Intestinal Tuberculosis (ITB) presents a significant diagnostic challenge due to its nonspecific clinical presentation and the lack of comprehensive local data to guide diagnostic strategies. This study aims to fill the gap by conducting a twelve-year retrospective cross-sectional analysis at a tertiary hospital in Cebu, Philippines. Electronic records of 209 patients aged 18 years old and above were first reviewed, focusing on clinical features, laboratory results, endoscopic findings, and CT scan of the abdomen. Initial screening identified 54 patients meeting the predefined criteria for gastrointestinal tuberculosis (GITB). In addition, statistical analyses, including logistic regression models, were employed to identify significant predictors of ITB which can further enhance the ITB diagnosis and management in the region.

Clinical manifestations observed include: symptoms and signs resembling those observed in malignancies and inflammatory bowel diseases, such as abdominal pain (92.6%), ascites (57.4%), fever (51.9%), hematochezia (25.9%), abdominal mass (24.1%) and intestinal obstruction (5.6%). The findings from CT scans of the abdomen were consistent with other studies, including the presence of matted mesenteric lymph nodes (79.6%), concentric mural thickening (57.4%), ileocecal involvement (44.4%). However, dilated bowel loops (20.4%), intestinal perforation (5.4%) and strictures (3.7%) were observed in only a few cases. Ileocecal involvement was found to be a dependable predictor among all the variables when logistic regression analysis was employed, emphasizing its diagnostic utility.

Our findings highlight the importance of local epidemiological insights in improving diagnostic strategies and patient outcomes. Consolidating the clinical profiles and diagnostic markers contributes to evidence-based strategies tailored to the Philippine context. This localized approach can further help medical professionals in making more informed decisions. Future studies could validate these findings to develop region-specific predictive tools, for a more time sensitive management of ITB.