Investigation of the performance of an automatic arterial oxygen controller

Premature infants often require respiratory support with a varying concentration of the fraction of inspired oxygen (FiO2) to keep the arterial oxygen saturation (SpO2) within the desired range to avoid both hypoxemia and hyperoxemia. Currently, manual adjustment of FiO2 is the common practice in neonatal intensive care units (NICUs). The automation of this adjustment is a topic of interest. The research team, at University of Missouri-Columbia (UMC), has developed a novel automatic arterial oxygen saturation controller. In this study, a systematic approach has been developed to investigate both non-clinical and clinical performance of this device. The non-clinical investigation of the performance was performed using a neonatal respiratory model (hardware-in-the-loop test). A factorial experimental design was utilized to generate challenging model responses of SpO2, which were addressed by the controllers. With this study, we demonstrate the stability and ability of the adaptive PI-controller to improve oxygen saturation control over manual control by increasing the proportion of time where SpO2 of the neonatal respiratory model was within the desired range and by minimizing the variability of the SpO2. In addition, the controller ability to significantly reduce the number of hypoxemic events of the neonatal respiratory model was reported. Results of this investigation show the competence of the controller estimation system for estimating neonatal respiratory model parameters while the adaptive PI-controller was in use. Also, the functionality of the controller with no mechanical or communication failure was validated non-clinically before heading forward to the clinical trial. The clinical investigation of the performance was performed by conducting a clinical trial at the NICU of the MU Women's and Children's Hospital. The crossover design was used for the clinical trial to allow within-subject comparison and to eliminate interpatient variability. Two human subjects, with two different target ranges of SpO2, were enrolled in the study. The adaptive automatic PI-controller shows clinical feasibility to improve the maintenance of SpO2 within the intended range. With this study, we demonstrate the potential of the automatic controller to minimize the variability of SpO2. In addition, the controller shows the ability to reduce the bradycardia and the hypoxemia. Moreover, the hardware and software of the controller show an ability to transition from manual to automatic mode, and vice versa with no pronounced “bump” or step variation in the control signal, and stability and performance were not adversely affected during the transitions.Includes bibliographical reference

Polynomial system identification modeling and adaptive model predictive control of arterial oxygen saturation in premature infants

The automation of the regulation of the fraction of inspired oxygen (FiO2) in neonatal mechanical ventilation to treat respiratory distress syndrome has proven challenging due to competing objectives: maintaining arterial oxygen saturation levels (SpO2) while simultaneously not inducing complications such as retrolental fibroplasia. Historically, models of the dynamics of the neonatal respiratory system were first order transfer function approximations. This work used higher order polynomial system identification methods with the model structures of autoregressive with exogenous inputs (ARX) and Box-Jenkins (BJ) models to investigate possible improved modeling of the dynamic relationship between the FiO2, Heart Rate (HR), and Respiratory Rate (RR) to the SpO2. Through a parameter sweep of different of polynomial orders and sampling delays, 3,456 ARX models and 13,176 BJ models were created, with four being selected for comparison based upon modeling performance metrics. From these best performing models, it was concluded that the FiO2 relationship to SpO2 could still be adequately approximated by a first order transfer function model with delay. The disturbance HR, RR, and the unmodeled dynamics did require higher order approximations. It was also shown that selecting a model based off the Akaike's Information Criterion was preferred in picking a model from a collection of identified models. With a singular winning model from the four best performing models, an adaptive model predictive controller (AMPC) was designed to adhere to clinical best practices to regulate the SpO2. Through a recursive polynomial model estimator (RPME), an ARX approximation of the unknown model's dynamics for the FiO2, HR, and RR relationship to the SpO2 could be used to update the internal model of the AMPC. Through this online model estimation, the AMPC could successfully feedforward reject the HR and RR disturbances improving the simulated time within the SpO2 target limits, 67.8% of simulation time, to a baseline PI controller's 56.6%, in periodic desaturation simulations.Includes bibliographical references (pages 63-65)

Forecasting using non-linear techniques in time series analysis : an overview of techniques and main issues

The development of techniques in non linear time series analysis has emerged from its time series background and developed over the last few decades into a range of techniques which aim to fill a gap in the ability to model and forecast certain types of data sets such a chaotic determinate systems. These systems are found in many diverse areas of natural and human spheres. This study outlines the background within which these techniques developed, the fundamental elements on which they are based and details some of the predictive techniques. This study aims to provide some insight into their mechanisms and their potential.peer-reviewe

Thirty years of artificial intelligence in medicine (AIME) conferences: A review of research themes

