Photo Credit: Mohammed Haneefa Nizamudeen

A recent modeling study has identified risk factors predictive of good and bad outcomes in non-diabetic CKD, such as gender, race, and albuminuria.

Using healthcare claims data, the researchers aiming to develop risk prediction models for non-diabetic CKD have identified factors predictive of kidney failure, the need for dialysis, hospitalization for heart failure, and deteriorating kidney function, according to findings published in BMC Nephrology.

“Even though treatments are indicated in non-diabetic CKD, studies that have evaluated their effectiveness have been conducted primarily on patients with diabetic CKD,” Christoph Wanner, MD, and colleagues wrote. “There are some theories that outcomes for patients with non-diabetic CKD are different compared with patients with diabetic CKD; however, it is generally hypothesized that the benefits observed in the diabetes-specific (types 1 and 2) studies extend to patients with a non-diabetic CKD etiology. This suggests that there is a need for further innovative therapies that will reduce cardiorenal risk for patients with non-diabetic CKD, but also for an improved understanding of risk factors and outcomes for patients with non-diabetic CKD.”

In the FLIEDER study, the researchers sought to characterize both patient characteristics, like demographics and clinical information, and to characterize treatment information and model disease outcomes. Wanner and colleagues used a US healthcare claims database to extract information on 504,687 adults with stage 3 or 4 non-diabetic chronic kidney disease. They developed multivariable time-to-first-event risk prediction models using data from patients’ clinical outcomes and follow-up results. The researchers performed modeling for each of three outcomes: kidney failure/need for dialysis, hospitalization for heart failure, and worsening kidney function.

Previously, Dr. Wanner and colleagues showed that 24% of patients experienced the primary composite outcome of kidney failure/need for dialysis over a median follow-up of 744 days. The incidence rate for this outcome was 10.3 events per 100 patient years. Approximately 11% of patients were hospitalized for heart failure (four events per 100 patient years), whereas another 11% saw their chronic kidney disease worsen to a later disease stage. (4.4 events per 100 patient years).

In the current study, female sex, normal-to-mildly-increased albuminuria A-1, and Asian or Hispanic race appeared to be among the protective factors that reduced patients’ risk for the three modeled outcomes, the researchers wrote. On the other hand, advanced-stage CKD, metastatic cancers, anemia, and heart problems were predictive of adverse outcomes. 

“The risk prediction models developed in this study have potential broad clinical applications in patient care because they include risk factors routinely collected by healthcare professionals,” the researchers concluded. “The use of risk prediction models in clinical practice may aid healthcare decision-making and improve patient outcomes in the non-diabetic CKD population. The next steps would be to validate these models in external data sources.”