Bowel sound in predicting the severity of acute pancreatitis: Protocol of a prospective, multi-center study

Acute pancreatitis (AP) is a common condition, with approximately 20% of patients developing severe acute pancreatitis (SAP), which is associated with a poor prognosis. Early identification and timely intervention for SAP patients are essential for improving clinical outcomes. This study aims to investigate whether bowel sound (BS) monitoring could facilitate the early recognition of SAP in AP patients.

This study is a prospective, multi-center prognostic study. We will consecutively recruit newly diagnosed AP patients within 48h of disease onset who have not developed SAP at enrollment from the emergency departments of three centers from 01/04/2024. Upon enrollment, each patient will undergo continuous bowel sound monitoring for at least 48 hours using standardized equipment and procedures. The primary outcome is the occurrence of SAP within the first 7 days of hospitalization. Collected bowel sound data will be analyzed using an automated algorithm to derive five bowel sound acoustic parameters: bowel sound occurrence rate, dominant frequency, duration, amplitude, and inter-bowel-sound interval. These parameters will be used as predictors of SAP. Bowel sound analysts will be blinded to patients' clinical information and outcomes, and treating clinicians will not have access to the bowel sound analysis results during the study. We will calculate the receiver operating characteristic (ROC) curve and area under the curve (AUC) to evaluate their predictive performance.

This study explores bowel sound monitoring as a non-invasive, real-time tool for assessing AP severity. Early BS changes correlate with prognosis, offering continuous physiological insights complementary to traditional scores. Despite confounders like clinical interventions, preliminary data show BS attenuation in SAP. Standardized protocols and noise-cancellation algorithms enhance reliability. Mechanistically, BS reflects intestinal dysmotility, aiding early deterioration detection. Future work will validate BS-guided interventions to optimize AP management.

ClinicalTrials.gov NCT06867120 Chinese Medical Research Registration and Filing Information System MR-11-25-024794.