A groundbreaking study led by UCLA has introduced a machine-learning model that significantly improves the prediction of short-term survival for patients undergoing continuous renal replacement therapy (CRRT). This research, published in Nature Communications, addresses a critical gap in treating critically ill patients who cannot undergo standard hemodialysis. CRRT is a continuous, gentler form of dialysis used for patients with severe kidney failure, but it has a disheartening survival rate, with approximately half of the adult patients not surviving the therapy.
The newly developed model uses advanced machine-learning techniques to analyze vast amounts of data from electronic health records. By integrating this model into clinical practice, doctors can better assess which patients are likely to benefit from CRRT, thereby improving decision-making processes. This predictive tool considers a multitude of variables, offering a more nuanced approach to patient assessment than traditional methods.
Dr. Ira Kurtz, the study’s senior author and chief of the UCLA Division of Nephrology, emphasized the model’s potential impact. «CRRT is often used as a last resort, but many patients do not survive it, leading to wasted resources and false hope for families,» he explained. By accurately predicting patient outcomes, the model aims to enhance the efficacy of CRRT, reduce unnecessary treatments, and improve overall patient care.
Future clinical trials will be crucial in determining the model’s effectiveness outside the research environment and ensuring it delivers on its potential to transform patient care in critical situations.
Article written by Medical Xpress
10/07/2024
Source:
Medical Xpress
https://medicalxpress.com/news/2024-07-ai-renal-therapy-survival.html