Beacon D is a system which enables measurement and monitoring of pulmonary function during mechanical ventilation. It measures the pulmonary gas exchange properties of the patient’s lungs using small changes in inspired oxygen, coupled with routine clinical measurements of respiratory gasses, and pulse oximetry and a mathematical model of the lungs, the core of Beacon D. In doing so a deep picture of pulmonary function is provided which can be used in monitoring the patient, for example the effects of changes ventilator settings such as PEEP. As such Beacon D provides an aid in selecting the most appropriate ventilator settings.
The above screen illustrates a typical application of Beacon D in helping the clinician to understand the effects of changing ventilator settings. The screen illustrates two measurements performed with Beacon D. The first (blue dot and blue graph) is taken at the current ventilator settings, and the second (green dot and green graph) is taken following an increase in PEEP of 5 cmH2O. The results are shown across the top of the screen for the current measurement, and on the left hand side for all the most recent measurements. The results on the left hand side provide a description of the effects of changing PEEP for this patient in terms of the effects of ventilation/perfusion (V/Q) matching in the lungs. Pulmonary shunt has reduced from 24% to 8%, indicating effective recruitment of the lung. ∆PO2, which is an expression of the low V/Q regions of the lungs, has decreased, probably illustrating that little of the recruited areas have become regions with low V/Q. ∆PCO2, which is an expression of the high V/Q regions of the lungs, has not changed significantly, probably illustrating that the recruitment has not resulted in detrimental over-distention of the lung. In this case the pulmonary effects of changing PEEP are justified and Beacon D illustrates an improved V/Q distribution in the lung.
The right hand side of the screen illustrates the results of Beacon D in a different format. Beacon D measures pulmonary gas exchange using variation in inspired oxygen (FiO2) and measurements of ventilation and arterial oxygenation (SpO2). The right hand side of the screen shows the relationship between FiO2 and SpO2 at the two different PEEP levels, illustrating the improved oxygenation, i.e. a shift of the curve, following change in PEEP. Data describing FiO2, SpO2, ventilation and arterial blood gas are input into the mathematical physiological models included in Beacon D. These mathematical models represent the core of Beacon D and enable calculation of pulmonary shunt, low V/Q (∆PO2) and high V/Q (∆PCO2).
The underlying cause of the impaired gas exchange is mainly due to mismatch between ventilation and perfusion of the lungs (V/Q mismatch), ranging from V/Q equals zero i.e. pulmonary shunt, to infinitely high V/Q i.e. alveolar dead space. In intensive care patients with Acute Lung Injury (ALI) or Acute Respiratory Distress Syndrome (ARDS) the primary cause of abnormal gas exchange is pulmonary shunt, low & high V/Q as illustrated below
VA/Q mismatch in the gas exchange model can be transformed into low VA/Q described by a change in oxygen partial pressure (∆PO2) and high VA/Q described by a change in carbon dioxide partial pressure (∆PCO2). ∆PO2 describes the drop in partial pressure of O2 from the alveoli to the capillaries leaving the lungs, just before being mixed with shunted blood and ∆PCO2 describes the increase in carbon dioxide pressure from ventilated alveoli to capillary blood.
Clinically, ∆PO2 describes the extra amount of FiO2 needed to counter for oxygenation problems caused by VA/Q mismatch. ∆PCO2 describes the CO2 gas transport problem occurring due to lung regions with high VA/Q. If ∆PCO2 is above 0 kPA the removal of CO2 is insufficient and the minute volume may need to be increased.
The figure above illustrates simulations of the end-tidal fraction of oxygen (FetO2) versus arterial oxygen saturation (SaO2) and end-tidal fraction of carbon dioxide (FetCO2) versus arterial partial pressure of carbon dioxide (PaCO2). The first figure from the left shows simulations performed with ∆PO2 = 0 kPa, ∆PCO2 = 0kPa and shunt varying from 0 to 30%. The second figure from left shows simulations performed with shunt = 5 %, ∆PCO2 = 0 kPa and ∆PO2 varying from 0 to 30 kPa. To the right are model simulations performed with shunt = 5 %, ∆PO2 = 0 kPa and ∆PCO2 varying from 0 to 2 kPa. In all simulations, oxygen consumption were held constant at 0.25 l/min, carbon dioxide production constant at 0.218 l/min and alveolar minute volume held constant at 5.25 l. Parameter
When the parameters of the gas exchange model has been identified from the procedure of varying FiO2, the gas exchange model can be used to predict SaO2 and PaCO2 at different levels of FiO2. Furthermore, the gas exchange model can be used to predict SaO2 and PaCO2 from different values of shunt, ∆PO2, and ∆PCO2.
Beacon D provides two standard monitoring facilities as well as an intelligent monitoring strategy.
The above window illustrates the standard monitoring window included in Beacon D. It can be customized by the user to include any of the following variables measured continuously by the Beacon Caresystem. These include pulse oximetry oxygen satuation (SpO2), inspiratory (FiO2, FiCO2) or expiratory (FeO2, FecO2) oxygen and carbon dioxide fractions, and calculated oxygen consumption (VO2) or carbon dioxide production (VCO2).
In addition Beacon D provides a historical monitoring view of the data as illustrated below
This view illustrates measured continuous variables, plus the measurement actions performed related to ventilator management. These include arterial blood gas sampling, measurement of pulmonary gas exchange or measurement of cardiac output. A full description of all data associated with the individual action can be accessed by clicking on the symbol marked on the action line.
