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Diagnostic value of digital continuous bowel sounds in critically ill patients with acute gastrointestinal injury: A prospective observational study

Year: 2024
Type: article
Study design: Observational Study
Abstract: BACKGROUND Acute gastrointestinal injury (AGI) is common in intensive care unit (ICU) and worsens the prognosis of critically ill patients. The four-point grading system proposed by the European Society of Intensive Care Medicine is subjective and lacks specificity. Therefore, a more objective method is required to evaluate and determine the grade of gastrointestinal dysfunction in this patient population. Digital continuous monitoring of bowel sounds and some biomarkers can change in gastrointestinal injuries. We aimed to develop a model of AGI using continuous monitoring of bowel sounds and biomarkers. AIM To develop a model to discriminate AGI by monitoring bowel sounds and biomarker indicators. METHODS We conducted a prospective observational study with 75 patients in an ICU of a tertiary-care hospital to create a diagnostic model for AGI. We recorded their bowel sounds, assessed AGI grading, collected clinical data, and measured biomarkers. We evaluated the model using misjudgment probability and leave-one-out cross-validation. RESULTS Mean bowel sound rate and citrulline level are independent risk factors for AGI. Gastrin was identified as a risk factor for the severity of AGI. Other factors that correlated with AGI include mean bowel sound rate, amplitude, interval time, Sequential Organ Failure Assessment score, Acute Physiology and Chronic Health Evaluation II score, platelet count, total protein level, blood gas potential of hydrogen (pH), and bicarbonate (HCO3-) level. Two discriminant models were constructed with a misclassification probability of < 0.1. Leave-one-out cross-validation correctly classified 69.8% of the cases. CONCLUSION Our AGI diagnostic model represents a potentially effective approach for clinical AGI grading and holds promise as an objective diagnostic standard for AGI.
Authors: Yuan-Hui Sun, Yun-Yun Song, Sha Sha, Qi Sun, Deng-Chao Huang, Lan Gao, Hao Li, Qin-Dong Shi
Language: English
Subfield: Epidemiology
Field: Medicine
Open Access status: diamond
OpenAlex ID: W4404732060
FWCI: 0.265
Cites: 33
Cited by: 1
Related to: 10