External validation of a model to predict risk of keratinocyte skin cancer after renal transplantation in a Western European cohort
Authors: E.J. Verhage, P.J. Nelemans, S.M.J. Kuijk, M.H.L. Christiaans, N.W.J. Kelleners‐Smeets
Journal: British Journal of Dermatology (2020)

Dear Editor, Renal transplant recipients (RTRs) face an increased risk of developing keratinocyte skin cancer (KSC) due to their lifelong immunosuppressive drug use.1 Current recommendations call for 6–12-monthly skin examinations for all RTRs.2 To develop a more personalized screening procedure, Urwin et al.3 identified the most significant KSC risk factors in an Australian sample and developed a predictive index (PI) that stratifies patients into high-, medium- and low-risk groups. We have externally validated this PI and evaluated its geographical transportability to the Netherlands. Based on the PI score a distinction was made between a high-risk group with a score ≥ 7 (screen after 6 months), a medium-risk group with a score of 5 or 6 (screen after 2 years) and a low-risk group with a score ≤ 4 (screen after 5 years). A cumulative probability of remaining KSC free < 70% was used as a criterion for selection of the screening intervals. In this study, the PI was validated in a cohort of patients who received a renal transplant at the Maastricht University Medical Centre in the Netherlands between 1982 and 2015. Information about the predictors was gathered through a questionnaire, while skin cancer data were obtained from a national registry. Model performance in terms of discrimination and calibration was assessed graphically with Kaplan–Meier plots, and a C-index and hazard ratios were calculated.4 Data on predictor variables were complete in 417 patients out of 424 responders, and 507 patients who did not complete the questionnaire were excluded. In total, 131 KSCs were observed during a median follow-up of 9.5 years (range 6 months to 36 years). Harrell’s C-index was 0.71 [95% confidence interval (CI) 0.65–0.76], indicating moderate discriminative ability of the PI scores. The hazard ratios of developing KSC across the risk groups (high vs. low risk and medium vs. low risk) are 7.90 (95% CI 3.40–18.33) and 2.82 (95% CI 1.92–4.12), respectively. Calibration was assessed by comparing the observed cumulative probabilities of remaining KSC free in each group at 6 months, 2 years, 3 years and 5 years post-transplantation in the Dutch and Australian populations (Table 1). Kaplan–Meier estimates for remaining KSC free were rather similar in the low-risk group, but estimates in the medium-risk group were substantially higher in the Dutch group than in the Australian sample. Only seven patients were stratified as high risk and the precision of the Kaplan–Meier estimates for this subgroup is very low, illustrated by the wide 95% CIs. The lower KSC risk in the Netherlands may be due to less ultraviolet radiation exposure than in Australia.4, 5 However, KSC risk in the Netherlands has increased over time, and Table 1 also shows that in patients who received their transplant between 2011 and 2015, the KSC risk was more comparable between the Dutch and Australian cohorts. We assume that improvements in skin cancer registration combined with increased awareness account for the higher KSC incidence in later periods.6, 7 The model was developed to provide more personalized advice on when to start skin cancer screening. Urwin et al. considered the time when KSC-free survival dropped below 70% as a critical threshold and therefore recommended screening the high-, medium- and low-risk groups at 6 months, 2 years and 5 years post-transplantation, respectively. This threshold is reached later in the Dutch medium-risk group, after more than 3 years and not after 2 years, even in the period 2011–2015. Which threshold is used depends on the risk one is willing to take. In every scheme, some patients will develop tumours before their first screening visit; therefore, self-screening remains crucial. The study has some limitations. More than half of the eligible RTRs did not respond, which increases the likelihood of selection bias. Also, inaccurate recall of the presence or absence of predictors may have resulted in nondifferential misclassification of patients into risk groups and bias towards underestimation of the discriminative ability of the PI scores. The high-risk group consisted of only seven patients, which does not allow for any robust conclusions. A larger-scale study including a cost-effectiveness analysis will be necessary to confirm the calibration and fine tune the screening intervals.