Shah noted that older age also increases the likelihood of a poorer recovery for patients with kidney failure due to AKI, so younger people received more points in the prediction model.
The scoring model was developed as a way to help staff providing clinical care in dialysis units quickly triage patients based on features that are easily available from their medical history, explains the study's lead author Charuhas Thakar, MD, PhD, professor at Queen's University Belfast, UK, and former chief of the department of nephrology at the University of Cincinnati.
“Patients who develop AKI requiring dialysis are typically treated in a dialysis facility with other patients with end-stage renal disease,” Thakar says. “Our goal was to quickly triage and determine who has a good, medium or poor chance of recovery based on clinical features in the patient's medical history.”
“This is our broad approach: the higher the score, the more likely they are to recover,” adds Thakur. “Patients with a high chance of recovery can be closely monitored for their prospects and we can focus on preserving their kidney function. At the same time, patients with a low chance of recovery should be allowed to undergo a long-term plan, including transplantation. In summary, our study not only facilitates the efficient use of treatments and resources, but also paves the way for personalized care.”
Shah says it's important for patients with kidney failure due to AKI to understand that they can recover.
“About a quarter of patients recover within 90 days, and about a third recover within 12 months,” Shah says. “There are several factors that can predict recovery rates. If you have a lower BMI, if you're black, if you have congestive heart failure, or if you've had an amputation, you're less likely to recover.”
“It helps us create a risk prediction score and at the same time, it helps communicate to patients and healthcare professionals what their expected recovery rates are,” Shah adds. “If you fall into the high-scoring category, you have a 57% chance of recovery within 90 days, which is very encouraging for both patients and doctors.”
She noted that the researchers used the National Renal Data System, the largest data set in the United States, to capture all dialysis patients in the country, including information on women, men, and different races and ethnicities.
“One of the greatest strengths of this paper is that it is comprehensive and the score is individualized,” Shah said, “which can help with counseling, risk prediction, and customizing treatment specifically for patients with dialysis-dependent AKI, and can inform patients of their chances of recovery based on their score.”
Other co-authors on the University of Cincinnati study are Anthony Leonard, PhD, professor in the Department of Environmental and Public Health Sciences; Karthikeyan Meganathan, PhD, Annette Christianson, and Kathleen Harrison, Department of Nephrology. Jia Ng, MD, assistant professor in the Hofstra University School of Medicine, also co-authored the study.
Silvi Shah and Jia Ng received Career Development Awards from the National Institutes of Health. Ng has also received support from the Breslin Family Foundation. Shah has received intramural funding from the University of California, Department of Nephrology.
Disclosure: Jia Ng has received consulting fees from Vifor Pharmaceuticals. She is the founder of PublishedMD Consulting LLC.
