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Table 3 Multivariable analysis of predictors of living-donor transplant

From: Interaction between socioeconomic deprivation and ethnicity for likelihood of receiving living-donor kidney transplantation

 

Univariable

Multivariable

Odds Ratio (95% CI)

p-Value

Odds Ratio (95% CI)

p-Value

IMD (per Quintile)

1.30 (1.22–1.39)

< 0.001

1.23 (1.13–1.34)

< 0.001

Age (per Decade)

0.84 (0.79–0.90)

< 0.001

0.95 (0.87–1.04)

0.288

Gender (Male)

1.10 (0.92–1.33)

0.298

1.11 (0.88–1.40)

0.372

Ethnicity

 

< 0.001

 

0.005

White

South Asian

0.37 (0.28–0.47)

< 0.001

0.65 (0.48–0.89)

0.007

Black

0.28 (0.18–0.45)

< 0.001

0.54 (0.32–0.90)

0.018

Other

0.89 (0.61–1.28)

0.522

1.17 (0.75–1.82)

0.487

Body Mass Index (per 5 km/m2)

0.90 (0.81–1.00)

0.044

0.98 (0.87–1.11)

0.751

Diabetes (Yes)

0.62 (0.46–0.85)

0.002

0.67 (0.45–0.98)

0.041

Hypertension (Yes)

1.16 (0.96–1.41)

0.124

1.19 (0.94–1.51)

0.146

Previous Transplants (Yes)

0.89 (0.59–1.34)

0.571

1.39 (0.85–2.28)

0.193

On Dialysis (Yes)

0.36 (0.30–0.44)

< 0.001

0.61 (0.48–0.78)

< 0.001

Time on Waiting List*

 

< 0.001

 

< 0.001

  < 12 Months

12–23 Months

0.66 (0.50–0.87)

0.003

0.67 (0.49–0.92)

0.013

24–35 Months

0.30 (0.22–0.41)

< 0.001

0.33 (0.24–0.47)

< 0.001

36–59 Months

0.13 (0.09–0.17)

< 0.001

0.13 (0.09–0.19)

< 0.001

60+ Months

0.12 (0.08–0.16)

< 0.001

0.14 (0.09–0.20)

< 0.001

Year of Transplant (per Decade)

0.40 (0.32–0.52)

< 0.001

0.35 (0.26–0.49)

< 0.001

  1. Results of the univariable analysis are from individual binary logistic regression models for each factor. All factors were then entered into a multivariable binary logistic regression model; this analysis was based on N = 1724 cases (N = 644 events), after exclusion of those with missing data for any of the factors considered. Odds ratios are reported for the stated category relative to the reference category for categorical variables, or per an increase of the stated number of units for ordinal/continuous factors. Bold p-values are significant at p < 0.05. *Goodness of fit testing indicated poor model fit when the time on the waiting list was treated as continuous (Hosmer-Lemeshow test: p < 0.001), hence it was divided into categories and treated as nominal for analysis