Validating Machine Learning Models Against the Saline Test Gold Standard for Primary Aldosteronism Diagnosis

Abstract

Background: In this study, we developed and validated machine learning models to predict primary aldosteronism (PA) in hypertensive East-Asian patients, comparing their performance against the traditional saline infusion test. The motivation for this development arises from the need to provide a more efficient and standardized diagnostic approach, because the saline infusion test, although considered a gold standard, is often cumbersome, is time-consuming, and lacks uniform protocols. By offering an alternative diagnostic method, this study seeks to enhance patient care through quicker and potentially more reliable PA detection.

Objectives: This study sought to both develop and evaluate the performance of machine learning models in detecting PA among hypertensive participants, in comparison to the standard saline loading test.

Methods: We used patient data from 3 distinct cohorts: TAIPAI (Taiwan Primary Aldosteronism Investigation), CONPASS (Chongqing Primary Aldosteronism Study), and a South Korean cohort. Random Forest's importance scores, XGBoost, and deep learning techniques are adopted to identify the most predictive features of primary aldosteronism.

Results: We present detailed results of the model's performance, including accuracy, sensitivity, and specificity. The Random Forest model achieved an accuracy of 0.673 (95% CI: 0.640-0.707), significantly outperforming the baseline models.

Conclusions: In our discussion, we address both the strengths and limitations of our study. Although the machine learning models demonstrated superior performance in predicting primary aldosteronism, the generalizability of these findings may be limited to East-Asian hypertensive populations. Future studies are needed to validate these models in diverse demographic settings to enhance their applicability.