to a better understanding of the progression of AKI-S and will offer an innovative tool for the early detection of patients at risk of renal deterioration , improving their prognosis and quality of life.