Non-invasive ventilation treatment for patients with chronic obstructive pulmonary disease.

Chronic obstructive pulmonary disease (COPD) affects the lives of millions of patients worldwide. Patients with advanced COPD may require non-invasive ventilation (NIV) to support the resultant deficiencies of the respiratory system. The purpose of this study was to evaluate the effects of varying the continuous positive airway pressure (CPAP) and oxygen supplementation components of NIV on simulated COPD patients by using an established and detailed model of the human respiratory system. The model used in the study simulates features of advanced COPD including the effects on the changes in ventilation control, increases in respiratory dead space and airway resistance, and the acid-base shifts in the blood seen in these patients over time. The results of the study have been compared with and found to be in general agreement with available clinical data. Our results demonstrate that under non-emergency conditions, low levels of oxygen supplementation combined with low levels of CPAP therapy seem to improve hypoxemia and hypercapnia in the model, whereas prolonged high-level CPAP and moderate-to-high levels of oxygen supplementation do not. The authors conclude that such modelling may be useful to help guide beneficial interventions for COPD patients using NIV.

Computerised decision support for differential lung ventilation.

Differential lung ventilation treatment is a mechanical ventilation strategy used for unilateral lung disease or injury. This treatment can be provided to patients who fail to respond to conventional mechanical ventilation to both lungs and is technically challenging to medical personnel. An effective computerised decision support system (CDSS) can be used as a support system to intensivists in providing this treatment to their patients. In this study, a CDSS for differential lung ventilation is presented. By using this system, the mode of ventilation to each lung can be pressure controlled or volume controlled and all ventilation parameters including the peak inspiratory pressure (P insp), tidal volume (V t), positive end-expiratory pressure, fraction of inspired oxygen ( F I O 2 ), and the respiratory rate (f) can be assigned individually to each lung. The proposed CDSS has the potential to be used as a support system to clinicians in providing differential lung ventilation treatments to patients.

Continuous Positive Airway Pressure treatment of premature infants; application of a computerized decision support system.

The predictions of a computerized decision-support system (CDSS) are compared to clinical data obtained from a group of premature infants. The infants were suffering from respiratory distress syndrome (RDS) and were treated by the Continuous Positive Airway Pressure (CPAP) therapy. The predictions of the CDSS are found to be in general agreement with clinical measurements. The CDSS is also used to determine the effect of low level oxygen treatment on arterial oxygen pressure if the infant׳s oxygenation is low despite CPAP therapy. Based on the computational results, application of low levels of supplemental inspired fraction of oxygen ( [Formula: see text] ) would result in significant improvement in oxygenation of premature infants provided such treatment is carefully controlled to avoid hyperoxemia.

A computerized decision support system to predict the variations in the cerebral blood flow of mechanically ventilated infants.

A computerized decision support system is described to predict the changes in the cerebral blood flow (CBF) of mechanically ventilated infants in response to different ventilatory settings. A CBF controller was developed and combined with a mathematical model of the infant's respiratory system to simulate the effects of ventilatory settings on the infant's CBF. The performance of the system was examined under various ventilatory treatments and the results were compared with available experimental data. The comparisons showed good agreement between the simulation results and experimental data for preterm infants. These included the results obtained under conditions of hypoventilation, hyperventilation, hypoxia, and hyperoxia. The presented decision support system has the potential to be used as an aide to the intensivist in choosing appropriate ventilation treatments for infants to prevent the untoward consequences of hazardous changes in CBF in mechanically ventilated infants such as hypoxic-ischemic brain injuries.

A control system for mechanical ventilation of passive and active subjects.

Synchronization of spontaneous breathing with breaths supplied by the ventilator is essential for providing optimal ventilation to patients on mechanical ventilation. Some ventilation techniques such as Adaptive Support Ventilation (ASV), Proportional Assist Ventilation (PAV), and Neurally Adjusted Ventilatory Assist (NAVA) are designed to address this problem. In PAV, the pressure support is proportional to the patient's ongoing effort during inspiration. However, there is no guarantee that the patient receives adequate ventilation. The system described in this article is designed to automatically control the support level in PAV to guarantee delivery of patient's required ventilation. This system can also be used to control the PAV support level based on the patient's work of breathing. This technique further incorporates some of the features of ASV to deliver mandatory breaths for passive subjects. The system has been tested by using computer simulations and the controller has been implemented by using a prototype.

A closed-loop system for control of the fraction of inspired oxygen and the positive end-expiratory pressure in mechanical ventilation.

A system for automatic control of the fraction of inspired oxygen (F(IO2)), and positive end-expiratory pressure (PEEP) for patients on mechanical ventilation is presented. In this system, F(IO2) is controlled by using two interacting mechanisms; a fine control mechanism and a fast stepwise procedure used when patient's oxygen saturation level (S(pO2)) falls abruptly. The PEEP level is controlled automatically and in relation to F(IO2) to prevent hypoxemia. The system has been tested by using bench studies and computer simulations. The results show the potential of the system as an aide in effective oxygenation of patients on mechanical ventilation.

