Machine learning can identify patients at risk of hyperparathyroidism without known calcium and intact parathyroid hormone

Background To prove the concept of diagnosing primary hyperparathyroidism (pHPT) without calcium and parathyroid hormone (PTH) values and identifying potential risk factors for pHPT. Methods Data were extracted from the clinical data warehouse (CDW) at the University of Arkansas for Medical Sciences (UAMS) Epic EHR (2014-2019). Results 1737 patients with over 185 000 rows of clinical data were provided in a relational structure and processed/flattened to facilitate modeling. Phenotype elements were identified for pHPT without advance knowledge of calcium and PTH levels. The area under the curve (AUC) for the prediction of pHPT using our model was 0.86 with sensitivity and specificity of 0.8953 and 0.6686, respectively, using a 0.45 probability threshold. Conclusion Primary hyperparathyroidism was predicted from a dataset excluding calcium and PTH data with 86% accuracy. This approach needs to be validated/refined on larger samples of data and plans are in place to do this with other regional/national datasets.

Automated summary

The study demonstrated the concept of diagnosing pHPT without calcium and PTH values, and identified potential risk factors for pHPT. Data from the clinical data warehouse at UAMS was used to develop a predictive model for pHPT, achieving an accuracy of 86%. Further validation and refinement on larger datasets are planned for the future.