ASSESSMENT OF RISK IN PRETERM INFANTS USING POINT PROCESS AND MACHINE LEARNING APPROACHES

Preemies, infants who are born too soon, have a higher incidence of Life-Threatening Events (LTE’s) such as apnea (cessation of breathing), bradycardia (slowing of heart rate) and hypoxemia (oxygen desaturation) also termed as ABD (Apnea, Bradycardia, and Desaturation) events. Clinicians at Neonatal Intensive Care Units (NICU) are facing the demanding task of assessing the risk of infants based on their physiological signals. The aim of this thesis is to develop a risk stratification algorithm using a machine-learning framework with the features related to pathological fluctuations derived from point process model that will be embedded into the current physiological recording system to assess the risk of life-threatening events well in advance of occurrence in individual infants in the NICU. We initially propose a point process algorithm of heart rate dynamics for risk stratification of preterm infants. Based on this analysis, point process indices were tested to determine whether they were useful as precursors for life-threatening events. Finally, a machine-learning framework using point process indices as precursors were designed and tested to classify the risk of preterm infants. This work helps to predict the number of bradycardia events, N, in the subsequent hours measuring point process indices for the current hour. The model proposed uses Quadratic Support Vector Machine (QSVM), a machine learning classifier, which can solve class optimization problems and execute data at an exponential speed with higher accuracy for risk assessment that might facilitate effective management and treatment for preterm infants in NICU. The findings are relevant to risk assessment by analyzing the fluctuations in physiological signals that can act as precursors for the future life-threatening events

ASSESSMENT OF CARDIORESPIRATORY INTERACTIONS DURING LIFE THREATENING EVENTS IN PRETERM INFANTS USING POINT PROCESS AND BIVARIATE ALGORITHMS

Cardiorespiratory interactions considered as an important indicator of neurodevelopment of preterm infants. The strength of cardiorespiratory interactions are presumed to be weak and rapidly fluctuating. The current signal processing algorithms are insufficient to capture such time varying weak interactions. In addition, detection of these interactions becomes difficult during life threatening events due to lack of information available due to apnea (absence of output from respiratory system) and the transient temporal destabilization of cardiac system due to bradycardia. To detect the cardiorespiratory interactions, a point process algorithm of cardiac system with respiration as covariates is proposed. The bivariate model is embedded on the point process-modeling framework to capture the time varying weak interactions between cardiac and respiratory system. This integrated framework is employed to detect the cardiorespiratory interactions in preterm infants during their life-threatening events

Advanced analyses of physiological signals and their role in Neonatal Intensive Care

Preterm infants admitted to the neonatal intensive care unit (NICU) face an array of life-threatening diseases requiring procedures such as resuscitation and invasive monitoring, and other risks related to exposure to the hospital environment, all of which may have lifelong implications. This thesis examined a range of applications for advanced signal analyses in the NICU, from identifying of physiological patterns associated with neonatal outcomes, to evaluating the impact of certain treatments on physiological variability. Firstly, the thesis examined the potential to identify infants at risk of developing intraventricular haemorrhage, often interrelated with factors leading to preterm birth, mechanical ventilation, hypoxia and prolonged apnoeas. This thesis then characterised the cardiovascular impact of caffeine therapy which is often administered to prevent and treat apnoea of prematurity, finding greater pulse pressure variability and enhanced responsiveness of the autonomic nervous system. Cerebral autoregulation maintains cerebral blood flow despite fluctuations in arterial blood pressure and is an important consideration for preterm infants who are especially vulnerable to brain injury. Using various time and frequency domain correlation techniques, the thesis found acute changes in cerebral autoregulation of preterm infants following caffeine therapy. Nutrition in early life may also affect neurodevelopment and morbidity in later life. This thesis developed models for identifying malnutrition risk using anthropometry and near-infrared interactance features. This thesis has presented a range of ways in which advanced analyses including time series analysis, feature selection and model development can be applied to neonatal intensive care. There is a clear role for such analyses in early detection of clinical outcomes, characterising the effects of relevant treatments or pathologies and identifying infants at risk of later morbidity

