Predicting prolonged postoperative ileus in gastric cancer patients based on bowel sounds using intelligent auscultation and machine learning

Background: Prolonged postoperative ileus (PPOI) delays the postoperative recovery of gastrointestinal function in patients with gastric cancer (GC), leading to longer hospitalization and higher healthcare expenditure. However, effective monitoring of gastrointestinal recovery in patients with GC remains challenging because of the lack of noninvasive methods.

Aim: To explore the risk factors for delayed postoperative bowel function recovery and evaluate bowel sound indicators collected via an intelligent auscultation system to guide clinical practice.

Methods: This study included data from 120 patients diagnosed with GC who had undergone surgical treatment and postoperative bowel sound monitoring in the Department of General Surgery II at Shaanxi Provincial People's Hospital between January 2019 and January 2021. Among them, PPOI was reported in 33 cases. The patients were randomly divided into the training and validation cohorts. Significant variables from the training cohort were identified using univariate and multivariable analyses and were included in the model.

Results: The analysis identified six potential variables associated with PPOI among the included participants. The incidence rate of PPOI was 27.5%. Age ≥ 70 years, cTNM stage (I and IV), preoperative hypoproteinemia, recovery time of bowel sounds (RTBS), number of bowel sounds (NBS), and frequency of bowel sounds (FBS) were independent risk factors for PPOI. The Bayesian model demonstrated good performance with internal validation: Training cohort [area under the curve (AUC) = 0.880, accuracy = 0.823, Brier score = 0.139] and validation cohort (AUC = 0.747, accuracy = 0.690, Brier score = 0.215). The model showed a good fit and calibration in the decision curve analysis, indicating a significant net benefit.

Conclusion: PPOI is a common complication following gastrectomy in patients with GC and is associated with age, cTNM stage, preoperative hypoproteinemia, and specific bowel sound-related indices (RTBS, NBS, and FBS). To facilitate early intervention and improve patient outcomes, clinicians should consider these factors, optimize preoperative nutritional status, and implement routine postoperative bowel sound monitoring. This study introduces an accessible machine learning model for predicting PPOI in patients with GC.