Adaptive Modelling and Image-Based Monitoring for Artificially Ventilated Patients in the Intensive Care Unit (ICU)

The Intensive Care Unit (ICU) is where the critically-ill are treated. The first 24-hours (‘the golden hours’) of treatment is crucial to determine patient’s recovery and survival, and mechanical ventilation plays a major role as the main life support system in the ICU. The efficiency of mechanical ventilation and its management strategy are assessed by observing the arterial blood gases (ABG), which are sampled every few hours using a catheter inserted into the patient’s artery. This procedure is invasive thus can only be performed a handful of times each day. The ICU also has an abundance of underutilized data which until recently can only be translated by expert clinicians, who unfortunately always have clinical responsibilities to undertake concomitantly. This thesis proposes a series of new fuzzy logic-based models with a new type of fuzzy sets (type-2), which have not been investigated before in this clinical setting, for the relative dead-space (Kd), the carbon-dioxide production (VCO2), and the shunt sub-components for the SOPAVent (Sheffield Simulation of Patients under Artificial Ventilation) system, which performs predictions of arterial blood gases non-invasively and automatically. The Kd model, the VCO2 model and the resulting overall SOPAVent model are validated with retrospective real ICU patient data obtained from the Sheffield Royal Hallamshire Hospital (UK). The SOPAVent model is also validated with newly obtained data from patients diagnosed with Faecal Peritonitis (FP), from the Sheffield Royal Hallamshire Hospital (UK). Results showed an improved prediction accuracy for the Kd and the VCO2 sub-components when compared to existing systems. The prediction capability of SOPAVent is also improved from previous models for arterial blood gases before and after ventilator settings changes are made. A second new simplified model for predicting ABG using ventilator settings is also proposed with excellent prediction outcomes. Additionally, this thesis also looks into Electrical Impedance Tomography (EIT) as a potential bedside monitoring tool for pulmonary functions. EIT has the ability to provide a non-invasive, portable, and a relatively low cost alternative to other medical imaging systems. This thesis details the development of the hardware for a compact 16-electrode EIT measurement system, with the objective for future pulmonary applications. A method to generate three-dimensional (3D) images of the lungs from two-dimensional (2D) medical images of the thorax is also proposed with the estimation of lung volumes being presented

Model-based development of a fuzzy logic advisor for artificially ventilated patients.

This thesis describes the model-based development and validation of an advisor for the maintenance of artificially ventilated patients in the intensive care unit (ICU). The advisor employs fuzzy logic to represent an anaesthetist's decision making process when adjusting ventilator settings to safely maintain a patient's blood-gases and airway pressures within desired limits. Fuzzy logic was chosen for its ability to process both quantitative and qualitative data. The advisor estimates the changes in inspired O2 fraction (FI02), peak inspiratory pressure (PEEP), respiratory rate (RR), tidal volume (VT) and inspiratory time (TIN), based upon observations of the patient state and the current ventilator settings. The advisor rules only considered the ventilation of patients on volume control (VC) and pressure regulated volume control (PRVC) modes. The fuzzy rules were handcrafted using known physiological relationships and from tacit knowledge elicited during dialogue with anaesthetists. The resulting rules were validated using a computer-based model of human respiration during artificial ventilation. This model was able to simulate a wide range of patho-physiology, and using data collected from ICU it was shown that it could be matched to real clinical data to predict the patient's response to ventilator changes. Using the model, five simulated patient scenarios were constructed via discussion with an anaesthetist. These were used to test the closed-loop performance of the prototype advisor and successfully highlighted divergent behaviour in the rules. By comparing the closed-loop responses against those produced by an anaesthetist (using the patient-model), rapid rule refinement was possible. The modified advisor demonstrated better decision matching than the prototype rules, when compared against the decisions made by the anaesthetist. The modified advisor was also tested using data collected from ICU. Direct comparisons were made between the decisions given by an anaesthetist and those produced by the advisor. Good decision matching was observed in patients with well behaved physiology but soon ran into difficulties if a patients state was changing rapidly or if the patient observations contained large measurement errors

