Top

Published in:

01-06-2022 | Artificial Intelligence | Special Issue Insight

Machine-assisted nutritional and metabolic support

Authors: Jean Reignier, Yaseen M. Arabi, Jean-Charles Preiser

Published in: Intensive Care Medicine | Issue 10/2022

Excerpt

Nutritional and metabolic support in the intensive care unit (ICU) involves a complex decision-making process addressing a multitude of time-varying biological and clinical parameters. Machine-assisted computer-guided nutritional and metabolic support could help caregivers (a) tailor prescription to individual patients (accounting for nutritional state, weight, gender, type and severity of acute disease and organ failure, course of acute illness and current metabolic state, (b) manage medical nutrition to achieve adequate provision of nutrients, (c) give alerts for failure of nutrition delivery or inadequacy and for variations in patients metabolism and d) detect intolerance to nutritional support (Table 1).

Table 1

Examples of machine-assisted nutritional and metabolic support in critically ill patients

Nutritional and metabolic goal	Available devices	Desirable functions	Future directions
Measurement of energy expenditure	Indirect calorimetry Ventilator-derived carbon dioxide consumption (EEVCO2)		Reliable measurements in different clinical settings including hemodynamic instability or high FiO₂
Stable blood glucose control	Intravascular continuous glucose monitoring devices Interstitial continuous glucose monitoring devices	Real-time glucose data Detection of hypoglycemic and hyperglycemic excursions Prediction of impending hypoglycemia Prediction of glycemic variability	Improvement in the performance of interstitial devices and of the lifespan of intravascular devices Cost-effective solutions Combination with closed-loop control systems similar to artificial pancreas
Achievement of adequate enteral feeding	Feeding pumps	Automatic and manual priming Dose setting with target volume alarm The ability to provide incremental increases in flow rate Continuous and intermittent feed programs Advanced memory	Integration of data from feeding pumps into AI algorithms
Monitoring of muscle mass and function	Bioelectrical impedance analysis compound muscle action potential (CMAP) Ultrasound CT scan	Reliable, easy-to-use tool, that is not affected by fluid shifts	Clinical studies evaluating whether a nutrition strategy based on monitoring of muscle mass improve patient-centered outcomes
Assessment of enteral feeding intolerance	Point of care ultrasound	Ability to be performed by ICU intensivist, nurse, or dietitian	Clinical studies evaluating the feasibility for wide implementation and the impact on patient-centered outcomes

EEVCO₂ energy expenditure estimated by ventilator-derived carbon dioxide consumption, FiO₂ fraction of inspired oxygen, CT computed tomography, ICU intensive care unit

…

White H, King L (2014) Enteral feeding pumps: efficacy, safety, and patient acceptability. Med Dev (Auckl) 7:291–298. https://doi.org/10.2147/MDER.S50050CrossRef

Hermans G, Van Mechelen H, Clerckx B et al (2014) Acute outcomes and 1-year mortality of intensive care unit-acquired weakness. A cohort study and propensity-matched analysis. Am J Respir Crit Care Med 190:410–420. https://doi.org/10.1164/rccm.201312-2257OCCrossRefPubMed

Thibault R, Makhlouf AM, Mulliez A et al (2016) Fat-free mass at admission predicts 28-day mortality in intensive care unit patients: the international prospective observational study Phase Angle Project. Intensive Care Med 42:1445–1453. https://doi.org/10.1007/s00134-016-4468-3CrossRefPubMed

Kuchnia A, Earthman C, Teigen L et al (2017) Evaluation of bioelectrical impedance analysis in critically ill patients: results of a multicenter prospective study. J Parenter Enteral Nutr 41:1131–1138. https://doi.org/10.1177/0148607116651063CrossRef

Hermans G, Van Mechelen H, Bruyninckx F et al (2015) Predictive value for weakness and 1-year mortality of screening electrophysiology tests in the ICU. Intensive Care Med 41:2138–2148. https://doi.org/10.1007/s00134-015-3979-7CrossRefPubMed

Reignier J, Mercier E, Le Gouge A et al (2013) Effect of not monitoring residual gastric volume on risk of ventilator-associated pneumonia in adults receiving mechanical ventilation and early enteral feeding: a randomized controlled trial. J Am Med Assoc 309:249–256. https://doi.org/10.1001/jama.2012.196377CrossRef

