External validation of the KFRE and Grams prediction models for kidney failure and death in a Spanish cohort of patients with advanced chronic kidney disease

Gallego-Valcarce, Eduardo; Shabaka, Amir; Tato-Ribera, Ana María; Landaluce-Triska, Eugenia; León-Poo, Mariana; Roldan, Deborah; Gruss, Enrique

doi:10.1007/s40620-023-01819-1

Cited by 2 publications

References 26 publications

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Adding biomarker change information to the kidney failure risk equation improves predictive ability for dialysis dependency in eGFR <30 ml/min/1.73 m2

Okada,

Aso,

Kurakawa

et al. 2024

Clinical Kidney Journal

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Background Although the Kidney Failure Risk Equation (KFRE), a well-known predictive model for predicting dialysis dependency, is useful, it remains unclear whether the addition of biomarker changes to the KFRE model in patients with an estimated glomerular filtration rate (eGFR) < 30 mL/min/1.73 m2 will improve its predictive value. Methods We retrospectively identified adults with eGFR < 30 mL/min/1.73 m2 without dialysis dependency, and available health checkup data for 2 successive years using a large Japanese claims database (DeSC, Tokyo, Japan). We dichotomized the entire population into 50% training set and the other half as validation set. To assess the incremental value in the predictive ability for dialysis dependency by the addition of changes in eGFR and proteinuria, we calculated the difference in the C-statistics and net reclassification index (NRI). Results We identified 4,499 individuals and observed 422 individuals (incidence of 45.2 per 1000 person-years) who developed dialysis dependency during the observation period (9,343 person-years). Adding biomarker changes to the KFRE model improved C-statistics from 0.862 to 0.921, with an improvement of 0.060 (95% CI of 0.043–0.076, P <0.001). The corresponding NRI was 0.773 (95% CI: 0.637– 0.908), with an NRI for events of 0.544 (95% CI of 0.415–0.672) and NRI for non-events of 0.229 (95% CI of 0.186–0.272). Conclusions The KFRE model was improved by incorporating yearly changes in its components. The added information may help clinicians identify high-risk individuals and improve their care.

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Adding biomarker change information to the kidney failure risk equation improves predictive ability for dialysis dependency in eGFR <30 ml/min/1.73 m2

Okada,

Aso,

Kurakawa

et al. 2024

Clinical Kidney Journal

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Machine Learning based Diagnosis of Kidney Abnormality Recognition on CT Scan Images

Pallab,

Mojumdar,

Chakraborty

et al. 2024

2024 11th International Conference on Computing for Sustainable Global Development (INDIACom)

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External validation of the KFRE and Grams prediction models for kidney failure and death in a Spanish cohort of patients with advanced chronic kidney disease

Cited by 2 publications

References 26 publications

Adding biomarker change information to the kidney failure risk equation improves predictive ability for dialysis dependency in eGFR <30 ml/min/1.73 m2

Adding biomarker change information to the kidney failure risk equation improves predictive ability for dialysis dependency in eGFR <30 ml/min/1.73 m2

Machine Learning based Diagnosis of Kidney Abnormality Recognition on CT Scan Images

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External validation of the KFRE and Grams prediction models for kidney failure and death in a Spanish cohort of patients with advanced chronic kidney disease

Cited by 2 publications

References 26 publications

Adding biomarker change information to the kidney failure risk equation improves predictive ability for dialysis dependency in eGFR &lt;30 ml/min/1.73 m2

Adding biomarker change information to the kidney failure risk equation improves predictive ability for dialysis dependency in eGFR &lt;30 ml/min/1.73 m2

Machine Learning based Diagnosis of Kidney Abnormality Recognition on CT Scan Images

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Adding biomarker change information to the kidney failure risk equation improves predictive ability for dialysis dependency in eGFR <30 ml/min/1.73 m2

Adding biomarker change information to the kidney failure risk equation improves predictive ability for dialysis dependency in eGFR <30 ml/min/1.73 m2