Key result
Grams prediction model shows moderate discrimination for 2-year kidney replacement therapy in advanced CKD.
Cohort (n=1,333)
Yes
The Grams prediction model demonstrates moderate applicability for estimating the risks of kidney replacement therapy, cardiovascular disease, and death in Chinese patients with advanced CKD, but its performance could be enhanced by recalibration and the inclusion of additional predictors like albumin.
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Should not yet guide practice in Chinese advanced CKD without recalibration; extends Grams model validation while highlighting overestimation of cardiovascular and mortality risks.
. The model demonstrated moderate to good discrimination across outcomes, performing well in predicting kidney replacement therapy (KRT) but overestimating the risks of cardiovascular disease (CVD) and mortality. Calibration for KRT was accurate, while other outcomes required recalibration to improve alignment with observed data. Although recalibration enhanced calibration, it did not improve the model's discrimination. Importantly, the study identified key predictors, such as albumin levels, that were not included in the original Grams model but significantly improved prognostic accuracy when incorporated. These findings suggest that while the Grams model has moderate applicability to the Chinese CKD population, its predictive performance can be improved by including additional critical variables. Further efforts are needed to improve the Grams model prior to application in the Chinese CKD population, such as the inclusion of novel valuable predictors.
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Yao et al. (2025) conducted a cohort in Advanced Chronic Kidney Disease (n=1,333). Grams prognostic model vs. Observed outcomes was evaluated on Model discrimination (C-index) for predicting 2-year kidney replacement therapy (KRT) (C-index 0.720, 95% CI 0.688-0.752). The Grams prediction model demonstrated moderate discrimination for predicting 2-year kidney replacement therapy in a Chinese population with advanced chronic kidney disease, but overestimated cardiovascular and mortality risks.
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