Enhancing Kidney Failure Analysis: Web Application Development for Longitudinal Trajectory Clustering
Gu, Y.; Gong, Y.; Wang, M.; Jiang, S.; Li, Z.; Yuan, Z.
Show abstract
Kidney failure is a critical health condition with significant impact on patient well-being and healthcare systems worldwide. Analyzing the longitudinal trajectory of kidney function is crucial for understanding disease progression, predicting outcomes, and personalizing treatment strategies. This paper proposes a novel approach utilizing latent longitudinal trajectory clustering techniques by incorporating survival information to analyze kidney failure and explore patterns within patient populations. Besides, we also developed a web application to provide visualize and intuitive way to explore the relationship between estimated glomerular filtration rate (EGFR) progression and survival outcomes, helping researchers and clinicians gain valuable insights. By identifying distinct subgroups, this analysis can aid in early detection, risk stratification, and treatment optimization. The proposed methodology holds promise for improving patient care and outcomes in the field of nephrology.
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