Intelligent decision support systems for mechanical ventilation

Computational physiological models for individualised mechanical ventilation: a systematic literature review focussing on quality, availability, and clinical readiness

BACKGROUND

Individualised optimisation of mechanical ventilation (MV) remains cumbersome in modern intensive care medicine. Computerised, model-based support systems could help in tailoring MV settings to the complex interactions between MV and the individual patient's pathophysiology. Therefore, we critically appraised the current literature on computational physiological models (CPMs) for individualised MV in the ICU with a focus on quality, availability, and clinical readiness.

METHODS

A systematic literature search was conducted on 13 February 2023 in MEDLINE ALL, Embase, Scopus and Web of Science to identify original research articles describing CPMs for individualised MV in the ICU. The modelled physiological phenomena, clinical applications, and level of readiness were extracted. The quality of model design reporting and validation was assessed based on American Society of Mechanical Engineers (ASME) standards.

RESULTS

Out of 6,333 unique publications, 149 publications were included. CPMs emerged since the 1970s with increasing levels of readiness. A total of 131 articles (88%) modelled lung mechanics, mainly for lung-protective ventilation. Gas exchange (n = 38, 26%) and gas homeostasis (n = 36, 24%) models had mainly applications in controlling oxygenation and ventilation. Respiratory muscle function models for diaphragm-protective ventilation emerged recently (n = 3, 2%). Three randomised controlled trials were initiated, applying the Beacon and CURE Soft models for gas exchange and PEEP optimisation. Overall, model design and quality were reported unsatisfactory in 93% and 21% of the articles, respectively.

CONCLUSION

CPMs are advancing towards clinical application as an explainable tool to optimise individualised MV. To promote clinical application, dedicated standards for quality assessment and model reporting are essential. Trial registration number PROSPERO- CRD42022301715 . Registered 05 February, 2022.

Modeling Mechanical Ventilation In Silico-Potential and Pitfalls

Computer simulation offers a fresh approach to traditional medical research that is particularly well suited to investigating issues related to mechanical ventilation. Patients receiving mechanical ventilation are routinely monitored in great detail, providing extensive high-quality data-streams for model design and configuration. Models based on such data can incorporate very complex system dynamics that can be validated against patient responses for use as investigational surrogates. Crucially, simulation offers the potential to "look inside" the patient, allowing unimpeded access to all variables of interest. In contrast to trials on both animal models and human patients, in silico models are completely configurable and reproducible; for example, different ventilator settings can be applied to an identical virtual patient, or the same settings applied to different patients, to understand their mode of action and quantitatively compare their effectiveness. Here, we review progress on the mathematical modeling and computer simulation of human anatomy, physiology, and pathophysiology in the context of mechanical ventilation, with an emphasis on the clinical applications of this approach in various disease states. We present new results highlighting the link between model complexity and predictive capability, using data on the responses of individual patients with acute respiratory distress syndrome to changes in multiple ventilator settings. The current limitations and potential of in silico modeling are discussed from a clinical perspective, and future challenges and research directions highlighted.

Prolonged Field Care Research Approach and Its Relevance to Civilian Medicine

In early March 2020, Johns Hopkins University Applied Physics Lab hosted an Association of Military Surgeons of the United States regional conference to address medical demands of the future battlefield for which prolonged field care is expected. Arising from this conference, we propose here an approach to prolonged field care research-and also summarize the major concepts discussed at the conference. We draw parallels to prolonged field care investments and advancements that apply beyond the combat environment. The exceedingly daunting medical challenges of the future battlefield, on land and at sea, must be addressed to maintain an effective force able to compete with modern highly capable adversaries. Since the human element, and its health, will allow future mission success, we propose here an approach to making soldier health-related research most impactful.

The dawn of physiological closed-loop ventilation-a review

The level of automation in mechanical ventilation has been steadily increasing over the last few decades. There has recently been renewed interest in physiological closed-loop control of ventilation. The development of these systems has followed a similar path to that of manual clinical ventilation, starting with ensuring optimal gas exchange and shifting to the prevention of ventilator-induced lung injury. Systems currently aim to encompass both aspects, and early commercial systems are appearing. These developments remain unknown to many clinicians and, hence, limit their adoption into the clinical environment. This review shows the evolution of the physiological closed-loop control of mechanical ventilation.

