How to predict abnormal acid reflux: recent developments

Recent advances in physiology and technology have led to the identification of additional parameters that have the potential to enhance diagnostic accuracy and inform the management of Gastroesophageal reflux disease (GERD). Whilst traditional pH monitoring and acid exposure time (AET) remain central to diagnosis, recent advances have introduced novel physiological markers that improve diagnostic accuracy, especially in cases where results are inconclusive.

This review summarizes recent advances in predicting abnormal acid reflux. Novel physiologic indices include baseline impedance as markers of mucosal integrity, and the post-reflux swallow-induced peristaltic wave (PSPW) as a measure of chemical clearance. High resolution esophageal manometry (HRM) provides indirect evidence through EGJ contractile integral (EGJ-CI), multiple rapid swallows, and straight leg raise testing. Finally, artificial intelligence (AI) offers powerful methods to automate reflux event detection, PSPW calculation, and endoscopic classification, reducing interobserver variability and supporting precision diagnosis.

While AET remains central, novel physiologic metrics and AI integration substantially enhance prediction of abnormal acid reflux, particularly in patients with borderline findings. The standardization of thresholds and validation in large multicentre studies is required for the establishment of these tools in clinical practice.