Development and validation of a prognostic index for allograft outcome in kidney recipients with transplant glomerulopathy.
Bottom Line: Based on the Cox model, we developed a prognostic index and classified patients into risk groups.The hazard ratios were 2.18 (median survival 19 months) and 16.27 (median survival 1.6 months), respectively, for patients in the medium and high-risk groups, compared to the low-risk group (median survival 47 months).Our prognostic index model did well in measures of discrimination and calibration.
We studied 92 patients with transplant glomerulopathy to develop a prognostic index based on the risk factors for allograft failure within five years of diagnosis (Development cohort). During 60 months (median) follow-up, 64 patients developed allograft failure. A chronic-inflammation score generated by combining Banff ci, ct and ti scores, serum creatinine and proteinuria at biopsy, were independent risk factors for allograft failure. Based on the Cox model, we developed a prognostic index and classified patients into risk groups. Compared to the low-risk group (median allograft survival over 60 months from diagnosis), patients in the medium risk group had a hazard ratio of 2.83 (median survival 25 months), while those in the high-risk group had a hazard ratio of 5.96 (median survival 3.7 months). We next evaluated the performance of the prognostic index in an independent external cohort of 47 patients with transplant glomerulopathy (Validation cohort). The hazard ratios were 2.18 (median survival 19 months) and 16.27 (median survival 1.6 months), respectively, for patients in the medium and high-risk groups, compared to the low-risk group (median survival 47 months). Our prognostic index model did well in measures of discrimination and calibration. Thus, risk stratification of transplant glomerulopathy based on our prognostic index may provide informative insight for both the patient and physician regarding prognosis and treatment.
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Mentions: There was no difference in the outcome between patients who did and who did not receive anti-rejection therapy (Figure 2). Because patients were not randomly assigned to the two groups (anti-rejection therapy versus no anti-rejection therapy), we did propensity score (PS) analysis to mimic a quasi-randomized trial using the observational data. We used the baseline variables to generate PS, defined as the probability of receiving anti-rejection therapy. The resulting concordance index of the model was 0.89 suggesting that the model discriminated the two groups well. We then used the PS as a covariate in the Cox analysis. The difference in allograft outcome between the two groups, adjusted for the PS, was not statistically significant (hazard ratio [HR] 0.89, 95% confidence interval [0.45–1.76], p=0.74).
No MeSH data available.