Reducing opioid therapy often causes an increase in the patient’s respiratory drive. An increase in respiratory drive will mean that the patient has a greater alveolar ventilation, and lower end tidal CO2 than predicted by the physiological models included in the Beacon Caresystem. This is recognized by the system and the user is directed to measure an arterial blood gas, with the following screen displayed.
The Beacon Caresystem uses the measured arterial acid-base status and alveolar ventilation to re-tune the model estimating the patient’s new respiratory drive. The advice provided by the Beacon Caresystem will then reflect this new respiratory drive, potentially reducing support to allow the patient greater control. The ability of the Beacon Caresystem to identify when the models are a poor description of patient state can direct the user both to measurement of blood gas and to measurement of pulmonary gas exchange.
Beacon D uses the current measurements and ventilator settings along with the physiological mathematical models to “learn” the patient’s physiological condition in terms of pulmonary gas exchange, lung mechanics, metabolism, circulation, the properties of the blood, and the patient’s respiratory drive. These values also allow the system to automatically notify the clinician when the patient’s state is changing. These notifications are Beacon D’s “intelligent alarms”. They are intelligent as, unlike other alarms, they are not based upon single measurements such a SpO2, but rather upon deeper physiological changes in the patient’s state such as pulmonary function, metabolism, blood and respiratory drive. These are beneficial as alarms based on single measurement variables are a poor help in aiding the clinician in understanding the deeper cause of the problem. For example, a decrease in oxygenation of arterial blood can be due to numerous causes. The patient may have become active or developed a fever, in which case metabolic demand may have increased. The patient’s lung function may have deteriorated such that oxygen transports less freely from the lungs to blood, or it may just be the case that the clinician has inadvertently reduced inspired oxygen to a level where arterial values are compromised. Measurements and alarms indicating reduced arterial oxygenation are therefore useful but are not the complete picture. The ability of Beacon D to provide deeper “intelligent alarms” completes the monitoring picture and provides valuable support to the clinician.
Application of BEACON D (ALPE) in ICU patients
- Karbing DS, Kjærgaard S, Andreassen S, Espersen K, Rees SE. Minimal model quantification of pulmonary gas exchanges in intensive care patients. Med Eng Phys. 2011 Mar;33(2):240-8.
- S Kjærgaard, S Rees, J Malczynski, J.A Nielsen, P Thorgaard, E Toft, S Andreassen. Non-invasive estimation of shunt and ventilation-perfusion mismatch. Intensive Care Medicine 2003 May;29(5):727-34.
- D.S Karbing, S Kjaergaard, B.W Smith, K Espersen, C Allerod, S Andreassen, S.E Rees. Variation in the PaO2/FiO2 ratio with FiO2: Mathematical and experimental description, and clinical relevance. Critical Care. 2007 ; Vol. 11, No. 6.
Use of BEACON D (ALPE) to evaluate the effects of anaesthesia and surgery on pulmonary gas exchange
- B.S Rasmussen, H Laugesen, J Sollid, J Grønlund, S.E Rees, E Toft, J Gjedsted, C Dethlefsen, E Tønnesen. Oxygenation and release of inflammatory mediators after off-pump compared to after on-pump coronary artery bypass surgery. Acta Anaesthesiologica Scandinavica, 2007, 51(9):1202-10.
- B.S Rasmussen, J Sollid, S.E Rees, S Kjærgaard, D Murley, E Toft. Oxygenation within the first 120 h following coronary artery bypass grafting. Influence of systemic hypothermia (32 degrees C) or normothermia (36 degrees C) during the cardiopulmonary bypass: a randomized clinical trial. Acta Anaesthesiologica Scandinavica. 2006 Jan;50(1):64-71.
- S Kjærgaard, S.E Rees, J.A. Nielsen, M Freundlich, P Thorgaard and S Andreassen. Modelling of hypoxaemia after gynaecological laparotomy. Acta Anaesthesiologica Scandinavica 2001 Mar;45(3):349-356.
Evaluation of BEACON D (ALPE) against MIGET
- S.E Rees, S Kjærgaard, S Andreassen, G. Hedenstierna. Reproduction of inert gas and oxygenation data – a comparison of the MIGET and a simple model of pulmonary gas exchange. Intensive Care Medicine 2010, 36:2117-2124.
- S.E Rees, S Kjærgaard, S Andreassen, G. Hedenstierna. Reproduction of MIGET retention and excretion data using a simple model of gas exchange in lung damage caused by oleic acid infusion. Journal of Applied Physiology, 2006 Sep;101(3):826-32.
Use of BEACON D (ALPE) technology in decision support for mechanical ventilation
- Karbing DS, Allerød C, Thomsen LP, Espersen K, Thorgaard P, Andreassen S, Kjærgaard S, Rees SE. Retrospective evaluation of a dicision support system for controlled mechanical ventilation. Med Biol Eng Comput. 2011 Nov 22. [Epub ahead of print]
- Karbing DS, Allerød C, Thorgaard P, CariusAM, FrilevL, Andreassen S, Kjærgaard S, Rees SE. Prospective evaluation of a decision support system for setting inspired oxygen in intensive care patients. Journal of Critical Care, 2010, 25(3):367-74.
- S.E Rees, C Allerød, D Murley, Y Zhao, B.W Smith, S Kjærgaard, P Thorgaard, S Andreassen. Using physiological models and decision theory for selecting appropriate ventilator settings. Journal of Clinical Monitoring and Computing, 2006; Dec;20(6):421-429.
A summary report describing research activities.
- S.E Rees. The Intelligent Ventilator (INVENT) project: the role of mathematical models in translating physiological knowledge into clinical practice. Computer Methods and Programs in Biomedicine. 2011, vol 104, suppl , ppS1-S30.