Over the past 30 years, the international conference on Artificial Intelligence in MEdicine (AIME) has been organized at different venues across Europe every 2 years, establishing a forum for scientific exchange and creating an active research community. The Artificial Intelligence in Medicine journal has published theme issues with extended versions of selected AIME papers since 1998

Patterns of Weight Change in infants With Congenital Heart Disease Following Neonatal Surgery: Potential Predictors of Growth Failure

Abstract Patterns of Weight Change in Infants with Congenital Heart Disease Following Neonatal Surgery: Potential predictors of growth failure Sharon Y Irving DISSERTATION SUPERVISOR: BARBARA MEDOFF-COOPER, RN, PhD Congenital heart disease (CHD) is reported to have an incidence of 9 to 14 per 1000 live births with a prevalence estimated between 650,000 and 1.3 million persons in the United States (US). It is a structural malformation(s) of one or more heart chamber(s) and/or deformity of one or more of the major intrathoracic blood vessel(s) and the ensuing malady occurring during embryonic development. Up to one-third of infants with CHD, require surgical intervention. Improved surgical technique over the last several decades has seen an increased survival of neonates with CHD. Concomitantly there has been an emergence of co-morbidities. Growth failure is a common co-morbidity following neonatal surgery for CHD. More than 30% of these infants fall below the third percentile for weight early in their lives. Postsurgical physiology, disease severity, feeding dysfunction, and a hypermetabolic state may all contribute to growth failure, which has been associated with deficits in cognitive development, intellectual ability and neurodevelopment, effecting maturation and school performance. Early recognition and intervention of growth failure can improve health outcomes. The objective of this work is to identify patterns of growth and growth failure in infants with CHD and explore potential predictors that may be modifiable to mitigate growth failure and prevent the associated untoward consequences

The effectiveness of neonatal health care services in the primary health care units in the north-west of Ethiopia

Improving the quality of newborn care services and accelerating the service utilization of sick young infants is required to contribute to the reduction of neonatal mortality and improve the wellbeing of the newborns. The purpose of this study was to explore the effectiveness of neonatal healthcare services in the primary healthcare units in the north-west of Ethiopia and develop guideline for effective neonatal care. A mixed method approach with a sequential explanatory design was employed to explore factors affecting the effectiveness of the neonatal healthcare services. Interviewer-administered questionnaires were administered to 221 health workers and health extension workers in 142 health facilities; and service statistics abstracted for 767 sick young infants’ from the sick young infant registers. Data was entered in the EpiData 3.1, exported to SPSS and STATA for analysis. In the qualitative study, twenty-six participants from the health centers and health posts were interviewed through focus group discussions. Thematic analysis was undertaken to explore factors affecting neonatal healthcare services. Results: The quality of newborn care with the domains of newborn resuscitation, follow-up care after resuscitation and thermal care; immediate care and breastfeeding advice for very low birthweight babies were found to be moderate at primary hospitals and urban health centres; low at rural health centres and health posts. The availability of essential equipment is significantly associated with the quality of neonatal care provision in the health facilities (p < 0.05). More than forty percent of health facilities were not meeting the quality of case management tasks for sick young infants, and the newborn care knowledge of health providers is significantly associated with the quality of sick young infants’ management (p < 0.05). The sick young infants’ service utilisation was only 6.3 percent from the expected sick young infants’ population. Overall, the effectiveness of the neonatal healthcare services has a significant association with the health facilitates readiness [95%CI: 0.134-0.768]. Conclusion: The quality of neonatal healthcare provision is low to moderate; and the service utilization of sick young infants is very low. Thus, the rural health centers and health posts should be prioritised for the effective neonatal care.Health StudiesD. Litt. et Phil. (Health Studies

New Insight into Brucella Infection and Foodborne Diseases

Brucellosis is an important zoonotic disease. More than half a million new cases from 100 countries are reported annually to the World Health Organization (WHO). The majority of patients are living in developing countries. Brucellosis is a systemic infection with a broad clinical spectrum, ranging from an asymptomatic disease to a severe and fatal illness. Clinical and laboratory features vary widely. The main presentations are acute febrile illness, localized infection, and chronic infection. Laboratory tools for diagnosis of brucellosis include culture, serology, and polymerase chain reaction (PCR). The goal of brucellosis therapy is to control the illness and prevent complications, relapses, and sequelae. Important principles of brucellosis treatment include use of antibiotics with activity in the acidic intracellular environment, use of combination regimens, and prolonged duration of treatment. This book is the result of several months of outstanding efforts by the authors and the revision of the content by experts in the field of brucellosis. This book is a valid resource and is intended for everyone interested in infectious disease to learn the most important aspects of brucellosis