Assessment of goal-directed closed-loop management in intensive care medicine

Given an aging population, shortage of nursing staff and a continuously increasing workload, automation in the medical sector is an important aspect of future intensive care. Although automation and machine learning are current research topics, progress is still very limited in comparison to other application areas. Probably one of the most serious problems is data shortage in a heterogeneous landscape of medical devices with limited interfaces and various protocols. In addition, the recording of data or, even more so, the evaluation of automation is limited by a complex legal framework. Given these complications and the sensitive legal nature of medical records, only very limited data is accessible for further analysis and development of automated systems. For this reason, within the context of this thesis various solutions for data acquisition and automation were developed and evaluated concomitant to two clinical studies utilizing a large animal model in a realistic intensive care setting at the University Hospital Tübingen. Foremost, to overcome the problems of data availability and interconnection of medical devices, a software framework for data collection and remote control using a client-server architecture was developed and significant amounts of research data could be collected in a central database. Furthermore, a closed-loop controller based on fuzzy logic was developed and used for management of end-tital CO2, glucose, and other parameters to stabilize the animal subjects during therapy and reduce caregivers’ workload. In addition to the fuzzy controller, closed-loop management for temperature and anticoagulation could be established by developing hardware interfaces for a forced-air warming unit and a point-of-care analysis device, respectively. Besides further reduction of caregivers’ workload, such systems can provide additional patient safety and allow management in settings where human supervision may not be present at all times. One general and encountered problem for closed-loop control in a medical setting is limited availability of measurements, especially if manual blood withdrawals are required. As an initial step to address this problem, measured parameters from other devices as potential surrogates were evaluated in a comparison between different regression approaches. The required training data, a matched set of blood gas and monitoring parameters, was obtained by utilizing a developed algorithm for automated detection of withdrawal events. Yet, besides any specific implementations and analysis, many general aspects regarding the physical implementation of such a system and interaction with caregivers could be evaluated in the experimental setting and might guide further development of clinical automation.Angesichts der alternden Bevölkerung, des Mangels an Pflegekräften und der ständig steigenden Arbeitsbelastung ist Automatisierung ein wichtiger Aspekt zukünftiger Intensivmedizin. Obwohl Automatisierung und maschinelles Lernen aktuelle Forschungsthemen sind, ist der Fortschritt im Vergleich zu anderen Anwendungsbereichen jedoch noch sehr begrenzt. Eines der größten Probleme ist wohl die Datenknappheit in einer heterogenen Medizinproduktelandschaft mit begrenzten Schnittstellen und zahlreichen unterschiedlichen Protokollen. Darüber hinaus sind die Datenerfassung und erst recht die Erprobung einer Automatisierung durch ein komplexes rechtliches Rahmenwerk eingeschränkt. Aufgrund dieser Komplikationen und der sensiblen Rechtslage für Patientendaten sind diese nur sehr begrenzt für weitere Analysen und die Entwicklung automatisierter Systeme zugänglich. Im Rahmen dieser Dissertation wurden daher verschiedene Lösungen zur Datenerfassung und Automatisierung begleitend zu zwei klinischen Studien des Universitätsklinikums Tübingen am Großtiermodell in einer realitätsnahen Intensivstation entwickelt und evaluiert. Um die Probleme der Datenverfügbarkeit und Vernetzung medizinischer Geräte zu lösen, wurde vorrangig ein Software-Framework für die Datenerfassung und Steuerung mittels einer Client-Server-Architektur entwickelt und umfangreiche Forschungsdaten in einer zentralen Datenbank gesammelt. Darüber hinaus wurde ein auf Fuzzy-Logik basierender Regler entwickelt, welcher zur Stabilisierung des endtitalen CO2, Glukose und anderen Parametern verwendet wurde und damit die Arbeitsbelastung der Pflegekräfte reduzieren konnte. Zusätzlich zum Fuzzy-Regler konnten durch die Entwicklung von Hardware-Schnittstellen für Geräte zum Temperaturmanagement mittels luftbasierter Wärmedecken und zur Messung der Blutgerinnung geschlossene Regelkreise aufgebaut werden. Neben einer weiteren Arbeitserleichterung für die Pflegekräfte können solche Systeme zusätzliche Sicherheit für den Patienten bieten und die Anwendung in nicht ständig überwachten Bereichen ermöglichen. Ein allgemeines und auch beobachtetes Problem für Regelkreise im medizinischen Bereich ist die begrenzte Verfügbarkeit von Messwerten, insbesondere bei manuellen Blutentnahmen. Als erster Schritt zur Lösung dieses Problems wurden Messparameter anderer Geräte als potentielle Ersatzparameter mit verschiedenen Regressionsansätzen analysiert und verglichen. Die dazu erforderlichen Trainingsdaten, Paare von Blutgas- und weiteren Vitaldaten, wurden mit Hilfe eines entwickelten Algorithmus zur automatisierten Erkennung von Blutentnahmen erzeugt. Abgesehen von diesen konkreten Anwendungen und Analysen konnten in der experimentellen Evaluation auch viele generelle Aspekte der realen Implementierung eines solchen Systems und die Interaktion mit Ärzten und Pflegekräften untersucht werden und damit der Entwicklung weiterer klinischen Automatisierung dienen

