| Ju Hee Lee | 3 Articles |
Purpose
The purposes of this study were to develop a prediction model for pressure injury using a machine learning algorithm and to integrate it into clinical practice. Methods This was a retrospective study of tertiary hospitals in Seoul, Korea. It analyzed patients in 12 departments where many pressure injuries occurred, including 8 general wards and 4 intensive care units from January 2018 to May 2022. In total, 182 variables were included in the model development. A pressure injury prediction model was developed using the gradient boosting algorithm, logistic regression, and decision tree methods, and it was compared to the Braden scale. Results Among the 1,389,660 general ward cases, there were 451 cases of pressure injuries, and among 139,897 intensive care unit cases, there were 297 cases of pressure injuries. Among the tested prediction models, the gradient boosting algorithm showed the highest predictive performance. The area under the receiver operating characteristic curve of the gradient boosting algorithm's pressure injury prediction model in the general ward and intensive care unit was 0.86 (95% confidence interval, 0.83~0.89) and 0.83 (95% confidence interval, 0.79~0.87), respectively. This model was integrated into the electronic health record system to show each patient's probability for pressure injury occurrence, and the risk factors calculated every hour. Conclusion The prediction model developed using the gradient boosting algorithm exhibited higher performance than the Braden scale. A clinical decision support system that automatically assesses pressure injury risk allows nurses to focus on patients at high risk for pressure injuries without increasing their workload.
PURPOSE
The purpose of this study was to review articles that used the Pittsburgh Sleep Quality Index (PSQI) scale to measure sleep quality among adults with trauma experiences. METHODS Databases such as PubMed, CINAHL, Embase, Cochrane, Medline, Google Scholar, Riss, NDSL, and reference data were searched systematically for relevant studies from July 5 to 6, 2018. A quality assessment was conducted using the COSMIN (COnsensus-based Standards for the selection of health Measurement INstruments) risk of bias checklist, including content validity, structural validity, internal consistency, reliability, measurement error, hypotheses testing, cross-cultural validity, criterion validity, and responsiveness. RESULTS Fourteen studies met the inclusion criteria. All selected studies confirmed the overall quality of sleep by total score, but only 28.6% of the papers presented subscales. The quality of the methodology used varied within and between studies. Internal consistency of 21.5%, cross-cultural validity of 7.1%, and criterion validity of 14.2% were all evaluated as sufficient (+). Reliability and measurement errors were not presented in selected studies. CONCLUSION Our findings revealed that overall quality of sleep was measured by analyzing the characteristics and measurement attributes of the PSQI. However, as a result of the varying purposes of each study, the full potential of the tool could not be optimized. Future research should assess the attributes of the PSQI based on the content specified in the COSMIN evaluation standard, using it carefully to consider the target population's socio-cultural characteristics. Citations Citations to this article as recorded by
PURPOSE
To evaluate undergraduate nursing students' ability in clinical competence, critical thinking, and problem solving following enrollment in a clinical reasoning course. METHODS A clinical reasoning course utilizing a human patient simulator and scenarios was offered to 22 senior students at a College of Nursing in Seoul. Students' clinical competence was measured with a checklist of 15 items by analyzing students' performance recorded on video tapes for eight scenarios. Critical thinking disposition and problem solving were measured by a self-administered questionnaire before and after the course. Data were analyzed using descriptive statistics and Wilcoxon signed-rank test. RESULTS The high scored items of clinical competence were: 'obtain relevant subjective/objective data', 'interpret vital signs', 'communicate with healthcare providers', and 'utilize standard precautions including handwashing.' Students' critical thinking and problem solving scores following the course were increased with statistical significance. CONCLUSION A clinical reasoning course utilizing a human patient simulator creates a realistic clinical environment for nursing students and provides the opportunity to obtain clinical competence, critical thinking, and problem solving skills.
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