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Development and validation of a risk prediction model for gastroesophageal reflux disease: Gastroesophageal Reflux
Shanmathi Subramanian1, Umashri Sundararaju1, Hamrish Kumar Rajakumar2
1Department of General Surgery, Government Medical College, Omandurar Government Estate, Chennai 600002, Tamil Nādu, India.
Background:
The rising global prevalence of gastroesophageal reflux disease (GERD) has been closely linked to lifestyle changes driven by globalization. GERD imposes a substantial public health burden, affecting quality of life and leading to potential complications. Early intervention through lifestyle modification can prevent disease onset; however, there is a lack of effective risk prediction models that emphasize primary prevention.
Aim:
To develop and validate a GERD Risk Scoring System (GRSS) aimed at identifying high-risk individuals and promoting primary prevention strategies.
Methods:
A 45-item questionnaire encompassing major lifestyle and demographic risk factors was developed and validated. It was administered to healthy controls and GERD patients. Two regression models-one using continuous variables and another using categorized variables-were used to develop a computational prediction equation and a clinically applicable scoring scale. An independent validation cohort of 355 participants was used to assess model performance in terms of discrimination (C-index), calibration, sensitivity, specificity, internal consistency (Cronbach's alpha), and test-retest reliability (intraclass correlation coefficient, Bland-Altman analysis).
Results:
Significant associations were observed between GERD and key lifestyle factors. The derived GRSS equation and scoring scale demonstrated strong discriminative ability, with high sensitivity and specificity. The scoring system exhibited excellent internal consistency (Cronbach's alpha) and strong test-retest reliability. The C-index indicated excellent predictive accuracy in both derivation and validation cohorts.
Conclusion:
GRSS offers a novel and validated approach to GERD risk prediction, combining a robust equation for digital applications and a practical scale for clinical use. Its ability to accurately identify at-risk individuals supports a paradigm shift toward primary prevention, underscoring its significance in addressing the growing burden of GERD at the population level.
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