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

PLoS One. 2016 Jul 11;11(7):e0158732. doi: 10.1371/journal.pone.0158732. eCollection 2016.

Abstract

Background: 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.

Patients and methods: 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.

Results: 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).

Conclusions: 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.

MeSH terms

  • Adolescent
  • Adult
  • Aged
  • Case-Control Studies
  • Child
  • Disease-Free Survival
  • Epidemiological Monitoring*
  • Female
  • Humans
  • Kidney Transplantation*
  • Male
  • Middle Aged
  • Neoplasms / epidemiology*
  • Neoplasms / therapy
  • Retrospective Studies
  • Risk Assessment
  • Risk Factors
  • Young Adult

Grants and funding

This work was supported by a grant from the German Federal Ministry of Education and Research (reference number: 01EO1302) to authors HS and MB. The contributions provided by author AG who is also professionally affiliated with a commercial company have been funded within the framework of the same grant agreement with the German Federal Ministry of Education and Research in the context of a consultancy agreement on quality management methodology. The commercial affiliation of author AG and the funder of the grant support for this study did not have any additional role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript. The specific roles of all authors are articulated in the ‘author contributions’ section.