Intelligent Decision Support Systems in Ventilation Management

Introduction: Intensive Care Unit (ICU) medical personnel, in an ongoing process termed ventilation management, utilize patient physiology and pathology data to define ventilator apparatus settings. Aims: The aim of the research is to develop and evaluate in comparison hybrid ventilation advisor systems, that could support ventilation management process, specific to lung pathology for patients ventilated in control mode. Methodology: A questionnaire was designed and circulated to Intensivists. Patient data, as defined by the questionnaire analysis, were collected and categorized into three lung pathologies. Three ICU doctors evaluated correlation analysis of the recorded data. Evaluation results were used for identifying models basic architecture. Two custom software toolboxes were developed for developing hybrid systems; namely the EVolution Of Fuzzy INference Engines (EVOFINE) and the FUzzy Neural (FUN) toolbox. Eight hybrid systems developed with EVOFINE, FUN, ANFIS and ANN techniques were evaluated against applied clinical decisions and patient scenarios. Results: Seventeen (17) models were designed for each of the eight (8) modeling techniques. The modelled process consisted of twelve physiology variables and six ventilator settings. The number of models’ inputs ranged from single to six based on correlation and evaluation findings. Evaluation against clinical recommendations has shown that ANNs performed better; mean average error as percentage for four of the applied techniques was 0.16%, 1.29% & 0.62 for ANN empirical, 0.05%, 2.23% & 2.30% for ANFIS, 0.93%, 2.33% & 1.89% for EVOFINE and 0.73%, 2.63% & 6.56 for FUN NM, in Normal, COPD and ALI-ARDS categories respectively. Additionally evaluation against clinical disagreement SD has shown that 70.6% of the NN empirical models were performing in 90% of their suggestions within clinical SD, while the percentages were 53%, 53% and 59% for the EVOFINE, ANFIS and NN Normalized models respectively. The EVOFINE and ANFIS produced Fuzzy Systems whose architecture is transparent for the user. Visual observation of ANFIS architectures revealed possibly hazardous advices. Evaluation against clinical disagreement has shown that the NN empirical was not producing hazardous advices, while EVOFINE, ANFIS and NN Normalized were shown to produce potentially hazardous advice in 17.6%, 23% and 5.8% of the developed models

Acute lung injury in paediatric intensive care: course and outcome

Introduction: Acute lung injury (ALI) and acute respiratory distress syndrome (ARDS) carry a high morbidity and mortality (10-90%). ALI is characterised by non-cardiogenic pulmonary oedema and refractory hypoxaemia of multifactorial aetiology [1]. There is limited data about outcome particularly in children. Methods This retrospective cohort study of 85 randomly selected patients with respiratory failure recruited from a prospectively collected database represents 7.1% of 1187 admissions. They include those treated with High Frequency Oscillation Ventilation (HFOV). The patients were admitted between 1 November 1998 and 31 October 2000. Results: Of the 85, 49 developed acute lung injury and 47 had ARDS. There were 26 males and 23 females with a median age and weight of 7.7 months (range 1 day-12.8 years) and 8 kg (range 0.8-40 kg). There were 7 deaths giving a crude mortality of 14.3%, all of which fulfilled the Consensus I [1] criteria for ARDS. Pulmonary occlusion pressures were not routinely measured. The A-a gradient and PaO2/FiO2 ratio (median + [95% CI]) were 37.46 [31.82-43.1] kPa and 19.12 [15.26-22.98] kPa respectively. The non-survivors had a significantly lower PaO2/FiO2 ratio (13 [6.07-19.93] kPa) compared to survivors (23.85 [19.57-28.13] kPa) (P = 0.03) and had a higher A-a gradient (51.05 [35.68-66.42] kPa) compared to survivors (36.07 [30.2-41.94]) kPa though not significant (P = 0.06). Twenty-nine patients (59.2%) were oscillated (Sensormedics 3100A) including all 7 non-survivors. There was no difference in ventilation requirements for CMV prior to oscillation. Seventeen of the 49 (34.7%) were treated with Nitric Oxide including 5 out of 7 non-survivors (71.4%). The median (95% CI) number of failed organs was 3 (1.96-4.04) for non-survivors compared to 1 (0.62-1.62) for survivors (P = 0.03). There were 27 patients with isolated respiratory failure all of whom survived. Six (85.7%) of the non-survivors also required cardiovascular support.Conclusion: A crude mortality of 14.3% compares favourably to published data. The A-a gradient and PaO2/FiO2 ratio may be of help in morbidity scoring in paediatric ARDS. Use of Nitric Oxide and HFOV is associated with increased mortality, which probably relates to the severity of disease. Multiple organ failure particularly respiratory and cardiac disease is associated with increased mortality. ARDS with isolated respiratory failure carries a good prognosis in children

