Ventilation management

Clinical challenge

In the US alone, ventilated patients consume 37% ($26Bn) of the Intensive Care Unit (ICU) costs. (Ref: Am. Journal of Resp. Critical Care Med Vol 165 #6 and Europan Respiratory Journal Vol 19 #5 )

 

The key to reducing these costs is to ensure the patient can be removed from mechanical ventilation sooner, i.e. optimise the ventilator management process.

 

One of the main difficulties in optimizing ventilation management for ICU patients is that the respiratory physician has to make decisions about appropriate ventilatory therapy in a matter of minutes during their clinical rounds.

 

These decisions need to be based upon the patient’s individual physiological state (cardio and pulmonary status) and the current ventilation strategy, requiring the clinician to interpret all this information with their previous clinical experience to make a rational decision.

 

This is a challenging process which today costs $26bn in the US alone, and can place the patient at increased risk of lung trauma and a prolonged hospital stay (post ICU phase).

Tools to assist the respiratory therapist in optimizing the ventilatory management are therefore required, particularly an Intelligent Automatic Ventilation Management System, which can advise on how to optimize the patients’ ventilation management, based on the patients’ physiological status, current settings of the ventilator and clinical heuristics.


Current technologies

Current technologies for ICU ventilation management on the market today are rule or protocol-based closed- or open-loop systems controlling the ventilatory support to the patient, meaning that the level of support is adjusted based on fixed clinical rules and guidelines for patient groups.

 

The shortcomings are that the ventilatory support is fixed for individual patients within those groups and does not adjust to the patient condition or current state of physiology. Or in other words:

 

  • The competing goals of ventilation may not be adequately balanced
  • Changes in the patient’s physiological state may be misinterpreted

 

Therefore, these systems are appropriate for keeping a patient in a “zone of comfort” but not for understanding the underlying physiological state of each individual patient.

 

The inclusion of physiological models of the ventilatory management system, as in the BEACON Caresystem, can help clinicians gain a deeper insight into the status of the patient and support the selection of ventilator settings on an individual patient basis.

 

Furthermore, model-based DSS – rather than rule or protocol-based systems – provide the possibility of simulating effects of different ventilator settings and thereby answer “what if” questions.

 

The BEACON Caresystem is the only DSS of the model-based DSS outlined above that has been evaluated both retrospectively and prospectively for ICU patients.


BEACON Caresystem overview

The BEACON Caresystem is a decision support system, implemented as an extra device on the side of the mechanical ventilator, which provides advice as to appropriate ventilator settings for a number of ventilators and in a number of modes. The figure illustrates an overview of the functionality and strategy of the BEACON Caresystem.

BEACON concept

As shown in this figure, the system is based upon a number of linked mathematical models of physiology, which enable simulation of changes in the patient when modifying ventilator settings. Input to these models are measurements, including: end tidal O2 and CO2, SpO2, respiratory flows and pressures, the ventilator’s current settings and user input values of arterial blood gas. These inputs are used by the mathematical model in two ways: a) to learn about the patient and b) to provide appropriate advice.

Learning about the patient is performed by “tuning” the mathematical models so that they describe the individual patient’s current measurements and settings. In doing so the patient is described in terms of their metabolism, lung mechanics, pulmonary gas exchange, acid-base status and respiratory drive. The system continues to learn about the patient as new measurements present. In this way the system adapts to the patient’s physiological changes, with every new measurement or change in ventilator setting being used to understand the patient’s physiological state. This learning allows for “intelligent monitoring” and “intelligent alarms” where the deep physiological picture of the patient is monitored and alarms provided on detrimental changes in the patient’s physiological state.

 

Providing appropriate advice is performed by the system in two stages. In the first, the mathematical models are used to simulate the effects of changes in ventilator settings for the individual patient. In the second, the BEACON Caresystem weighs the pros and cons of the simulated strategy, finding the ventilator strategy which provides the best balance between these. As illustrated in the figure above, this weighing is performed in terms of three sets of competing goals, illustrated in a single hexagon form. The three factors on the top of this hexagon represent over-ventilation, and the three factors on the bottom represent under-ventilation.

 

For the individual patient, the shape drawn within the hexagon represents the result of the model-simulated balances between over- and under-ventilation for each of the three sets of competing goals:

 

1)   For support modes of ventilation, the balance between the risk of atrophy of the respiratory muscles on over-ventilation is weighed against the risk of patient stress and fatigue due to under-ventilation

2)   The risk of lung trauma due to over ventilation is weighed against the risk of acidosis on under-ventilation

3)   The risk of the damaging physiological effects of high FiO2 on the lung, are weighed against the risk of hypoxaemia. The BEACON Caresystem automatically searches through possible ventilator strategies, advising on the results in model simulations which best balance all these competing goals simultaneously.

 

The BEACON Caresystem is available in three different versions (with an easy upgrade path between them):

 

  • BEACON-D:
    • Pulmonary diagnostic device for non-invasive V/Q estimation on ICU ventilated patients (SHUNT, Low and High V/Q estimation)More about BEACON-D
  • BEACON-3
    • Physiological Based Ventilation Assist and Intelligent Monitoring System, which provides advice for the setting of FiO2, P/Vt and Rf on the ICU VentilatorMore about BEACON-3
  • BEACON-5
    • Physiological Based Ventilation Assist and Intelligent Monitoring System, which provides advice for the setting of FiO2, P/Vt, Rf, PEEP and I:E on the ICU VentilatorMore about BEACON-5

BEACON Caresystem clinical value

Physiological Based Ventilator Assist System

  • The BEACON Caresystem provides advice on the optimal FiO2, P/Vt, f, PEEP and I:E setting regardless of patient clinical state.
  • The BEACON advice is based on physiological models, clinical preferences and Step2Target simulations:
    • Physiological models are tuned to patient-specific clinical state enabling individualized treatment, improving patient safety and efficacy.
    • Clinical preferences enabling standardized care describe the trade-offs made when selecting mechanical ventilator strategy, and are separate from physiological knowledge
    • Step2Target enables simulation of the effects of changing ventilator strategy, reducing the risks associated with trial and error strategies
  • The BEACON Caresystem is in contrast to rule-based systems which use the same medical protocol or rules of thumb for all patients.
  • The BEACON Caresystem’s model parameters characterize the deeper picture of the patient’s state and physiology.
  • Monitoring is based on changes in physiology instead of a single point of measure, enabling improved patient safety.
  • Monitoring is in contrast to other systems which, for example, measure and report a drop in SpO2 and do not explain why the drop happened.

Intelligent monitoring and alarms

  • The BEACON Caresystem’s model parameters characterize the deeper picture of the patient’s state and physiology.
  • Monitoring is based on changes in physiology instead of a single point of measure, enabling improved patient safety.
  • Monitoring is in contrast to other systems which, for example, measure and report a drop in SpO2 and do not explain why the drop happened.

 

 

 

 

 

 

 

 

 


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