Prognostic capability of the Maximum Acute Gastrointestinal Injury Score and of caloric intake in patients requiring vasopressors: A multicenter prospective cohort study

Loudet, Cecilia; Marchena, María Cecilia; Tumino, Leandro; Cabana, María L.; Capurro, Gabriela; Astegiano, Paulina; Velásquez, Mariela A.; Casanova, Matías; Bugueiro, María J. Rodríguez; Roth, Mária; Roda, Gisela; Gimbernat, Rolando; Balmaceda, Yanina Del Valle; Okurzaty, Patricia; Perman, Mario; González, Ana Laura; Reina, Rosa; Bakker, Jan

doi:10.1016/j.jcrc.2020.04.007

Cited by 5 publications

(1 citation statement)

References 27 publications

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“…The severity of the score was associated with an increase in mortality and confirmed another score, which was developed previously [13], the Acute Gastrointestinal Injury (AGI) score. Loubet et al [14], in a prospective multicenter cohort study, showed that patients with higher AGI score had a higher mortality and a lower calorie intake (17% had calorie intake lower than 10 kcal/ kg/day). However, these scores are still not validated and do not significantly impact the SOFA score.…”

Section: Gastrointestinal Intolerancementioning

confidence: 99%

Gastrointestinal failure, big data and intensive care

Singer

Robinson

Raphaeli

2023

Current Opinion in Clinical Nutrition &Amp; Metabolic Care

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Purpose of review Enteral feeding is the main route of administration of medical nutritional therapy in the critically ill. However, its failure is associated with increased complications. Machine learning and artificial intelligence have been used in intensive care to predict complications. The aim of this review is to explore the ability of machine learning to support decision making to ensure successful nutritional therapy. Recent findings Numerous conditions such as sepsis, acute kidney injury or indication for mechanical ventilation can be predicted using machine learning. Recently, machine learning has been applied to explore how gastrointestinal symptoms in addition to demographic parameters and severity scores, can accurately predict outcomes and successful administration of medical nutritional therapy. Summary With the rise of precision and personalized medicine for support of medical decisions, machine learning is gaining popularity in the field of intensive care, first not only to predict acute renal failure or indication for intubation but also to define the best parameters for recognizing gastrointestinal intolerance and to recognize patients intolerant to enteral feeding. Large data availability and improvement in data science will make machine learning an important tool to improve medical nutritional therapy.

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Section: Gastrointestinal Intolerancementioning

confidence: 99%

Gastrointestinal failure, big data and intensive care

Singer

Robinson

Raphaeli

2023

Current Opinion in Clinical Nutrition &Amp; Metabolic Care

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Development of the Gastrointestinal Dysfunction Score (GIDS) for critically ill patients – A prospective multicenter observational study (iSOFA study)

et al. 2021

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Background & aims: To develop a five grade score (0e4 points) for the assessment of gastrointestinal (GI) dysfunction in adult critically ill patients. Methods: This prospective multicenter observational study enrolled consecutive adult patients admitted to 11 intensive care units in nine countries. At all sites, daily clinical data with emphasis on GI clinical symptoms were collected and intra-abdominal pressure measured. In five out of 11 sites, the biomarkers citrulline and intestinal fatty acid-binding protein (I-FABP) were measured additionally. Cox models with time-dependent scores were used to analyze associations with 28-and 90-day mortality. The models were estimated with stratification for study center. Results: We included 540 patients (224 with biomarker measurements) with median age of 65 years (range 18e94), the Simplified Acute Physiology Score II score of 38 (interquartile range 26e53) points, and Sequential Organ Failure Assessment (SOFA) score of 6 (interquartile range 3e9) points at admission. Median ICU length of stay was 3 (interquartile range 1e6) days and 90-day mortality 18.9%. A new five grade Gastrointestinal Dysfunction Score (GIDS) was developed based on the rationale of the previously developed Acute GI Injury (AGI) grading. Citrulline and I-FABP did not prove their potential for scoring of GI dysfunction in critically ill. GIDS was independently associated with 28-and 90-day

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Enteral feeding strategies in patients with acute gastrointestinal injury: From limited to progressive to open feeding

Wang,

Li,

et al. 2024

Nutrition

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Prognostic capability of the Maximum Acute Gastrointestinal Injury Score and of caloric intake in patients requiring vasopressors: A multicenter prospective cohort study

Cited by 5 publications

References 27 publications

Gastrointestinal failure, big data and intensive care

Gastrointestinal failure, big data and intensive care

Development of the Gastrointestinal Dysfunction Score (GIDS) for critically ill patients – A prospective multicenter observational study (iSOFA study)

Enteral feeding strategies in patients with acute gastrointestinal injury: From limited to progressive to open feeding

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