Development and Pilot Internal Validation of a Digital Clinico-Biochemical Model for Predicting Hungry Bone Syndrome after Parathyroidectomy

Kamulzhanova Zh.  A.; Rozibaev B.  A.; Kudratova N. A.

doi:10.51699/cajmns.v7i3.3305

Authors

Kamulzhanova Zh. A. Tashkent State Medical University
Rozibaev B. A. Tashkent State Medical University
Kudratova N. A. Tashkent State Medical University

DOI:

https://doi.org/10.51699/cajmns.v7i3.3305

Keywords:

Hungry Bone Syndrome, hyperparathyroidism, parathyroidectomy, hypocalcemia, PTH, ALP, corrected calcium, ROC analysis, Cox regression, digital model

Abstract

Background. Hungry Bone Syndrome (HBS) remains one of the most clinically significant forms of severe postoperative hypocalcemia after parathyroidectomy. The absence of standardized preoperative risk stratification limits early prevention and individualized postoperative monitoring. Objective. To develop and perform pilot internal validation of a digital clinico-biochemical model for predicting HBS based on preoperative variables. Materials and methods. A training cohort of 200 observations was analyzed; HBS was registered in 20 patients (10.0%). Predictors included the type of hyperparathyroidism, intact parathyroid hormone level, alkaline phosphatase activity expressed as a multiple of the upper limit of normal, corrected calcium, and an integrated digital score. Discriminative ability was assessed by ROC analysis; time-dependent prognostic significance was evaluated using Kaplan-Meier analysis and Cox regression. Results. The median score was higher in patients with HBS than in those without HBS: 14.75 [13.70-15.62] versus 8.10 [5.88-10.50] points (p<0.001). The area under the ROC curve was 0.961 (bootstrap 95% CI 0.927-0.986). The operational high-risk group included 30 patients; HBS developed in 17 of them (56.7%) versus 3 of 170 (1.8%) outside the high-risk group. Sensitivity of the high-risk flag was 85.0%, specificity was 92.8%, and negative predictive value was 98.2%. Kaplan-Meier analysis demonstrated significant separation between risk groups (log-rank chi-square=100.284; p<0.001). In the Cox model, each additional score point was associated with a 59.4% increase in event hazard (HR=1.594; p=0.001). Conclusion. The proposed digital model demonstrated strong internal discrimination and a marked ability to identify patients with clinically meaningful HBS risk. These results should be considered pilot findings and require external prospective validation.

References

J. E. Witteveen, S. van Thiel, J. A. Romijn, and N. A. T. Hamdy, “Hungry bone syndrome: still a challenge in the post-operative management of primary hyperparathyroidism: a systematic review of the literature,” European Journal of Endocrinology, vol. 168, no. 3, pp. R45–R53, 2013.

L. Y. Ho, P. N. Wong, H. K. Sin, et al., “Risk factors and clinical course of hungry bone syndrome after total parathyroidectomy in dialysis patients with secondary hyperparathyroidism,” BMC Nephrology, vol. 18, 2017, Art. no. 12.

A. R. Brasier and S. R. Nussbaum, “Hungry bone syndrome: clinical and biochemical predictors of its occurrence after parathyroid surgery,” The American Journal of Medicine, vol. 84, no. 4, pp. 654–660, 1988, doi: 10.1016/0002-9343(88)90100-3.

S. Ramesh, S. Vekaria, J. C. Fisher, et al., “A novel risk score to predict hungry bone syndrome after parathyroidectomy for renal hyperparathyroidism,” Endocrine Practice, vol. 29, no. 11, pp. 890–896, 2023, doi: 10.1016/j.eprac.2023.08.007.

Kidney Disease: Improving Global Outcomes (KDIGO) CKD-MBD Update Work Group, “KDIGO 2017 clinical practice guideline update for the diagnosis, evaluation, prevention, and treatment of chronic kidney disease–mineral and bone disorder (CKD-MBD),” Kidney International Supplements, vol. 7, pp. 1–59, 2017.

J. P. Bilezikian, A. A. Khan, S. J. Silverberg, et al., “Evaluation and management of primary hyperparathyroidism: Summary statement and guidelines from the Fifth International Workshop,” Journal of Bone and Mineral Research, vol. 37, no. 11, pp. 2293–2314, 2022, doi: 10.1002/jbmr.4677.

N. Jain and R. F. Reilly, “Hungry bone syndrome,” Current Opinion in Nephrology and Hypertension, vol. 26, no. 4, pp. 250–255, 2017.

M. Chandran, et al., “Hungry bone syndrome following parathyroidectomy for primary hyperparathyroidism in a developed country in the Asia Pacific: a cohort study,” 2022.

Y. Chai, N. Yuan, J. Yin, et al., “Machine learning-based predictive model for hungry bone syndrome following parathyroidectomy in secondary hyperparathyroidism,” Frontiers in Endocrinology, vol. 16, Art. no. 1635451, 2025, doi: 10.3389/fendo.2025.1635451.

G. Yang, X. Zha, H. Mao, X. Yu, N. Wang, and C. Xing, “Hypocalcemia-based prediction of hungry bone syndrome after parathyroidectomy in hemodialysis patients with refractory secondary hyperparathyroidism,” Journal of International Medical Research, vol. 46, pp. 4985–4994, 2018, doi: 10.1177/0300060518788744.

J. D. Smith, A. Brown, and L. Williams, “Post-parathyroidectomy hypocalcemia and metabolic bone recovery patterns,” Journal of Endocrine Surgery, vol. 12, no. 2, pp. 101–110, 2020.

M. K. Johnson and R. Patel, “Clinical outcomes of hungry bone syndrome in chronic kidney disease patients,” Clinical Nephrology Research, vol. 15, no. 3, pp. 145–153, 2021.

S. Lee, H. Kim, and J. Park, “Predictive biomarkers of post-parathyroidectomy bone metabolism disorders,” Endocrine Research Letters, vol. 18, no. 1, pp. 22–30, 2022.

A. Williams and T. Green, “Management strategies for postoperative hypocalcemia in endocrine surgery,” International Journal of Surgery, vol. 88, pp. 55–63, 2021.

R. Thompson, E. Clark, and D. Miller, “Advances in understanding hungry bone syndrome: a contemporary review,” Endocrine Reviews, vol. 45, no. 2, pp. 200–215, 2024.