ADDING INSIGHT BY USING CER TECHNIQUES WHEN EXAMINING RACIAL DISPARITIES IN TRANSPLANTATION

David Taber Division of Transplant Surgery Department of Surgery Medical University of South Carolina

MUSC DATA – Graft Survival for All Adult Kidney Transplants Since 1999

Survival non-AA AA Abs Dif p-Value 1-yr 92% 91% 1% 0.411 3-yr 86% 82% 4% 0.030 5-yr 80% 75% 5% 0.010 10-yr 64% 58% 6% 0.005

AA

non-AA

Etiologies for graft survival disparities in Black kidney transplant recipients

NEJM 2002;346:580-90.

• ↑ HLA Mismatch

• ↑ MHC Polymorphisms

• Hyper-immune responsiveness

• ↑ Drug doses & adjustments

• ↑ Non-adherence

• Socioeconomic barriers

• ↓ Living Donors

• ↑ Delayed Graft Function

• ↑ Time on Dialysis

• ↑ Hypertension

• ↑ Doses Immuno-suppressants

• ↑ Diabetes

Racial Disparities in Transplantation

• African-Americans have numerous disadvantageous factors contributing to the risk of graft loss

•  Pre-Transplant •  Access to care, socioeconomics, time on dialysis, comorbidities

•  Peri-Transplant •  Donor characteristics, immunologic characteristics

•  Post-Transplant •  Immunologic risks, comorbidities

• Due to the multidimensional interaction with these factors, difficult to determine salient etiologies

• Use of a CER approach; propensity scoring, and sequential multivariate analysis of archival transplant data may help discern the important contributory factors

Population •  Large-scale single center retrospective longitudinal cohort

study of 1,910 adult solitary kidney transplant recipients transplanted between 1999 - 2012

•  Included detailed baseline and follow-up data collection and analysis •  Sociodemographics (donor and recipient), peri-operative transplant

characteristics, post-transplant outcomes

• Utilize propensity scoring (binary logistic regression) with subsequent sequential MV modeling

Results •  1,910 adult solitary kidney transplant recipients included

•  Transplanted between 1999 - 2012 •  55% were African-American •  Mean follow-up 6.1 ± 3.8 years

• Similar to previous studies, large number of dissimilar characteristics based on race

Baseline Sociodemographics Characteristic   Non-African-American

(n=861)  African-American

(n=1049)   p-Value  

Age   51±14   49±13   0.014  Gender   39%   43%   0.064  BMI   27±6   28±6   <0.001  Did Not Graduate HS   5%   7%   0.023  Medicare Only Insurance   7%   10%   0.001  Working at the Time of Transplant  

17%   9%  <0.001  

Receiving Income from Disability   20%   28%   <0.001  Primary Diagnosis DM   23%   34%   <0.001  Primary Diagnosis HTN   81%   87%   <0.001  Primary Diagnosis PKD   14%   4%   <0.001  Primary Diagnosis FSGS   5%   8%   0.019  Primary Diagnosis IgA   6%   1%   <0.001  Cardiovascular History Heart Disease CHF Hyperlipidemia CVA Cath/CABG Acute MI PVD Smoker  

21% 3%

40% 5%

15% 5% 5%

23%  

16% 5%

35% 7%

10% 3% 3%

17%  

0.017 0.465 0.141 0.239 0.002 0.093 0.118 0.005  

Pre-Transplant Dialysis   68%   89%   <0.001  Type of Dialysis PD HD  

20% 45%  

13% 74%  

<0.001  

Years on Dialysis   2±2   4±3   <0.001  Re-Transplant   11%   7%   0.004  

Characteristic  Non-African-

American (n=861)  

African-American (n=1049)  

p-Value  

Re-Transplant   11%   7%   0.004  PRA   13±27   13±25   0.905  HLA Mismatches   3.5±1.8   4.4±1.4   <0.001  CIT WIT  

13±10 35±12  

17±9 36±14  

<0.001 0.427  

Living Donor   30%   7%   <0.001  ECD   8%   10%   0.083  Donor Age   35±16   33±17   0.014  Donor AA   10%   21%   <0.001  Cytolytic Induction Therapy   30%   34%   0.066  Baseline CNI FK   62%   63%   0.383  DGF   10%   20%   <0.001  Acute Rejection   13%   20%   <0.001  Mean eGFR   51±16   54±18   0.001  

Donor and Immunologic Characteristics

Characteristic   Non-African-American (n=861)  

African-American (n=1049)   p-Value  

Mean LDL <100 mg/dL   53%   48%   0.216  Mean BP at goal   25%   21%   0.267  Mean TG <150 mg/dL   44%   63%   <0.001  NODAT DM  

9% 34%  

11% 47%  

0.312 <0.001  

DM Controlled   55%   43%   0.002  On Beta Blocker   61%   64%   0.285  On Ace/ARB   55%   58%   0.409  On Statin   60%   61%   0.900  On Other Lipid Therapy   41%   33%   <0.001  On Antiplatelet Therapy   37%   38%   0.901  

Post-Transplant Cardiovascular Risks and Control

Propensity Score Analysis and Ranking based on Recipient Race

Included in the binary logistic model (AA=dependent variable): Age, gender, insurance, education, working status, disability status,

diagnosis, CV history, dialysis history

AA Recipients

Non-AA Recipients

Propensity Score Analysis and Ranking based on Recipient Race

Included in the binary logistic model (AA=dependent variable): Retransplant, panel reactive antibody, HLA mismatches, cold ischemic

time, warm ischemic time, type of donor, donor age, donor race

AA Recipients

Non-AA Recipients

Propensity Score Analysis and Ranking based on Recipient Race

Included in the binary logistic model (AA=dependent variable): Antibody induction therapy, maintenance immunosuppression, delayed

graft function, acute rejection, eGFR

AA Recipients

Non-AA Recipients

Propensity Score Analysis and Ranking based on Recipient Race

Included in the binary logistic model (AA=dependent variable): LDL at goal, TG at goal, BP at goal, DM controlled, ACE/ARB use, BB

use, statin use, other lipid therapy use, antiplatelet therapy use

AA Recipients

Non-AA Recipients

Risk for Graft Lost in AA Recipients Using Sequential Propensity Score Analysis

Unadjusted Risk in AA

Adjusted for Baseline Sociodemographics

+ Baseline Txp/Donor

+ Post-Txp

+ Cardiovascular Risk Control

Summary •  There are numerous disadvantageous characteristics that are more common

in AAs which contribute to this disparity •  Baseline sociodemographics, donor and immunologic characteristics, post-

transplant allograft outcomes, post-transplant cardiovascular risk factor control

•  Propensity score analysis within these four domains can aid to discern predominant factors associated with outcomes within a complex medical issue

•  Future studies, utilizing large national datasets with clinical follow-up data, will allow for comparisons of sub-populations with similar propensity scores

AA Recipients Non-AA Recipients

Acknowledgements •  Mentors

•  PK Baliga, MD, LE Egede MD, MS, KD Chavin, MD, PhD, T Srinivas, MD •  Collaborators

•  CF Bratton, MD, JM McGillicuddy, MD, NA Pilch, PharmD, MSCR •  Data and Regulatory Assistance

•  K Douglass, S Shapiro, D Davis, C Schaffner, C Hurman, G Johnson •  Statistical Assistance & Study Design Guidance

•  K Simpson, PhD, P Mauldin, PhD