A physiology-based mathematical model for the selection of appropriate ventilator controls for lung and diaphragm protection

Mechanical ventilation is used to sustain respiratory function in patients with acute respiratory failure. To aid clinicians in consistently selecting lung- and diaphragm-protective ventilation settings, a physiology-based decision support system is needed. To form the foundation of such a system, a comprehensive physiological model which captures the dynamics of ventilation has been developed. The Lung and Diaphragm Protective Ventilation (LDPV) model centers around respiratory drive and incorporates respiratory system mechanics, ventilator mechanics, and blood acid–base balance. The model uses patient-specific parameters as inputs and outputs predictions of a patient’s transpulmonary and esophageal driving pressures (outputs most clinically relevant to lung and diaphragm safety), as well as their blood pH, under various ventilator and sedation conditions. Model simulations and global optimization techniques were used to evaluate and characterize the model. The LDPV model is demonstrated to describe a CO₂ respiratory response that is comparable to what is found in literature. Sensitivity analysis of the model indicate that the ventilator and sedation settings incorporated in the model have a significant impact on the target output parameters. Finally, the model is seen to be able to provide robust predictions of esophageal pressure, transpulmonary pressure and blood pH for patient parameters with realistic variability. The LDPV model is a robust physiological model which produces outputs which directly target and reflect the risk of ventilator-induced lung and diaphragm injury. Ventilation and sedation parameters are seen to modulate the model outputs in accordance with what is currently known in literature.

Clinical decision support recommending ventilator settings during noninvasive ventilation

NIV therapy is used to provide positive pressure ventilation for patients. There are protocols describing what ventilator settings to use to initialize NIV; however, the guidelines for titrating ventilator settings are less specific. We developed an advisory system to recommend NIV ventilator setting titration and recorded respiratory therapist agreement rates at the bedside. We developed an algorithm (NIV advisor) to recommend when to change the non-invasive ventilator settings of IPAP, EPAP, and FiO₂ based on patient respiratory parameters. The algorithm utilized a multi-target approach; oxygenation, ventilation, and patient effort. The NIV advisor recommended ventilator settings to move the patient's respiratory parameters in a preferred target range. We implemented a pilot study evaluating the usability of the NIV advisor on 10 patients receiving critical care with non-invasive ventilation (NIV). Respiratory therapists were asked their agreement on recommendations from the NIV advisor at the patient's bedside. Bedside respiratory therapists agreed with 91% of the ventilator setting recommendations from the NIV advisor. The POB and VT values were the respiratory parameters that were most often out of the preferred target range. The IPAP ventilator setting was the setting most often considered in need of changing by the NIV advisor. The respiratory therapists agreed with the majority of the recommendations from the NIV advisor. We consider the IPAP recommendations informative in providing the respiratory therapist assistance in targeting preferred POB and Vt values, as these values were frequently out of the target ranges. This pilot implementation was unable to produce the results required to determine the value of the EPAP recommendations. The FiO₂ recommendations from the NIV advisor were treated as ancillary information behind the IPAP recommendations.

Optimising mechanical ventilation through model-based methods and automation

Mechanical ventilation (MV) is a core life-support therapy for patients suffering from respiratory failure or acute respiratory distress syndrome (ARDS). Respiratory failure is a secondary outcome of a range of injuries and diseases, and results in almost half of all intensive care unit (ICU) patients receiving some form of MV. Funding the increasing demand for ICU is a major issue and MV, in particular, can double the cost per day due to significant patient variability, over-sedation, and the large amount of clinician time required for patient management. Reducing cost in this area requires both a decrease in the average duration of MV by improving care, and a reduction in clinical workload. Both could be achieved by safely automating all or part of MV care via model-based dynamic systems modelling and control methods are ideally suited to address these problems. This paper presents common lung models, and provides a vision for a more automated future and explores predictive capacity of some current models. This vision includes the use of model-based methods to gain real-time insight to patient condition, improve safety through the forward prediction of outcomes to changes in MV, and develop virtual patients for in-silico design and testing of clinical protocols. Finally, the use of dynamic systems models and system identification to guide therapy for improved personalised control of oxygenation and MV therapy in the ICU will be considered. Such methods are a major part of the future of medicine, which includes greater personalisation and predictive capacity to both optimise care and reduce costs. This review thus presents the state of the art in how dynamic systems and control methods can be applied to transform this core area of ICU medicine.

