ArticleEndocrine2024
Dynamic nomogram for predicting hungry bone syndrome before parathyroidectomy.
Article in Endocrine, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
4 citing papers in PubMed, 5 citations in OpenAlex.
- Predicting Hungry Bone Syndrome with Interpretable Machine Learning: A Single-Center Cohort of Dialysis Patients Undergoing Parathyroidectomy.Diagnostics (Basel, Switzerland) · 2026Article
- Predictors of Hungry Bone Syndrome After Parathyroidectomy in Secondary Hyperparathyroidism: A Narrative Review of Bone Turnover Biomarkers and Risk Prediction Tools.Journal of clinical medicine · 2025Review
- Machine learning-based predictive model for hungry bone syndrome following parathyroidectomy in secondary hyperparathyroidism.Frontiers in endocrinology · 2025Article
- Construction and validation of a risk prediction model for early hungry bone syndrome in maintenance hemodialysis patients post-parathyroidectomy.Zhong nan da xue xue bao. Yi xue ban = Journal of Central South University. Medical sciences · 2024Article
Corrections and comments
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Authors and funding
4 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purposeThe objective of this study was to develop a dependable and uncomplicated prediction model utilizing clinical information readily accessible to patients before surgery. This model aimed to assess the likelihood of hungry bone syndrome occurrence in post-surgery patients with secondary hyperparathyroidism (SHPT), and to assist clinicians in adjusting treatment plans promptly.
methodsIn this study, we constructed an online nomogram utilizing independent variables determined through multiple logistic regression to predict the probability of HBS occurrence after parathyroidectomy in patients with secondary hyperparathyroidism. To evaluate the precision and dependability of the nomogram, we used receiver operating characteristic (ROC) curve, calibration curve, and decision curve analysis (DCA).
resultsMultivariate logistic regression analyses on 136 eligible patients identified age, parathyroid hormone (PTH), and blood calcium as independent HBS risk factors, which were then integrated into the nomogram. The area under ROC curve demonstrated the nomogram's strong predictive accuracy. The calibration curve demonstrates consistency between the model's prediction probability and observed probability, reflecting high prediction accuracy of the nomogram. Dynamic nomograms were found to hold significant practical clinical value as demonstrated by clinical decision analysis. It can be accessed on https://min115.shinyapps.io/dynnomapp/ .
conclusionIn patients with secondary hyperparathyroidism, the dynamic nomogram based on age, parathyroid hormone, and blood calcium can more accurately predict the likelihood of HBS after parathyroidectomy, allowing doctors to make clinical decisions more quickly and adjust treatment plans in a timely manner to reduce the incidence of HBS.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.