ArticleEuropean journal of medical research2024
A nomogram prediction model for hungry bone syndrome in dialysis patients with secondary hyperparathyroidism after total parathyroidectomy.
Article in European journal of medical research, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
The trial behind it
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
10 citing papers in PubMed, 1 synthesis or guideline pooled it, 7 citations in OpenAlex.
- Parathyroidectomy in the treatment of uremic tumoral calcinosis: a 9-year case report and meta-analysis.BMC nephrology · 2025Pooled it
- A Clinically Aligned Two-Stage Machine Learning Framework for Predicting Hungry Bone Syndrome After Parathyroidectomy.Endocrinology, diabetes & metabolism · 2026Article
- Development of a nomogram for predicting severe post-parathyroidectomy hypocalcemia in patients with secondary hyperparathyroidism: a retrospective cohort study.Gland surgery · 2026Article
- Pre-Parathyroidectomy PTH as an Integrated Biomarker of Glandular Remodeling and Skeletal Turnover in Secondary Hyperparathyroidism.International journal of molecular sciences · 2026Review
- 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
- Hungry Bone Syndrome After Parathyroidectomy for Secondary Hyperparathyroidism: Pathogenesis and Contemporary Clinical Considerations.Journal of clinical medicine · 2025Review
- Development and validation of risk prediction models for permanent hypocalcemia after total thyroidectomy in patients with papillary thyroid carcinoma.Scientific reports · 2025Article
- Machine learning-based predictive model for hungry bone syndrome following parathyroidectomy in secondary hyperparathyroidism.Frontiers in endocrinology · 2025Article
- Brown Tumors: The Hidden Face of Primary and Renal Hyperparathyroidism Amid Real-Life Settings.Journal of clinical medicine · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectiveSecondary 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.
methodsClinical 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.
resultsAmong 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.
conclusionsPreoperative iPTH and preoperative ALP are the risk factors for postoperative HBS, which can be used to guide the early clinical intervention.
Indexed as
Identifiers
What OpenQuestion holds
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.