Development and validation of a predictive model for feeding intolerance in intensive care unit patients with sepsis

Role of artificial intelligence in critical care nutrition support and research

Nutrition plays a key role in the comprehensive care of critically ill patients. Determining optimal nutrition strategy, however, remains a subject of intense debate. Artificial intelligence (AI) applications are becoming increasingly common in medicine, and specifically in critical care, driven by the data-rich environment of intensive care units. In this review, we will examine the evidence regarding the application of AI in critical care nutrition. As of now, the use of AI in critical care nutrition is relatively limited, with its primary emphasis on malnutrition screening and tolerance of enteral nutrition. Despite the current scarcity of evidence, the potential for AI for more personalized nutrition management for critically ill patients is substantial. This stems from the ability of AI to integrate multiple data streams reflecting patients' changing needs while addressing inherent heterogeneity. The application of AI in critical care nutrition holds promise for optimizing patient outcomes through tailored and adaptive nutrition interventions. A successful implementation of AI, however, necessitates a multidisciplinary approach, coupled with careful consideration of challenges related to data management, financial aspects, and patient privacy.

Enhancing sepsis therapy: the evolving role of enteral nutrition

Sepsis is a life-threatening organ dysfunction syndrome caused by a dysregulated response to infection in the body. Effective treatment of sepsis poses a significant challenge in today's clinical field. In recent years, enteral nutrition has garnered significant attention as an essential supportive therapeutic strategy. Serving as a means to provide ample nutritional support directly through the gastrointestinal tract, enteral nutrition not only addresses the nutritional depletion caused by the disease but also holds potential advantages in regulating immune function, maintaining intestinal mucosal barrier integrity, and promoting tissue repair. This article delves into the latest advancements of enteral nutrition in the treatment of sepsis, with a particular focus on its application effectiveness in clinical practice, potential mechanisms, and challenges faced. By examining relevant basic and clinical research, the aim is to provide a deeper understanding of nutritional therapy for sepsis patients and offer valuable insights for future research and clinical practice.

Clinical practice guidelines for nutritional assessment and monitoring of adult ICU patients in China

The Chinese Society of Critical Care Medicine (CSCCM) has developed clinical practice guidelines for nutrition assessment and monitoring for patients in adult intensive care units (ICUs) in China. This guideline focuses on nutrition evaluation and metabolic monitoring to achieve optimal and personalized nutrition therapy for critically ill patients. This guideline was developed by experts in critical care medicine and evidence-based medicine methodology and was developed after a thorough review of the system and a summary of relevant trials or studies published from 2000 to July 2023. A total of 18 recommendations were formed and consensus was reached through discussions and reviews by expert groups in critical care medicine, parenteral and enteral nutrition, and surgery. The recommendations are based on currently available evidence and cover several key fields, including screening and assessment, evaluation and assessment of enteral feeding intolerance, metabolic and nutritional measurement and monitoring during nutrition therapy, and organ function evaluation related to nutrition supply. Each question was analyzed according to the Population, Intervention, Comparison, and Outcome (PICO) principle. In addition, interpretations were provided for four questions that did not reach a consensus but may have potential clinical and research value. The plan is to update this nutrition assessment and monitoring guideline using the international guideline update method within 3-5 years.

The future of artificial intelligence in clinical nutrition

PURPOSE OF REVIEW

Artificial intelligence has reached the clinical nutrition field. To perform personalized medicine, numerous tools can be used. In this review, we describe how the physician can utilize the growing healthcare databases to develop deep learning and machine learning algorithms, thus helping to improve screening, assessment, prediction of clinical events and outcomes related to clinical nutrition.

RECENT FINDINGS

Artificial intelligence can be applied to all the fields of clinical nutrition. Improving screening tools, identifying malnourished cancer patients or obesity using large databases has been achieved. In intensive care, machine learning has been able to predict enteral feeding intolerance, diarrhea, or refeeding hypophosphatemia. The outcome of patients with cancer can also be improved. Microbiota and metabolomics profiles are better integrated with the clinical condition using machine learning. However, ethical considerations and limitations of the use of artificial intelligence should be considered.

SUMMARY

Artificial intelligence is here to support the decision-making process of health professionals. Knowing not only its limitations but also its power will allow precision medicine in clinical nutrition as well as in the rest of the medical practice.