Evaluation of model-based methods in estimating respiratory mechanics in the presence of variable patient effort

Monitoring of respiratory mechanics is required for guiding patient-specific mechanical ventilation settings in critical care. Many models of respiratory mechanics perform poorly in the presence of variable patient effort. Typical modelling approaches either attempt to mitigate the effect of the patient effort on the airway pressure waveforms, or attempt to capture the size and shape of the patient effort. This work analyses a range of methods to identify respiratory mechanics in volume controlled ventilation modes when there is patient effort. The models are compared using 4 Datasets, each with a sample of 30 breaths before, and 2-3 minutes after sedation has been administered. The sedation will reduce patient efforts, but the underlying pulmonary mechanical properties are unlikely to change during this short time. Model identified parameters from breathing cycles with patient effort are compared to breathing cycles that do not have patient effort. All models have advantages and disadvantages, so model selection may be specific to the respiratory mechanics application. However, in general, the combined method of iterative interpolative pressure reconstruction, and stacking multiple consecutive breaths together has the best performance over the Dataset. The variability of identified elastance when there is patient effort is the lowest with this method, and there is little systematic offset in identified mechanics when sedation is administered.

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 (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$F_{{\rm I}{\rm O}_2}$\end{document}FIO2), 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.

Informatics for the Modern Intensive Care Unit

Advanced informatics systems can help improve health care delivery and the environment of care for critically ill patients. However, identifying, testing, and deploying advanced informatics systems can be quite challenging. These processes often require involvement from a collaborative group of health care professionals of varied disciplines with knowledge of the complexities related to designing the modern and "smart" intensive care unit (ICU). In this article, we explore the connectivity environment within the ICU, middleware technologies to address a host of patient care initiatives, and the core informatics concepts necessary for both the design and implementation of advanced informatics systems.

Computerized Decision Support System for Traumatic Brain Injury Management

Mortality and morbidity related to traumatic brain injury (TBI) present a major health care burden. Patients with severe TBI must be managed rapidly and efficiently to minimize secondary brain injury potentially leading to permanent sequelae. This is especially important in young patients, whose brain is still in development, making them particularly susceptible to secondary insults. The complexity of both brain injury pathophysiology and the intensive care unit environment makes the management of these patients challenging, with a risk of delayed response and/or patient instability contributing to worsened outcome. Computerized assistance in TBI appears likely to improve patient management, by helping clinicians quickly analyze and respond to ongoing clinical changes and optimizing patient status by guiding management. Currently, computerized decision support systems (CDSSs) do not feature continuous medical assistance with individualized treatment plans. This review presents new developments in CDSSs specialized in TBI. We also present the framework for future CDSSs needed to improve TBI management in real time, taking into account individual patient characteristics.

A data-driven acute inflammation therapy

Acute inflammation is a severe medical condition defined as an inflammatory response of the body to an infection. Its rapid progression requires quick and accurate decisions from clinicians. Inadequate and delayed decisions makes acute inflammation the 10th leading cause of death overall in United States with the estimated cost of treatment about $17 billion annually. However, despite the need, there are limited number of methods that could assist clinicians to determine optimal therapies for acute inflammation. We developed a data-driven method for suggesting optimal therapy by using machine learning model that is learned on historical patients' behaviors. To reduce both the risk of failure and the expense for clinical trials, our method is evaluated on a virtual patients generated by a mathematical model that emulates inflammatory response. In conducted experiments, acute inflammation was handled with two complimentary pro- and anti-inflammatory medications which adequate timing and doses are crucial for the successful outcome. Our experiments show that the dosage regimen assigned with our data-driven method significantly improves the percentage of healthy patients when compared to results by other methods used in clinical practice and found in literature. Our method saved 88% of patients that would otherwise die within a week, while the best method found in literature saved only 73% of patients. At the same time, our method used lower doses of medications than alternatives. In addition, our method achieved better results than alternatives when only incomplete or noisy measurements were available over time as well as it was less affected by therapy delay. The presented results provide strong evidence that models from the artificial intelligence community have a potential for development of personalized treatment strategies for acute inflammation.

