Predicted Cure and Survival Among Transplant Recipients With a Previous Cancer Diagnosis

A previous cancer diagnosis is a negative consideration in evaluating patients for possible solid organ transplantation. Statistical models may improve selection of patients with cancer evaluated for transplantation.

We fitted statistical cure models for patients with cancer in the US general population using data from 13 cancer registries. Patients subsequently undergoing solid organ transplantation were identified through the Scientific Registry of Transplant Recipients. We estimated cure probabilities at diagnosis (for all patients with cancer) and transplantation (transplanted patients). We used Cox regression to assess associations of cure probability at transplantation with subsequent cancer-specific mortality.

Among 10,524,326 patients with 17 cancer types in the general population, the median cure probability at diagnosis was 62%. Of these patients, 5,425 (0.05%) subsequently underwent solid organ transplantation and their median cure probability at transplantation was 94% (interquartile range, 86%-98%). Compared with the tertile of transplanted patients with highest cure probability, those in the lowest tertile more frequently had lung or breast cancers and less frequently colorectal, testicular, or thyroid cancers; more frequently had advanced-stage cancer; were older (median 57 v 51 years); and were transplanted sooner after cancer diagnosis (median 3.6 v 8.6 years). Patients in the low-cure probability tertile had increased cancer-specific mortality after transplantation (adjusted hazard ratio, 2.08; 95% CI, 1.48 to 2.93; v the high tertile), whereas those in the middle tertile did not differ.

Patients with cancer who underwent solid organ transplantation exhibited high cure probabilities, reflecting selection on the basis of existing guidelines and clinical judgment. Nonetheless, there was a range of cure probabilities among transplanted patients and low probability predicted increased cancer-specific mortality after transplantation. Cure probabilities may facilitate guideline development and evaluating individual patients for transplantation.