Construction and validation of a predictive model for the serum phosphorus reduction after total parathyroidectomy in patients with secondary hyperparathyroidism - Yingnan Feng; Yingnan Feng; Yinghui Zhou; Xiaodong Feng; Qila Sa; Qila Sa; Ningyuan Zhang; Ningyuan Zhang; Wantao Xie; Wantao Xie; Bailiang Liu; Fengyang Chen; et al. (Frontiers in Endocrinology)
ObjectiveWe aimed to construct a predictive scoring model for the factors influencing serum phosphorus reduction following total parathyroidectomy (tPTX) in secondary hyperparathyroidism (SHPT) and provide a reference for identifying patients who can successfully correct hyperphosphatemia before surgery.MethodsThe clinical data of 529 patients with SHPT who underwent tPTX were retrospectively analyzed according to the inclusion and exclusion criteria. Univariate and multivariate analyses were conducted to determine the independent factors and establish a predictive scoring model. The receiver operating characteristic curve (ROC) was applied to verify the model in the training and validation groups, respectively.ResultsIn the whole group, 315 patients had a significant decrease in serum phosphorus after tPTX. Univariate and multivariate analysis showed that preoperative alkaline phosphatase (AKP), intact parathyroid hormone (iPTH) and free triiodothyronine (FT3) were independent influencing factors to promote the decrease of serum phosphorus after tPTX; Serum phosphorus and bone pain were inhibitory factors (all P<0.05). According to the cut-off value, AKP>193.33 U/L, iPTH>1808 pg/mL, FT3>2.825 pg/mL, serum phosphorus>2.285 mmol/L and bone pain were used to establish the predictive scoring model for serum phosphorus decline. The results showed that the success rate of serum phosphorus reduction was 67.55% at 10~14 points and 95.35% at 15~24 points. The area under ROC curves (AUC) for the training and validation group were 0.818 (95% CI=0.775~0.861) and 0.840 (95% CI=0.780~0.901, both P<0.05).ConclusionThe established prediction score model for serum phosphorus decrease has a good prediction efficiency which is helpful for the early identification. The model provides important clinical guidance for the postoperative management and treatment of SHPT.