An investigation of the suitability of Artificial Neural Networks for the prediction of core and local skin temperatures when trained with a large and gender-balanced database

Neural networks have been proven to successfully predict the results of complex non-linear problems in a variety of research fields, including medical research. Yet there is paucity of models utilising intelligent systems in the field of thermoregulation. They are under-utilized for predicting seemingly random physiological responses and in particular never used to predict local skin temperatures; or core temperature with a large dataset. In fact, most predictive models in this field (non-artificial intelligence based) focused on predicting body temperature and average skin temperature using relatively small gender-unbalanced databases or data from thermal dummies due to a lack of larger datasets. This paper aimed to address these limitations by applying Artificial Intelligence to create predictive models of core body temperature and local skin temperature (specifically at forehead, chest, upper arms, abdomen, knees and calves) while using a large and gender-balanced experimental database collected in office-type situations. A range of Neural Networks were developed for each local temperature, with topologies of 1–2 hidden layers and up to 20 neurons per layer, using Bayesian and the Levemberg-Marquardt back-propagation algorithms, and using various sets of input parameters (2520 NNs for each of the local skin temperatures and 1760 for the core temperature, i.e. a total of 19400 NNs). All topologies and configurations were assessed and the most suited recommended. The recommended Neural Networks trained well, with no sign of over-fitting, and with good performance when predicting unseen data. The recommended Neural Network for each case was compared with previously reported multi-linear models. Core temperature was avoided as a parameter for local skin temperatures as it is impractical for non-contact monitoring systems and does not significantly improve the precision despite it is the most stable parameter. The recommended NNs substantially improve the predictions in comparison to previous approaches. NN for core temperature has an R-value of 0.87 (81% increase), and a precision of ±0.46 °C for an 80% CI which is acceptable for non-clinical applications. NNs for local skin temperatures had R-values of 0.85-0.93 for forehead, chest, abdomen, calves, knees and hands, last two being the strongest (increase of 72% for abdomen, 63% for chest, and 32% for calves and forehead). The precision was best for forehead, chest and calves, with about ±1.2 °C, which is similar to the precision of existent average skin temperature models even though the average value is more stable

Credibility Evidence for Computational Patient Models Used in the Development of Physiological Closed-Loop Controlled Devices for Critical Care Medicine

Physiological closed-loop controlled medical devices automatically adjust therapy delivered to a patient to adjust a measured physiological variable. In critical care scenarios, these types of devices could automate, for example, fluid resuscitation, drug delivery, mechanical ventilation, and/or anesthesia and sedation. Evidence from simulations using computational models of physiological systems can play a crucial role in the development of physiological closed-loop controlled devices; but the utility of this evidence will depend on the credibility of the computational model used. Computational models of physiological systems can be complex with numerous non-linearities, time-varying properties, and unknown parameters, which leads to challenges in model assessment. Given the wide range of potential uses of computational patient models in the design and evaluation of physiological closed-loop controlled systems, and the varying risks associated with the diverse uses, the specific model as well as the necessary evidence to make a model credible for a use case may vary. In this review, we examine the various uses of computational patient models in the design and evaluation of critical care physiological closed-loop controlled systems (e.g., hemodynamic stability, mechanical ventilation, anesthetic delivery) as well as the types of evidence (e.g., verification, validation, and uncertainty quantification activities) presented to support the model for that use. We then examine and discuss how a credibility assessment framework (American Society of Mechanical Engineers Verification and Validation Subcommittee, V&V 40 Verification and Validation in Computational Modeling of Medical Devices) for medical devices can be applied to computational patient models used to test physiological closed-loop controlled systems