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Survival of patients on RRT: Recent Issues and future plans

Survival of patients on RRT: Recent Issues and future plans. UK Renal Registry 2013 Annual Audit Meeting. Dr Damian Fogarty Acting Medical Director, UK Renal Registry Retha Steenkamp Research and Analysis Manager, UK Renal Registry. Outline. UK Renal Registry 2013 Annual Audit Meeting.

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Survival of patients on RRT: Recent Issues and future plans

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  1. Survival of patients on RRT:Recent Issues and future plans UK Renal Registry2013 Annual Audit Meeting Dr Damian FogartyActing Medical Director, UK Renal Registry Retha Steenkamp Research and Analysis Manager, UK Renal Registry

  2. Outline UK Renal Registry2013 Annual Audit Meeting • Survival overview • Current Issues • Future plans

  3. Overview of survival in the current annual report UK Renal Registry2013 Annual Audit Meeting

  4. INPUTS AND OUTPUTS 4 UK Renal Registry2013 Annual Audit Meeting

  5. analyses presented Established renal failure (ERF) synonymous with the terms end-stage renal failure (ESRF) and end stage renal disease (ESRD) which are in more widespread international usage and ESKD. Survival from the start of renal replacement therapy in incident patients on RRT Survival amongst all prevalent RRT patients alive on 31st December 2010 Cause of death for incident and prevalent patients and Projected life years remaining for patients starting RRT

  6. analyses presented-2 Analyses of survival within the 1st year of starting RRT include patients who were recorded as having started RRT for established renal failure (as opposed to acute kidney injury) but who had died within the first 90 days of starting RRT, a group excluded from most other countries’ registry data. Survival analyses are also presented for the first year after 90 days. Prevalent dialysis patients on 31st December 2010 were followed up in 2011 and were censored at transplantation. When a patient is censored at transplantation, this means that the patient is considered as alive up to the point of transplantation, but the patient’s status post-transplant is not considered. Censoring at transplantation systematically removes younger fitter patients from the survival data. The differences are likely to be small due to the relatively small proportion of patients being transplanted in a given year compared to the whole dialysis population (about 12% of the dialysis population aged under 65 and 2% of the population aged 65 years and over). To understand survival of patients, including survival following transplantation, the incident patient analyses should be viewed.

  7. Caveats & process • UKRR can adjust for the effects of the different age distributions of patients in different centres (→age 60) • UKRR not able to adjust for primary renal diagnosis, ethnicity & other comorbidities at start of RRT (especially diabetes) • Lack of case mix information makes interpretation of differences in survival between centres difficult. • Despite the uncertainty about any apparent differences in outcome for centres which appear to be outliers, the UKRR will follow the clinical governance procedures. • Letters to CDs asking them to ensure the results are conveyed to senior CEO level • Encouraged to check cases, M & M reviews etc

  8. incident (new RRT) patient survival Age: 18-64: 97.5% >65: 90.1% Age: 18-64: 93.5% >65: 80.6%

  9. Survival by Dialysis Modality trends

  10. One year incident death rate per 1,000 patient years by age group, 1997-2010 cohort UK Renal Registry2013 Annual Audit Meeting

  11. Medium & long term survival for incident patientsAGE 18-64

  12. Medium & long term survival for incident patientsAGE >65

  13. UK Renal Registry2013 Annual Audit Meeting Growth in prevalent patients, by treatment modality at the end of each year 1982-2011 +2.8% PD +23% HHD +2.8% HD +5.4% Tx

  14. Funnel plot for age adjusted 1 year after 90 days survival, 2006–2009 incident cohort UK Renal Registry2013 Annual Audit Meeting

  15. 2011 data: Funnel plot for age adjusted 1 year after 90 days survival, 2007-2010 incident cohort UK Renal Registry2013 Annual Audit Meeting

  16. One year funnel plot of prevalent dialysis patients ineach centre adjusted to age 60, 2009 cohort UK Renal Registry2013 Annual Audit Meeting

  17. 2011 data: One year survival funnel plot of prevalent dialysis patients by centre adjusted to age 60, 2010 cohort UK Renal Registry2013 Annual Audit Meeting

