Multivariate Logistic Regression Model for significant predictors for inclusion on CKD QOF register.

<p>*p<0.0001.</p>i<p>For an increase in years from the mean age.</p>ii<p>In comparison with male gender.</p>iii<p>Indicates the reference indicator.</p>iv<p>The presence of the disease in comparison to those without it. The disease was ascertained by having a Read code for the disease.</p

Age standardised prevalence (%) of stages 3–5 CKD from 2005–2009.

<p>CKD stage was defined by the last two consecutive laboratory eGFRS at least seven days apart in the year in question. Note figures are approximated to the nearest decimal place and therefore may be slightly different from the total figure.</p

CKD Management according to QOF standards between 1st January 2008 to 1st April 2009⁺.

a<p>Patients with two consecutive eGFRS under 60 at least seven days apart.</p>b<p>Patients with a QOF business Rule Read code for CKD.</p>c<p>Patients with two consecutive eGFRS under 60 at least seven days apart and a QOF business rule Read code.</p>d<p>Patients with a QOF business Rule Read code for CKD but no sustained eGFRs below 60.</p>e<p>Patients with two consecutive eGFRS under 60 at least seven days apart but no QOF business Rule Read code for CKD.</p><p>Proportions quoted are of the total in that group.</p><p>*When comparing groups d and e/c and e, p<0.0001.</p>+<p>QOF pay for performance business rules look back 15 months hence time period.</p

Demographics of CKD patients between 1st January 2008 to 1st April 2009 (The proportions in brackets are not adjusted and crude percentages).

a<p>Patients with two consecutive eGFRS under 60 at least seven days apart.</p>b<p>Patients with a QOF business Rule Read code for Chronic Kidney Disease.</p>c<p>Patients with 2 consecutive eGFRS <60 at least 7 days apart and a QOF business rule Read code.</p>d<p>Patients with a QOF business Rule Read code for CKD but no sustained eGFRs below 60.</p>e<p>Patients with 2 consecutive eGFRS <60 at least 7 days apart but no QOF Read code for CKD.</p>f<p>Where reported as not all patients have Ethnic group reported.</p>g<p>Diabetes Mellitus – Type 1&2, CHD = Coronary Heart Disease, PVD = peripheral vascular disease.</p

Prevalence of CKD by year combining CKD stage derived from laboratory eGFRs and eGFR calculated from serum creatinine.

<p>Prevalence of CKD by year combining CKD stage derived from laboratory eGFRs and eGFR calculated from serum creatinine.</p

The definition of Stages 3–5 CKD in the prevalence cohort.

<p>The definition of Stages 3–5 CKD in the prevalence cohort.</p

Causes of patient death following kidney transplantation.

<p>Causes of patient death following kidney transplantation.</p

Kaplan Meier survival curve showing death censored graft survival for the presence or absence of HLA-C2 allele in the recipient.

<p>The presence of an HLA-C2 allele in the recipient was associated with a significant improvement in death censored graft survival (10-year survival: 74.2% versus 54.3%).</p

Demographics and distribution of parameters that are known to affect graft and patient survival after kidney transplantation.

<p>As the analysis involved the comparison between absence of HLA-C2 in the recipient versus presence of HLA-C2 in the recipient groups were divided accordingly. Absence of HLA-C2 represents HLA-C1 homozygous whereas presence of HLA-C2 combines both heterozygous and HLA-C2 homozygous groups. CI is 95% confidence interval.</p><p>*indicates significance by ANOVA test,</p>+<p>indicates significance by Kendall's tau-b test.</p

Kaplan Meier survival curve showing patient survival for the presence or absence of HLA-C2 allele in the recipient.

<p>The presence of an HLA-C2 allele in the recipient was associated with a significant improvement in patient survival (10-year survival: 88.5% versus 80.4%).</p