Predictive ability of pretransplant comorbidities to predict long-term graft loss and death.
Whether to include additional comorbidities beyond diabetes in future kidney allocation schemes is controversial. We investigated the predictive ability of multiple pretransplant comorbidities for graft and patient survival. We included first-kidney transplant deceased donor recipients if Medicare was the primary payer for at least one year pretransplant. We extracted pretransplant comorbidities from Medicare claims with the Clinical Classifications Software (CCS), Charlson and Elixhauser comorbidities and used Cox regressions for graft loss, death with function (DWF) and death. Four models were compared: (1) Organ Procurement Transplant Network (OPTN) recipient and donor factors, (2) OPTN + CCS, (3) OPTN + Charlson and (4) OPTN + Elixhauser. Patients were censored at 9 years or loss to follow-up. Predictive performance was evaluated with the c-statistic. We examined 25 270 transplants between 1995 and 2002. For graft loss, the predictive value of all models was statistically and practically similar (Model 1: 0.61 [0.60 0.62], Model 2: 0.63 [0.62 0.64], Models 3 and 4: 0.62 [0.61 0.63]). For DWF and death, performance improved to 0.70 and was slightly better with the CCS. Pretransplant comorbidities derived from administrative claims did not identify factors not collected on OPTN that had a significant impact on graft outcome predictions. This has important implications for the revisions to the kidney allocation scheme.
Machnicki
G
G
Center for Outcomes Research, Saint Louis University School of Medicine, St. Louis, MO, USA.
Pinsky
B
B
Takemoto
S
S
Balshaw
R
R
Salvalaggio
P R
PR
Buchanan
P M
PM
Irish
W
W
Bunnapradist
S
S
Lentine
K L
KL
Burroughs
T E
TE
Brennan
D C
DC
Schnitzler
M A
MA
D000293:Adolescent; D000328:Adult; D002138:Calibration; D015897:Comorbidity; D003643:Death; D005260:Female; D005500:Follow-Up Studies; D006084:Graft Rejection / Q000276:immunology* / Q000401:mortality*; D006801:Humans; D008297:Male; D008875:Middle Aged; D008954:Models, Biological; D013997:Time Factors; D014015:Tissue Banks / Q000706:statistics & numerical data; D014019:Tissue Donors / Q000706:statistics & numerical data