Robust fractional order PI control for cardiac output stabilisation

Drug regulatory paradigms are dependent on the hemodynamic system as it serves to distribute and clear the drug in/from the body. While focusing on the objective of the drug paradigm at hand, it is important to maintain stable hemodynamic variables. In this work, a biomedical application requiring robust control properties has been used to illustrate the potential of an autotuning method, referred to as the fractional order robust autotuner. The method is an extension of a previously presented autotuning principle and produces controllers which are robust to system gain variations. The feature of automatic tuning of controller parameters can be of great use for data-driven adaptation during intra-patient variability conditions. Fractional order PI/PD controllers are generalizations of the well-known PI/PD controllers that exhibit an extra parameter usually used to enhance the robustness of the closed loop system. (C) 2019, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved

Robust Control of Maintenance-Phase Anesthesia

In biomedical systems, feedback control can be applied whenever adequate sensors, actuators, and sufficiently accurate mathematical models are available. The key issue is the capacity of the control algorithm to tackle the large levels of uncertainty, both structured and unstructured, associated with patient dynamics. In the particular case of intravenous anesthesia considered here, manipulated variables are drug infusion rates, administered by syringe pumps, and the measured signal outputs are the levels of hypnosis or depth of anesthesia (DoA) and of neuromuscular blockade (NMB). Figure 1 provides an example of a loop closed for the control of NMB

Modelos de resposta à interação entre fármacos anestésicos : análise de regressão e análise de Clusters

Os avanços tecnológicos e científicos, na área da saúde, têm vindo a aliar áreas como a Medicina e a Matemática, cabendo à ciência adequar de forma mais eficaz os meios de investigação, diagnóstico, monitorização e terapêutica. Os métodos desenvolvidos e os estudos apresentados nesta dissertação resultam da necessidade de encontrar respostas e soluções para os diferentes desafios identificados na área da anestesia. A índole destes problemas conduz, necessariamente, à aplicação, adaptação e conjugação de diferentes métodos e modelos das diversas áreas da matemática. A capacidade para induzir a anestesia em pacientes, de forma segura e confiável, conduz a uma enorme variedade de situações que devem ser levadas em conta, exigindo, por isso, intensivos estudos. Assim, métodos e modelos de previsão, que permitam uma melhor personalização da dosagem a administrar ao paciente e por monitorizar, o efeito induzido pela administração de cada fármaco, com sinais mais fiáveis, são fundamentais para a investigação e progresso neste campo. Neste contexto, com o objetivo de clarificar a utilização em estudos na área da anestesia de um ajustado tratamento estatístico, proponho-me abordar diferentes análises estatísticas para desenvolver um modelo de previsão sobre a resposta cerebral a dois fármacos durante sedação. Dados obtidos de voluntários serão utilizados para estudar a interação farmacodinâmica entre dois fármacos anestésicos. Numa primeira fase são explorados modelos de regressão lineares que permitam modelar o efeito dos fármacos no sinal cerebral BIS (índice bispectral do EEG – indicador da profundidade de anestesia); ou seja estimar o efeito que as concentrações de fármacos têm na depressão do eletroencefalograma (avaliada pelo BIS). Na segunda fase deste trabalho, pretende-se a identificação de diferentes interações com Análise de Clusters bem como a validação do respetivo modelo com Análise Discriminante, identificando grupos homogéneos na amostra obtida através das técnicas de agrupamento. O número de grupos existentes na amostra foi, numa fase exploratória, obtido pelas técnicas de agrupamento hierárquicas, e a caracterização dos grupos identificados foi obtida pelas técnicas de agrupamento k-means. A reprodutibilidade dos modelos de agrupamento obtidos foi testada através da análise discriminante. As principais conclusões apontam que o teste de significância da equação de Regressão Linear indicou que o modelo é altamente significativo. As variáveis propofol e remifentanil influenciam significativamente o BIS e o modelo melhora com a inclusão do remifentanil. Este trabalho demonstra ainda ser possível construir um modelo que permite agrupar as concentrações dos fármacos, com base no efeito no sinal cerebral BIS, com o apoio de técnicas de agrupamento e discriminantes. Os resultados desmontram claramente a interacção farmacodinâmica dos dois fármacos, quando analisamos o Cluster 1 e o Cluster 3. Para concentrações semelhantes de propofol o efeito no BIS é claramente diferente dependendo da grandeza da concentração de remifentanil. Em suma, o estudo demostra claramente, que quando o remifentanil é administrado com o propofol (um hipnótico) o efeito deste último é potenciado, levando o sinal BIS a valores bastante baixos.Mathematics has been playing an important role in the technological and scientific developments in the health area. When the areas of Medicine and Mathematics are combined science is most effective in linking research, diagnosis, monitoring and therapeutics. The developed methods and studies presented in this dissertation are a result of the search for solutions to different challenges identified in the area of anaesthesia. The nature of these problems leads, necessarily, to the development, adaptation and conjugation of diverse methods and models in the different areas of mathematics. Induction of anaesthesia in patients, in a safe and reliable way, leads to a huge variety of situations that must be taken into account; therefore there is a demand for intensive studies. Methods and models of foreknowledge are crucial to research and improvement in this field, so as to allow for patient dosage’s adaptation. Models may be used to help the clinician predict the individual drug dose required to induced a desired effect. In this context, the aim is to develop a foreknowledge model towards the brain effect of two drugs during sedation. To this purpose statistical analysis will be used. Data obtained from volunteers will be used to study the pharmacodynamics interaction between the two anaesthetic drugs. In the first phase, linear regression models are explored, which allow to model the effect of the drugs on the brain signal BIS (bispectral index of the EEG – measure of depth of anaesthesia); that is to model of the drugs’ concentration on the central nervous systems depression (as assess by BIS). In the second phase of this work, the different drug interactions are identified by means of Clusters analysis, as well as the validation of the corresponding model through Discriminative Analysis, identifying homogeneous groups in the obtained sample, through clustering techniques. On an exploratory phase, the number of groups in the sample was determined through hierarchical clustering and the characterization of the identified groups was defined using the k-means clustering. The reproducibility of the achieved clustering models was tested through discriminative analysis. The main conclusions are that Linear Regression model is highly meaningful to estimate the effect of the hypnotic and analgesic drug on the brain signal BIS. propofol and remifentanil anaesthetic drugs influence BIS substantially, and the model improves with the inclusion of the remifentanil concentration. This research also shows that it is possible to build a model with the support of mathematical techniques (clustering and discriminating); that allows the clustering of drugs concentration based on its’ effect on the brain signal BIS. The results when we analyse the different clusters, clearly show the pharmacodynamics interaction of both drugs. For similar propofol concentrations, the effect on BIS is totally different and dependent on the level of the remifentanil concentration

