Vulnerable newborn types: Analysis of population-based registries for 165 million births in 23 countries, 2000-2021.

OBJECTIVE: To examine the prevalence of novel newborn types among 165 million live births in 23 countries from 2000 to 2021. DESIGN: Population-based, multi-country analysis. SETTING: National data systems in 23 middle- and high-income countries. POPULATION: Liveborn infants. METHODS: Country teams with high-quality data were invited to be part of the Vulnerable Newborn Measurement Collaboration. We classified live births by six newborn types based on gestational age information (preterm 90th centile) for gestational age, according to INTERGROWTH-21st standards. We considered small newborn types of any combination of preterm or SGA, and term + LGA was considered large. Time trends were analysed using 3-year moving averages for small and large types. MAIN OUTCOME MEASURES: Prevalence of six newborn types. RESULTS: We analysed 165 017 419 live births and the median prevalence of small types was 11.7% - highest in Malaysia (26%) and Qatar (15.7%). Overall, 18.1% of newborns were large (term + LGA) and was highest in Estonia 28.8% and Denmark 25.9%. Time trends of small and large infants were relatively stable in most countries. CONCLUSIONS: The distribution of newborn types varies across the 23 middle- and high-income countries. Small newborn types were highest in west Asian countries and large types were highest in Europe. To better understand the global patterns of these novel newborn types, more information is needed, especially from low- and middle-income countries

Spatial-Temporal Signals and Clinical Indices in Electrocardiographic Imaging (I): Preprocessing and Bipolar Potentials

During the last years, Electrocardiographic Imaging (ECGI) has emerged as a powerful and promising clinical tool to support cardiologists. Starting from a plurality of potential measurements on the torso, ECGI yields a noninvasive estimation of their causing potentials on the epicardium. This unprecedented amount of measured cardiac signals needs to be conditioned and adapted to current knowledge and methods in cardiac electrophysiology in order to maximize its support to the clinical practice. In this setting, many cardiac indices are defined in terms of the so-called bipolar electrograms, which correspond with differential potentials between two spatially close potential measurements. Our aim was to contribute to the usefulness of ECGI recordings in the current knowledge and methods of cardiac electrophysiology. For this purpose, we first analyzed the basic stages of conventional cardiac signal processing and scrutinized the implications of the spatial-temporal nature of signals in ECGI scenarios. Specifically, the stages of baseline wander removal, low-pass filtering, and beat segmentation and synchronization were considered. We also aimed to establish a mathematical operator to provide suitable bipolar electrograms from the ECGI-estimated epicardium potentials. Results were obtained on data from an infarction patient and from a healthy subject. First, the low-frequency and high-frequency noises are shown to be non-independently distributed in the ECGI-estimated recordings due to their spatial dimension. Second, bipolar electrograms are better estimated when using the criterion of the maximum-amplitude difference between spatial neighbors, but also a temporal delay in discrete time of about 40 samples has to be included to obtain the usual morphology in clinical bipolar electrograms from catheters. We conclude that spatial-temporal digital signal processing and bipolar electrograms can pave the way towards the usefulness of ECGI recordings in the cardiological clinical practice. The companion paper is devoted to analyzing clinical indices obtained from ECGI epicardial electrograms measuring waveform variability and repolarization tissue properties

A new approach to the intracardiac inverse problem using Laplacian distance kernel

Abstract Background The inverse problem in electrophysiology consists of the accurate estimation of the intracardiac electrical sources from a reduced set of electrodes at short distances and from outside the heart. This estimation can provide an image with relevant knowledge on arrhythmia mechanisms for the clinical practice. Methods based on truncated singular value decomposition (TSVD) and regularized least squares require a matrix inversion, which limits their resolution due to the unavoidable low-pass filter effect of the Tikhonov regularization techniques. Methods We propose to use, for the first time, a Mercer’s kernel given by the Laplacian of the distance in the quasielectrostatic field equations, hence providing a Support Vector Regression (SVR) formulation by following the principles of the Dual Signal Model (DSM) principles for creating kernel algorithms. Results Simulations in one- and two-dimensional models show the performance of our Laplacian distance kernel technique versus several conventional methods. Firstly, the one-dimensional model is adjusted for yielding recorded electrograms, similar to the ones that are usually observed in electrophysiological studies, and suitable strategy is designed for the free-parameter search. Secondly, simulations both in one- and two-dimensional models show larger noise sensitivity in the estimated transfer matrix than in the observation measurements, and DSM−SVR is shown to be more robust to noisy transfer matrix than TSVD. Conclusion These results suggest that our proposed DSM−SVR with Laplacian distance kernel can be an efficient alternative to improve the resolution in current and emerging intracardiac imaging systems

Spatial-Temporal Signals and Clinical Indices in Electrocardiographic Imaging (II): Electrogram Clustering and T-Wave Alternans

