Projects & Collaborations 2 foundShow per page10 10 20 50 Predictive value of heart rate variability on cardiorespiratory events of preterm infants routinely immunised in the hospital Research Project | 2 Project MembersPreterm birth is a major challenge of health care systems across the globe, affecting about 10% of all infants born worldwide, resulting in almost 13 million preterm births per year. The autonomic nervous system of preterm infants is characterized by instability of heart rate and breathing, requiring continuous monitoring of vital signs over several months and long-term respiratory support. Cardiorespiratory events due to this instability, summarised under the term 'apnoea of prematurity' (AOP), affect at least 80% of very preterm infants born before 32 weeks of gestation. AOP may lead to severe hypoxaemia requiring immediate resuscitation and recent data show that repetitive episodes of AOP increase the risk of post-discharge death and long-term neurodevelopmental impairment. Most importantly, severity and frequency of AOP may drastically increase upon challenging the autonomic system by routine immunisation. It is, however, very important to provide timely immunisation and establish early immunity against typical vaccine-preventable diseases in preterm infants as they are particularly vulnerable to complications arising from those diseases. Current recommendations are to initially immunise preterm infants in the hospital under continuous monitoring of vital signs if the treating physician considers an infant to be at risk of post-immunisation AOP. However, there are no objective criteria to predict post-immunisation AOP. Although the first immunisation of very preterm infants typically takes place in the hospital under continuous monitoring of vital signs, immunisations of infants at risk of AOP are often delayed due to fear of AOP or may be initiated in non-intensive care settings (normal wards) where adequate respiratory support cannot be provided but may be needed due to post-immunisation AOP. Also, due to an international trend of early discharge home of preterm infants, immunisations may be arranged in the rooms of the family paediatrician without further monitoring of vital signs and no specific knowledge of the individual risk of post-immunisation AOP. Thus, developing of new biomarkers and objective criteria to better understand and assess the risk of post-immunisation AOP is urgently needed. We recently developed a systematic quality control algorithm for assessing heart rate variability data in a standardised manner and demonstrated that the sample entropy (SampEn) of interbeat intervals, a parameter of heart rate variability derived from nonlinear time series analysis, predicts cardiorespiratory stability in preterm infants. SampEn reflects the regularity of heart rate and the presence of spikes in a given time series of heart beats and has been validated to be a reliable predictor of incipient events such as sepsis. SampEn of heart rate can be obtained non-invasively from electrocardiogram monitors, which are routinely used to monitor preterm infants immunised in the hospital. We aim to evaluate whether real-time calculation of SampEn at a) 32 and 36 weeks corrected age, b) upon primary routine immunisation in the hospital, c) at discharge from the hospital after initial prematurity-related hospital stay, and d) on readmission for immunisation in the hospital based on previous post-immunisation AOP or referral of the family paediatrician has prognostic utility for the risk of post-immunisation AOP in very preterm infants. We will further assess whether immunisation itself initiates a step response in SampEn and compare SampEn values from preterm infants to those of term healthy infants to study maturational effects. The biomarker SampEn provides a unique opportunity to objectively prognosticate autonomic stability with the goal of optimising risk stratification and establishing timely immunisation in preterm infants. Such real-time display of SampEn thus could become a valuable tool to better understand autonomic regulation in preterm infants and guide physicians in providing an optimal level of care for immunisation based on personalised risk assessment in order to provide an adequate setting and staffing. This approach combines both novel scientific aspects on prognostic value of nonlinear time series analysis and pragmatic utility of SampEn for decision-making on within hospital risk-stratification and necessity of readmission for immunisation. Indo-European Research Network in Mathematics for Health and Disease Research Project | 1 Project MembersHealth and disease are regulated, to a large extent, by our immune system. Current challenges for health and disease that would benefit from mathematical, statistical, and computational approaches to integrate experimental and clinical data include: (1) What are the relevant mechanisms of viral pathogenesis and immune responses, and how do these relate to a pathogenic and molecular characterisation of the virus, (2) What are the mechanisms that regulate immune cell differentiation and fate, as well as ageing, (3) How does receptor-mediated signalling correlate with cellular responses, and (4) How can we quantify the gene diversity of a species with pathogenic potential, such as M. tuberculosis. These questions can now be addressed with dual experimental/clinical and mathematical/computational approaches. In particular, modelling (mathematical and computational) helps to (i) interpret and integrate experimental data, (ii) frame and test hypotheses, (iii) suggest novel experiments allowing for more conclusive and quantitative interpretations of biological, immunological and disease-related processes, and (iv) help towards the 3Rs objectives to reduce, refine and develop replacement strategies as alternatives to animal testing. More concretely, the main research objective of this research network is to develop, by means of the Marie Curie Research Staff Exchange Scheme, four long-term directions in Mathematics for Health and Disease. Given the clinical and experimental expertise of the Indian, EU and Australian partners, and the mathematical and computational expertise of the Indian, EU, USA and Canadian partners, we plan (i) to develop mathematical and computational models of host-pathogen and virus dynamics, with a focus on pathogenic and molecular characterisation of HIV-1, and the distribution of virulence in intra-host HIV quasispecies, in order to understand if regulation of immune activation can be a potentially optimum way for disease management, (ii) to develop mathematical and computational models of immune cellular processes, such as differentiation and cellular fate, as well as ageing, validated by experimental data, with a focus on T cells, (iii) to develop stochastic mathematical models of receptor-mediated processes in health and disease, with a focus on the CCR5 receptor, VEGF receptor, T cell receptor and B cell receptor, and (iv) to develop statistical tools and methods, using evolutionary game theory, to characterise the genomic fluidity of human pathogens, in order to understand microbial pathogen evolution and what constitutes the boundary between commensal and pathogenic organisms. 1 1
