Skin Cancer Risk Prediction in Heart Transplant Recipients

Nandini Nair, Zhiyong Hu, Dongping Du

Research output: Contribution to journalArticlepeer-review


Objectives: This study was undertaken to derive a risk prediction model for skin cancer utilizing the United Network for Organ Sharing database population. Materials and Methods: Of the 24 734 adults (>18 years old) heart transplant recipients (2000-2015) in the United Network for Organ Sharing database, 2625 recipients developed skin cancer. Univariate and multivariate Cox regression analyses were performed; P values, hazard ratios, and confidence intervals were derived. The model was tested using receiver operating characteristics curves and area under the curves. MATLAB software (MathWorks) was used for analyses. Results: Multivariate analysis showed that White patients had a hazard ratio of 31.7 compared with Black patients (P <.001). Male patients had a hazard ratio of 2.52 (P <.001) compared with female patients. Malignancy at listing showed a hazard ratio of 1.77 (P <.001). Thymoglobulin had a hazard ratio of 1.19 (P =.005) compared with other induction agents. The receiver operating characteristic curves generated for 5 years, 8 years, and 10 years after transplant showed area under the curve values of 0.78, 0.77, and 0.76, respectively, in the training set and 0.75, 0.75, and 0.74, respectively, in the validation set. Conclusions: Male sex, White ethnicity, older age, malignancy at the time of listing or at time of transplant, and thymoglobulin induction are major risk factors for skin cancers after transplant. This risk prediction model has a C statistic of 0.75. To our knowledge, this is the first time such a model has been generated for skin cancers in this population.

Original languageEnglish (US)
Pages (from-to)41-46
Number of pages6
JournalExperimental and Clinical Transplantation
Issue number1
StatePublished - Jan 2023

All Science Journal Classification (ASJC) codes

  • Transplantation


Dive into the research topics of 'Skin Cancer Risk Prediction in Heart Transplant Recipients'. Together they form a unique fingerprint.

Cite this