The Usefulness of Intraoperative PTH as a Predictor for Successful Parathyroidectomy in Secondary Hyperparathyroidism

Introduction: Secondary hyperparathyroidism (SHPT) is a multisystemic syndrome that affects calcium and bone homeostasis in patients with chronic kidney disease (CKD). Despite medical treatment, 1-2% of patients require parathyroidectomy annually. The use of an intraoperative parathormone protocol (IOPTH) to predict cure is still in debate, due to the lack of standardized protocols, the use of different assays, and uneven PTH clearance. This study aimed to determine the diagnostic accuracy of an IOPTH in patients with SHPT for predicting successful surgery after parathyroidectomy. Methods: About 30 patients were enrolled. A prospective observational study (cohort) was performed in patients who were submitted to parathyroidectomy by an endocrine surgeon for SHPT. All were submitted to a bilateral neck exploration with a subtotal parathyroidectomy. Three IOPTH determinations were withdrawn: at anesthetic induction (PTH₀), 15 min (PTH₁₅), and 30 min (PTH₃₀) after completion of gland resection. Another sample was taken 24 h after the procedure (PTH₂₄), values <150 pg/mL defined a successful surgery, and patients were assigned to the success or failure group. IOPTH drop was analyzed to predict successful surgery with drops of 70 and 90% at 15 and 30 min, respectively. Results: A total of 26 patients were included, 19 patients were in the successful group. IOPTH showed a significant difference between groups in their absolute PTH₁₅ and PTH₃₀ values. A significant difference was also found in their PTH drop at 30 min (81 vs. 91%, p = 0.08). For predicting a successful surgery, having a PTH drop >90% at 30 min was the most significant factor [Odds Ratio (OR) 3.0 (1.5-4) IC 95%]. Conclusions: This study points toward a stricter and staggered IOPTH protocol to predict a successful surgery. Our results suggest taking a PTH₁₅ expecting a PTH drop of >90%. If this is not achieved, reexploration and a PTH₃₀ sample are suggested to accurately predict success.