Objectives: This study aimed to determine practice and confidence in electrocardiogram (ECG) interpretation among intensive care unit (ICU) nurses in Fujian Province, China, and identify predictors of ECG interpretation practice.
Research Methodology/design: A quantitative cross-sectional study was conducted between October 2021 and December 2021 among 357 respondents.
Setting: Conducted online at twenty-one hospitals in all nine cities of Fujian Province.
Main Outcome Measures: Purposive and convenient sampling techniques were employed in selecting hospitals and respondents, respectively. A validated and pre-tested Chinese version of the questionnaire was used in data collection. We conducted binary logistic regression to identify the predictors of ICU nurses' ECG interpretation practice, and linear regression to analyze the relationship between ECG interpretation practice and confidence. We considered statistically significant a p-value < 0.05.
Results: The practice mean score of the respondents was 5.54 (SD = 2.26) out of 10 points, and only 2.2 % of nurses correctly interpreted all the patient ECG strips. Few ICU nurses (25.5 %) had good ECG interpretation practice, with a confidence mean score of 2.02 (SD = 0.99) out of 4 points in their overall ability to interpret patient ECG strips. Currently working unit in comparison to cardiac ICU (emergency ICU: AOR = 5.71, 95 % CI: 1.84-17.75); previous ECG training (AOR = 2.02, 95 % CI: 1.10-3.70); source of ECG training (university/school) (AOR = 2.02, 95 % CI: 1.12-3.65); and ECG knowledge (AOR = 16.18, 95 % CI: 7.43-35.25) were significantly associated with the ECG interpretation practice.
Conclusions: ICU nurses' ECG interpretation practice in the current study was relatively poor. An ECG education program is recommended to impart ICU nurses with basic ECG knowledge for enhancing good ECG interpretation practice and confidence in nursing care provision.
Implications For Clinical Practice: Good ECG interpretation skills are paramount among ICU nurses for better patient outcomes. ECG knowledge among ICU nurses is an important predictor of effective ECG monitoring for cardiac arrhythmias. Therefore, frequent, continuouszgood practice and boost confidence in the provision of quality nursing care.
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http://dx.doi.org/10.1016/j.iccn.2024.103835 | DOI Listing |
Am J Emerg Med
January 2025
UniCamillus-Saint Camillus International University of Health Science, Rome, Italy; Department of Internal Medicine, Intermediate Care Unit, Hospital Alto Vicentino (AULSS-7), Santorso, Italy.
Sensors (Basel)
January 2025
Institute of Artificial Intelligence in Sports, Capital University of Physical Education and Sports, Beijing 100191, China.
This study investigates mental fatigue in sports activities by leveraging deep learning techniques, deviating from the conventional use of heart rate variability (HRV) feature analysis found in previous research. The study utilizes a hybrid deep neural network model, which integrates Residual Networks (ResNet) and Bidirectional Long Short-Term Memory (Bi-LSTM) for feature extraction, and a transformer for feature fusion. The model achieves an impressive accuracy of 95.
View Article and Find Full Text PDFChildren (Basel)
December 2024
School of Medicine, University of Crete, 71 003 Heraklion, Crete, Greece.
Background: Screening for cardiovascular disease (CVD) and its associated risk factors in childhood facilitates early detection and timely preventive interventions. However, limited data are available regarding screening tools and their diagnostic yield when applied in unselected pediatric populations.
Aims: To evaluate the performance of a CVD screening program, based on history, 12-lead ECG and phonocardiography, applied in primary school children.
Children (Basel)
December 2024
Department of Cardiovascular Medicine, Mayo Clinic, Jacksonville, FL 32224, USA.
Artificial intelligence (AI) is revolutionizing healthcare by offering innovative solutions for diagnosis, treatment, and patient management. Only recently has the field of pediatric cardiology begun to explore the use of deep learning methods to analyze electrocardiogram (ECG) data, aiming to enhance diagnostic accuracy, expedite workflows, and improve patient outcomes. This review examines the current state of AI-enhanced ECG interpretation in pediatric cardiology applications, drawing insights from adult AI-ECG research given the progress in this field.
View Article and Find Full Text PDFBiomedicines
January 2025
Department of Emergency Medicine, Faculty of Medicine, University of Debrecen, 4032 Debrecen, Hungary.
Background/objectives: Autoimmune inflammation enhances the electrical instability of the atrial myocardium in patients with systemic sclerosis (SSc); thus, atrial arrhythmia risk is increased, which might be predicted by evaluating the P wave interval and dispersion of a 12-lead surface electrocardiogram (ECG).
Methods: We examined 26 SSc patients and 36 healthy controls and measured the P wave interval and P wave dispersion of the 12-lead surface ECG in each patient. Furthermore, echocardiography and 24-h Holter ECG were performed and levels of inflammatory laboratory parameters, including serum progranulin (PGRN), sVCAM-1, sICAM-1, leptin and C-reactive protein (CRP), were determined.
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