A model-based decision support system for critiquing mechanical ventilation treatments.

A computerized system for critiquing mechanical ventilation treatments is presented that can be used as an aide to the intensivist. The presented system is based on the physiological model of the subject's respiratory system. It uses modified versions of previously developed models of adult and neonatal respiratory systems to simulate the effects of different ventilator treatments on the patient's blood gases. The physiological models that have been used for research and teaching purposes by many researchers in the field include lungs, body tissue, and the brain tissue. The lung volume is continuously time-varying and the effects of shunt in the lung, changes in cardiac output and cerebral blood flow, and the arterial transport delays are included in the system. Evaluation tests were done on adult and neonate patients with different diagnoses. In both groups combined, the differences between the arterial partial pressures of CO(2) predicted by the system and the experimental values were 1.86 ± 1.6 mmHg (mean ± SD), and the differences between the predicted arterial hemoglobin oxygen saturation values, S(aO2), and the experimental values measured by using pulse oximetry, S(pO2), were 0.032 ± 0.02 (mean ± SD). The proposed system has the potential to be used alone or in combination with other decision support systems to set ventilation parameters and optimize treatment for patients on mechanical ventilation.

Critiquing treatment and setting ventilatory parameters by using physiological modeling.

A modeling system is presented that can be used to predict the effects of ventilatory settings on the blood gases of patients on mechanical ventilation. The system uses a physiological model of the patient that includes lungs, body tissue, and brain tissue compartments. The model includes the effects of changes in the cardiac output and cerebral blood flow and lung mechanical factors. The system has applications in critiquing different treatment options and can be used alone or in combination with decision support systems to set ventilatory parameters and optimize treatment for patients on mechanical ventilation.

Evaluation of a computerized system for mechanical ventilation of infants.

OBJECTIVE: To evaluate a computerized system for mechanical ventilation of infants. METHODS: FLEX is a computerized system that includes the features of a patented mode known as adaptive-support ventilation (ASV). In addition, it has many other features including adjustment of positive end-expiratory pressure (PEEP), fraction of inspired oxygen (F(IO2)), minute ventilation, and control of weaning. It is used as an open-loop decision support system or as a closed-loop technique. Blood gas and ventilation data were collected from 12 infants in the neonatal intensive care at baseline and at the next round of evaluation. This data were input to open-loop version of FLEX. The system recommendations were compared to clinical determinations. RESULTS: FLEX recommended values for ventilation were on the average within 25% and 16.5% of the measured values at baseline and at the next round of evaluation, respectively. For F(IO2) and PEEP, FLEX recommended values were in general agreement with the clinical settings. FLEX recommendations for weaning were the same as the clinical determinations 50% of the time at baseline and 55% of the time at the next round of evaluation. FLEX did not recommend weaning for infants with weak spontaneous breathing effort or those who showed signs of dyspnea. CONCLUSIONS: A computerized system for mechanical ventilation is evaluated for treatment of infants. The results of the study show that the system has good potential for use in neonatal ventilatory care. Further refinements can be made in the system for very low-birth-weight infants.

Automatic control of mechanical ventilation. Part 2: the existing techniques and future trends.

OBJECTIVE: The major automatic techniques that are available in commercial ventilators are described and a discussion of the recently developed systems along with the future trends in the field is provided. METHODS: The major available automatic control techniques for mechanical ventilation are analyzed and the future trends are discussed in view of today's ICU requirements and the recently developed technologies. RESULTS: Several major automatic techniques are available in commercial ventilators at this time. Those techniques have been in use successfully and are accepted by clinicians. At the same time, more advanced techniques have been and continue to be developed by various researchers that are designed for more aggressive use of automation in control of ventilation and oxygenation in different phases of ventilatory treatment. CONCLUSIONS: Automatic control of mechanical ventilation can significantly improve patient care in the ICUs, reduce the mortality and morbidity rates associated with provision of inappropriate ventilatory treatments, and reduce healthcare costs. Development of more effective and robust systems that can have high noise immunity and provide effective treatment to patients automatically in different phases of treatment will likely gain increasing momentum in the years to come.

Automatic control of mechanical ventilation. Part 1: theory and history of the technology.

OBJECTIVE: In this article, automatic control technology as applied to mechanical ventilation is discussed and the techniques that have been reported in the literature are reviewed. METHODS: The information in the literature is reviewed and various techniques are compared. RESULTS: Automatic control has been applied in many ways to mechanical ventilation since several decades ago. More aggressive techniques aimed at automatic and more optimal control of the main outputs of the machine have emerged and continue to be enhanced with time. CONCLUSIONS: Development of more efficient automatic techniques and/or enhancement of the present methods are likely to be pursued to make this technology more compatible with future healthcare requirements.