Statistical methods for NHS incident reporting data

The National Reporting and Learning System (NRLS) is the English and Welsh NHS’ national repository of incident reports from healthcare. It aims to capture details of incident reports, at national level, and facilitate clinical review and learning to improve patient safety. These incident reports range from minor ‘near-misses’ to critical incidents that may lead to severe harm or death. NRLS data are currently reported as crude counts and proportions, but their major use is clinical review of the free-text descriptions of incidents. There are few well-developed quantitative analysis approaches for NRLS, and this thesis investigates these methods. A literature review revealed a wealth of clinical detail, but also systematic constraints of NRLS’ structure, including non-mandatory reporting, missing data and misclassification. Summary statistics for reports from 2010/11 – 2016/17 supported this and suggest NRLS was not suitable for statistical modelling in isolation. Modelling methods were advanced by creating a hybrid dataset using other sources of hospital casemix data from Hospital Episode Statistics (HES). A theoretical model was established, based on ‘exposure’ variables (using casemix proxies), and ‘culture’ as a random-effect. The initial modelling approach examined Poisson regression, mixture and multilevel models. Overdispersion was significant, generated mainly by clustering and aggregation in the hybrid dataset, but models were chosen to reflect these structures. Further modelling approaches were examined, using Generalized Additive Models to smooth predictor variables, regression tree-based models including Random Forests, and Artificial Neural Networks. Models were also extended to examine a subset of death and severe harm incidents, exploring how sparse counts affect models. Text mining techniques were examined for analysis of incident descriptions and showed how term frequency might be used. Terms were used to generate latent topics models used, in-turn, to predict the harm level of incidents. Model outputs were used to create a ‘Standardised Incident Reporting Ratio’ (SIRR) and cast this in the mould of current regulatory frameworks, using process control techniques such as funnel plots and cusum charts. A prototype online reporting tool was developed to allow NHS organisations to examine their SIRRs, provide supporting analyses, and link data points back to individual incident reports

New Insights in the Genetics and Genomics of Adrenocortical Tumors and Pheochromocytomas

This book includes 17 papers published in the Special Issue/Article Collectoin “New Insights in the Genetics and Genomics of adrenocortical tumors and pheochromocytomas” including an editorial, 10 research papers and six review articles. Adrenal tumors represent a hot topic in contemporary endocrine oncology. Significant advancements in the genetics of genomics of these tumors have been made in recent years, and these articles give a useful and comprehensive overview of these issues. Questions regarding molecular pathogenesis, diagnosis (biomarkers) and even treatment are discussed in the papers written by international leaders of the field. Manuscripts are focused on three main topics: i. primary aldosteronism (the most common cause of secondary endocrine hypertension), ii. adrenocortical cancer and iii. pheochromocytoma/paraganglioma, which are the tumors with the highest heritability in humans. The book is edited by Prof. Peter Igaz (Department of Endocrinology, Faculty of Medicine, Semmelweis University)