Table 2.

Coefficients (standard errors) and performance of the models developed in the development dataset

Model	Variables						Model Fit
	intercept	logCys	logScr	logAge	Female	Black	RMSE	R2
							(95% CI)*	(95% CI)
1. Cystatin	4.34	−1.18					0.237	0.817
	(0.01)	(0.01)					(0.227, 0.248)	(0.800,0.833)
2. Cystatin, age, sex, race	4.78	−1.15		−0.12	−0.07	0.06	0.232	0.824
	(0.08)	(0.01)		(0.02)	(0.01)	(0.01)	(0.222,0.243)	(0.808,0.840)
3. Creatinine	4.38		−1.10				0.282	0.742
	(0.01)		(0.01)				(0.271,0.292)	(0.720,0.764)
4. Creatinine, age, sex, race	5.40		−1.12	−0.27	−0.26	0.20	0.228	0.831
	(0.07)		(0.01)	(0.02)	(0.01)	(0.01)	(0.217,0.239)	(0.813,0.848)
5. Creatinine, Cystatin	4.39	−0.82	−0.41				0.220	0.843
	(0.01)	(0.02)	(0.02)				(0.210,0.230)	(0.827,0.857)
6. Creatinine, Cystatin, age, sex, race	5.13	−0.60	−0.62	−0.19	−0.17	0.11	0.199	0.872
	(0.07)	(0.02)	(0.02)	(0.02)	(0.01)	(0.01)	(0.188,0.209)	(0.857,0.886)
7. MDRD Study equation	5.16		−1.15	−0.203	−0.298	0.19	0.231	0.829
							(0.220,0.242)	(0.811,0.846)

Scr, serum creatinine; Cys, serum Cystatin C; RMSE, root mean square error; CI, confidence interval; MDRD Study, Modification of Diet in Renal Disease Study.

^*The 95% confidence interval around the estimates of bias, IQR, P₃₀ and RMSE provides the range of values which is likely to include the parameter in 95% of circumstances. Comparison of confidence intervals around any metric for two equations provides information as to difference between them. If the two metrics were the same, the chance that the upper and lower bounds of the confidence intervals would not overlap is less than 5%. Therefore, it can be concluded that they are statistically significantly different