Introduction: Parathyroid venous sampling (PVS) has been reported to be a useful adjunctive test in localizing lesions in elusive cases of primary hyperparathyroidism (PHPT). Conventional cutoff (twofold) is now widely being used, but optimal cutoff threshold for PVS gradient based on discriminatory performance remains unclear. Materials and methods: Among a total of 197 consecutive patients (mean age 58.2 years, female 74.6%) with PHPT who underwent parathyroidectomy at a tertiary center between 2012 and 2018, we retrospectively analyzed 59 subjects who underwent PVS for persistent or recurrent disease after previous parathyroidectomy, or for equivocal or negative results from conventional imaging modalities including ultrasonography (US) and Tc99m-Sestamibi SPECT-CT (MIBI). True parathyroid lesions were confirmed by combination of surgical, pathological findings, and intraoperative parathyroid hormone (PTH) changes. Optimal PVS cutoff were determined by receiver-operating characteristics (ROC) analysis with Youden and Liu method. Results: Compared to subjects who did not require PVS, PVS group tends to have lower PTH (119.8 pg/mL vs 133.7 pg/mL, p = 0.075). A total of 79 culprit parathyroid lesions (left 40; right 39) from 59 patients (left 24; right 26; bilateral 9) were confirmed by surgery. The optimal cutoff for PVS gradient was estimated as 1.5-fold gradient (1.5 ×) with sensitivity of 61.8% and specificity of 84%. When 1.5 × cutoff was applied, PVS improved the discrimination for true parathyroid lesions substantially based on area under ROC (0.892 to 0.942, p < 0.001) when added to US and MIBI. Conclusion: Our findings suggest that PVS with cutoff threshold 1.5 × can provide useful complementary information for pre-operative localization in selected cases.
Bibliographical noteFunding Information:
This research was funded by Severance Hospital Research fund for Clinical excellence (SHRC C-2019-0032).
The authors would like to thank Dong-Su Jang, MFA (Medical Illustrator, Medical Research Support Section, Yonsei University College of Medicine, Seoul, Korea) for his support with the medical illustration. We thank SENTINEL (Severance endocrinology data science platform) team members Doori Cho and Minheui Yoo for supporting literature review and data extraction process.
All Science Journal Classification (ASJC) codes
- Endocrinology, Diabetes and Metabolism
- Orthopedics and Sports Medicine