Dynamic nomogram for predicting hungry bone syndrome before parathyroidectomy.

PURPOSE: The 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. METHODS: In 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). RESULTS: Multivariate 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/ . CONCLUSION: In 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.

SEER-based risk stratification system for patients with primary non-cirrhotic liver cancer.

BACKGROUND: Little research has been done on the factors affecting the survival of patients with non-cirrhotic hepatocellular carcinoma (HCC-NCL). Our aim was to develop and validate a nomogram and a new risk stratification system that can evaluate overall survival (OS) in HCC-NCL patients. METHODS: We retrospectively analyzed data from the Surveillance, Epidemiology, and End Results (SEER) database from 2010 to 2019 to study HCC-NCL patients. The patients were randomly split into training and validation groups at a 7:3 ratio and subjected to single-factor and multi-factor COX regression analysis. We then developed a nomogram and evaluated its accuracy and clinical validity using time-dependent ROC, DCA, and calibration curves. We compared the nomogram with the AJCC staging system by calculating C-index, NRI, and IDI. Finally, we used Kaplan-Meier curves to compare the nomogram and AJCC staging. These analyses were performed without altering the original intended meaning. RESULTS: AFP levels, surgical intervention, T-stage, tumor size, and M-stage were independent prognostic indicators for overall survival among the HCC-NCL population studied. We developed a nomogram based on these factors, and time-dependent ROC, calibration curves, DCA analyses, and C-index proved its accuracy. Compared to the AJCC staging system, the nomogram showed better prognostic accuracy through time-dependent ROC, DCA analyses, C-index, NRI, IDI, and Kaplan-Meier curves. CONCLUSION: We have developed and validated a survival nomogram applicable to HCC-NCL patients, with risk stratification. Our nomogram offers personalized treatment and management options superior to those provided by the AJCC staging system.