A novel nomogram for predicting the risk of persistent hyperparathyroidism after kidney transplantation

• Published in: Endocrine Vol. 86; no. 1; pp. 400 - 408
• Main Authors: Ma, Changyu, Shen, Congrong, Tan, Haotian, Chen, Ziyin, Ding, Zhenshan, Zhao, Ying, Zhou, Xiaofeng
• Format: Journal Article
• Language: English
• Published: New York Springer US 01.10.2024; Springer Nature B.V
• Subjects: Adult; Aged; Bone grafts; Bone loss; Calcium - blood; Cardiovascular diseases; Diabetes; Dialysis; Endocrinology; Female; Humanities and Social Sciences; Humans; Hyperparathyroidism; Hyperparathyroidism - etiology; Internal Medicine; Kidney transplantation; Kidney Transplantation - adverse effects; Kidney transplants; Male; Medicine; Medicine & Public Health; Middle Aged; multidisciplinary; Nomograms; Original Article; Parathyroid hormone; Parathyroid Hormone - blood; Patients; Postoperative Complications - epidemiology; Postoperative Complications - etiology; Prediction models; Prognosis; Retrospective Studies; Risk Assessment; Risk Factors; Science; Persistent hyperparathyroidism; Nomogram; Predictive model; Kidney transplantation
• ISSN: 1559-0100; 1355-008X; 1559-0100
• DOI: 10.1007/s12020-024-03963-5

Purpose Persistent hyperparathyroidism (PTHPT) in kidney transplant recipients is associated with bone loss, graft dysfunction and cardiovascular mortality. There is no clear consensus on the management of PTHPT. Accurate risk prediction of the disease is needed to support individualized treatment decisions. We aim to develop a useful predictive model to provide early intervention for hyperparathyroidism in these patients. Methods We retrospectively analyzed 263 kidney transplantations in the urology department of China-Japan Friendship Hospital from January 2018 to December 2022. The overall cohort was randomly assigned 70% of the patients to the training cohort and 30% to the validation cohort. Univariate and multivariate logistic regression analyses were used to identify independent risk factors for PTHPT and to construct the predictive model. This model was assessed regarding discrimination, consistency, and clinical benefit. Results The occurrence of PTHPT was 25.9% (68 out of 263 patients) in this study. Dialysis duration, postoperative 3-month intact parathyroid hormone (iPTH), 3-month corrected calcium (cCa), and 3-month phosphorus (P) are independent risk factors for the development of PTHPT. The nomogram showed good discrimination with the area under the curve (AUC) value of 0.926 in the training cohort and 0.903 in the validation cohort. The calibration curve and decision curve also showed that the model was well-evaluated. Conclusion We developed a validated nomogram model to predict PTHPT after kidney transplantation. This can help the clinic prevent and control PTHPT early and improve patients’ prognosis.