My interest in acute kidney injury (AKI) has led me to explore various innovative approaches in its study and management. In my published work in Critical Care, We introduced a novel technique to noninvasively measure kidney intracapsular pressure using ultrasound surface wave elastography. This study showed a substantial correlation with direct pressure measurements in a swine model. This research has the potential to transform how we diagnose and manage AKI, offering a less invasive yet effective method to assess kidney health.
I also designed and conducted a study featured in Mayo Clinic Proceedings, where I aimed to identify risk factors for AKI in hospitalized non-ICU patients. Here, I developed and validated an AKI risk prediction model based on patient data right at admission. This model represents a step towards enhancing early intervention and continuous monitoring in patients at high risk, potentially mitigating the severity of AKI in vulnerable populations.
Additionally, my interest in integrating technology into nephrology led me to create a program, hosted on GitHub, that analyses a series of creatinine laboratory values to identify and plot the occurrences and trends of AKI. This tool is designed to assess a patient’s baseline and peak creatinine levels, providing crucial insights into their kidney function during hospitalization.
Collectively, these studies and developments highlight my commitment to advancing our understanding of AKI and underscore my efforts to open new avenues for diagnosis, treatment, and risk prediction in nephrology. My goal remains to contribute meaningfully to the field, improving patient outcomes through research and innovation.