Model 1a
\[ \text{Probability by model 1a} = \frac{{e^{1.250165 \times \text{female} + 0.0638349 \times \text{age_65} + 0.8517973 \times \text{mdrd_ckd} - 0.4269041 \times \text{diuretic} - 6.421989}}}{{e^{1.250165 \times \text{female} + 0.0638349 \times \text{age_65} + 0.8517973 \times \text{mdrd_ckd} - 0.4269041 \times \text{diuretic} - 6.421989} + 1}} \]
Model 1b
\[ \text{Probability by model 1b} = \frac{{e^{0.8456204 \times \text{female} + 0.4766383 \times \text{age_65} + 0.3254639 \times \text{mdrd_ckd} + 0.1819375 \times \text{sua} + 2.034854 \times \text{diuretic} - 7.969009}}}{{e^{0.8456204 \times \text{female} + 0.4766383 \times \text{age_65} + 0.3254639 \times \text{mdrd_ckd} + 0.1819375 \times \text{sua} + 2.034854 \times \text{diuretic} - 7.969009} + 1}} \]
Model 2
\[ \text{Probability by model 2} = \frac{{e^{1.2303453 \times \text{female} + 0.4373190 \times \text{age_65} + 1.0254634 \times \text{mdrd_ckd} + 0.0144714 \times \text{allo_dose} + 1.0091512 \times \text{diuretic} - 8.6836693}}}{{e^{1.2303453 \times \text{female} + 0.4373190 \times \text{age_65} + 1.0254634 \times \text{mdrd_ckd} + 0.0144714 \times \text{allo_dose} + 1.0091512 \times \text{diuretic} - 8.6836693} + 1}} \]