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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