PURPOSE: The purpose of this descriptive study was to investigate the pattern of physical restraints used in ICUs and to identify influencing factors of application and removal of restraints. METHOD: The subjects of this study were 90 restrained patients out of 215 patients over 6 years old who were admitted to 6 ICUs in SMC during a 2 weeks period. The data was collected through a questionnaire of characte-ristics, guidelines and nursing care of restraint uses. The data were analyzed by non-parametric statistic with the use of the SAS program. RESULTS: The restraints were applied to 31.4% of subjects. Mean time of physical restraint was 36.76 55.7 hours. There were significant difference with mean time and frequency according to duty shift. GCS, restless behavior and discomfort factors, medical devices, and life sustaining devices had significant relation with application of restraints. In addition, the mean time of restraints used were related significantly with GCS, restless behavior, and discomfort factors. CONCLUSION: The used of restraints were dependent on mainly the nurses' decision. Thus ICU nurses have to develop the guidelines to applying restraints and removal of restraints in regard to patients rights and ethics. Continuous monitoring and evaluation of application of the restraints is essential in professional nursing.
Child ; Ethics ; Humans ; Intensive Care Units ; Nursing ; Nursing Care ; Patient Rights ; Restraint, Physical* ; Surveys and Questionnaires

OBJECTIVES: Electrocardiogram (ECG) data are important for the study of cardiovascular disease and adverse drug reactions. Although the development of analytical techniques such as machine learning has improved our ability to extract useful information from ECGs, there is a lack of easily available ECG data for research purposes. We previously published an article on a database of ECG parameters and related clinical data (ECG-ViEW), which we have now updated with additional 12-lead waveform information. METHODS: All ECGs stored in portable document format (PDF) were collected from a tertiary teaching hospital in Korea over a 23-year study period. We developed software which can extract all ECG parameters and waveform information from the ECG reports in PDF format and stored it in a database (meta data) and a text file (raw waveform). RESULTS: Our database includes all parameters (ventricular rate, PR interval, QRS duration, QT/QTc interval, P-R-T axes, and interpretations) and 12-lead waveforms (for leads I, II, III, aVR, aVL, aVF, V1, V2, V3, V4, V5, and V6) from 1,039,550 ECGs (from 447,445 patients). Demographics, drug exposure data, diagnosis history, and laboratory test results (serum calcium, magnesium, and potassium levels) were also extracted from electronic medical records and linked to the ECG information. CONCLUSIONS: Electrocardiogram information that includes 12 lead waveforms was extracted and transformed into a form that can be analyzed. The description and programming codes in this case report could be a reference for other researchers to build ECG databases using their own local ECG repository.
Calcium ; Cardiovascular Diseases ; Demography ; Diagnosis ; Drug-Related Side Effects and Adverse Reactions ; Electrocardiography* ; Electronic Health Records ; Hospitals, Teaching ; Korea ; Machine Learning ; Magnesium ; Potassium