Development of a Machine Learning Model to Predict Therapeutic Responses in Laryngopharyngeal Reflux Disease
Su Il Kim1, Young-Gyu Eun2, Young Chan Lee3
1Department of Otolaryngology-Head and Neck Surgery, Kyung Hee University School of Medicine, Kyung Hee University Hospital at Gangdong, Seoul, Korea.
Objectives:
Laryngopharyngeal reflux disease (LPRD) is a challenging condition requiring effective treatment. Thus, understanding the factors that influence therapeutic response in LPRD is crucial. This study leverages machine learning models to predict the therapeutic responses and identify the key influencing factors in LPRD.
Methods:
Patients with typical LPRD symptoms showing more than one pharyngeal reflux episode on 24-hour multichannel intraluminal impedance (MII)-pH monitoring were collected retrospectively from two independent otolaryngologic clinics. Patients who were prescribed proton pump inhibitor therapy and followed up for at least 2 months were included. Patients who showed a ≥50% decrease in the follow-up reflux symptom index score during treatment periods compared with pre treatment were defined as responders. Among various demographic and 24-hour MII-pH monitoring parameters, features showing the absolute correlation coefficients ≥0.1 with response were selected. Four machine learning models-logistic regression, random forest, support vector machine, and gradient boosting-were applied to the training cohort and assessed in the internal and external validation cohorts.
Results:
Patients from two otolaryngologic clinics were assigned to the internal dataset (n = 157) and external dataset (n = 53). All four models showed comparable predictive performances, illustrating their potential utility in clinical decision-making. Among them, the logistic regression model demonstrated the best performance with accuracy and F1 scores of 82.98% and 88.24% in the internal validation cohort and 84.91% and 86.21% in the external validation cohort predicting therapeutic responses in LPRD. Feature importance analysis revealed vital factors, such as proximal total reflux time and weak acid time, influencing therapeutic response, and offering insights into LPRD management.
Conclusions:
This study provides valuable insights into the factors influencing the therapeutic response in LPRD, underscoring the utility of machine learning in refining treatment strategies. Our findings suggest that integrating machine learning models into clinical practice can significantly improve LPRD management.
