Enteral Feeding Controller Reflux Prediction
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Solution Overview
Problem
Current manual assessment methods for enteral feeding in ICU patients are inadequate, leading to inaccurate energy prescription due to predictive equations like Harris-Benedict, resulting in under or over nutrition, which increases morbidity and mortality, and lack of technology to support medical staff in implementing nutritional guidelines.
Innovation Solution
A system that includes a processor to monitor gastric reflux-related parameters, train a classifier model to predict future reflux events, and adjust the enteral feeding profile to reduce reflux risk while meeting nutritional goals, using inputs such as feeding rate, patient location changes, and medication administration.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If predictive equations like Harris-Benedict are used for energy prescription, then feeding can be initiated without direct measurement, but accuracy of energy prescription deteriorates leading to under or over nutrition
Solution Approach 1:
The patent replaces manual predictive equations with an automated indirect calorimetry system that directly measures oxygen consumption and carbon dioxide production. This substitution of measurement methodology eliminates the inaccuracy of predictive equations while maintaining ease of operation through automated monitoring and calculation of energy expenditure.
2Quantity of substance
If higher calories target is provided to meet nutritional needs, then nutritional goals can be achieved, but mortality increases due to over nutrition
Solution Approach 1:
The patent implements continuous feedback monitoring through indirect calorimetry that measures actual energy expenditure in real-time. The system compares measured energy expenditure against prescribed calorie targets and automatically adjusts feeding rates to maintain appropriate energy balance, preventing both undernutrition and overnutrition-related mortality.
3Quantity of substance
If feeding rate is increased to compensate for GRV losses, then caloric uptake increases, but reflux events may be triggered
Solution Approach 1:
The patent employs dynamic adjustment of feeding rates based on real-time monitoring of gastric residual volumes and patient tolerance. Rather than static compensation, the system continuously adapts the feeding rate to balance caloric delivery needs against reflux risk, adjusting parameters dynamically based on measured patient response and tolerance thresholds.
Data Source
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AI summary
A computer-implemented method of treating a patient's and automated enteral feeding, comprising: monitoring a plurality of reflux-related parameters and at least one reflux event while the patient is automatically enterally fed by an enteral feeding controller according to a baseline feeding profile including a target nutritional goal, training a classifier component of a model for predicting likelihood of a future reflux event according to an input of scheduled and/or predicted plurality of reflux-related parameters, the classifier trained according to computed correlations between the plurality of reflux-related parameters and the at least one reflux event, feeding scheduled and/or predicted reflux-related parameters into the trained classifier component of the model for outputting risk of likelihood of a future reflux event, and computing, by the model, an adjustment to the baseline feeding profile for reducing likelihood of the future reflux event and for meeting the target nutritional goal.