Oxygenation advisor recommends appropriate positive end expiratory pressure and FIO2 settings: retrospective validation study

A decision support, rule-based oxygenation advisor that provides guidance for setting positive end expiratory pressure (PEEP) and fractional inhaled oxygen concentration (FIO2) for patients with respiratory failure is described. The target oxygenation goal is to achieve and maintain pulse oximeter oxygen saturation (SpO2) ≥ 88 and ≤ 95%, as posited by the Acute Respiratory Distress Syndrome Network, by recommending appropriate combinations of PEEP and FIO2. For patient safety, the oxygenation advisor monitors mean arterial blood pressure (MAP) to ensure it is ≥ 65 mmHg for hemodynamic stability and inspiratory plateau pressure (Pplt) so it is ≤ 30 cm H2O for lung protection. The purpose of this validation study was to compare attending physicians' recommendations to those recommendations of the oxygenation advisor for setting PEEP and FIO2. Adults with respiratory failure (n = 117) receiving ventilatory support were studied. PEEP, FIO2, SpO2, MAP, and Pplt are input variables into the advisor. Recommendations to increase, maintain, or decrease PEEP and FIO2 are the oxygenation advisor's output variables. Physicians' recommendations for setting PEEP and FIO2 were recorded; the oxygenation advisor's recommendations were also recorded for comparison. At all times, ventilator settings were based on recommendations from attending physicians. PEEP ranged from 2 to 22 cm H2O and FIO2 ranged from 0.30 to 0.65. A total of 326 recommendations by the oxygenation advisor and attending physicians were made to increase, maintain, or decrease PEEP and FIO2. There was a very significant relationship (p < 0.0001) between recommendations of the oxygenation advisor and attending physicians for setting PEEP and FIO2. The agreement rate for recommendations by the oxygenation advisor and attending physicians was 92%. The K statistic, a test of the strength of agreement of recommendations between the oxygenation advisor and attending physicians, was 0.82 (p < 0.0001), indicating "almost perfect agreement". Relationships for recommendations made by the oxygenation advisor and attending physicians for setting PEEP and FIO2 were excellent, PEEP: r = 0.98 (p < 0.01), r(2) = 0.96; FIO2: r = 0.91 (p < 0.01), r(2) = 0.83, bias and precision values were negligible. A novel oxygenation advisor provided continuous and automatic recommendations for setting PEEP and FIO2 that were shown to be as good as the clinical judgment of experienced attending physicians. For all patients, the target oxygenation goal was achieved. Concerning patient safety, the oxygenation advisor detected those occasions when MAP and Pplt were in potentially unsafe ranges.

Decision support at home (DS@HOME)--system architectures and requirements

Background

Demographic change with its consequences of an aging society and an increase in the demand for care in the home environment has triggered intensive research activities in sensor devices and smart home technologies. While many advanced technologies are already available, there is still a lack of decision support systems (DSS) for the interpretation of data generated in home environments. The aim of the research for this paper is to present the state-of-the-art in DSS for these data, to define characteristic properties of such systems, and to define the requirements for successful home care DSS implementations.

Methods

A literature review was performed along with the analysis of cross-references. Characteristic properties are proposed and requirements are derived from the available body of literature.

Results

79 papers were identified and analyzed, of which 20 describe implementations of decision components. Most authors mention server-based decision support components, but only few papers provide details about the system architecture or the knowledge base. A list of requirements derived from the analysis is presented. Among the primary drawbacks of current systems are the missing integration of DSS in current health information system architectures including interfaces, the missing agreement among developers with regard to the formalization and customization of medical knowledge and a lack of intelligent algorithms to interpret data from multiple sources including clinical application systems.

Conclusions

Future research needs to address these issues in order to provide useful information – and not only large amounts of data – for both the patient and the caregiver. Furthermore, there is a need for outcome studies allowing for identifying successful implementation concepts.

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.

