APD Regimen Generation via Closed-Loop Feedback

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Solution Overview

Problem

Automated peritoneal dialysis (APD) devices lack the capability to provide feedback on therapy effectiveness and adjust parameters based on actual measured data, leading to inadequate solute clearance and ultrafiltration, resulting in conditions like fluid overload and hypertension, with current methods relying heavily on patient reporting and clinician adjustment.

Innovation Solution

A system with multiple prescription optimization modules, including an advanced peritoneal equilibration test (PET) for ultrafiltration data collection, regimen generation using patient physiological data, filtering for preferred or required therapy parameters, inventory tracking, trending of therapy parameters, and prescription recall and adjustment, enabling the APD machine to automatically select optimal therapy prescriptions based on real-time data.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Reliability

If APD devices run open loop without feedback capability, then device complexity is reduced, but therapy effectiveness deteriorates leading to inadequate solute clearance and ultrafiltration

Engineering Contradiction:
Improvetherapy effectivenessVSAvoiddevice complexity
Core Design Contradiction:
ReliabilityVSDevice complexity

Solution Approach 1:

The patent implements a closed-loop feedback system where the APD device continuously monitors therapy parameters (solute clearance, ultrafiltration) and automatically adjusts treatment prescriptions based on measured effectiveness. This feedback mechanism resolves the contradiction by maintaining high therapy effectiveness while managing device complexity through automated control algorithms.

Inventive Principle:
Principle #23Feedback

Solution Approach 2:

The system enables self-service by allowing the APD device to autonomously optimize therapy parameters without requiring constant clinician intervention. The device performs self-adjustment based on real-time data, improving therapy effectiveness while reducing the operational burden on healthcare providers.

Inventive Principle:
Principle #25Self-service

2Reliability

If therapy parameters are not adjusted frequently based on actual measured data, then ease of operation is improved, but patient outcomes deteriorate due to fluid overload and hypertension

Engineering Contradiction:
Improvepatient outcomesVSAvoidease of operation
Core Design Contradiction:
ReliabilityVSEase of operation

Solution Approach 1:

The system implements continuous feedback monitoring of patient status (weight, blood pressure, ultrafiltration) and automatically triggers therapy parameter adjustments when thresholds are exceeded. This ensures reliable patient outcomes through frequent adjustments while maintaining ease of operation through automated decision-making algorithms.

Inventive Principle:
Principle #23Feedback

Solution Approach 2:

The patent replaces manual clinician assessment and adjustment mechanisms with automated electronic monitoring and control systems. Sensors continuously measure patient parameters and algorithms automatically modify therapy, improving outcomes through frequent adjustments while eliminating the operational burden of manual intervention.

Inventive Principle:
Principle #28Mechanics substitution (Replace mechanical system)

3Adaptability or versatility

If multiple prescription options are generated and filtered, then adaptability to patient preferences is improved, but device complexity increases

Engineering Contradiction:
Improveadaptability to patient preferencesVSAvoiddevice complexity
Core Design Contradiction:
Adaptability or versatilityVSDevice complexity

Solution Approach 1:

The system dynamically generates multiple prescription options based on real-time patient data and automatically filters them according to patient preferences and clinical criteria. This dynamic adaptation improves versatility while managing complexity through algorithmic optimization that automatically selects the most appropriate prescription from generated options.

Inventive Principle:
Principle #15Dynamics

Solution Approach 2:

The patent utilizes parameter changes by varying therapy parameters (dwell times, exchange volumes, solution concentrations) to generate multiple prescription options. The system systematically explores parameter space to create customized treatments, improving adaptability while managing complexity through structured parameter optimization algorithms.

Inventive Principle:
Principle #35Parameter changes

4Productivity

If automated feedback and adjustment capabilities are added to APD devices, then productivity of therapy optimization is improved, but device complexity increases

Engineering Contradiction:
Improveproductivity of therapy optimizationVSAvoiddevice complexity
Core Design Contradiction:
ProductivityVSDevice complexity

Solution Approach 1:

The system implements self-service capabilities where the APD device automatically performs therapy optimization without requiring external intervention. The device autonomously analyzes patient data, generates prescription options, and implements adjustments, dramatically improving productivity while managing complexity through integrated automated control systems.

Inventive Principle:
Principle #25Self-service

Solution Approach 2:

The patent replaces manual therapy optimization processes with automated electronic systems. Algorithms continuously analyze patient data and automatically adjust prescriptions, increasing productivity by eliminating time-consuming manual assessments while managing complexity through software-based control mechanisms.

Inventive Principle:
Principle #28Mechanics substitution (Replace mechanical system)

Data Source

PatentEP2331164B1Dialysis system having regimen generation methodology
Publication Date: 2016.06.01 BAXTER INT INC
  • EP2331164B1 patent drawingFigure 1
  • EP2331164B1 patent drawingFigure 2A
  • EP2331164B1 patent drawingFigure 2B

AI summary

A peritoneal dialysis system includes a logic implementer configured to: (i) accept at least one therapy target input; (ii) accept at least one patient transport characteristic input; (iii) accept at least one solution input; (iv) accept at least one fluid volume input; (v) accept at least one therapy time input, at least one of the inputs (ii) to (v) including an input range; and (vi) generate each therapy regimen using the inputs of (ii), (iii), (iv) and (v), including each of the possibilities within the at least one input range, that satisfy the at least one therapy target input.