Incorporating kidney disease measures into cardiovascular risk prediction: Development and validation in 9 million adults from 72 datasets
Why the study?
Most major clinical guidelines lack a standardized approach to incorporate CKD measures into CVD risk prediction, despite their ability to improve prediction of incident CVD.
Does adding a 'CKD Patch' (incorporating eGFR and albuminuria) to established risk equations (PCE and SCORE) improve the prediction of ASCVD and CVD mortality in adults?
Population
4,143,535 adults across 35 datasets for development and 4,932,824 adults across 37 datasets for validation
Comparison
Addition of CKD Patch vs original PCE and SCORE equations alone
Design
Risk prediction model development and validation study across 72 datasets
Authors
Discussion
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Member takes
Implication
May refine CVD risk equations with CKD measures; leaves open guideline adoption pending prospective validation.
Structured PICO
Does adding a 'CKD Patch' (incorporating eGFR and albuminuria) to established risk equations (PCE and SCORE) improve the prediction of ASCVD and CVD mortality in adults?
P
Population
9,076,359 adults from 72 datasets (4,143,535 adults from 35 datasets for development, and 4,932,824 adults from 37 independent datasets for validation)
I
Intervention
CKD Patch incorporating eGFR and albuminuria added to Pooled Cohort Equation (PCE) and Systematic COronary Risk Evaluation (SCORE)
C
Comparator
Original PCE and SCORE equations (recalibrated in each dataset)
O
Outcome
Prediction of risk of atherosclerotic CVD (ASCVD) and CVD mortalityhard clinical
Incorporating eGFR and albuminuria via the 'CKD Patch' significantly improves the accuracy of established cardiovascular risk prediction models (PCE and SCORE), particularly by correcting risk underestimation in patients with chronic kidney disease.
Cite This Study
Matsushita et al. (2020) studied this question.