Nutritional support feeding efficiency

Inventors

Elia, Liron • Iddan, Gavriel J.

Assignees

Art Medical Ltd

Abstract

There is provided a method comprising: computing a target nutritional goal to reach at an end of a time interval based on a real time energy expenditure of a patient, wherein the target nutritional goal comprises a volume to be delivered (VTBD) by the end of the time interval corresponding to a target amount of energy expenditure of the patient over the time interval, computing a target feeding profile defining a target feeding rate for enteral feeding of the patient for reaching the VTBD by the end of the time interval, continuously monitoring the real time energy expenditure, adapting the target nutritional goal and corresponding VTBD to compute a maximum VTBD to reach at the end of the time interval according to the monitoring, and dynamically adapting the target feeding rate and the corresponding target feeding profile for a remaining portion of the time interval for reaching the maximum VTBD.

Core Innovation

The invention relates to a system for automated enteral feeding of a subject that uses a target feeding profile and a baseline feeding profile to define a target nutritional goal at an end of a time interval. A feeding deficit is detected as a deficit to reach the target nutritional goal when comparing the target feeding profile with the baseline feeding profile.

After detecting the feeding deficit, the system dynamically sends a control signal to an automated enteral feeding device. The control signal causes delivery feedings by adjusting the baseline feeding profile to a higher feeding delivery rate than a feeding delivery rate of the corresponding target feeding profile in order to compensate the feeding deficit for reaching the target nutritional goal at the end of the time interval. When the feeding deficit has been compensated, the system reduces the feeding delivery rate to reach the target nutritional goal at the end of the time interval.

The disclosed approach further includes continuously monitoring energy expenditure and using real-time energy expenditure to compute and dynamically adapt the target nutritional goal and the target feeding profile over the time interval, including max VTBD. A machine learning approach is also described for predicting and reducing future gastric reflux risk by learning correlations between reflux-related parameters and events and using classifier outputs to pause, slow, or increase feeding within maximal rate bounds, while compensating for feeding gaps through pump rate control.

Claims Coverage

The independent claims cover three inventive aspects: detecting and compensating a feeding deficit by dynamically adjusting a baseline feeding profile above a target feeding profile and later reducing back to the target, the corresponding method implementation, and a non-transitory medium storing program instructions that cause a processor to perform the same logic.

Across the independent claims, the core claim coverage is the detection of a feeding deficit between a target feeding profile and a baseline feeding profile, the dynamic sending of a control signal that increases feeding above the target profile to compensate the deficit, and the subsequent reduction of feeding back to reach the target nutritional goal at the end of the time interval.

Stated Advantages

Documented Applications