  18. 07/08 SHMI (Summary Hospital Mortality Index) Age & Sex only – Poisson Funnel Plot UK Renal Registry2013 Annual Audit Meeting

  19. Linkage to Hospital Episodes dataJames Fotheringham - PhD HES 2.8 Million Episodes 1996-2011 290,000 Hospital Admissions (~13 per patient) 2 Million Outpatient Appt. 21,633 Incident RRT Patients 2002 – 2006 UKRR Data until Oct 2009 11,547 Deaths up to 31/12/2010 14.4% At Home Hospital Associated Mortality Renal Centre & Hospital Level 2 Length of Stay & Freq. of Admission Start of RRT & End of Life Comprehensively Adjusted Survival Late presentation, Comorbidity & PRD School of Health and Related Research

  20. Funnel plots detailing centre specific three year survival following adjustmentCentre specific predicted survival at three years adjusted for age and sex Mean Centre-Specific Survival at three years adjusted to age 65 and male: 69.7%, range 60.2 – 78.7%. Six centres with worse than expected survival highlighted in red.

  21. Centre specific survival adjusted for age, sex, ethnicity, socioeconomic status and year of start of renal replacement therapy Mean Centre-Specific Survival at three years adjusted to white 65 year old male in most deprived group starting renal replacement therapy in 2002: 67.9%, range 60.5 – 75.02%. Four centres with worse than expected survival highlighted in red.

  22. Centre specific survival adjusted for age, sex, ethnicity, socioeconomic status, year of start of renal replacement therapy and 16 comorbid conditions Mean Centre-Specific Survival at three years adjusted to all characteristics including demography and comorbidity: 78.8%, range 72.9 – 86.3%. 1 centre with worse than expected survival highlighted in red.

  23. The effect on survival after sequential adjustment for age, PRD and comorbidity on survival, 2006-2010 incident cohort UK Renal Registry2013 Annual Audit Meeting

  24. Treated and untreated ESRD in Australia UK Renal Registry2013 Annual Audit Meeting

  25. New EDTA codes-a quick word Former one 40 years old Incomplete & inflexible 273 different codes New rare diseases Maps to established ICD-10 and SNOMED

  26. Current issues with survival analyses UK Renal Registry2013 Annual Audit Meeting

  27. 2011 data One year survival funnel plot of prevalent dialysis patients by centre adjusted to age 60, 2010 cohort UK Renal Registry2013 Annual Audit Meeting ONLY AGE ADJUSTED!

  28. Problems caused by missing data Not adjusting for important data items - lead to inadequate case-mix adjustment Case-mix adjustment in statistical models are limited to complete cases - Loss of statistical power - Loss of information - Selection bias - Lack of generalizability Most standard statistical methods assumes complete data

  29. Unadjusted 1 year survival of incident RRT patients, 1997-2010 Selection bias! UK Renal Registry2013 Annual Audit Meeting

  30. Missing data

  31. Missing data All patients, 2005-2011 N=46,078 With PRD N=42,961 With Ethnicity N=35,975 With comorbidity N=19,185 41.6% UK Renal Registry2013 Annual Audit Meeting

  32. Future plans UK Renal Registry2013 Annual Audit Meeting

  33. Future plans UK Renal Registry2013 Annual Audit Meeting • Improve data completeness • Focus on increasing data completeness • Regular HES linkage • Multiple imputation • New analyses

  34. HES linkage and ethnicity UK Renal Registry2013 Annual Audit Meeting 1 Hospital admissions and outpatient appointments ethnic groups combined 2 UKRR and HES ethnic groups combined

  35. HES linkage and comorbidity

  36. HES linkage and comorbidity UK Renal Registry2013 Annual Audit Meeting

  37. What is multiple imputation? Imputation: 1. Missing values are replaced by imputations The imputation procedure is repeated many times with each dataset having the same observed values and different sets of imputed values for missing observations 2. Analyse using standard statistical methods 3. Pooling parameter estimates

  38. Thank you for all your unit’s hard work supplying the data Retha Steenkamp Retha.Steenkamp@renalregistry.nhs.uk renalreg@renalreg.com UKRR Office: 0117 323 5665 Damian Fogarty d.fogarty@qub.ac.uk T: 07825 140 158 www.renalreg.com 38

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