Intelligent decision support systems for mechanical ventilation.

OBJECTIVE: An overview of different methodologies used in various intelligent decision support systems (IDSSs) for mechanical ventilation is provided. The applications of the techniques are compared in view of today's intensive care unit (ICU) requirements. METHODS: Information available in the literature is utilized to provide a methodological review of different systems. RESULTS: Comparisons are made of different systems developed for specific ventilation modes as well as those intended for use in wider applications. The inputs and the optimized parameters of different systems are discussed and rule-based systems are compared to model-based techniques. The knowledge-based systems used for closed-loop control of weaning from mechanical ventilation are also described. Finally, in view of increasing trend towards automation of mechanical ventilation, the potential utility of intelligent advisory systems for this purpose is discussed. CONCLUSIONS: IDSSs for mechanical ventilation can be quite helpful to clinicians in today's ICU settings. To be useful, such systems should be designed to be effective, safe, and easy to use at patient's bedside. In particular, these systems must be capable of noise removal, artifact detection and effective validation of data. Systems that can also be adapted for closed-loop control/weaning of patients at the discretion of the clinician, may have a higher potential for use in the future.

Flex: a new computerized system for mechanical ventilation.

OBJECTIVE: To describe and evaluate a new weaning and decision support system for mechanical ventilation. BACKGROUND: FLEX is a computerized weaning and decision support system for mechanical ventilation that unlike previous rule-based systems derives many of its rules on the basis of the conditions of individual patients. This system can be used in a wide range of ventilatory modes as well as automatic control of weaning. It incorporates the features of the patented ventilatory mode known as Adaptive Support Ventilation (ASV) along with other new features for control of weaning, and control of patient's oxygenation by adjustment of PEEP and the fraction of inspired oxygen. METHODS: Ventilator data was collected for 10 patients in medical/surgical ICU at baseline and about 24 hours later. Required data fields for each patient for these two time points were also entered into the FLEX program. Comparison of clinical data and FLEX recommendations were made with regard to minute ventilation, alarms, weaning institution and other variables. RESULTS: At baseline, 7 patients were being treated with AC, the remainder with IMV/PS. There was good agreement between the measured and recommended minute ventilations; variances were seen in some patients being treated with permissive hypercapnea and those with evidence of high oxygen needs or other metabolic derangements. At 24 hours, there was improved correlation between measured minute ventilation and that recommended by FLEX, suggesting that clinical adjustments were in-line with Flex recommendations over time. Furthermore, FLEX made recommendations with regard to FIO(2) and PEEP that would potentially diminish the risk of oxygen toxicity, hypoxemia, and barotrauma in selected patients. FLEX has also been implemented as a closed loop system in an initial set up. CONCLUSION: A new weaning and decision support system for mechanical ventilation is presented. The recommendations made by the system were found to be in line with clinical determinations. Further refinements in the FLEX predictions can be easily made by including inputs which represent permissive hypercapnea or increased metabolic demand for selected patients.

A new decision support system for mechanical ventilation.

A decision support system has been developed for the treatment and management of patients on mechanical ventilation. The following criteria have been used in the design of the system; a) to regulate arterial blood gases within the normal range, b) to optimize ventilatory treatment in order to minimize the breathing work rate, and c) to reduce the weaning time from the ventilator. The system incorporates many safety features and can be used as an advisory tool as well as an informative source of patient data. Another application of the system is in automatic control of weaning.

Function of brainstem neurons in optimal control of respiratory mechanics.

An optimization control procedure is developed to describe the function of the human respiratory controller in determination of the respiratory frequency, the expiratory reserve volume, and the physiological dead space volume at all levels of human activity. The required level of alveolar ventilation is considered to have been determined based on the inputs from the peripheral and central chemoreceptors. The proposed procedure describes the mechanical control of breathing in which the excitation signals are adjusted and transferred from the neuron pools in the brainstem to the respiratory muscles to control the rate and depth of breathing. The criterion of minimum average respiratory work rate is used to find the optimal characteristics of respiration. The respiratory frequency, physiologic dead space volume, and expiratory reserve volume are used simultaneously as the optimization variables to minimize the average respiratory work rate. The optimization procedure has been applied by using different airflow patterns at various levels of ventilation. The theoretical results of the study have been compared with the experimental data in exercise taken from the literature. The results show a close agreement between the experimentally measured data and the theoretical values found by the optimization control procedure. The findings attest to the validity of the minimum average work rate criterion and the proposed multivariable optimization procedure compared with other procedures suggested in the literature in control of respiratory mechanics.