**Appendix A**

*Appendix A.1. Risk Model for ICU Admission*

In a multivariate binary logistic regression analysis, BMI, hypertensive nephropathy and coronary artery disease had significant impact on ICU admission. The prediction probability (P) of an ICU stay for each individual patient was calculated with the equation

$$P = \frac{1}{1 + e^{-z}}$$

in which the logit *z* is

$$z = 3.557 + 4.004 \times \text{FIN} + 1.495 \times \text{CAD} - 0.221 \times \text{BMI}$$


The optimal cut-off for the predicted probability of ICU admission was calculated via ROC analysis by using a Youden index (see Figure A1). By setting the cut-off to 0.08, this risk model gained a sensitivity of 94.1%, specificity of 51.1%, false positive rate of 48.9%, false negative rate of 5.9%, positive predictive value of 27.1% and negative predictive value of 97.8% (see Table A1).

**Figure A1.** ROC analysis examining the relationship between the predicted probability of ICU stay and actual ICU admission.

**Table A1.** Crosstabulation illustrating case assignment in our cohort by risk model.


#### *Appendix A.2. Graft and Patient Survival Stratified for Donor and Recipient Age*


**Table A2.** Mortality table with age-dependent comparison stratified for donor age (very old donors ≥75 vs. old donors) or recipient age (very old recipients ≥70 years vs. old recipients).

#### *Appendix A.3. Underlying Renal Diseases*

**Table A3.** Underlying renal diseases for patients with or without ICU stay after KT.

