Machine Learning-Based Prediction of Elevated PTH Levels Among the US General Population

Abstract

Context: Although elevated parathyroid hormone (PTH) levels are associated with higher mortality risks, the evidence is limited as to when PTH is expected to be elevated and thus should be measured among the general population.

Objective: This work aimed to build a machine learning-based prediction model of elevated PTH levels based on demographic, lifestyle, and biochemical data among US adults.

Methods: This population-based study included adults aged 20 years or older with a measurement of serum intact PTH from the National Health and Nutrition Examination Survey (NHANES) 2003 to 2006. We used the NHANES 2003 to 2004 cohort (n = 4096) to train 6 machine-learning prediction models (logistic regression with and without splines, lasso regression, random forest, gradient-boosting machines [GBMs], and SuperLearner). Then, we used the NHANES 2005 to 2006 cohort (n = 4112) to evaluate the model performance including area under the receiver operating characteristic curve (AUC).

Results: Of 8208 US adults, 753 (9.2%) showed PTH greater than 74 pg/mL. Across 6 algorithms, the highest AUC was observed among random forest (AUC [95% CI] = 0.79 [0.76-0.81]), GBM (AUC [95% CI] = 0.78 [0.75-0.81]), and SuperLearner (AUC [95% CI] = 0.79 [0.76-0.81]). The AUC improved from 0.69 to 0.77 when we added cubic splines for the estimated glomerular filtration rate (eGFR) in the logistic regression models. Logistic regression models with splines showed the best calibration performance (calibration slope [95% CI] = 0.96 [0.86-1.06]), while other algorithms were less calibrated. Among all covariates included, eGFR was the most important predictor of the random forest model and GBM.

Conclusion: In this nationally representative data in the United States, we developed a prediction model that potentially helps us to make accurate and early detection of elevated PTH in general clinical practice. Future studies are warranted to assess whether this prediction tool for elevated PTH would improve adverse health outcomes.

Keywords: NHANES; hyperparathyroidism; machine learning; parathyroid hormone; prediction model.

Figure 1.

Receiver operating characteristic curve of the logistic regression model, tree-based algorithms, and SuperLearner to predict elevated parathyroid hormone levels. GBM, gradient-boosting machines; Lasso, logistic regression with lasso regularization, Logistic, logistic regression model; RF, random forest.

Figure 2.

Receiver operating characteristic curve of the logistic regression model, tree-based algorithms, and SuperLearner to predict elevated parathyroid hormone levels without information on serum calcium and phosphorus levels. GBM, gradient-boosted machines; Lasso, logistic regression with lasso regularization, Logistic, logistic regression model; RF, random forest.

Figure 3.

Calibration plots of the logistic regression model, tree-based algorithms, and SuperLearner for elevated parathyroid hormone levels.

Figure 4.

Variable importance of each predictor in the random forest and gradient-boosting machine algorithms. The variable importance is a measure scaled to have a maximum value of 100. A, random forest; B, gradient-boosting machines.