{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,31]],"date-time":"2026-07-31T13:31:20Z","timestamp":1785504680486,"version":"3.56.0"},"reference-count":25,"publisher":"AACN Publishing","issue":"1","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025,1,1]]},"abstract":"<jats:sec>\n                  <jats:title>Background<\/jats:title>\n                  <jats:p>Enteral feeding intolerance, a common type of gastrointestinal dysfunction leading to underfeeding, is associated with increased mortality. Tracheal pepsin A, an indicator of microaspiration, was found in 39% of patients within 24 hours of enteral feeding. Tracheal pepsin A is a potential biomarker of enteral feeding intolerance.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Objective<\/jats:title>\n                  <jats:p>To identify predictors of microaspiration (tracheal or oral pepsin A). It was hypothesized that variables predicting the presence of tracheal pepsin A might be similar to predictors of enteral feeding intolerance.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Methods<\/jats:title>\n                  <jats:p>In this secondary analysis, machine learning models were fit for 283 adults receiving mechanical ventilation who had tracheal and oral aspirates obtained every 12 hours for up to 14 days. Pepsin A levels were measured using the proteolytic enzyme assay method, and values of 6.25 ng\/mL or higher were classified as indicating microaspiration. Demographics, comorbidities, and variables associated with enteral feeding were analyzed with 3 machine learning models\u2014random forest, XGBoost, and support vector machines with recursive feature elimination\u2014using 5-fold cross-validation tuning.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Results<\/jats:title>\n                  <jats:p>Random forest for tracheal pepsin A was the best-performing model (area under the curve, 0.844 [95% CI, 0.792-0.897]; accuracy, 87.55%). The top 20 predictors of tracheal pepsin A were identified.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Conclusion<\/jats:title>\n                  <jats:p>Four predictor variables for tracheal pepsin A (microaspiration) are also reported predictors of enteral feeding intolerance, supporting the exploration of tracheal pepsin A as a potential biomarker of enteral feeding intolerance. Identification of predictor variables using machine learning models may facilitate treatment of patients at risk for enteral feeding intolerance.<\/jats:p>\n               <\/jats:sec>","DOI":"10.4037\/ajcc2025349","type":"journal-article","created":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T02:00:43Z","timestamp":1735696843000},"page":"67-71","source":"Crossref","is-referenced-by-count":2,"title":["Use of Machine Learning Models to Predict Microaspiration Measured by Tracheal Pepsin A"],"prefix":"10.4037","volume":"34","author":[{"given":"Annette","family":"Bourgault","sequence":"first","affiliation":[{"name":"Annette Bourgault is an associate professor in Nursing, University of Central Florida College of Nursing, Orlando."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ilana","family":"Logvinov","sequence":"additional","affiliation":[{"name":"Ilana Logvinov is a PhD student in Nursing, University of Central Florida College of Nursing, 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