PURPOSE: This study aimed to identify types of quality of life (QoL) based on the 5 dimensions of EQ-5D and predict factors affecting types of QoL. METHODS: This study was a secondary analysis using data from the Korean Health Panel Survey-??2012). Participants were 2,071 middle-aged men who had completed the additional survey in 2012 and the data were analyzed using SPSS 20.0 and Mplus 5.21 for latent analysis. RESULTS: Three latent classes of QoL were identified: serious (2.4% of the sample), threatened (15.5%), and stable types (82.0%). The types and characteristics of QoL among the latent classes differed. On comparing latent type 1 with latent type 2, the socioeconomic status (p < .05), employment status (p < .05), and subjective health status (p < .001) were found to be significant. On comparing latent type 1 with latent type 3, the socioeconomic status (p < .05), current smoking status (p < .001), and subjective health status (p < .001) were found to be significant. On comparing latent type 2 with latent type 3, the socioeconomic status (p < .05), subjective health status (p < .001), stress (p < .001) were found to be significant. CONCLUSION The results showed significant heterogeneity in types of QoL and the predictors of QoL by types were different. These findings provide basic information for developing nursing interventions to improve QoL. Specific characteristics depending on the subtypes should be considered during the development of interventions.

PURPOSE: This study aimed to identify types of quality of life (QoL) based on the 5 dimensions of EQ-5D and predict factors affecting types of QoL. METHODS: This study was a secondary analysis using data from the Korean Health Panel Survey-??2012). Participants were 2,071 middle-aged men who had completed the additional survey in 2012 and the data were analyzed using SPSS 20.0 and Mplus 5.21 for latent analysis. RESULTS: Three latent classes of QoL were identified: serious (2.4% of the sample), threatened (15.5%), and stable types (82.0%). The types and characteristics of QoL among the latent classes differed. On comparing latent type 1 with latent type 2, the socioeconomic status (p < .05), employment status (p < .05), and subjective health status (p < .001) were found to be significant. On comparing latent type 1 with latent type 3, the socioeconomic status (p < .05), current smoking status (p < .001), and subjective health status (p < .001) were found to be significant. On comparing latent type 2 with latent type 3, the socioeconomic status (p < .05), subjective health status (p < .001), stress (p < .001) were found to be significant. CONCLUSION: The results showed significant heterogeneity in types of QoL and the predictors of QoL by types were different. These findings provide basic information for developing nursing interventions to improve QoL. Specific characteristics depending on the subtypes should be considered during the development of interventions.

Objective: This study examined the predictive values of presepsin compared with that of other biomarkers (C-reactive protein and procalcitonin) for intensive care unit (ICU) admission and the 28-day mortality in patients with a suspected infection presenting to the emergency department (ED). Methods: This single-center retrospective cohort study was conducted from January 2021 to December 2021 at the ED of a university hospital. The serum presepsin levels were obtained from adult patients suspected of having an infection based on the judgment of clinicians. Results: Of the 1,772 patients analyzed, 389 patients (22.0%) were admitted to the ICU, and 190 patients (10.7%) died within 28 days. The presepsin levels were significantly higher in the ICU admission group (P<0.001) and non-survivor group (P<0.001) than in the other patients. The receiver operating characteristic (ROC) curve for predicting ICU admission indicated an area under the curve (AUC) of 0.660 for presepsin (vs. 0.640 and 0.700 for the C-reactive protein and procalcitonin, respectively). The ROC curve for predicting the 28-day mortality indicated an AUC of 0.719 for presepsin (vs. 0.675 and 0.720 for C-reactive protein and procalcitonin, respectively). Conclusion The initial presepsin level might be useful for predicting ICU admission and 28-day mortality in patients with a suspected infection in the ED.

Objective: This study examined the predictive values of presepsin compared with that of other biomarkers (C-reactive protein and procalcitonin) for intensive care unit (ICU) admission and the 28-day mortality in patients with a suspected infection presenting to the emergency department (ED). Methods: This single-center retrospective cohort study was conducted from January 2021 to December 2021 at the ED of a university hospital. The serum presepsin levels were obtained from adult patients suspected of having an infection based on the judgment of clinicians. Results: Of the 1,772 patients analyzed, 389 patients (22.0%) were admitted to the ICU, and 190 patients (10.7%) died within 28 days. The presepsin levels were significantly higher in the ICU admission group (P<0.001) and non-survivor group (P<0.001) than in the other patients. The receiver operating characteristic (ROC) curve for predicting ICU admission indicated an area under the curve (AUC) of 0.660 for presepsin (vs. 0.640 and 0.700 for the C-reactive protein and procalcitonin, respectively). The ROC curve for predicting the 28-day mortality indicated an AUC of 0.719 for presepsin (vs. 0.675 and 0.720 for C-reactive protein and procalcitonin, respectively). Conclusion The initial presepsin level might be useful for predicting ICU admission and 28-day mortality in patients with a suspected infection in the ED.