A method to detect and represent temporal patterns from time series data and its application for analysis of physiological data streams

In critical care, complex systems and sensors continuously monitor patients??? physiological features such as heart rate, respiratory rate thus generating significant amounts of data every second. This results to more than 2 million records generated per patient in an hour. It???s an immense challenge for anyone trying to utilize this data when making critical decisions about patient care. Temporal abstraction and data mining are two research fields that have tried to synthesize time oriented data to detect hidden relationships that may exist in the data. Various researchers have looked at techniques for generating abstractions from clinical data. However, the variety and speed of data streams generated often overwhelms current systems which are not designed to handle such data. Other attempts have been to understand the complexity in time series data utilizing mining techniques, however, existing models are not designed to detect temporal relationships that might exist in time series data (Inibhunu & McGregor, 2016). To address this challenge, this thesis has proposed a method that extends the existing knowledge discovery frameworks to include components for detecting and representing temporal relationships in time series data. The developed method is instantiated within the knowledge discovery component of Artemis, a cloud based platform for processing physiological data streams. This is a unique approach that utilizes pattern recognition principles to facilitate functions for; (a) temporal representation of time series data with abstractions, (b) temporal pattern generation and quantification (c) frequent patterns identification and (d) building a classification system. This method is applied to a neonatal intensive care case study with a motivating problem that discovery of specific patterns from patient data could be crucial for making improved decisions within patient care. Another application is in chronic care to detect temporal relationships in ambulatory patient data before occurrence of an adverse event. The research premise is that discovery of hidden relationships and patterns in data would be valuable in building a classification system that automatically characterize physiological data streams. Such characterization could aid in detection of new normal and abnormal behaviors in patients who may have life threatening conditions

Defining The Difficult-To-Sedate Clinical Phenotype In Critically Ill Children

Each year thousands of critically-ill children receive sedation to help them tolerate intensive care therapies. A significant number of these children do not respond as expected to appropriately dosed sedation and remain agitated for some period, leading to iatrogenic injury and increased stress, as well as increased resource use. Children who remain under-sedated despite optimal therapy are considered “difficult-to-sedate”, but, to date, little data have been available to support an accurate description of this group of children. Recent attention to heterogeneity of treatment effect has spurred the development of clinical phenotypes that describe subgroups of patients within a disease process who differ in their clinical attributes and responses to therapy. Defining the difficult-to-sedate clinical phenotype in critically ill children is important because it will allow the use of sedation therapy targeted to the unique clinical, physiological, and developmental characteristics of the child. The three papers developed in this dissertation study explored the concept of the difficult-to-sedate child clinical phenotype. A comprehensive review of the literature identified the lack of an operational definition and identified factors contributing to the clinical phenotype. These factors were used to develop an initial operational definition and to construct a conceptual model. Expert critical care clinicians validated the elements of the operational definition through an assessment of face and content validity and proposed additional factors for inclusion in the model. A refined definition was tested using data from the RESTORE study. Characteristics identified through latent class and classification and regression tree analysis were consistent with the conceptual model proposed. Decreasing the ambiguity that currently exists around the concept of the difficult-to-sedate child clinical phenotype is a major achievement of this study. A clear operational definition of the concept promotes its consistent measurement and facilitates future investigation, and allows useful comparisons across studies. The conceptual model and operational definition require further investigation and refinement, as well as prospective validation by other investigators. This study suggests that a clinically meaningful population of difficult-to-sedate children requiring mechanical ventilation for a critical illness exists. Documentation of this phenotype promotes the development of evidence to support best practices in the care of these children

