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Respiratory Volumes and Capacities I

Respiratory Volumes and Capacities I

Assessing the respiratory rate and rhythm for a complete minute is crucial for evaluating the breathing pattern. Even a minor increase in the patient's average respiratory rate, by as little as three to five breaths per minute, is an early and vital indicator of respiratory distress. Patients with a respiratory rate exceeding twenty-four breaths per minute require close monitoring to determine the physiological alterations. This careful observation is essential for prompt recognition and...

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Preterm EEG: A Multimodal Neurophysiological Protocol

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Predicting Apnoeic Events in Preterm Infants.

Kathleen Lim^1,2, Haimin Jiang³, Andrew P Marshall^1,3

¹Menzies Institute for Medical Research, College of Health and Medicine, University of Tasmania, Hobart, TAS, Australia.

Frontiers in Pediatrics

|October 12, 2020

Summary

This summary is machine-generated.

Apnoea, a pause in respiration, is common in preterm infants. This paper reviews challenges and potential for developing predictive models for apnoeic events, aiming for proactive intervention.

Keywords:

apnoea of prematurity machine learning neonatal intensive care prediction preterm infants

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Area of Science:

Neonatal Medicine
Respiratory Physiology
Biomedical Engineering

Background:

Apnoea is frequent in preterm infants (<30 weeks gestation), leading to hypoxemia and bradycardia.
Current reactive management of apnoea is labor-intensive and cannot prevent adverse neurodevelopmental outcomes.
Predicting apnoea is crucial for proactive intervention in vulnerable preterm infants.

Purpose of the Study:

To identify essential components for developing apnoea prediction models in preterm infants.
To highlight current challenges and gaps in apnoea prediction research.
To review existing studies on predicting apnoea in preterm infants.

Main Methods:

Literature review of studies on apnoea prediction in preterm infants.
Analysis of technological advancements and signal processing in predictive modeling.
Identification of requisite components and research gaps.

Main Results:

Developing predictive models for apnoea in preterm infants is challenging but shows potential.
Technological advances enable new approaches to apnoea prediction.
Existing research is nascent, with significant gaps in model development and validation.

Conclusions:

Proactive prediction of apnoea in preterm infants is a promising but underdeveloped field.
Further research is needed to overcome challenges in developing accurate and reliable prediction models.
Successful models could revolutionize apnoea management, improving infant outcomes.