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

Anemia, Apnea of Prematurity, and Blood Transfusions

Objective To compare the frequency and severity of apneic events in very low birth weight (VLBW) infants before and after blood transfusions using continuous electronic waveform analysis. Study design We continuously collected waveform, heart rate, and oxygen saturation data from patients in all 45 neonatal intensive care unit beds at the University of Virginia for 120 weeks. Central apneas were detected using continuous computer processing of chest impedance, electrocardiographic, and oximetry signals. Apnea was defined as respiratory pauses of \u3e 10, \u3e 20, and \u3e 30 seconds when accompanied by bradycardia ( \u3c 100 beats per minute) and hypoxemia ( \u3c 80% oxyhemoglobin saturation as detected by pulse oximetry). Times of packed red blood cell transfusions were determined from bedside charts. Two cohorts were analyzed. In the transfusion cohort, waveforms were analyzed for 3 days before and after the transfusion for all VLBW infants who received a blood transfusion while also breathing spontaneously. Mean apnea rates for the previous 12 hours were quantified and differences for 12 hours before and after transfusion were compared. In the hematocrit cohort, 1453 hematocrit values from all VLBW infants admitted and breathing spontaneously during the time period were retrieved, and the association of hematocrit and apnea in the next 12 hours was tested using logistic regression. Results Sixty-seven infants had 110 blood transfusions during times when complete monitoring data were available. Transfusion was associated with fewer computer-detected apneic events (P \u3c .01). Probability of future apnea occurring within 12 hours increased with decreasing hematocrit values (P \u3c .001). Conclusions Blood transfusions are associated with decreased apnea in VLBW infants, and apneas are less frequent at higher hematocrits. (J Pediatr 2012;161:417-21)

Prediction of fetal acidemia in placental abruption

BACKGROUND: To determine the major predictive factors for fetal acidemia in placental abruption. METHODS: A retrospective review of pregnancies with placental abruption was performed using a logistic regression model. Fetal acidemia was defined as a pH of less than 7.0 in umbilical artery. The severe abruption score, which was derived from a linear discriminant function, was calculated to determine the probability of fetal acidemia. RESULTS: Fetal acidemia was seen in 43 survivors (43/222, 19%). A logistic regression model showed bradycardia (OR (odds ratio) 50.34, 95% CI 11.07 – 228.93), and late decelerations (OR 15.13, 3.05 – 74.97), but not abnormal ultrasonographic findings were to be associated with the occurrence of fetal acidemia. The severe abruption score was calculated for the occurrence of fetal acidemia, using 6 items including vaginal bleeding, gestational age, abdominal pain, abnormal ultrasonographic finding, late decelerations, and bradycardia. CONCLUSIONS: An abnormal FHR pattern, especially bradycardia is the most significant risk factor in placental abruption predicting fetal acidemia, regardless of the presence of abnormal ultrasonographic findings or gestational age

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

The Impact of Risk on the Developmental Course of Visual Attention in Infants Born Prematurely

The developmental course of attention has been documented in full-term infants, but the growth parameters of visual attention in preterm infants and the impact of medical and environmental risk on these measures have not been investigated. The purposes of the current investigation were twofold: 1) to examine the developmental course of attention over the first year of life in a sample of 71 infants born prematurely; and 2) to examine the impact of risk on these growth parameters in infants with varying levels of medical severity. Overall, the preterm sample demonstrated a general decline in peak look duration from 2- to 12-months corrected age that was best captured by a non-linear function. The construct of medical risk was not found to be significantly associated with either the intercept or slope factors in this model. Future considerations with regards to medical risk, inclusion of process environmental variables, as well as examining the relationship between these trajectories of attention and later developmental outcome, are discussed