Objective: This study examined the predictive values of presepsin compared with that of other biomarkers (C-reactive protein and procalcitonin) for intensive care unit (ICU) admission and the 28-day mortality in patients with a suspected infection presenting to the emergency department (ED). Methods: This single-center retrospective cohort study was conducted from January 2021 to December 2021 at the ED of a university hospital. The serum presepsin levels were obtained from adult patients suspected of having an infection based on the judgment of clinicians. Results: Of the 1,772 patients analyzed, 389 patients (22.0%) were admitted to the ICU, and 190 patients (10.7%) died within 28 days. The presepsin levels were significantly higher in the ICU admission group (P<0.001) and non-survivor group (P<0.001) than in the other patients. The receiver operating characteristic (ROC) curve for predicting ICU admission indicated an area under the curve (AUC) of 0.660 for presepsin (vs. 0.640 and 0.700 for the C-reactive protein and procalcitonin, respectively). The ROC curve for predicting the 28-day mortality indicated an AUC of 0.719 for presepsin (vs. 0.675 and 0.720 for C-reactive protein and procalcitonin, respectively). Conclusion The initial presepsin level might be useful for predicting ICU admission and 28-day mortality in patients with a suspected infection in the ED.

PURPOSE: The use of mechanical compression devices may be considered as an alternative method to treat cardiac arrest. This study aimed to assess the influence of vertical location of cardiac arrest scene to survival rate. Furthermore, it set out to examine the effect of mechanical compression devices on the survival rate. METHODS: This is retrospective, observational study of cardiac arrest patients from Gangdong-gu, Seoul between September 2015 and February 2016. The data were collected by 119 rescue records and cardiac arrest summary list, and the resuscitation result variables were analyzed. We also conducted a survey on 119 paramedics regarding the subjective valuation and drawback of using mechanical compression devices. RESULTS: The odd ratio of return of spontaneous circulation (ROSC) in vertical location of cardiac arrest scene was 0.40 (95% confidence interval, 0.17 to 0.98; p=0.044). The odd ratio of survival to admission comparing manual compression with mechanical compression was 0.73 (95% confidence interval, 0.26 to 1.99; p=0.532). A total of 54 paramedics completed the survey, and 46 (85.2%) of them answered that there was a decrease in the quality of chest compression if the scene was other than the 1st floor. Fifty-three (93.1%) replied that mechanical compression devices can be a counter-measure. CONCLUSION: Vertical location of cardiac arrest scene independently effects ROSC of out of hospital cardiac arrest. However, there was no difference in the survival rate between manual and mechanical compressions.
Allied Health Personnel ; Cardiopulmonary Resuscitation* ; Heart Arrest* ; Humans ; Methods ; Observational Study* ; Out-of-Hospital Cardiac Arrest* ; Resuscitation ; Retrospective Studies* ; Seoul ; Survival Rate ; Thorax

Objective: Bystander cardiopulmonary resuscitation is important for the survival of cardiac arrest patients. The rapid recognition of cardiac arrest by dispatchers leads to quicker initiation of bystander cardiopulmonary resuscitation. This study examines the use of video phone by dispatchers in recognizing cardiac arrest. Methods: Between June to October 2018, 227 participants were recruited (114 lay people and 113 dispatchers) for this study. Lay people participants answered prepared questionnaires after watching 6 scenario video clips of a collapsed virtual patient with unresponsiveness. Dispatchers were divided into two groups: audio call group and video call group. The audio call group judged for occurrence of a cardiac arrest in the 6 scenarios, referring only to questionnaires of the lay people. The video call group judged the same scenarios referring to questionnaires and scenario video clips. The score of correct answers of each scenario was subsequently analyzed. Results: Significant difference was obtained for the total score among the audio call group and the video call group (3.67 ±1.3 vs. 4.66±1.11, respectively; P<0.001). In 2 scenarios concerning cardiac arrest with gasping, the correct answer rate was higher in the video call group (63.9% vs. 85.2%, P<0.001; 59.5% vs. 74.8%, P=0.014). In 2 other scenarios showing non-cardiac arrest with normal breathing, the correct answer rate was also higher in the video call group (52.7% vs. 71.4, P=0.010; 38.2% vs. 63.6%, P<0.001). Conclusion This study determined that compared to the audio call group, dispatchers of the video call group were more accurate in recognizing cardiac arrest. This indicates that video phones are more helpful in determining gasping and in discerning cardiac arrest with syncope or seizure.

