Aim To address alarm fatigue, a new alarm management system which ensures a quicker delivery of alarms together with waveform information on nurses' handheld devices was implemented and settings optimised. The effects of this clinical implementation on alarm rates and nurses' responsiveness were measured in an 18‐bed single family rooms neonatal intensive care unit (NICU). Methods The technical implementation of the alarm management system was followed by clinical workflow optimisation. Alarms and vital parameters from October 2017 to December 2019 were analysed. Measures included monitoring alarms, nurses' response to alarms and time spent by patients in different saturation ranges. A survey among nurses was performed to evaluate changes in alarm rate and use of protocols. Results A significant reduction of monitoring alarms per patient days was detected after the optimisation phase (in particular for SpO2 ≤ 80%, P < .001). More time was spent by infants within the optimal peripheral oxygen saturation range (88% < SpO2 < 95%, P < .001). Results from the surveys showed that false alarms are less likely to cause an inappropriate response after the optimisation phase. Conclusion The implementation of an alarm management solution and an optimisation programme can safely reduce the alarm burden inside of the NICU environment.

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SUMMARY Optimization of water management in low‐temperature polymer electrolyte fuel cells is an important subject for the achievement of high performance and sufficient lifetime. Indeed, good hydration of the electrolyte membrane is essential for high proton conductivity; on the contrary, water in excess may lead to electrode flooding especially at the cathode where water is produced. Water transport through the gas diffusion layer, from cathode electrode to distributor flow field, occurs via diffusion and permeation mechanisms: these phenomena are not fully explained, and the onset of the second one, usually named as breakthrough, can play an important role on cathode water balance. This work aims to investigate diffusive and permeative water transport through the diffusion layer under fuel cell representative operating conditions. The effect of microporous layer on diffusion layer effective diffusivity is estimated, and the contribution of the Knudsen diffusion is evaluated. The microporous layer presence determines a strong increase in breakthrough pressure; moreover, cracks are responsible for breakthrough. At high water saturation in the diffusion layer, a considerable hysteresis effect on breakthrough pressure is present. Copyright © 2013 John Wiley & Sons, Ltd.

The aim of this study is to develop an explainable late-onset sepsis (LOS) prediction algorithm based on continuously measured multi-channel physiological signals that can be applied to a bedside patient monitor for preterm infants in a neonatal intensive care unit (NICU). The study highlights the complementary predictive value of motion information for LOS prediction when combined with cardiorespiratory information. The algorithm uses features that contain information on heart rate variability (HRV), respiration, and motion, based on continuously measured physiological waveforms including electrocardiogram (ECG) and chest impedance (CI). In this study, 127 preterm infants were included, of whom 59 were bloodculture-proven LOS patients and 68 were control patients. Features in 24 hours before the onset of sepsis (for the LOS group), and an age-matched onset time point (for the control group) were extracted and fed into machine learning classifiers together with gestational age (GA) and birth weight. We compared the prediction performance of several well-known classifiers using features extracted from different signal channels (HRV, respiration, and motion) individually as well as their combinations. The prediction performance was evaluated using the area under the receiver-operating-characteristics curve (AUC). The best performance for LOS prediction was achieved by an XGB classifier combining features from all signal channels, with an AUC of 0.88, a positive predictive value of 0.80, and a negative predictive value of 0.83 during the 6 hours preceding LOS onset. This feasibility study demonstrates the complementary predictive value of motion information in addition to cardiorespiratory information for LOS prediction. Furthermore, visualization of how each feature measuredThis research was supported by grant from e/MTIC.