Reintam Blaser A, Preiser JC, Fruhwald S et al (2020) Gastrointestinal dysfunction in the critically ill: a systematic scoping review and research agenda proposed by the Section of Metabolism, Endocrinology and Nutrition of the European Society of Intensive Care Medicine. Crit Care 24:224. https://doi.org/10.1186/s13054-020-02889-4CrossRefPubMedPubMedCentral

Hamada SR, Garcon P, Ronot M et al (2014) Ultrasound assessment of gastric volume in critically ill patients. Intensive Care Med 40:965–972. https://doi.org/10.1007/s00134-014-3320-xCrossRefPubMed

Fraipont V, Preiser J-C (2013) Energy estimation and measurement in critically ill patients. J Parenter Enteral Nutr 37:705–713. https://doi.org/10.1177/0148607113505868CrossRef

10.

Allingstrup MJ, Kondrup J, Wiis J et al (2017) Early goal-directed nutrition versus standard of care in adult intensive care patients: the single-centre, randomised, outcome assessor-blinded EAT-ICU trial. Intensive Care Med 43:1637–1647. https://doi.org/10.1007/s00134-017-4880-3CrossRefPubMed

11.

Bohe J, Abidi H, Brunot V et al (2021) Individualised versus conventional glucose control in critically-ill patients: the CONTROLING study-a randomized clinical trial. Intensive Care Med 47:1271–1283. https://doi.org/10.1007/s00134-021-06526-8CrossRefPubMedPubMedCentral

12.

Righy Shinotsuka C, Brasseur A, Fagnoul D et al (2016) Manual versus Automated moNitoring Accuracy of GlucosE II (MANAGE II). Crit Care 20:380. https://doi.org/10.1186/s13054-016-1547-3CrossRefPubMedPubMedCentral

13.

Preiser JC, Lheureux O, Thooft A et al (2018) Near-continuous glucose monitoring makes glycemic control safer in ICU patients. Crit Care Med 46:1224–1229. https://doi.org/10.1097/CCM.0000000000003157CrossRefPubMed

14.

Wernerman J, Desaive T, Finfer S et al (2014) Continuous glucose control in the ICU: report of a 2013 round table meeting. Crit Care 18:226. https://doi.org/10.1186/cc13921CrossRefPubMedPubMedCentral

15.

Leelarathna L, English SW, Thabit H et al (2013) Feasibility of fully automated closed-loop glucose control using continuous subcutaneous glucose measurements in critical illness: a randomized controlled trial. Crit Care 17:R159. https://doi.org/10.1186/cc12838CrossRefPubMedPubMedCentral

16.

van de Sande D, van Genderen ME, Huiskens J et al (2021) Moving from bytes to bedside: a systematic review on the use of artificial intelligence in the intensive care unit. Intensive Care Med 47:750–760. https://doi.org/10.1007/s00134-021-06446-7CrossRefPubMedPubMedCentral

17.

Ettori F, Henin A, Zemmour C et al (2019) Impact of a computer-assisted decision support system (CDSS) on nutrition management in critically ill hematology patients: the NUTCHOCO study (nutritional care in hematology oncologic patients and critical outcome). Ann Intensive Care 9:53. https://doi.org/10.1186/s13613-019-0527-6CrossRefPubMedPubMedCentral

Title: Machine-assisted nutritional and metabolic support
Authors: Jean Reignier
Yaseen M. Arabi
Jean-Charles Preiser
Publication date: 01-06-2022
Publisher: Springer Berlin Heidelberg
Keywords: Artificial Intelligence
Nutrition
Published in: Intensive Care Medicine / Issue 10/2022
Print ISSN: 0342-4642
Electronic ISSN: 1432-1238
DOI: https://doi.org/10.1007/s00134-022-06753-7

Keynote webinar | Spotlight on medication adherence

Springer Medicine

Machine-assisted nutritional and metabolic support

Excerpt

Keynote webinar | Spotlight on medication adherence

Springer Medicine

Excerpt

Please log in to get access to this content

Other articles of this Issue 10/2022

Invasive arterial pressure monitoring: much more than mean arterial pressure!

Phrenic nerve stimulation to protect the diaphragm, lung, and brain during mechanical ventilation

Machines that save lives in the intensive care unit: the ultrasonography machine

Update on prevention of intra-vascular accesses complications

The physiological underpinnings of life-saving respiratory support

Manipulating temperature: devices for targeted temperature management (TTM) in brain injury