Development and validation of a nomogram for predicting postoperative hypocalcemia in patients undergoing surgery for...

This study aimed to identify risk factors for hypocalcemia following differentiated thyroid cancer (DTC) surgery and develop and validate a nomogram model to predict its occurrence.

A retrospective cohort study included 315 DTC patients who underwent surgery between January 2023 and January 2025. Clinical data encompassing demographics, surgical parameters (e.g., central lymph node dissection, capsular invasion, operation time), comorbidities (e.g., diabetes), and preoperative biomarkers (albumin, Lp-PLA2, Nesfatin-1) were analyzed. Variables were screened using univariate analysis, followed by multivariate logistic regression with a backward stepwise selection procedure to identify independent predictors. A nomogram was constructed using RStudio (version 3.4), and its performance was assessed for discrimination and calibration. Internal validation was performed using a bootstrapping technique.

Postoperative hypocalcemia occurred in 32.06% of patients (101/315). Multivariate analysis identified diabetes (OR=2.1, 95%CI: 1.4-3.3), central lymph node dissection (OR=3.4, 95%CI: 2.1-5.6), capsular invasion (OR=2.8, 95%CI: 1.7-4.7), prolonged operation time (OR=1.9, 95%CI: 1.3-3.0), elevated preoperative albumin (OR=1.7, 95%CI: 1.2-2.5), and Lp-PLA2 (OR=2.3, 95%CI: 1.5-3.7) as significant risk factors. Serum Nesfatin-1 emerged as a protective factor (OR=0.6, 95%CI: 0.4-0.9). The nomogram demonstrated excellent discrimination, with an area under the curve (AUC) of 0.850 (95%CI: 0.825-0.930), which remained robust after bootstrap validation (bias-corrected AUC=0.845). The model showed good calibration, confirmed by a calibration plot and the Hosmer-Lemeshow test (P=0.30). Decision curve analysis indicated a strong clinical utility.

This study developed and internally validated a nomogram integrating clinical, surgical, and biochemical predictors to quantify individualized risk of hypocalcemia after DTC surgery. The model shows robust predictive accuracy, offering clinicians a practical tool for preoperative risk stratification and targeted interventions. However, external validation in multicenter cohorts is necessary to enhance generalizability.