Abstract
OBJECTIVE: Secondary hyperparathyroidism (SHPT) is a common complication of chronic kidney disease (CKD). Hungry bone syndrome (HBS) after parathyroidectomy (PTX) is a serious complication, which can lead to diarrhea, convulsion, arrhythmia and even death. This study was aimed to determine the risk factors for HBS after PTX in dialysis patients with SHPT and construct a nomogram prediction model to predict the incidence of postoperative complications. METHODS: Clinical data were collected from 80 maintenance hemodialysis (MHD) patients with SHPT who received total PTX in the Second Hospital of Jilin University from January 2018 to September 2021. In line with the inclusion and exclusion criteria, totally 75 patients were finally enrolled for analysis. Patients were divided into two groups for retrospective analysis according to the severity of postoperative HBS, including HBS group and non-HBS (N-HBS) group. Univariate and multivariate logistic regression analyses were conducted to determine the risk factors for postoperative HBS. Afterwards, the receiver operating characteristic (ROC) curves were plotted based on the statistical analysis results, aiming to compare the prediction effects of different predicting factors. Finally, the nomogram was established to evaluate the occurrence probability of postoperative complications predicted by the risk factors. RESULTS: Among the 75 patients, 32 had HBS (HBS group), while 43 did not have HBS (N-HBS group). Univariate analysis results indicated that, the preoperative intact parathyroid hormone (iPTH) and serum alkaline phosphatase (ALP) levels in HBS group were significantly higher than those in N-HBS group, while preoperative hemoglobin and preoperative albumin (Alb) levels were significantly lower than those in N-HBS group. As discovered by multivariate logistic regression analysis, preoperative iPTH (OR = 1.111, P = 0.029) and ALP (OR = 1.010, P < 0.001) were the independent risk factors for postoperative HBS. ROC curve analysis suggested that the area under the curve (AUC) values of these two indicators were 0.873 and 0.926, respectively (P < 0.0001). Subsequently, the nomogram model for predicting HBS was constructed. The model verification results indicated that the predicted values were basically consistent with the measured values, with the C-index of 0.943 (95% CI 0.892-0.994). Besides, the calibration curve was consistent with the ideal curve, demonstrating the favorable accuracy and discrimination of the model. CONCLUSIONS: Preoperative iPTH and preoperative ALP are the risk factors for postoperative HBS, which can be used to guide the early clinical intervention.