The goal of this study was the characterization of long-term cancer risks after liver transplantation (LT) with implications for prevention and detection. Site-specific cancer incidence rates and characteristics were compared retrospectively for 2000 LT patients from a single institution (January 1, 1983 to December 31, 2010) and the general German population with standardized incidence ratios (SIRs); the total follow-up at December 31, 2011 was 14,490 person-years. The cancer incidence rates for the LT recipients were almost twice as high as those for the age- and sex-matched general population (SIR = 1.94, 95% CI = 1.63-2.31). Significantly increased SIRs were observed for vulvar carcinoma (SIR = 23.80), posttransplant lymphoproliferative disorder/non-Hodgkin lymphoma (SIR = 10.95), renal cell carcinoma (SIR = 2.65), lung cancer (SIR = 1.85), and colorectal cancer (SIR = 1.41). The mean time between transplantation and diagnosis was 6.8 years. The mean age at the time of diagnosis was significantly lower for the cohort versus the general population with similar malignancies [50 years (both sexes) versus 69 and 68 years (males and females), P ≤ 0.006]. Tumors were diagnosed at more advanced stages, and there was a trend of higher grading, which suggested more aggressive tumor growth. Tumor treatment was performed according to accepted guidelines. Surprisingly, 5-year survival was slightly better in the study cohort versus the general population for renal cell carcinoma, lung cancer, colorectal cancer, and thyroid cancer. Long-term immunosuppression with different protocols did not lead to significantly different SIRs, although patients treated with mycophenolate mofetil had the lowest SIR for de novo cancers (1.65, 95% CI = 1.2-2.4). Alcoholic liver disease (SIR = 2.30) and primary sclerosing cholangitis (SIR = 3.40) as indications for LT were associated with an increased risk of de novo malignancies. In conclusion, risk-adapted cancer surveillance is proposed. Tumor treatment performed according to accepted guidelines appears adequate. Mycophenolate may lead to lower long-term risks for de novo cancers.

BackgroundThe 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.Patients and Methods1655 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.ResultsCancer 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).ConclusionsRisk-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.