▲ ⓒSeverance Hospital
A predictive model was developed to evaluate the risk of developing trigeminal hyperparathyroidism, which occurs frequently in kidney transplant patients. Through this development, it is possible to predict the risk before transplantation, and it is expected that it will be possible to establish customized monitoring and surgical and pharmacological treatment plans for each individual.
Yonsei University Severance Hospital Endocrine Internal Medicine Professor Lee Yu-mi, Hong Nam-ki, transplant surgeon Gyu-ha Huh, Lee Ju-han, and nephrology professor Kim Hyung-woo’s research team announced on the 19th that they have developed a model that can predict the risk of parathyroidectomy due to trigeminal hyperparathyroidism following kidney transplantation.
The results of this study were published in the latest issue of the Clinical Journal of the American Society of Nephrology (IF 10.671).
Trigeminal hyperparathyroidism refers to a phenomenon in which hypercalcemia persists due to the continuous secretion of parathyroid hormone due to the autonomy of the existing parathyroid gland following kidney transplantation. As a result, symptoms such as fatigue, constipation, loss of appetite, memory loss, and increased urination may make daily life difficult.
If not treated in a timely manner, hypercalcemia leads to problems such as bone loss, loss of transplanted kidney function, and increased cardiovascular risk, in many cases requiring parathyroidectomy treatment.
If the risk of developing trigeminal hyperparathyroidism can be accurately assessed before kidney transplantation, effective treatment can be provided by establishing various treatment strategies for each individual patient, such as early drug therapy and setting the follow-up period.
The research team developed a model to predict the risk of parathyroidectomy due to trigeminal hyperparathyroidism following kidney transplantation using the clinical data of 669 patients who underwent kidney transplantation at Severance Hospital between 2009 and 2015 and data of 542 patients from a multicenter registry.
DPC (Dialysis duration, Parathyroid hormone, Calcium) score, which is calculated by quantifying the dialysis duration, pre-transplantation parathyroid hormone concentration, and calcium concentration in kidney transplant patients as key predictors, reflecting the risk according to each numerical interval, and quantifying it as an integer score (Integer 0 to 15 points) A model was devised.
The research team confirmed the risk classification performance of the DPC score prediction model using data from kidney transplant patients and multicenter registry data from Severance Hospital.
As a result, in the case of data from Severance Hospital, the median (median) of the DPC score was 13 points in the parathyroidectomy group, which was higher than 3 points in the non-resection group.
Even when the multicenter registry data was entered, 13 points in the parathyroidectomy group and 4 points in the non-resection group were obtained. A DPC score of 13 or higher was associated with a high risk of parathyroidectomy.
The DPC score showed good performance in multicenter data in predicting the risk of trigeminal hyperparathyroidism following kidney transplant requiring surgical treatment.
In particular, when the DPC score is 13 or more twice or more through repeated measurements at 3-month intervals within 12 months prior to kidney transplantation, the risk prediction accuracy is higher than when the DPC score is measured once (NRI; net reclassification improvement 0.28, p=0.03) was 28% higher.
Professor Lee Yoo-mi said, “The development of a DPC score prediction model enables us to accurately assess the risk of parathyroidectomy following transplantation in kidney transplant patients, enabling us to establish various treatment strategies such as preemptive surgery, early drug therapy, and follow-up period setting.” .
“The DPC score model is a model that can easily and simply measure risk even in the clinical field without any special tools, and we plan to conduct extended research through clinical application in the future.”
