Department of Internal Medicine, Endocrine Research Institute, Yonsei University College of Medicine, Seoul, South Korea.
Department of Surgery, The Research Institute for Transplantation, Yonsei University College of Medicine, Seoul, South Korea.
Clin J Am Soc Nephrol. 2022 Jul;17(7):1026-1035. doi: 10.2215/CJN.15921221. Epub 2022 Jun 10.
Tertiary hyperparathyroidism in kidney allograft recipients is associated with bone loss, allograft dysfunction, and cardiovascular mortality. Accurate pretransplant risk prediction of tertiary hyperparathyroidism may support individualized treatment decisions. We aimed to develop an integer score system that predicts the risk of tertiary hyperparathyroidism using machine learning algorithms.
DESIGN, SETTING, PARTICIPANTS, & MEASUREMENTS: We used two separate cohorts: a derivation cohort with the data of kidney allograft recipients (=669) who underwent kidney transplantation at Severance Hospital, Seoul, Korea between January 2009 and December 2015 and a multicenter registry dataset (the Korean Cohort Study for Outcome in Patients with Kidney Transplantation) as an external validation cohort (=542). Tertiary hyperparathyroidism was defined as post-transplant parathyroidectomy. The derivation cohort was split into 75% training set (=501) and 25% holdout test set (=168) to develop prediction models and integer-based score.
Tertiary hyperparathyroidism requiring parathyroidectomy occurred in 5% and 2% of the derivation and validation cohorts, respectively. Three top predictors (dialysis duration, pretransplant intact parathyroid hormone, and serum calcium level measured at the time of admission for kidney transplantation) were identified to create an integer score system (dialysis duration, pretransplant serum parathyroid hormone level, and pretransplant calcium level [DPC] score; 0-15 points) to predict tertiary hyperparathyroidism. The median DPC score was higher in participants with post-transplant parathyroidectomy than in those without (13 versus three in derivation; 13 versus four in external validation; <0.001 for all). Pretransplant dialysis duration, pretransplant serum parathyroid hormone level, and pretransplant calcium level score predicted post-transplant parathyroidectomy with comparable performance with the best-performing machine learning model in the test set (area under the receiver operating characteristic curve: 0.94 versus 0.92; area under the precision-recall curve: 0.52 versus 0.47). Serial measurement of DPC scores (≥13 at least two or more times, 3-month interval) during 12 months prior to kidney transplantation improved risk classification for post-transplant parathyroidectomy compared with single-time measurement (net reclassification improvement, 0.28; 95% confidence interval, 0.02 to 0.54; =0.03).
A simple integer-based score predicted the risk of tertiary hyperparathyroidism in kidney allograft recipients, with improved classification by serial measurement compared with single-time measurement.
Korean Cohort Study for Outcome in Patients with Kidney Transplantation (KNOW-KT), NCT02042963 PODCAST: This article contains a podcast at https://www.asn-online.org/media/podcast/CJASN/2022_06_10_CJN15921221.mp3.
肾移植受者的三发性甲状旁腺功能亢进与骨丢失、移植肾功能障碍和心血管死亡率有关。准确预测三发性甲状旁腺功能亢进的移植前风险，可能有助于个体化治疗决策。我们旨在开发一种整数评分系统，使用机器学习算法预测三发性甲状旁腺功能亢进的风险。
设计、地点、参与者和测量：我们使用了两个独立的队列：一个是来自韩国首尔 Severance 医院的肾移植受者（=669）的数据的推导队列，这些患者在 2009 年 1 月至 2015 年 12 月期间接受了肾移植；另一个是多中心注册数据库（韩国肾移植患者结局研究）作为外部验证队列（=542）。三发性甲状旁腺功能亢进定义为移植后甲状旁腺切除术。推导队列分为 75%的训练集（=501）和 25%的保留测试集（=168），以开发预测模型和基于整数的评分。
在推导和验证队列中，分别有 5%和 2%的患者需要甲状旁腺切除术来治疗三发性甲状旁腺功能亢进。确定了三个主要预测因素（透析时间、移植前完整甲状旁腺激素和移植前入院时的血清钙水平）来创建一个整数评分系统（透析时间、移植前血清甲状旁腺激素水平和移植前钙水平[DPC]评分；0-15 分），以预测三发性甲状旁腺功能亢进。与未行甲状旁腺切除术的患者相比，术后行甲状旁腺切除术的患者的中位 DPC 评分更高（推导组中为 13 分与 3 分；外部验证组中为 13 分与 4 分；均<0.001）。移植前透析时间、移植前血清甲状旁腺激素水平和移植前钙水平评分与测试集中表现最佳的机器学习模型具有相当的预测术后甲状旁腺切除术的性能（接受者操作特征曲线下面积：0.94 与 0.92；精确召回曲线下面积：0.52 与 0.47）。在移植前 12 个月内，DPC 评分（至少两次或更多次达到≥13，间隔 3 个月）的连续测量提高了术后甲状旁腺切除术的风险分类，优于单次测量（净重新分类改善，0.28；95%置信区间，0.02 至 0.54；=0.03）。
一种简单的基于整数的评分预测了肾移植受者发生三发性甲状旁腺功能亢进的风险，与单次测量相比，连续测量可改善分类。
韩国肾移植患者结局研究（KNOW-KT），NCT02042963 播客：本文包含在 https://www.asn-online.org/media/podcast/CJASN/2022_06_10_CJN15921221.mp3 上的播客。