Different definitions of feeding intolerance and their associations with outcomes of critically ill adults receiving enteral nutrition: a systematic review and meta-analysis

BACKGROUND

A unified clinical definition of feeding intolerance (FI) is urged for better management of enteral nutrition (EN) in critically ill patients. We aimed to identify optimum clinical FI definitions based on reported evidence.

METHODS

We searched clinical studies comparing FI with non-FI with a clear definition, summarized the evidence by random-effect meta-analyses, and rated the certainty of evidence by the Grading of Recommendations Assessment, Development and Evaluation frameworks.

RESULTS

Five thousand five hundred twenty-five records were identified, of which 26 eligible studies enrolled 25,189 adult patients. Most patient-centered outcomes were associated with FI overall. Low to very low certainty evidence established FI defined as large gastric residual volume (GRV) ≥ 250 ± 50 mL combined with any other gastrointestinal symptoms (GIS) had a significant association with high mortalities in particular all-cause hospital mortality (odds ratio [OR] 1.90, 95% confidence interval [CI] 1.40-2.57), the incidence of pneumonia (OR 1.54, 95% CI 1.13-2.09) and prolonged length of hospital stay (mean difference 4.20, 95% CI 2.08-6.32), with a moderate hospital prevalence (41.49%, 95% CI 31.61-51.38%). 3-day enteral feeding (EF) delivered percentage < 80% had a moderate hospital prevalence (38.23%, 95% CI 24.88-51.58) but a marginally significant association with all-cause hospital mortality (OR 1.90, 95% CI 1.03-3.50).

CONCLUSIONS

In critically ill adult patients receiving EN, the large-GRV-centered GIS to define FI seemed to be superior to 3-day EF-insufficiency in terms of both close associations with all-cause hospital mortality and acceptable hospital prevalence (Registered PROSPERO: CRD42022326273).

TRIAL REGISTRATION

The protocol for this review and meta-analysis was registered with PROSPERO: CRD42022326273. Registered 10 May 2022.

Using Machine-Learning to Assess the Prognostic Value of Early Enteral Feeding Intolerance in Critically Ill Patients: A Retrospective Study

BACKGROUND

The association between gastrointestinal intolerance during early enteral nutrition (EN) and adverse clinical outcomes in critically ill patients is controversial. We aimed to assess the prognostic value of enteral feeding intolerance (EFI) markers during early ICU stays and to predict early EN failure using a machine learning (ML) approach.

METHODS

We performed a retrospective analysis of data from adult patients admitted to Beilinson Hospital ICU between January 2011 and December 2018 for more than 48 h and received EN. Clinical data, including demographics, severity scores, EFI markers, and medications, along with 72 h after admission, were analyzed by ML algorithms. Prediction performance was assessed by the area under the receiver operating characteristics (AUCROC) of a ten-fold cross-validation set.

RESULTS

The datasets comprised 1584 patients. The means of the cross-validation AUCROCs for 90-day mortality and early EN failure were 0.73 (95% CI 0.71-0.75) and 0.71 (95% CI 0.67-0.74), respectively. Gastric residual volume above 250 mL on the second day was an important component of both prediction models.

CONCLUSIONS

ML underlined the EFI markers that predict poor 90-day outcomes and early EN failure and supports early recognition of at-risk patients. Results have to be confirmed in further prospective and external validation studies.

Gut Microbiota and Enteral Nutrition Tolerance in Non-Abdominal Infection Septic ICU Patients: An Observational Study

Background: The effect of gut microbiota on enteral nutrition tolerance in critically ill patients is unclear. Methods: Non-abdominal sepsis patients in an ICU, sorted by whether they reached 20 Kcal/kg/day on the 3rd day of EN, were divided into tolerance and intolerance groups. Their feces on day 1 and day 3 of EN initiation were collected for 16s rDNA and short-chain fatty acid (SCFA) testing. Results: There were 14 patients included in the tolerance group and 10 in the intolerance group. On EN day 1, the OTUs and microbiota diversity were higher in the tolerance group than in the intolerance group. The ratio of Firmicutes to Bacteroidetes was higher in the intolerance group on EN day 1. The genus Parabacteroides were the most significantly elevated in the tolerance group. On EN day 3, the genus Escherichia-Shigella was the most significantly elevated in the tolerance group. On EN day 3, the levels of SCFA decreased more significantly in the intolerance group. Conclusion: Enteral nutrition tolerance is associated with microbiota features and short-chain fatty acid levels. A higher ratio of Firmicutes to Bacteroidetes and microbiota diversity on EN day 1 may help in the early prediction of EN tolerance.