Univariate model

Characteristic N **30 % eGFRCYS<eGFRCRE Univariate logistic regression **
OR1 95% CI1 p-value
age 312 1.02 1.00, 1.05 0.034
Female 312 1.16 0.69, 1.98 0.577
White 312 1.32 0.65, 2.91 0.472
bmi_cat 297
    Normal Range — —
    Obse 1.91 0.97, 3.85 0.064
    Overweight 1.36 0.66, 2.81 0.407
    Underweight 2.44 0.48, 10.3 0.238
smoking 312 1.89 1.11, 3.26 0.020
htn 312 1.65 0.84, 3.50 0.167
cad 312 1.96 1.14, 3.40 0.016
dm 312 1.62 0.95, 2.77 0.076
cirrhosis 312 5.06 1.57, 17.6 0.007
malnutrition 312 1.53 0.86, 2.67 0.142
ace_arb 312 1.41 0.83, 2.44 0.213
ppi 312 1.90 0.99, 3.90 0.066
diu 312 2.33 1.31, 4.31 0.005
steroids 312 2.33 1.31, 4.31 0.005
alb_cat 285
    >=4 — —
    <3 16.1 3.30, 117 0.001
    3-3.99 4.58 2.50, 8.48 <0.001
hgb_cat 293
    >=12 — —
    <=10 2.85 1.44, 5.63 0.002
    10-11.99 1.30 0.66, 2.51 0.435
sarcopenia 312
    0 — —
    1 1.77 0.92, 3.32 0.078
sarcopenia_old 312
    0 — —
    1 2.39 1.38, 4.25 0.002
sarcopenia_SMA 312
    0 — —
    1 2.07 1.20, 3.56 0.009
1 OR = Odds Ratio, CI = Confidence Interval

heat map by sex

## `summarise()` has grouped output by 'male', 'Q3_SMA', 'Variable'. You can
## override using the `.groups` argument.

eGFR_CRE_CYS

eGFR_CRE