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The role of telemetry monitoring: From diagnosing arrhythmia to predictive models of patient instability.
J Electrocardiol
December 2024
Emory University, Atlanta, GA, USA. Electronic address:
Over the past sixty years, telemetry monitoring has become integral to hospital care, offering critical insights into patient health by tracking key indicators like heart rate, respiratory rate, blood pressure, and oxygen saturation. Its primary application, continuous electrocardiographic (ECG) monitoring, is essential in diverse settings such as emergency departments, step-down units, general wards, and intensive care units for the early detection of cardiac rhythms signaling acute clinical deterioration. Recent advancements in data analytics and machine learning have expanded telemetry's role from observation to prognostication, enabling predictive models that forecast inhospital events indicative of patient instability.
View Article and Find Full Text PDFCall for Decision Support for Electrocardiographic Alarm Administration Among Neonatal Intensive Care Unit Staff: Multicenter, Cross-Sectional Survey.
J Med Internet Res
December 2024
Shanghai Engineering Research Center of Intelligence Pediatrics, Shanghai Children's Medical Center, School of Medicine, Shanghai Jiao Tong University, Shanghai, China.
Background: Previous studies have shown that electrocardiographic (ECG) alarms have high sensitivity and low specificity, have underreported adverse events, and may cause neonatal intensive care unit (NICU) staff fatigue or alarm ignoring. Moreover, prolonged noise stimuli in hospitalized neonates can disrupt neonatal development.
Objective: The aim of the study is to conduct a nationwide, multicenter, large-sample cross-sectional survey to identify current practices and investigate the decision-making requirements of health care providers regarding ECG alarms.
Introduction: Frequent and long-term exposure to clinical alarms can cause emergency nurses to lose their trust in alarms, delay their response, and even disable or mute these alarms.
Methods: A cross-sectional study was conducted to assess emergency nurses' knowledge, perceptions, and practices toward clinical alarm fatigue and investigate the perceived obstacles they face when managing clinical alarms.
Results: Less than half of emergency nurses were unfamiliar with the term "alarm fatigue" (40.
Machine and Deep Learning Models for Hypoxemia Severity Triage in CBRNE Emergencies.
Diagnostics (Basel)
December 2024
Research Center CHU Ste-Justine Centre Hospitalier Universitaire Mère-Enfant, 3175 Boulevard de la Côte-Sainte-Catherine Drive, Montréal, QC H3T 1C5, Canada.
Background/objectives: This study develops machine learning (ML) models to predict hypoxemia severity during emergency triage, particularly in Chemical, Biological, Radiological, Nuclear, and Explosive (CBRNE) scenarios, using physiological data from medical-grade sensors.
Methods: Tree-based models (TBMs) such as XGBoost, LightGBM, CatBoost, Random Forests (RFs), Voting Classifier ensembles, and sequential models (LSTM, GRU) were trained on the MIMIC-III and IV datasets. A preprocessing pipeline addressed missing data, class imbalances, and synthetic data flagged with masks.
Neonatal apnea and hypopnea prediction in infants with Robin sequence with neural additive models for time series.
PLOS Digit Health
December 2024
Department of Neonatology, University Hospital Tübingen, Tübingen, Germany.
Neonatal apneas and hypopneas present a serious risk for healthy infant development. Treating these adverse events requires frequent manual stimulation by skilled personnel, which can lead to alarm fatigue. This study aims to develop and validate an interpretable model that can predict apneas and hypopneas.
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