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Background <p>Hypocalcemia is a frequent complication in patients undergoing maintenance hemodialysis and is closely linked to disturbances in mineral metabolism, increased cardiovascular risk, and bone disorders. Early identification of high-risk individuals is essential for effective prevention and management. This study aimed to evaluate risk factors associated with hypocalcemia and to develop a nomogram prediction model for individualized risk assessment.</p> Methods <p>This retrospective study included 386 adult patients receiving maintenance hemodialysis between January 2020 and December 2024. Hypocalcemia was defined as total serum calcium &lt; 2.1&#xa0;mmol/L. Patients were categorized into a hypocalcemia group (<i>n</i> = 135) and a normocalcemia group (<i>n</i> = 251). Demographic, dialysis-related, biochemical, and clinical variables were collected. Univariate and multivariate logistic regression analyses were performed to identify independent predictors. A nomogram prediction model was constructed and its discrimination, calibration, and clinical utility were assessed using the receiver operating characteristic (ROC) curve, calibration plots, and decision curve analysis (DCA). Internal validation was performed using bootstrap resampling with 1000 iterations to assess model stability.</p> Results <p>Multivariate logistic regression revealed that thyroid disease, elevated serum creatinine, and hyperphosphatemia were independent risk factors for hypocalcemia, while higher parathyroid hormone (PTH) levels and compound α-ketoacid use were protective factors. The nomogram incorporating these variables demonstrated good discrimination (AUC = 0.846, 95% CI 0.802–0.891), with a sensitivity of 81.5% and specificity of 77.3%. The calibration curve showed strong agreement between predicted and observed outcomes, and DCA indicated favorable net clinical benefit. Internal validation showed robust performance with a bootstrap-corrected area under the ROC curve (AUC) of 0.832.</p> Conclusions <p>The developed nomogram provides a reliable and clinically applicable tool for individualized prediction of hypocalcemia in hemodialysis patients, facilitating improved risk stratification and management.</p>

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Risk factors and nomogram prediction model for hypocalcemia in patients undergoing hemodialysis

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年2月3日

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  • Sha Chen,
  • Shu-Han Yu,
  • Juan-Juan Wang,
  • Qing-Xia Zhang,
  • Ping Yang

摘要

Background

Hypocalcemia is a frequent complication in patients undergoing maintenance hemodialysis and is closely linked to disturbances in mineral metabolism, increased cardiovascular risk, and bone disorders. Early identification of high-risk individuals is essential for effective prevention and management. This study aimed to evaluate risk factors associated with hypocalcemia and to develop a nomogram prediction model for individualized risk assessment.

Methods

This retrospective study included 386 adult patients receiving maintenance hemodialysis between January 2020 and December 2024. Hypocalcemia was defined as total serum calcium < 2.1 mmol/L. Patients were categorized into a hypocalcemia group (n = 135) and a normocalcemia group (n = 251). Demographic, dialysis-related, biochemical, and clinical variables were collected. Univariate and multivariate logistic regression analyses were performed to identify independent predictors. A nomogram prediction model was constructed and its discrimination, calibration, and clinical utility were assessed using the receiver operating characteristic (ROC) curve, calibration plots, and decision curve analysis (DCA). Internal validation was performed using bootstrap resampling with 1000 iterations to assess model stability.

Results

Multivariate logistic regression revealed that thyroid disease, elevated serum creatinine, and hyperphosphatemia were independent risk factors for hypocalcemia, while higher parathyroid hormone (PTH) levels and compound α-ketoacid use were protective factors. The nomogram incorporating these variables demonstrated good discrimination (AUC = 0.846, 95% CI 0.802–0.891), with a sensitivity of 81.5% and specificity of 77.3%. The calibration curve showed strong agreement between predicted and observed outcomes, and DCA indicated favorable net clinical benefit. Internal validation showed robust performance with a bootstrap-corrected area under the ROC curve (AUC) of 0.832.

Conclusions

The developed nomogram provides a reliable and clinically applicable tool for individualized prediction of hypocalcemia in hemodialysis patients, facilitating improved risk stratification and management.