Characteristics and outcomes of public bath-related out-of-hospital cardiac arrests in South Korea

Yoo, Yung Jae; Kim, Gi Woon; Lee, Choung Ah; Park, Yong Jin; Lee, Kyoung Mi; Cho, Jin Seong; Jeong, Won Jung; Choi, Heeseung; Heo, Nam Hun; Moon, Hyung Jun

doi:10.15441/ceem.19.071

Cited by 3 publications

(3 citation statements)

References 17 publications

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“…Trained managers visited the hospitals to review the medical records and complete the database. Additionally, they contacted the patients to verify information about the outcomes [19][20][21]. The Korea CDC approved the use of all data.…”

Section: Data Setsmentioning

confidence: 99%

Developing a Time-Adaptive Prediction Model for Out-of-Hospital Cardiac Arrest: Nationwide Cohort Study in Korea

Woong¹

2021

J Med Internet Res

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Background Out-of-hospital cardiac arrest (OHCA) is a serious public health issue, and predicting the prognosis of OHCA patients can assist clinicians in making decisions about the treatment of patients, use of hospital resources, or termination of resuscitation. Objective This study aimed to develop a time-adaptive conditional prediction model (TACOM) to predict clinical outcomes every minute. Methods We performed a retrospective observational study using data from the Korea OHCA Registry in South Korea. In this study, we excluded patients with trauma, those who experienced return of spontaneous circulation before arriving in the emergency department (ED), and those who did not receive cardiopulmonary resuscitation (CPR) in the ED. We selected patients who received CPR in the ED. To develop the time-adaptive prediction model, we organized the training data set as ongoing CPR patients by the minute. A total of 49,669 patients were divided into 39,602 subjects for training and 10,067 subjects for validation. We compared random forest, LightGBM, and artificial neural networks as the prediction model methods. Model performance was quantified using the prediction probability of the model, area under the receiver operating characteristic curve (AUROC), and area under the precision recall curve. Results Among the three algorithms, LightGBM showed the best performance. From 0 to 30 min, the AUROC of the TACOM for predicting good neurological outcomes ranged from 0.910 (95% CI 0.910-0.911) to 0.869 (95% CI 0.865-0.871), whereas that for survival to hospital discharge ranged from 0.800 (95% CI 0.797-0.800) to 0.734 (95% CI 0.736-0.740). The prediction probability of the TACOM showed similar flow with cohort data based on a comparison with the conventional model’s prediction probability. Conclusions The TACOM predicted the clinical outcome of OHCA patients per minute. This model for predicting patient outcomes by the minute can assist clinicians in making rational decisions for OHCA patients.

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Section: Data Setsmentioning

confidence: 99%

Developing a Time-Adaptive Prediction Model for Out-of-Hospital Cardiac Arrest: Nationwide Cohort Study in Korea

Woong¹

2021

J Med Internet Res

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“…[4][5][6] Many previous studies assessed survival rates or neurologic prognoses of patients with OHCA using tools such as the Cerebral Performance Category scale. [7][8][9] However, according to the European Resuscitation Council and European Society of Intensive Care Medicine guidelines, it is also important to perform functional assessments of nonphysical impairments and screen for cognitive and emotional problems in the long term in patients with OHCA. 10 In 2020, a sixth link in the chain of survival highlighting recovery was added to the American Heart Association guideline to emphasize the importance of recovery and survivorship in resuscitation outcomes.…”

Section: Introductionmentioning

confidence: 99%

“…Many previous studies assessed survival rates or neurologic prognoses of patients with OHCA using tools such as the Cerebral Performance Category scale . However, according to the European Resuscitation Council and European Society of Intensive Care Medicine guidelines, it is also important to perform functional assessments of nonphysical impairments and screen for cognitive and emotional problems in the long term in patients with OHCA .…”

Section: Introductionmentioning

confidence: 99%

Analysis of Anxiety or Depression and Long-term Mortality Among Survivors of Out-of-Hospital Cardiac Arrest

Lee

Cho

et al. 2023

JAMA Netw Open

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ImportanceThe recent American Heart Association guidelines added a sixth link in the chain of survival highlighting recovery and emphasized the importance of psychiatric outcome and recovery for survivors of out-of-hospital cardiac arrest (OHCA). The prevalence of psychiatric disorders among this population was higher than that in the general population.ObjectiveTo examine the prevalence of depression or anxiety and the association of these conditions with long-term mortality among individuals who survive OHCA.Design, Setting, and ParticipantsA longitudinal population-based cohort study was conducted to analyze long-term prognosis in patients hospitalized for OHCA between January 1, 2005, and December 31, 2015, who survived for 1 year or longer. Patients with cardiac arrest due to traumatic or nonmedical causes, such as injuries, poisoning, asphyxiation, burns, or anaphylaxis, were excluded. Data were extracted on depression or anxiety diagnoses in this population within 1 year from the database of the Korean National Health Insurance Service and analyzed April 7, 2022, and reanalyzed January 19 to 20, 2023.Main Outcomes and MeasuresFollow-up data were obtained for up to 14 years, and the primary outcome was long-term cumulative mortality. Long-term mortality among patients with and without a diagnosis of depression or anxiety were evaluated.ResultsThe analysis included 2373 patients; 1860 (78.4%) were male, and the median age was 53.0 (IQR, 44.0-62.0) years . A total of 397 (16.7%) patients were diagnosed with depression or anxiety, 251 (10.6%) were diagnosed with depression, and 227 (9.6%) were diagnosed with anxiety. The incidence of long-term mortality was significantly higher in the group diagnosed with depression or anxiety than in the group without depression or anxiety (141 of 397 [35.5%] vs 534 of 1976 [27.0%]; P = .001). With multivariate Cox proportional hazards regression analysis, the adjusted hazard ratio of long-term mortality for total patients with depression or anxiety was 1.41 (95% CI, 1.17-1.70); depression, 1.44 (95% CI, 1.16-1.79); and anxiety, 1.20 (95% CI, 0.94-1.53).Conclusions and RelevanceIn this study, among the patients who experienced OHCA, those diagnosed with depression or anxiety had higher long-term mortality rates than those without depression or anxiety. These findings suggest that psychological and neurologic rehabilitation intervention for survivors of OHCA may be needed to improve long-term survival.

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Impact of crowding in local ambulance demand on call-to-ambulance scene arrival in out-of-hospital cardiac arrest

Kim

Shin

et al. 2022

The American Journal of Emergency Medicine

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Characteristics and outcomes of public bath-related out-of-hospital cardiac arrests in South Korea

Cited by 3 publications

References 17 publications

Developing a Time-Adaptive Prediction Model for Out-of-Hospital Cardiac Arrest: Nationwide Cohort Study in Korea

Developing a Time-Adaptive Prediction Model for Out-of-Hospital Cardiac Arrest: Nationwide Cohort Study in Korea

Analysis of Anxiety or Depression and Long-term Mortality Among Survivors of Out-of-Hospital Cardiac Arrest

Impact of crowding in local ambulance demand on call-to-ambulance scene arrival in out-of-hospital cardiac arrest

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