Comparing machine learning techniques for neonatal mortality prediction: insights from a modeling competition.

Background: Predicting mortality risk in neonatal intensive care units (NICUs) is challenging due to complex, variable clinical and physiological data. Machine learning (ML) offers potential for more accurate risk stratification.

Objective: To compare the performance of various ML models in predicting NICU mortality using a team-based modeling competition.

Ordinal Outcome State-Space Models for Intensive Longitudinal Data.

Intensive longitudinal (IL) data are increasingly prevalent in psychological science, coinciding with technological advancements that make it simple to deploy study designs such as daily diary and ecological momentary assessments. IL data are characterized by a rapid rate of data collection (1+ collections per day), over a period of time, allowing for the capture of the dynamics that underlie psychological and behavioral processes. One powerful framework for analyzing IL data is state-space modeling, where observed variables are considered measurements for underlying states (i.

Highly comparative time series analysis of oxygen saturation and heart rate to predict respiratory outcomes in extremely preterm infants.

Highly comparative time series analysis (HCTSA) is a novel approach involving massive feature extraction using publicly available code from many disciplines. The Prematurity-Related Ventilatory Control (Pre-Vent) observational multicenter prospective study collected bedside monitor data from>700extremely preterm infants to identify physiologic features that predict respiratory outcomes..

Highly comparative time series analysis of oxygen saturation and heart rate to predict respiratory outcomes in extremely preterm infants.

Objective: Highly comparative time series analysis (HCTSA) is a novel approach involving massive feature extraction using publicly available code from many disciplines. The Prematurity-Related Ventilatory Control (Pre-Vent) observational multicenter prospective study collected bedside monitor data from > 700 extremely preterm infants to identify physiologic features that predict respiratory outcomes. We calculated a subset of 33 HCTSA features on > 7 10-minute windows of oxygen saturation (SPO2) and heart rate (HR) from the Pre-Vent cohort to quantify predictive performance.

Heart rate patterns predicting cerebral palsy in preterm infants.

Background: Heart rate (HR) patterns can inform on central nervous system dysfunction. We previously used highly comparative time series analysis (HCTSA) to identify HR patterns predicting mortality among patients in the neonatal intensive care unit (NICU) and now use this methodology to discover patterns predicting cerebral palsy (CP) in preterm infants.

Method: We studied NICU patients <37 weeks' gestation with archived every-2-s HR data throughout the NICU stay and with or without later diagnosis of CP (n = 57 CP and 1119 no CP).

Pathophysiological Responses to Bloodstream Infection in Critically Ill Transplant Recipients Compared With Non-Transplant Recipients.

Background: Identification of bloodstream infection (BSI) in transplant recipients may be difficult due to immunosuppression. Accordingly, we aimed to compare responses to BSI in critically ill transplant and non-transplant recipients and to modify systemic inflammatory response syndrome (SIRS) criteria for transplant recipients.

Methods: We analyzed univariate risks and developed multivariable models of BSI with 27 clinical variables from adult intensive care unit (ICU) patients at the University of Virginia (UVA) and at the University of Pittsburgh (Pitt).

Maturation of cardioventilatory physiological trajectories in extremely preterm infants.

Background: In extremely preterm infants, persistence of cardioventilatory events is associated with long-term morbidity. Therefore, the objective was to characterize physiologic growth curves of apnea, periodic breathing, intermittent hypoxemia, and bradycardia in extremely preterm infants during the first few months of life.

Methods: The Prematurity-Related Ventilatory Control study included 717 preterm infants <29 weeks gestation.

Smart chitosan-based microgels for enhanced photothermal-assisted antibacterial activity.

During recent years, antibiotic-resistant bacteria have rapidly emerged owing to the irrational use of antibiotics, rendering a global problem. Currently, few studies introduce customized antibacterial nanoplatforms to overcome antibiotic-resistance according to specific characteristic of bacteria, rather than abuse of antibiotic. Herein, with regard to personalized antibacterial nanoplatform, we design a novel antibiotic delivery nanocarrier composed of polyaniline-grafted-chitosan, presenting pH-responsive, conductive, photothermal, and biodegradable properties.

Cardiorespiratory Monitoring Data to Predict Respiratory Outcomes in Extremely Preterm Infants.

Immature control of breathing is associated with apnea, periodic breathing, intermittent hypoxemia, and bradycardia in extremely preterm infants. However, it is not clear if such events independently predict worse respiratory outcome. To determine if analysis of cardiorespiratory monitoring data can predict unfavorable respiratory outcomes at 40 weeks postmenstrual age (PMA) and other outcomes, such as bronchopulmonary dysplasia at 36 weeks PMA.

Cardiorespiratory signature of neonatal sepsis: development and validation of prediction models in 3 NICUs.

Background: Heart rate characteristics aid early detection of late-onset sepsis (LOS), but respiratory data contain additional signatures of illness due to infection. Predictive models using cardiorespiratory data may improve early sepsis detection. We hypothesized that heart rate (HR) and oxygenation (SpO) data contain signatures that improve sepsis risk prediction over HR or demographics alone.

Chemical-Reductant-Free Electrochemical Deuteration Reaction using Deuterium Oxide.

We report a method for the electrochemical deuteration of α,β-unsaturated carbonyl compounds under catalyst- and external-reductant-free conditions, with deuteration rates as high as 99 % and yields up to 91 % in 2 h. The use of graphite felt for both the cathode and the anode was key to ensuring chemoselectivity and high deuterium incorporation under neutral conditions without the need for an external reductant. This method has a number of advantages over previously reported deuteration reactions that use stoichiometric metallic reductants.