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Survival after graft failure Dr Lynsey Webb Registrar UK Renal Registry UK Renal Registry 2011 Annual Audit Meeting.

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Presentation on theme: "Survival after graft failure Dr Lynsey Webb Registrar UK Renal Registry UK Renal Registry 2011 Annual Audit Meeting."— Presentation transcript:

1 Survival after graft failure Dr Lynsey Webb Registrar UK Renal Registry UK Renal Registry 2011 Annual Audit Meeting

2 Overview Background Survival after transplant failure (UK) Defining a proxy group European analyses Future work

3 Background In 2009: 23,284 prevalent renal transplant patients 2.9% experienced graft failure (excluding death as cause of graft loss) Evidence (mostly North American) that kidney transplant loss associated with significant mortality and morbidity Why is this important? Growing transplant population Young patients Limited guidelines/ evidence for best practice Some deaths may be preventable Quality of life Financial implications

4 Survival analyses UKRR and NHSBT data Cases = Patients starting dialysis after 1 st transplant failure (>90 days function) 01/01/2000 - 31/12/2008 Controls = Patients starting RRT on dialysis 01/01/2000 - 31/12/2008, and wait-listed for transplantation within 2 years Followed to death, or loss to follow up, or 31/12/2010 Censored if transplanted Exclusions: <18 yrs, missing primary renal diagnosis Hazard ratios (case/control) for specific time periods calculated. Adjusted for age, gender, DM, ethnicity and modality

5 Final cohort UK incident adult RRT patients (1 st Jan 2000 - 31 st Dec 2008) in UK (n= 46,793 ) ControlsCases UK adult patients with failure of 1 st transplant (1 st Jan 2000 – 31 st Dec 2008) (n=3,568) Controls included in analysis (n= 11,742) Cases included in analysis (n=3,059) Pre-emptive transplants (n=1,645) Not listed (n=31,863) Listed >2 years from RRT start (n=1,543) Pre-emptive re- transplants (n=93) 1 st transplant <90days duration (n=416)

6 Demographics CasesControlsP value Number of subjects3,05911,742 Age mean (SD)48.2 (15.0)47.4 (13.5)0.003 Sex (% male)60.961.4n.s. Ethnicity – White (%) Ethnicity – Asian (%) Ethnicity – Black (%) Ethnicity – Other (%) Ethnicity – Missing N (%) 86.3 7.1 4.5 2.1 63 (2.1) 81.4 10.2 5.7 2.7 77 (0.7) <0.0001 Diabetes as PRD (%) Missing PRD N (%) 9.6 34 (1.1) 15.6 160 (1.4) <0.0001 Dialysis modality (% HD)7758<0.0001 Mean duration 1 st transplant (yrs) (SD)9.4 (6.5)N/A Mean duration dialysis pre-transplant (yrs)(SD)1.7 (2.1)N/A Mean duration on dialysis (yrs) (SD)2.3 (1.9)2.2 (1.6)n.s.

7 Demographics CasesControlsP value Number of subjects3,05911,742 Age mean (SD)48.2 (15.0)47.4 (13.5)0.003 Sex (% male)60.961.4n.s. Ethnicity – White (%) Ethnicity – Asian (%) Ethnicity – Black (%) Ethnicity – Other (%) Ethnicity – Missing N (%) 86.3 7.1 4.5 2.1 63 (2.1) 81.4 10.2 5.7 2.7 77 (0.7) <0.0001 Diabetes as PRD (%) Missing PRD N (%) 9.6 34 (1.1) 15.6 160 (1.4) <0.0001 Dialysis modality (% HD)7758<0.0001 Mean duration 1 st transplant (yrs) (SD)9.4 (6.5)N/A Mean duration dialysis pre-transplant (yrs)(SD)1.7 (2.1)N/A Mean duration on dialysis (yrs) (SD)2.3 (1.9)2.2 (1.6)n.s.

8 Survival: UK Hazard ratio (case/ control) Un- adjusted +age+gender+DM and DM*age + ethnicity +modality 0-90 days25.0 (11.1-56.3) 23.9 (10.6-53.8) 23.9 (10.6-53.8) 23.1 (10.2-52.1) 23.4 (10.3-52.8) 17.0 (7.5-38.5) 90-365 days 6.5 (4.7-9.2) 6.3 (4.5-8.9) 6.3 (4.5-8.9) 6.5 (4.6-9.1) 6.3 (4.4-8.8) 5.9 (4.1-8.4) 1-2 years4.0 (3.2-5.1) 4.1 (3.2-5.1) 4.1 (3.2-5.2) 4.3 (3.4-5.5) 4.2 (3.3-5.3) 3.9 (3.0-4.9) 2-3 years2.5 (2.0-3.1) 2.8 (2.2-3.4) 2.8 (2.2-3.5) 3.2 (2.5-4.0) 3.0 (2.4-3.8) 3.1 (2.4-3.9) 3-5 years1.4 (1.1-1.7) 1.7 (1.3-2.0) 1.7 (1.3-2.0) 1.8 (1.5-2.3) 1.8 (1.5-2.2) 1.8 (1.4-2.2) >5 years 0.8 (0.6-1.2) 1 (0.7-1.5) 1 (0.7-1.5) 1.2 (0.8-1.7) 1.3 (0.8-1.7) 1.2 (0.9-1.8)

