The need for biomarkers to determine response to enteral nutrition during and after critical illness: an update

Toward Precision in Nutrition Therapy

Precision in critical care nutrition is paramount, as it focuses nutrition interventions on those patients most likely to benefit, or those who might potentially be harmed. Critical care nutrition must therefore be tailored to individual metabolic needs as determined by factors that control the capacity for tissue homeostasis and anabolic responses. This ideally involves the accurate and timely assessment of macronutrient and micronutrient requirements, a careful evaluation of metabolic response mechanisms and the identification of circumstances that might interfere with the productive utilization of dietary substrates. Specific surrogate markers of metabolic response, such as blood glucose levels, urea levels, or nitrogen balance, might be used to evaluate the metabolic readiness for nutrition and to establish the timing, nature, and clinical effectiveness of nutrition interventions. Despite the pressing need to further develop more targeted approaches in critically ill patients, indices of immediate metabolic responses that correlate with favorable clinical outcomes are lacking. In addition, the development of precision approaches might address timely adjustments in protein, energy, or micronutrient supplementation based on evolving clinical conditions. Here, we review why precision tools are needed in critical care nutrition, our progress thus far, as well as promising approaches and technologies by which multidisciplinary healthcare teams can improve quality of care and clinical outcomes by individualizing nutrition interventions.

Use of Machine Learning Models to Predict Microaspiration Measured by Tracheal Pepsin A

BACKGROUND

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.

OBJECTIVE

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.

METHODS

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-random forest, XGBoost, and support vector machines with recursive feature elimination-using 5-fold cross-validation tuning.

RESULTS

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.

CONCLUSION

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.

Dynamic assessment of prealbumin for nutrition support effectiveness in critically ill patients

BACKGROUND & AIMS

Serum prealbumin is considered to be a sensitive predictor of clinical outcomes and a quality marker for nutrition support. However, its susceptibility to inflammation restricts its usage in critically ill patients according to current guidelines. We assessed the performance of the initial value of prealbumin and dynamic changes for predicting the ICU mortality and the effectiveness of nutrition support in critically ill patients.

METHODS

This monocentric study included patients admitted to the ICU between 2009 and 2016, having at least one initial prealbumin value available. Prospectively recorded data were extracted from the electronic ICU charts. We used both univariable and multivariable logistic regressions to estimate the performance of prealbumin for the prediction of ICU mortality. Additionally, the association between prealbumin dynamic changes and nutrition support was assessed via a multivariable linear mixed-effects model and multivariable linear regression. Performing subgroup analysis assisted in identifying patients for whom prealbumin dynamic assessment holds specific relevance.

RESULTS

We included 3136 patients with a total of 4942 prealbumin levels available. Both prealbumin measured at ICU admission (adjusted odds-ratio (aOR) 0.04, confidence interval (CI) 95% 0.01-0.23) and its change over the first week (aOR 0.02, CI 95 0.00-0.19) were negatively associated with ICU mortality. Throughout the entire ICU stay, prealbumin dynamic changes were associated with both cumulative energy (estimate: 33.2, standard error (SE) 0.001, p < 0.01) and protein intakes (1.39, SE 0.001, p < 0.01). During the first week of stay, prealbumin change was independently associated with mean energy (6.03e-04, SE 2.32e-04, p < 0.01) and protein intakes (1.97e-02, SE 5.91e-03, p < 0.01). Notably, the association between prealbumin and energy intake was strongest among older or malnourished patients, those suffering from increased inflammation and those with high disease severity. Finally, prealbumin changes were associated with a positive mean nitrogen balance at day 7 only in patients with SOFA <4 (p = 0.047).

CONCLUSION

Prealbumin measured at ICU admission and its change during the first-week serve as an accurate predictor of ICU mortality. Prealbumin dynamic assessment may be a reliable tool to estimate the effectiveness of nutrition support in the ICU, especially among high-risk patients.

Body composition in pediatric patients

Undernutrition is highly prevalent in children who are critically ill and is associated with increased morbidity and mortality, including a higher risk of infection due to transitory immunological disorders, inadequate wound healing, reduced gut function, longer dependency on mechanical ventilation, and longer hospital stays compared with eutrophic children who are critically ill. Nutrition care studies have proposed that early interventions targeting nutrition assessment can prevent or minimize the complications of undernutrition. Stress promotes an acute inflammatory response mediated by cytokines, resulting in increased basal metabolism and nitrogen excretion and leading to muscle loss and changes in body composition. Therefore, the inclusion of body composition assessment is important in the evaluation of these patients because, in addition to the nutrition aspect, body composition seems to predict clinical prognosis. Several techniques can be used to assess body composition, such as arm measurements, calf circumference, grip strength, bioelectrical impedance analysis, and imaging examinations, including computed tomography and dual-energy x-ray absorptiometry. This review of available evidence suggests that arm measurements seem to be well-established in assessing body composition in children who are critically ill, and that bioelectrical impedance analysis with phase angle, handgrip strength, calf circumference and ultrasound seem to be promising in this evaluation. However, further robust studies based on scientific evidence are necessary.