Predictive Modeling of Hyperparathyroidism in Patients With Benign Thyroid Nodules: A Cohort Study Using the Vizient Database.

Abstract

Predicting primary hyperparathyroidism in data may facilitate earlier diagnosis and treatment. Primary Hyperparathyroidism (pHPT) is the leading cause of hypercalcemia and up to 75% of hypercalcemic patients go undiagnosed. The purpose of this study was to examine the use of predictive modeling using a large clinical database to predict pHPT in patients with benign thyroid nodules. Retrospective analysis and predictive modeling of pHPT using a large discharge database. A predictive model of pHPT was created using logistic regression and compared to three machine learning algorithms: a Gaussian naive Bayes classifier, a stochastic gradient descent classifier, and a histogram-based gradient boosting classifier. Vizient hospital discharge database from over 1000 hospitals including academic health centers. Data from the Vizient Clinical Database (CDB), 2 541 901 patients with benign thyroid nodules were identified between 2020 and 2023, of whom 83 555 (3.29%) had pHPT. INTERVENTION(S) (FOR CLINICAL TRIALS) OR EXPOSURE(S) (FOR OBSERVATIONAL STUDIES): Analyses controlled for demographics (age, sex, race), comorbidities (body mass index (BMI), diabetes, hypertension, smoking status, renal disease) and use of proton pump inhibitors and bisphosphonates. The primary outcome measure was the presence of pHPT, which was identified using ICD-10 codes. Model performance was compared using the area under the receiver operating characteristics (ROC) curve. In the baseline predictive model, several demographic characteristics were significant predictors of pHPT. The logistic regression model had an area under the ROC curve of 68.1%, which was lower than that of the histogram gradient boosting model (68.7%) but equivalent to the gradient descent classifier (68.1%). Furthermore, the logistic regression model correctly classified 80.4% of pHPT cases, compared to 80.5% for both the histogram gradient boosting classifier and the gradient descent classifier. A threshold of 5% yielded a sensitivity of 38.5% and specificity of 81.8% for logistic regression. Predictive modeling of pHPT among patients with benign thyroid nodules is possible using a large clinical database. The predictive equation could be built into decision support systems to alert clinicians to potentially undiagnosed pHPT and aid in timely diagnosis and treatment of pHPT.