Objective: The incidence of bystander cardiopulmonary resuscitation in out-of-hospital cardiac arrest has increased rapidly over the past 10 years. On the other hand, automated external defibrillators (AEDs) are still only used in a minority of cases. This study investigated the feasibility of mobile video call guidance to facilitate AED use for laypeople. Methods: Ninety laypersons were randomized into three groups: mobile video call guided, voice call guided, and nonguided. The participants were exposed to a simulated cardiac arrest requiring AED use and guided by video call, voice call, or not. The simulation experiments were saved as a video clip, and other researchers blinded to simulation assessed the performance according to a prespecified checklist after the simulations. The performance score and analyzed time intervals from AED arrival to defibrillation in the three groups were compared. Results: The basic characteristics were similar in the three groups. Performance scores in the checklist for using AEDs were higher in the mobile video call guided group, particularly in a category of ‘power on AED’ and ‘correctly attaches pads’ than non-guided groups. The performance scores in the category of ‘safely delivers a shock and resume compression’ were also higher in the mobile video call group. On the other hand, the time interval to defibrillation was significantly longer in the mobile video call group. Conclusion This study showed that mobile video call guidance might be an alternative method for laypeople to facilitate AED use, but further well-designed research will be needed.

Objective: The coronavirus disease 2019 (COVID-19) pandemic saw rising cases of out-of-hospital cardiac arrests. In addition, there was an increase in the number of cardiac arrest terminations. This study analyzes the 3-year status (2018 to 2020) of out-of-hospital cardiac arrest resuscitation terminations. Methods: This study is a retrospective observational study based on the diary of 119 first aid activities in the Seoul area. Factors analyzed included patient demographics, history, location of occurrence, witness status, cause of cardiac arrest, electrocardiogram rhythm, reasons for cardiac arrest termination, and hospital transfer. Data obtained from the periods January 1, 2018, to December 31, 2019 (defined as the pre_COVID-19 period) and from January 1, 2020, to December 31, 2020 (defined as the COVID-19 period) were compared and analyzed. Results: There was an increase in the number of terminated resuscitation cases during the study period. Most cases were elderly men with chronic disease who underwent asystole arrest at home. The reasons for the termination of resuscitation were obvious evidence of death, refusal of cardiopulmonary resuscitation (CPR) by the guardian, medical guidance, and cessation of prior life-sustaining treatment. During the COVID-19 pandemic, clear evidence of death and refusal of CPR by caregivers were statistically and significantly increased. Conclusion The results of this study indicate a rise in the number of cases subjected to termination of resuscitation during the COVID-19 period. It is, therefore, necessary to find a way to improve this situation.

Objective: Automated external defibrillators (AEDs) have been installed for use on out-of-hospital cardiac arrest (OHCA) patients in Korea. However, there are very few cases in which the AED was used before the 119 paramedics arrived in cardiac arrest patients. This study investigated whether the locations of the AED in Korea were appropriate. Methods: This study analyzed the locations of OHCA patients, AED installations, and the number of OHCA patients within a 100-m grid around the AEDs in public and residential areas by province. The information on acute cardiac arrest patients was obtained from the original data of the Korea Disease Control and Prevention Agency. The installation of AEDs is registered with the National Emergency Medical Center. Results: The number of OHCA patients in public and residential areas was 28,434 and 95,713, respectively, and the number of installed AEDs in these locations was 15,387 and 11,420, respectively. The number of OHCA patients per AED was 1.8 in public areas and 8.4 in residential areas, and there were significant differences by province (P<0.001). The percentage of OHCA patients within the 100-m grid around the AEDs was 21.9% and 23.1% in public and residential areas, respectively (P<0.001). Conclusion There were significant differences in the AED placement locations in public and residential areas by province. In addition, the percentage of OHCA patients within a 100-m grid around AED was only 22.9%, indicating that the number of AEDs was insufficient. Efforts to enhance the AED placements and monitoring are needed to resolve the regional deviations.