Predictive value of heart rate variability on cardiorespiratory events of preterm infants routinely immunised in the hospital Research Project | 2 Project MembersPreterm birth is a major challenge of health care systems across the globe, affecting about 10% of all infants born worldwide, resulting in almost 13 million preterm births per year. The autonomic nervous system of preterm infants is characterized by instability of heart rate and breathing, requiring continuous monitoring of vital signs over several months and long-term respiratory support. Cardiorespiratory events due to this instability, summarised under the term 'apnoea of prematurity' (AOP), affect at least 80% of very preterm infants born before 32 weeks of gestation. AOP may lead to severe hypoxaemia requiring immediate resuscitation and recent data show that repetitive episodes of AOP increase the risk of post-discharge death and long-term neurodevelopmental impairment. Most importantly, severity and frequency of AOP may drastically increase upon challenging the autonomic system by routine immunisation. It is, however, very important to provide timely immunisation and establish early immunity against typical vaccine-preventable diseases in preterm infants as they are particularly vulnerable to complications arising from those diseases. Current recommendations are to initially immunise preterm infants in the hospital under continuous monitoring of vital signs if the treating physician considers an infant to be at risk of post-immunisation AOP. However, there are no objective criteria to predict post-immunisation AOP. Although the first immunisation of very preterm infants typically takes place in the hospital under continuous monitoring of vital signs, immunisations of infants at risk of AOP are often delayed due to fear of AOP or may be initiated in non-intensive care settings (normal wards) where adequate respiratory support cannot be provided but may be needed due to post-immunisation AOP. Also, due to an international trend of early discharge home of preterm infants, immunisations may be arranged in the rooms of the family paediatrician without further monitoring of vital signs and no specific knowledge of the individual risk of post-immunisation AOP. Thus, developing of new biomarkers and objective criteria to better understand and assess the risk of post-immunisation AOP is urgently needed. We recently developed a systematic quality control algorithm for assessing heart rate variability data in a standardised manner and demonstrated that the sample entropy (SampEn) of interbeat intervals, a parameter of heart rate variability derived from nonlinear time series analysis, predicts cardiorespiratory stability in preterm infants. SampEn reflects the regularity of heart rate and the presence of spikes in a given time series of heart beats and has been validated to be a reliable predictor of incipient events such as sepsis. SampEn of heart rate can be obtained non-invasively from electrocardiogram monitors, which are routinely used to monitor preterm infants immunised in the hospital. We aim to evaluate whether real-time calculation of SampEn at a) 32 and 36 weeks corrected age, b) upon primary routine immunisation in the hospital, c) at discharge from the hospital after initial prematurity-related hospital stay, and d) on readmission for immunisation in the hospital based on previous post-immunisation AOP or referral of the family paediatrician has prognostic utility for the risk of post-immunisation AOP in very preterm infants. We will further assess whether immunisation itself initiates a step response in SampEn and compare SampEn values from preterm infants to those of term healthy infants to study maturational effects. The biomarker SampEn provides a unique opportunity to objectively prognosticate autonomic stability with the goal of optimising risk stratification and establishing timely immunisation in preterm infants. Such real-time display of SampEn thus could become a valuable tool to better understand autonomic regulation in preterm infants and guide physicians in providing an optimal level of care for immunisation based on personalised risk assessment in order to provide an adequate setting and staffing. This approach combines both novel scientific aspects on prognostic value of nonlinear time series analysis and pragmatic utility of SampEn for decision-making on within hospital risk-stratification and necessity of readmission for immunisation.
Indo-European Research Network in Mathematics for Health and Disease Research Project | 1 Project MembersHealth and disease are regulated, to a large extent, by our immune system. Current challenges for health and disease that would benefit from mathematical, statistical, and computational approaches to integrate experimental and clinical data include: (1) What are the relevant mechanisms of viral pathogenesis and immune responses, and how do these relate to a pathogenic and molecular characterisation of the virus, (2) What are the mechanisms that regulate immune cell differentiation and fate, as well as ageing, (3) How does receptor-mediated signalling correlate with cellular responses, and (4) How can we quantify the gene diversity of a species with pathogenic potential, such as M. tuberculosis. These questions can now be addressed with dual experimental/clinical and mathematical/computational approaches. In particular, modelling (mathematical and computational) helps to (i) interpret and integrate experimental data, (ii) frame and test hypotheses, (iii) suggest novel experiments allowing for more conclusive and quantitative interpretations of biological, immunological and disease-related processes, and (iv) help towards the 3Rs objectives to reduce, refine and develop replacement strategies as alternatives to animal testing. More concretely, the main research objective of this research network is to develop, by means of the Marie Curie Research Staff Exchange Scheme, four long-term directions in Mathematics for Health and Disease. Given the clinical and experimental expertise of the Indian, EU and Australian partners, and the mathematical and computational expertise of the Indian, EU, USA and Canadian partners, we plan (i) to develop mathematical and computational models of host-pathogen and virus dynamics, with a focus on pathogenic and molecular characterisation of HIV-1, and the distribution of virulence in intra-host HIV quasispecies, in order to understand if regulation of immune activation can be a potentially optimum way for disease management, (ii) to develop mathematical and computational models of immune cellular processes, such as differentiation and cellular fate, as well as ageing, validated by experimental data, with a focus on T cells, (iii) to develop stochastic mathematical models of receptor-mediated processes in health and disease, with a focus on the CCR5 receptor, VEGF receptor, T cell receptor and B cell receptor, and (iv) to develop statistical tools and methods, using evolutionary game theory, to characterise the genomic fluidity of human pathogens, in order to understand microbial pathogen evolution and what constitutes the boundary between commensal and pathogenic organisms.
