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. 2022 May 30;12:9013. doi: 10.1038/s41598-022-13145-w

Table 2.

Equation performance in validation dataset (n = 266).

Formula Bias Precision Accuracy
MPE [95% CI] R2 [95% CI] RMSE [95% CI] MAPE [95% CI] P15 [95% CI] P30 [95% CI]
Cockcroft–Gault − 0.16 [− 0.60; 0.27] 0.40 [0.28; 0.52] 3.52 [3.07; 3.96] 27.2% [22.7–31.7%] 40% [34%; 46%] 77% [72%; 82%]
Ix 0.84 [0.44; 1.25] 0.42 [0.31; 0.52] 3.46 [3.07; 3.85] 30.3% [25.2–35.5%] 46% [40%; 52%] 71% [66%; 77%]
CRAFT 1 0.18 [− 0.13; 0.50] 0.63 [0.53; 0.72] 2.68 [2.34; 3.01] 21.0% [17.7–24.2%] 56% [50%; 62%] 81% [76%; 85%]
CRAFT 2 0.16 [− 0.17; 0.48] 0.61 [0.50; 0.70] 2.78 [2.43; 3.12] 22.3% [18.8–25.8] 49% [44%; 55%] 80% [76%; 85%]

Confidence intervals were calculated with the combined variance of multiple imputation (10×) and bootstrap (1000×).

MPE mean prediction error (mmol/day), MAPE mean absolute percentage error, RMSE root mean squared error (mmol/day), R2 the R2-value calculated with linear regression, p15/p30 the percentage of points that fall within 15%/30% of the outcome.