Supplementary Material for: Clinical characteristics and management of patients with secondary hyperparathyroidism undergoing hemodialysis: a feasibility analysis of electronic health records using Natural Language Processing

Introduction: This study aimed to assess the feasibility of applying Natural Language Processing (NLP) to analyze real-world data (RWD) and resolve clinical problems in patients with secondary hyperparathyroidism and chronic kidney disease undergoing hemodialysis (SHPT/CKD-HD). The primary objective was to evaluate how well the guidelines-recommended analytical goals are achieved in a Spanish cohort of SHPT/CKD-HD patients based on RWD. Methods: Unstructured data in the EHRs from 8 hospitals were retrospectively analyzed using the EHRead® technology, based on NLP and machine learning. Variables extracted from EHRs included demographics, CKD-related clinical characteristics, comorbidities, and complications, mineral and bone disorder parameters levels and treatments at baseline, 6 months and 12 months follow up. Results: A total of 623 prevalent SHPT/CKD-HD patients were identified; of those, 282 fulfilled the inclusion criteria. They were predominantly elderly males with cardiovascular comorbidities and first cause of CKD was diabetic nephropathy. Diagnosis of SHPT was associated with an improvement in median values for parathyroid hormone (PTH), calcium and phosphate. However, the percentage of patients with normal PTH ranges remained stable during the study period (52.8% to 60.4%) while the percentage of patients with within-target range serum calcium or phosphate values showed an increasing trend (43.2% to 60% and 38.8% to 50%). At baseline, 74.1% of patients were using SHPT-related medication, including at least one Vitamin D or analogue (63.1%), phosphate binders (46.8%), and/or calcimimetics (9.6%). Conclusions: This study represents the first attempt to use clinical NLP to analyze SHPT/CKD-HD patients based on unstructured clinical data. This methodology is useful to address clinical problems based on RWD and identified a high rate of out-of-range mineral-bone analytical values in patients with HPT/CKD-HD and an increasing trend of out-of-range values for serum calcium and phosphate.

研究背景：本研究旨在评估自然语言处理（Natural Language Processing, NLP）应用于真实世界数据（Real-World Data, RWD）分析、解决血液透析继发性甲状旁腺功能亢进合并慢性肾脏病（Secondary Hyperparathyroidism/Chronic Kidney Disease Undergoing Hemodialysis, SHPT/CKD-HD）患者临床问题的可行性。首要研究目标为基于真实世界数据，评估西班牙SHPT/CKD-HD患者队列对指南推荐分析目标的符合程度。

研究方法：本研究回顾性分析了8家医院电子健康档案（Electronic Health Records, EHRs）中的非结构化数据，采用基于自然语言处理与机器学习的EHRead®技术完成数据提取与处理。从电子健康档案中提取的变量包括人口统计学特征、慢性肾脏病相关临床特征、合并症与并发症、基线及随访6个月、12个月时的矿物质与骨代谢紊乱参数水平及治疗方案。

研究结果：本研究共确认623例现患SHPT/CKD-HD患者，其中282例符合入组标准。入组患者以合并心血管疾病的老年男性为主，慢性肾脏病的首位病因是糖尿病肾病。甲状旁腺激素（Parathyroid Hormone, PTH）、血钙与血磷的中位数水平均得到改善。然而，研究期间甲状旁腺激素处于正常参考范围的患者占比维持稳定（52.8%至60.4%），而血清钙、血清磷达到靶目标范围的患者占比均呈上升趋势（分别从43.2%升至60%、38.8%升至50%）。基线时，74.1%的患者使用了SHPT相关治疗药物，包括至少1种维生素D或其类似物（63.1%）、磷结合剂（46.8%）以及/或拟钙剂（9.6%）。

研究结论：本研究是首次采用临床自然语言处理技术，基于非结构化临床数据分析SHPT/CKD-HD患者的尝试。该方法可有效基于真实世界数据解决临床问题，研究发现SHPT/CKD-HD患者的矿骨代谢分析指标异常率较高，且血清钙、血清磷的异常比例呈上升趋势。