RESEARCH Open Access Prognostic limitations of the Eurotransplant-donor risk index in liver transplantation

Background: Liver transplantation is the only life-saving therapeutic option for end-stage liver disease. Progressive donor organ shortage and declining donor organ quality justify the evaluation of the leverage of the Donor-Risk-Index, which was recently adjusted to the Eurotransplant community’s requirements (ET-DRI). We analysed the prognostic value of the ET-DRI for the prediction of outcome after liver transplantation in our center within the Eurotransplant community. Results: 291 consecutive adult liver transplants were analysed in a single centre study with ongoing data collection. Determination of the area under the receiver operating characteristic curve (AUROC) was performed to calculate the sensitivity, specificity, and overall correctness of the Eurotransplant-Donor-Risk-Index (ET-DRI) for the prediction of 3-month and 1-year mortality, as well as 3-month and 1-year graft survival. Cut-off values were determined with the best Youden-index. The ET-DRI is unable to predict 3-month mortality (AUROC: 0.477) and 3-month graft survival (AUROC: 0.524) with acceptable sensitivity, specificity and overall correctness (54 % and 56.3%, respectively). Logistic regression confirmed this finding (p = 0.573 and p = 0.163, respectively). Determined cut-off values of the ET-DRI for these predictions had no significant influence on long-term patient and graft survival (p = 0.230 and p = 0.083, respectively; Kaplan-Meier analysis with Log-Rank test). Conclusions: The ET-DRI should not be used for donor organ allocation policies without further evaluation, e.g. in combination with relevant recipient variables. Robust and objective prognostic scores for donor organ allocation purposes are desperately needed to balance equity and utility in donor organ allocation

Independent Pre-Transplant Recipient Cancer Risk Factors after Kidney Transplantation and the Utility of G-Chart Analysis for Clinical Process Control.

The aim of this study is to identify independent pre-transplant cancer risk factors after kidney transplantation and to assess the utility of G-chart analysis for clinical process control. This may contribute to the improvement of cancer surveillance processes in individual transplant centers.1655 patients after kidney transplantation at our institution with a total of 9,425 person-years of follow-up were compared retrospectively to the general German population using site-specific standardized-incidence-ratios (SIRs) of observed malignancies. Risk-adjusted multivariable Cox regression was used to identify independent pre-transplant cancer risk factors. G-chart analysis was applied to determine relevant differences in the frequency of cancer occurrences.Cancer incidence rates were almost three times higher as compared to the matched general population (SIR = 2.75; 95%-CI: 2.33-3.21). Significantly increased SIRs were observed for renal cell carcinoma (SIR = 22.46), post-transplant lymphoproliferative disorder (SIR = 8.36), prostate cancer (SIR = 2.22), bladder cancer (SIR = 3.24), thyroid cancer (SIR = 10.13) and melanoma (SIR = 3.08). Independent pre-transplant risk factors for cancer-free survival were age <52.3 years (p = 0.007, Hazard ratio (HR): 0.82), age >62.6 years (p = 0.001, HR: 1.29), polycystic kidney disease other than autosomal dominant polycystic kidney disease (ADPKD) (p = 0.001, HR: 0.68), high body mass index in kg/m2 (p<0.001, HR: 1.04), ADPKD (p = 0.008, HR: 1.26) and diabetic nephropathy (p = 0.004, HR = 1.51). G-chart analysis identified relevant changes in the detection rates of cancer during aftercare with no significant relation to identified risk factors for cancer-free survival (p<0.05).Risk-adapted cancer surveillance combined with prospective G-chart analysis likely improves cancer surveillance schemes by adapting processes to identified risk factors and by using G-chart alarm signals to trigger Kaizen events and audits for root-cause analysis of relevant detection rate changes. Further, comparative G-chart analysis would enable benchmarking of cancer surveillance processes between centers

Distribution of de novo malignancy and univariable Cox regression model.

<p>Distribution of de novo malignancy and univariable Cox regression model.</p

T-stages and Grading.

<p>T-stages and Grading.</p

G-chart of Diagnosis Dates of Cancer.

<p>Cases marked with red dots and the number “1” above the upper control limit (UCL) indicate an unusually low rate of detected de novo malignancies (Test 1 result) which may raise concerns in regard to appropriate vigilance of doctors or possible decreased diagnostic sensitivity. Alternatively, this observation may also indicate decreased de novo cancer risk in these observed cases. Cases marked by the letter “B” and a red dot on the lower control limit (LCL) indicate a failed Benneyan test pointing to statistically relevant increases in the diagnostic event rate which should trigger clinical concerns in regard to causation, e.g. by intensified or uncontrolled immunosuppression. Alternatively, a more rigorous screening for cancer may also lead to an increased event rate.</p

Multivariable Cox regression model and G-chart alarm signals.

<p>Multivariable Cox regression model and G-chart alarm signals.</p