During the last years, attention and controversy have been present for the first commercially available equipment being used in Electrocardiographic Imaging (ECGI), a new cardiac diagnostic tool which opens up a new field of diagnostic possibilities. Previous knowledge and criteria of cardiologists using intracardiac Electrograms (EGM) should be revisited from the newly available spatial–temporal potentials, and digital signal processing should be readapted to this new data structure. Aiming to contribute to the usefulness of ECGI recordings in the current knowledge and methods of cardiac electrophysiology, we previously presented two results: First, spatial consistency can be observed even for very basic cardiac signal processing stages (such as baseline wander and low-pass filtering); second, useful bipolar EGMs can be obtained by a digital processing operator searching for the maximum amplitude and including a time delay. In addition, this work aims to demonstrate the functionality of ECGI for cardiac electrophysiology from a twofold view, namely, through the analysis of the EGM waveforms, and by studying the ventricular repolarization properties. The former is scrutinized in terms of the clustering properties of the unipolar an bipolar EGM waveforms, in control and myocardial infarction subjects, and the latter is analyzed using the properties of T-wave alternans (TWA) in control and in Long-QT syndrome (LQTS) example subjects. Clustered regions of the EGMs were spatially consistent and congruent with the presence of infarcted tissue in unipolar EGMs, and bipolar EGMs with adequate signal processing operators hold this consistency and yielded a larger, yet moderate, number of spatial–temporal regions. TWA was not present in control compared with an LQTS subject in terms of the estimated alternans amplitude from the unipolar EGMs, however, higher spatial–temporal variation was present in LQTS torso and epicardium measurements, which was consistent through three different methods of alternans estimation. We conclude that spatial–temporal analysis of EGMs in ECGI will pave the way towards enhanced usefulness in the clinical practice, so that atomic signal processing approach should be conveniently revisited to be able to deal with the great amount of information that ECGI conveys for the clinician

Monitoring Changes in Oxygen Muscle during Exercise with High-Flow Nasal Cannula Using Wearable NIRS Biosensors

Exercise increases the cost of breathing (COB) due to increased lung ventilation (V˙E), inducing respiratory muscles deoxygenation (∇SmO2), while the increase in workload implies ∇SmO2 in locomotor muscles. This phenomenon has been proposed as a leading cause of exercise intolerance, especially in clinical contexts. The use of high-flow nasal cannula (HFNC) during exercise routines in rehabilitation programs has gained significant interest because it is proposed as a therapeutic intervention for reducing symptoms associated with exercise intolerance, such as fatigue and dyspnea, assuming that HFNC could reduce exercise-induced ∇SmO2. SmO2 can be detected using optical wearable devices provided by near-infrared spectroscopy (NIRS) technology, which measures the changes in the amount of oxygen bound to chromophores (e.g., hemoglobin, myoglobin, cytochrome oxidase) at the target tissue level. We tested in a study with a cross-over design whether the muscular desaturation of m.vastus lateralis and m.intercostales during a high-intensity constant-load exercise can be reduced when it was supported with HFNC in non-physically active adults. Eighteen participants (nine women; age: 22 ± 2 years, weight: 65.1 ± 11.2 kg, height: 173.0 ± 5.8 cm, BMI: 21.6 ± 2.8 kg·m−2) were evaluated in a cycle ergometer (15 min, 70% maximum watts achieved in ergospirometry (V˙O2-peak)) breathing spontaneously (control, CTRL) or with HFNC support (HFNC; 50 L·min−1, fiO2: 21%, 30 °C), separated by seven days in randomized order. Two-way ANOVA tests analyzed the ∇SmO2 (m.intercostales and m.vastus lateralis), and changes in V˙E and ∇SmO2·V˙E−1. Dyspnea, leg fatigue, and effort level (RPE) were compared between trials by the Wilcoxon matched-paired signed rank test. We found that the interaction of factors (trial × exercise-time) was significant in ∇SmO2-m.intercostales, V˙E, and (∇SmO2-m.intercostales)/V˙E (p ∇SmO2-m.vastus lateralis. ∇SmO2-m.intercostales was more pronounced in CTRL during exercise since 5′ (p p ∇SmO2·V˙E−1 decreased during exercise, being lowest in CTRL since 5′. Lower dyspnea was reported in HFNC, with no differences in leg fatigue and RPE. We concluded that wearable optical biosensors documented the beneficial effect of HFNC in COB due to lower respiratory ∇SmO2 induced by exercise. We suggest incorporating NIRS devices in rehabilitation programs to monitor physiological changes that can support the clinical impact of the therapeutic intervention implemented

Data for: “Vulnerable newborn types: Analysis of population-based registries for 165 million births in 23 countries, 2000–2021”

Data collected as part of a multi-country, population-based study to explore the prevalence of Vulnerable Newborn Types combining gestational age (preterm vs term) and size of the newborn (small for gestational age [SGA 90th centile]) in 23 countries