Work

Can we predict mortality of older patients with advanced chronic kidney disease?

Year: 2025

Type: article

Abstract: Background Studies have shown that patients with advanced chronic kidney disease (CKD) desire to know their prognosis. Although many predictive tools exist, they are not widely used in clinical practice as their applicability is not clear, particularly for older patients. Aim To evaluate the validity of existing prognostic tools in older patients with advanced CKD. Methods This was a single‐centre retrospective validation cohort study on patients aged ≥65 years with grade 4 or 5 CKD between 2009 and 2018. The Ivory, Schmidt and Cohen models were tested by comparing predicted and actual mortality outcomes. Discriminatory ability was measured by area under the receiver operating characteristic curve (AUC), and calibration was assessed by the Hosmer–Lemeshow statistic. The global performance of the predictive regression models was assessed using the measure of goodness of fit with the coefficient of determination Nagalkerke R2. Results A total of 387 patients with a median age of 80 years (IQR 74–85) were included in the study. For the Ivory and Schmidt models, the c‐statistic was 0.617 (95% CI 0.47–0.74) and 0.60 (95% CI 0.47–0.71) respectively, indicating poor discrimination. The Hosmer–Lemeshow statistic was 1.42 (P = 0.22) and 1.59 (P = 0.15) respectively, indicating reasonable calibration. The Cohen model had an overall poor predictive value. Conclusion Existing prognostication tools demonstrate overall suboptimal performance in our validation cohort of older patients. Further research is needed for the development of a prognostic tool specific to the older advanced CKD population.

Source: Internal Medicine Journal

DOI: 10.1111/imj.70089

Authors: Angela Chou, Sanjay Farshid, Mark A. Brown, Kelly Chenlei Li

Institutions: The University of Sydney, UNSW Sydney, St George Hospital

Language: English

Topic: Palliative Care and End-of-Life Issues

Subfield: Public Health, Environmental and Occupational Health

Field: Medicine

Domain: Health Sciences

SDG: Good health and well-being

Open Access status: hybrid

Estimated APC paid: $3,570