9 Survival: UK Hazard ratio (case/ control) Un- adjusted +age+gender+DM and DM*age + ethnicity +modality 0-90 days25.0 (11.1-56.3) 23.9 (10.6-53.8) 23.9 (10.6-53.8) 23.1 (10.2-52.1) 23.4 (10.3-52.8) 17.0 (7.5-38.5) 90-365 days 6.5 (4.7-9.2) 6.3 (4.5-8.9) 6.3 (4.5-8.9) 6.5 (4.6-9.1) 6.3 (4.4-8.8) 5.9 (4.1-8.4) 1-2 years4.0 (3.2-5.1) 4.1 (3.2-5.1) 4.1 (3.2-5.2) 4.3 (3.4-5.5) 4.2 (3.3-5.3) 3.9 (3.0-4.9) 2-3 years2.5 (2.0-3.1) 2.8 (2.2-3.4) 2.8 (2.2-3.5) 3.2 (2.5-4.0) 3.0 (2.4-3.8) 3.1 (2.4-3.9) 3-5 years1.4 (1.1-1.7) 1.7 (1.3-2.0) 1.7 (1.3-2.0) 1.8 (1.5-2.3) 1.8 (1.5-2.2) 1.8 (1.4-2.2) >5 years 0.8 (0.6-1.2) 1 (0.7-1.5) 1 (0.7-1.5) 1.2 (0.8-1.7) 1.3 (0.8-1.7) 1.2 (0.9-1.8)

10 Re wait-listing analysis Of the 3,059 cases, 1,610 were listed for repeat transplantation within 2 yrs Hazard ratio (re wait-listed cases/control) AdjustedP value 0-365 days3.1 (1.3-7.3) 0.009 1-2 years1.6 (1.0-2.6) 0.04 2-3 years2.3 (1.6-3.2) <0.0001 3-5 years1.5 (1.1-2.1) 0.007 >5 years0.72 (0.4-1.3) 0.3

11 Discussion As would be predicted the greatest risk of death is in the first few weeks following transplant failure but persists until at least 5 years post transplant failure Even if only patients listed for further transplantation (i.e. fitter cases) are considered, an excess mortality risk is seen Limitations: choice of control group, lack of comorbidity data, data capture issues

12 Proxy group Analyses

13 Background Evolved from plans to undertake a European study ERA-EDTA registry doesn’t normally collect wait-listing data Is it possible (from the data available) to design a “proxy” cohort of patients fit for transplantation? Preliminary analyses using UK data Who would be likely to be fit for transplantation? <50 years old Non-diabetics

14 Cases vs. proxy controls (1) 01/01/2000-31/12/2008 Cases = as before, limiting to patients <50 years, non-diabetic Controls = incident dialysis patients <50 years, non-diabetic HR (Case/control) calculated Adjusted for age, gender, ethnicity and modality at start

15 Cases vs. proxy controls (2) Hazard ratio (case/ control)Adjusted HRP value 0-90 days1.7 (1.3-2.3)0.0002 90-365 days1.5 (1.2-1.9)<0.0001 2-5 years1.0 (0.8-1.3)0.9 Why are the results so different?

16 Cases vs. proxy controls (3) CasesProxy controls P value Number of subjects14748057 Age at dialysis start mean37.037.7n.s Sex (% male)907 (61.5)4,832 (60)n.s GN as PRD (%) Missing as PRD (%) Uncertain as PRD (%) 399 (27.1) 12 (0.8) 301(20.5) 1689 (21.0) 427 (5.3) 1924 (23.9) <0.0001 Dialysis modality (% HD)72.967.0<0.0001 UK wait-listing data used to split proxy group into true and proxy controls

17 Cases vs. proxy controls (4) CasesTrue controls Proxy controls P values Number of subjects147454072681 Age at dialysis start mean (SD)37 (8.7) 39.2 (8.3) Sex (% male)61.560.857.90.02 Ethnicity – White (%) Ethnicity – Missing N (%) 86.3 8 (0.5) 78.3 47 (0.9) 73.7 374 (14.0) <0.0001 GN as PRD (%) HTN as PRD (%) Pyelonephritis as PRD (%) Uncertain as PRD (%) Missing PRD N (%) 27.1 3.9 22.9 20.5 12 (0.8) 25.3 6.8 13.2 23.5 73 (1.4) 15.1 7.1 12.8 28.8 201 (7.5) <0.0001 Dialysis modality (% HD)72.959.283.1<0.0001 % who died (censoring at transplant)12.12.427.5<0.0001 % who died (not censored)13.24.027.8<0.0001

18 KM survival True controls Cases Proxy controls Days post dialysis start Survival function

19 Discussion Very hard to define a proxy group Higher proportion of missing data in non- listed proxy controls (previous UKRR work has shown missing data is associated with poorer outcomes) Accurate clinical reasoning – physicians do well at selecting patients suitable for transplants Local-level audit to generate some ideas

20 European Analyses

21 European analyses ERA-EDTA wait-listing data (1998-2005) for: Norway Netherlands Austria Scotland Group A >18yrs old Starting dialysis after failure of 1 st transplant (>90days function) 01/01/1998-31/12/2003 Group B Incident dialysis patients >18yrs commencing dialysis Wait-listed pre or within 2 yrs of RRT start Censoring: Transplantation/re- transplantation Lost to follow-up End of study 31/12/2008

22 KM survival (adjusted) UK (E&W) Controls (Group B) Cases (Group A) Days Survival function

23 Discussion There appears to be significant variation in outcomes after graft failure between countries Access to transplantation Transplantation practices Survival on dialysis Prevalence of comorbidities (e.g. High DM rate in Austria)

24 Future work

25 Questionnaire examining centre-level practices Service provision and staffing Service design Research and audit IT facilities Explore cause of death data and compare with MRIS death certificate information Funding for a case-control study

26 Acknowledgements Many thanks to: Supervisors (C. Tomson, Y. Ben-Shlomo) Anna Casula (UKRR statistician) UKRR transplant sub-group UK renal centres and patients Scottish Renal Registry Data and systems staff (UKRR) NHS Blood and Transplant ERA-EDTA and other participating registries

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