Development and implementation of explicit computerized protocols for mechanical ventilation in children

Mechanical ventilation can be perceived as a treatment with a very narrow therapeutic window, i.e., highly efficient but with considerable side effects if not used properly and in a timely manner. Protocols and guidelines have been designed to make mechanical ventilation safer and protective for the lung. However, variable effects and low compliance with use of written protocols have been reported repeatedly. Use of explicit computerized protocols for mechanical ventilation might very soon become a "must." Several closed loop systems are already on the market, and preliminary studies are showing promising results in providing patients with good quality ventilation and eventually weaning them faster from the ventilator. The present paper defines explicit computerized protocols for mechanical ventilation, describes how these protocols are designed, and reports the ones that are available on the market for children.

The Intelligent Ventilator (INVENT) project: the role of mathematical models in translating physiological knowledge into clinical practice

This dissertation has addressed the broad hypothesis as to whether building mathematical models is useful as a tool for translating physiological knowledge into clinical practice. In doing so it describes work on the INtelligent VENTilator project (INVENT), the goal of which is to build, evaluate and integrate into clinical practice, a model-based decision support system for control of mechanical ventilation. The dissertation describes the mathematical models included in INVENT, i.e. a model of pulmonary gas exchange focusing on oxygen transport, and a model of the acid-base status of blood, interstitial fluid and tissues. These models have been validated, and applied in two other systems: ALPE, a system for measuring pulmonary gas exchange and ARTY, a system for arterialisation of the acid-base and oxygen status of peripheral venous blood. The major contributions of this work are as follows. A mathematical model has been developed which can describe pulmonary gas exchange more accurately that current clinical techniques. This model is parsimonious in that it can describe pulmonary gas exchange from measurements easily available in the clinic, along with a readily automatable variation in F(I)O(2). This technique and model have been developed into a research and commercial tool (ALPE), and evaluated both in the clinical setting and when compared to the reference multiple inert gas elimination technique (MIGET). Mathematical models have been developed of the acid- base chemistry of blood, interstitial fluid and tissues, with these models formulated using a mass-action mass-balance approach. The model of blood has been validated against literature data describing the addition and removal of CO(2), strong acid or base, and haemoglobin; and the effects of oxygenation or deoxygenation. The model has also been validated in new studies, and shown to simulate accurately and precisely the mixing of blood samples at different PCO(2) and PO(2) levels. This model of acid-base chemistry of blood has been applied in the ARTY system. ARTY has been shown to accurately and precisely calculate arterial values of acid-base and oxygen status in patients residing in the ICU, and in those with chronic lung disease. The INtelligent VENTilator (INVENT) system has been developed for optimization of mechanical ventilator settings using physiological models and utility/penalty functions, separating physiological knowledge from clinical preference. The models can be tuned to the individual patient via parameter estimation, providing patient specific advice. The INVENT team has shown prospectively that the system provides advice on F(I)O(2) which is as good as clinical practice, and retrospectively that the system provides reasonable suggestions of tidal volume, respiratory frequency and F(I)O(2). In general, this dissertation has illustrated a further example of the role of modeling in describing and understanding complex systems. The dissertation has shown that when dealing with complexity the goal of the model must be in focus if a correct balance is to be maintained between system complexity and model parameterization. The original goal of the INVENT team, i.e. to build, evaluate and integrate a DSS for control of mechanical ventilation has not as yet been completed. However, the broader hypothesis that building models generates new and interesting questions has been successfully demonstrated. The ALPE model and system has been applied in intensive care, post operative care and cardiology and is currently being evaluated in new clinical domains. ARTY has been shown to have potential benefit in eliminating the need for painful arterial punctures, and may also be useful as a screening tool. These systems illustrate the benefits of investing in models as a mechanism for translating physiological knowledge to clinical practice.