Dynamical models for neonatal intensive care monitoring

The vital signs monitoring data of an infant receiving intensive care are a rich source of information about its health condition. One major concern about the state of health of such patients is the onset of neonatal sepsis, a life-threatening bloodstream infection. As early signs are subtle and current diagnosis procedures involve slow laboratory testing, sepsis detection based on the monitored physiological dynamics is a clinically significant task. This challenging problem can be thoroughly modelled as real-time inference within a machine learning framework. In this thesis, we develop probabilistic dynamical models centred around the goal of providing useful predictions about the onset of neonatal sepsis. This research is characterised by the careful incorporation of domain knowledge for the purpose of extracting the infant’s true physiology from the monitoring data. We make two main contributions. The first one is the formulation of sepsis detection as learning and inference in an Auto-Regressive Hidden Markov Model (AR-HMM). The model investigates the extent to which physiological events observed in the patient’s monitoring traces could be used for the early detection of neonatal sepsis. In addition, the proposed approach involves exact marginalisation over missing data at inference time. When applying the ARHMM on a real-world dataset, we found that it can produce effective predictions about the onset of sepsis. Second, both sepsis and clinical event detection are formulated as learning and inference in a Hierarchical Switching Linear Dynamical System (HSLDS). The HSLDS models dynamical systems where complex interactions between modes of operation can be represented as a twolevel hidden discrete hierarchical structure. For neonatal condition monitoring, the lower layer models clinical events and is controlled by upper layer variables with semantics sepsis/nonsepsis. The model parameterisation and estimation procedures are adapted to the specifics of physiological monitoring data. We demonstrate that the performance of the HSLDS for the detection of sepsis is not statistically different from the AR-HMM, despite the fact that the latter model is given “ground truth” annotations of the patient’s physiology

Patient Safety in Pediatrics: a Developing Discipline

__Abstract__ The publication of the breakthrough report “To Err is Human” by the Institute of Medicine was the launch of patient safety initiatives all over the world. In the intensive care unit (ICU) of the Erasmus MC-Sophia Children’s Hospital this resulted in the institution of a multimodal patient safety management system under the name Safety First in 2005. This system now includes nine major elements, representing monitoring and intervention activities. In this thesis we report on the results and the implementation of the patient safety management system called Safety First. __Outline of this thesis:__ In part I the concept of patient safety and the Safety First project are introduced. The rationale for selecting the elements of the patient safety management system is explained. As preventable mortality and morbidity are the public focus as outcome parameters for quality and safety of care, we have studied very long stay patients in our ICU (chapter 2). The goal of this study was to determine characteristics and mortality in these patients as well as modes of death. Chapter 3 presents an evaluation of potentially preventable deaths in our ICU. An important question was whether five years of patient safety efforts had resulted in fewer potentially preventable deaths. Part II reflects on the difficulties in monitoring adverse events. In chapter 4 we present numbers and types of adverse events identified with real time physicians’ registration during a 3-month period in general pediatric practice. The next chapter is a study into adverse events in the surgical pediatric ICU in a 2-year period. We combined the physicians’ registration with the Trigger Tool methodology as developed by the Institute for Healthcare, Boston, USA. The goals were to determine the rate and nature of the adverse events and to compare the two methods. In part III a number of elements of Safety First are described, as well as other studies into patient safety issues relevant to bedside ICU care. Chapter 6 brings the results of critical incident analysis with a focus on the factors contributing to the incident and the resultant recommendations. The next study evaluated the availability and reliability of drug formularies used in our ICU, which are crucial in safe drug prescription. In chapter 8 we discuss the safety of routine MRI scans in preterm infants at 30 weeks gestational age, as reflected by safety incidents and adverse events. In the next chapter, safety focused Mortality and Morbidity conference reports were scrutinized for numbers and types of recommendations stemming from these meetings. Chapter 10 is a study about nursing protocol violations established with the Critical Nursing Situation Index. Part IV describes a study of safety culture in the ICU, as it emerged from a safety attitude questionnaire administered to all staff. We aimed to compare findings to benchmark data and explore any deficiencies. In the general discussion in part V the results of the studies are commented on and future directions are given, including guidelines for optimal implementation of a patient safety management system and future benchmarking