Validation of an acoustic gastrointestinal surveillance biosensor for postoperative ileus

Brennan M R Spiegel1, Marc Kaneshiro, Marcia M Russell

  • 1Department of Medicine, VA Greater Los Angeles Healthcare System, Los Angeles, USA, bspiegel@mednet.ucla.edu.

Insights

A new acoustic gastro-intestinal surveillance (AGIS) biosensor accurately distinguishes postoperative ileus (POI) patients from healthy individuals. This non-invasive device shows promise for guiding feeding decisions after abdominal surgery.

Area of Science:

  • Gastroenterology
  • Surgical Innovation
  • Biomedical Engineering

Background:

  • Postoperative ileus (POI) complicates recovery, increasing costs and hospital stays.
  • Objective markers are needed to guide feeding protocols for surgical patients.
  • A novel acoustic gastro-intestinal surveillance (AGIS) biosensor was developed.

Purpose of the Study:

  • To evaluate the efficacy of the AGIS biosensor in differentiating healthy individuals from postoperative patients.
  • To assess the AGIS biosensor's ability to distinguish between patients who tolerate feeding and those with POI.

Main Methods:

  • The AGIS biosensor, a disposable device with a microphone, was adhered to the abdominal wall.
  • Intestinal acoustic event rates were recorded in healthy controls and two postoperative groups: those tolerating feeding and those with POI.
  • Statistical analysis (ANOVA, t-tests) compared acoustic rates among the groups.

Main Results:

  • Mean intestinal rates were significantly different across healthy controls (0.14 events/sec), fed patients (0.03 events/sec), and POI patients (0.016 events/sec) (ANOVA p < 0.001).
  • AGIS achieved 100% sensitivity and 97% specificity in distinguishing postoperative patients from controls.
  • Acoustic rates were significantly higher in fed patients compared to POI patients (p = 0.017).

Conclusions:

  • Non-invasive acoustic monitoring using AGIS effectively differentiates between POI and non-POI subjects.
  • Further research is warranted to explore AGIS for predicting POI risk and optimizing postoperative feeding strategies.
Abstract

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