Key result
A structural equation modeling-based estimate of latent kidney function did not significantly outperform cystatin C-based eGFR in predicting 10-year incident cardiovascular disease risk.
Why the study?
It is unclear which eGFR equation best predicts increased CVD risk and whether integrating multiple kidney function markers improves prediction.
Does a structural equation modeling (SEM)-based index of multiple kidney function markers improve 10-year CVD risk prediction compared to established eGFR equations in a population-based cohort?
Cohort (n=1,317)
No
Does a structural equation modeling (SEM)-based index of multiple kidney function markers improve 10-year CVD risk prediction compared to established eGFR equations in a population-based cohort?
Absolute Event Rate: 0.7% vs 0.69%
p-value: p=0.88
While integrating multiple kidney function markers via structural equation modeling predicts CVD risk, it does not outperform the simpler cystatin C-based eGFR equation.
Lower kidney function is known to enhance cardiovascular disease (CVD) risk. It is unclear which estimated glomerular filtration rate (eGFR) equation best predict an increased CVD risk and if prediction can be improved by integration of multiple kidney function markers. We performed structural equation modeling (SEM) of kidney markers and compared the performance of the resulting pooled indexes with established eGFR equations to predict CVD risk in a 10-year longitudinal population-based design. We split the study sample into a set of participants with only baseline data (n = 647; model-building set) and a set with longitudinal data (n = 670; longitudinal set). In the model-building set, we fitted five SEM models based on serum creatinine or creatinine-based eGFR (eGFRcre), cystatin C or cystatin-based eGFR (eGFRcys), uric acid (UA), and blood urea nitrogen (BUN). In the longitudinal set, 10-year incident CVD risk was defined as a Framingham risk score (FRS)>5% and a pooled cohort equation (PCE)>5%. Predictive performances of the different kidney function indexes were compared using the C-statistic and the DeLong test. In the longitudinal set, a SEM-based estimate of latent kidney function based on eGFRcre, eGFRcys, UA, and BUN showed better prediction performance for both FRS>5% (C-statistic: 0.70; 95% CI: 0.65-0.74) and PCE>5% (C-statistic: 0.75; 95%CI: 0.71-0.79) than other SEM models and different eGFR formulas (DeLong test p-values<3.21×10-6 for FRS>5% and <1.49×10-9 for PCE>5%, respectively). However, the new derived marker could not outperform eGFRcys (DeLong test p-values = 0.88 for FRS>5% and 0.20 for PCE>5%, respectively). SEM is a promising approach to identify latent kidney function signatures. However, for incident CVD risk prediction, eGFRcys could still be preferrable given its simpler derivation.
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Fujii et al. (2023) conducted a cohort in Cardiovascular disease risk (n=1,317). Structural equation modeling (SEM) of kidney function markers vs. Cystatin C-based eGFR (eGFRcys) was evaluated on Prediction of 10-year incident CVD risk (Framingham risk score > 5%) (95% CI 0.65-0.74, p=0.88). A structural equation modeling-based estimate of latent kidney function did not significantly outperform cystatin C-based eGFR in predicting 10-year incident cardiovascular disease risk.
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