Retrospective evaluation of a decision support system for controlled mechanical ventilation

Management of mechanical ventilation in intensive care patients is complicated by conflicting clinical goals. Decision support systems (DSS) may support clinicians in finding the correct balance. The objective of this study was to evaluate a computerized model-based DSS for its advice on inspired oxygen fraction, tidal volume and respiratory frequency. The DSS was retrospectively evaluated in 16 intensive care patient cases, with physiological models fitted to the retrospective data and then used to simulate patient response to changes in therapy. Sensitivity of the DSS's advice to variations in cardiac output (CO) was evaluated. Compared to the baseline ventilator settings set as part of routine clinical care, the system suggested lower tidal volumes and inspired oxygen fraction, but higher frequency, with all suggestions and the model simulated outcome comparing well with the respiratory goals of the Acute Respiratory Distress Syndrome Network from 2000. Changes in advice with CO variation of about 20% were negligible except in cases of high oxygen consumption. Results suggest that the DSS provides clinically relevant and rational advice on therapy in agreement with current 'best practice', and that the advice is robust to variation in CO.

Automated mechanical ventilation: adapting decision making to different disease states

The purpose of the present study is to introduce a novel methodology for adapting and upgrading decision-making strategies concerning mechanical ventilation with respect to different disease states into our fuzzy-based expert system, AUTOPILOT-BT. The special features are: (1) Extraction of clinical knowledge in analogy to the daily routine. (2) An automated process to obtain the required information and to create fuzzy sets. (3) The controller employs the derived fuzzy rules to achieve the desired ventilation status. For demonstration this study focuses exclusively on the control of arterial CO(2) partial pressure (p(a)CO(2)). Clinical knowledge from 61 anesthesiologists was acquired using a questionnaire from which different disease-specific fuzzy sets were generated to control p(a)CO(2). For both, patients with healthy lung and with acute respiratory distress syndrome (ARDS) the fuzzy sets show different shapes. The fuzzy set "normal", i.e., "target p(a)CO(2) area", ranges from 35 to 39 mmHg for healthy lungs and from 39 to 43 mmHg for ARDS lungs. With the new fuzzy sets our AUTOPILOT-BT reaches the target p(a)CO(2) within maximal three consecutive changes of ventilator settings. Thus, clinical knowledge can be extended, updated, and the resulting mechanical ventilation therapies can be individually adapted, analyzed, and evaluated.

Intelligent model-based advisory system for the management of ventilated intensive care patients. Part II: Advisory system design and evaluation

The optimisation of ventilatory support is a crucial issue for the management of respiratory failure in critically ill patients, aiming at improving gas exchange while preventing ventilator-induced dysfunction of the respiratory system. Clinicians often rely on their knowledge/experience and regular observation of the patient's response for adjusting the level of respiratory support. Using a similar data-driven decision-making methodology, an adaptive model-based advisory system has been designed for the clinical monitoring and management of mechanically ventilated patients. The hybrid blood gas patient model SOPAVent developed in Part I of this paper and validated against clinical data for a range of patients lung abnormalities is embedded into the advisory system to predict continuously and non-invasively the patient's respiratory response to changes in the ventilator settings. The choice of appropriate ventilator settings involves finding a balance among a selection of fundamentally competing therapeutic decisions. The design approach used here is based on a goal-directed multi-objective optimisation strategy to determine the optimal ventilator settings that effectively restore gas exchange and promote improved patient's clinical conditions. As an initial step to its clinical validation, the advisory system's closed-loop stability and performance have been assessed in a series of simulations scenarios reconstructed from real ICU patients data. The results show that the designed advisory system can generate good ventilator-setting advice under patient state changes and competing ventilator management targets.

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.

Service oriented architecture for the integration of clinical and physiological data for real-time event stream processing

This paper proposes a framework for the integration of physiological and clinical health data within a Service-Oriented architecture framework. This integration will subsequently be used in real-time event stream processing in intelligent patient monitoring devices. Service-oriented architecture offers a unique method of integrating health data as information is collected from multiple medical devices that lack any substantial means of standardization. Employing various services to facilitate the transmission and integration of these data will result in significant improvement in both efficacy and analytical velocity of intelligent patient monitoring systems. We demonstrate this approach within the Neonatal Intensive Care setting.

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.

Can fuzzy logic make things more clear?

Intensive care is a complex environment involving many signals, data and observations. Clinical decision support and artificial intelligence using fuzzy logic and closed loop techniques are methods that might help us to handle this complexity in a safe, effective and efficient way. Merouani and colleagues have performed a study using fuzzy logic and closed loop techniques to more effectively wean patients with sepsis from norepinephrine infusion.