Related Experiment Video
Updated: Jan 21, 2026

Robot-Assisted Kidney Transplantation
Published on: July 19, 2021
Dynamic Frailty Before Kidney Transplantation: Time of Measurement Matters
Nadia M Chu1,2, Arlinda Deng1, Hao Ying2
1Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD.
Background:
Frail kidney transplant (KT) recipients have higher risk of adverse post-KT outcomes. Yet, there is interest in measuring frailty at KT evaluation and then using this information for post-KT risk stratification. Given long wait times for KT, frailty may improve or worsen between evaluation and KT. Patterns, predictors, and post-KT adverse outcomes associated with these changes are unclear.
Methods:
Five hundred sixty-nine adult KT candidates were enrolled in a cohort study of frailty (November 2009-September 2017) at evaluation and followed up at KT. Patterns of frailty transitions were categorized as follows: (1) binary state change (frail/nonfrail), (2) 3-category state change (frail/intermediate/nonfrail), and (3) raw score change (-5 to 5). Adjusted Cox proportional hazard and logistic regression models were used to test whether patterns of frailty transitions were associated with adverse post-KT outcomes.
Results:
Between evaluation and KT, 22.0% became more frail, while 24.4% became less frail. Black race (relative risk ratio, 1.98; 95% confidence interval [CI], 1.07-3.67) was associated with frail-to-nonfrail transition, and diabetes (relative risk ratio, 2.56; 95% CI, 1.22-5.39) was associated with remaining stably frail. Candidates who became more frail between 3-category states (hazard ratio, 2.27; 95% CI, 1.11-4.65) and frailty scores (hazard ratio, 2.36; 95% CI, 1.12-4.99) had increased risk of post-KT mortality and had higher odds of length of stay ≥2 weeks (3-category states: odds ratio, 2.02; 95% CI, 1.20-3.40; frailty scores: odds ratio, 1.92; 95% CI, 1.13-3.25).
Conclusions:
Almost half of KT candidates experienced change in frailty between evaluation and KT, and those transitions were associated with mortality and longer length of stay. Monitoring changes in frailty from evaluation to admission may improve post-KT risk stratification.
Related Concept Videos
Kidney Transplant I: Introduction
Kidney Transplant II: Surgical Procedure
Kidney Transplant III: Nursing Management
Classifying Matter by State
Physical and Chemical Properties of Matter
Classifying Matter by Composition
According to its composition, the matter can be classified into two broad categories — pure substances and mixtures.
A pure substance is a form of matter that has a constant composition throughout with uniform properties. For example, any sample of sucrose has the same composition and same physical properties, such as melting point, color, and sweetness, regardless of the source from which it is isolated.
A mixture is composed of two or...

