Author's summary
Based on a multicenter prospective registry in South Korea, we aimed to identify the etiologies of out-of-hospital cardiac arrests (OHCAs) that were successfully resuscitated and to determine predictors of survival. The leading cause of OHCAs that achieved return of spontaneous circulation was obstructive coronary artery disease. However, sudden unexplained death syndrome was common despite thorough evaluation of cardiac and non-cardiac etiologies,—accounting for 67.5% of the survivors. The presence of ventricular arrhythmia (VA) during admission was a favorable predictor of survival to discharge. Active evaluation and prompt clinical intervention may benefit OHCA survivors, especially those with VA.
Keywords: Out-of-hospital cardiac arrest; Tachycardia, ventricular; Ventricular fibrillation; Sudden unexplained death syndrome
Abstract
Background and Objectives
The outcomes and characteristics of out-of-hospital cardiac arrest (OHCA) vary across geographic regions. The etiologies and prognoses of OHCA in Asian populations remain less established. This study aimed to investigate the etiologies and clinical characteristics of patients successfully resuscitated after OHCA and to identify predictors of survival outcomes.
Methods
Data were extracted from a South Korean multicenter prospective registry of OHCA that included 64 tertiary hospitals from 2015 to 2018 (n=7,577). The primary outcome was in-hospital mortality, and the secondary outcome was a Cerebral Performance Category (CPC) score of grade 1 at discharge.
Results
Of the 7,577 patients, 2,066 achieved return of spontaneous circulation (ROSC) and were hospitalized. A total of 915 (44.2%) presented with ventricular arrhythmia (VA) as their initial rhythm or on admission. The leading cause was obstructive coronary artery disease (n=413; 20.0%). Sudden unexplained death syndrome (SUDS) accounted for 67.5% of survivors and was significantly less common in patients with VA (82.7% vs. 48.3%, p<0.001). VA was an independent predictor of in-hospital mortality (adjusted hazard ratio, 0.774; 95% confidence interval [CI], 0.633–0.946; p=0.012) and the grade-1 CPC score at discharge (odds ratio, 2.822; 95% CI, 1.909–4.172; p<0.001). Other predictors of in-hospital mortality included age, diabetes mellitus, witnessed cardiac arrest, ROSC on arrival, total arrest time, alertness on admission, extracorporeal membrane oxygenation use, targeted temperature management, and coronary reperfusion.
Conclusions
SUDS was common in patients with ROSC after OHCA. VA was independently associated with favorable survival outcomes at discharge. Prompt clinical intervention may improve clinical outcomes in patients with OHCA, particularly those with VA.
Graphical Abstract
INTRODUCTION
Out-of-hospital cardiac arrest (OHCA) is a significant global health burden affecting 55 individuals per 100,000 annually.1),2) Although therapeutic strategies and emergency medical systems have improved over the past few decades, patient survival remains suboptimal. Despite efforts to improve the survival and outcomes of OHCA, most survivors develop neurological deficits, reducing their quality of life.3)
Patients with OHCA often present with shockable rhythms of ventricular arrhythmia (VA), such as ventricular fibrillation and tachycardia. VA is a strong predictor of favorable survival outcomes, with odds ratios (ORs) for survival and discharge of 2.9 and 20.6, respectively.4) Additionally, patients who with VAs have a high likelihood of underlying structural heart disease, which may be reversed by active reperfusion or medications. Acute coronary syndrome is the most common cause of VAs and OHCA, and more than half of the patients with OHCA are assumed to have significant coronary artery lesions.5),6) Therefore, confirming VA and identifying the cause of OHCA are crucial for determining the initial treatment strategy in the resuscitation phase and post-care after the return of spontaneous circulation (ROSC). VA is also important for secondary prevention, including cardioverter-defibrillator implantation.
The incidence and outcomes of OHCA vary across geographical regions and ethnicities, particularly in the Asian region due to vast heterogeneity across systems.7) In addition, community- and system-based intervention trials had limitations of sample size and resources, necessitating a large-scale registry. Therefore, based on a contemporary hospital-based research network of OHCA, this study to examine the etiologies and clinical characteristics of patients with OHCA with ROSC and identify predictors of survival outcomes. Further investigations should focus on VA and underlying cardiovascular conditions.
METHODS
Ethical statement
The protocols of the Korean Cardiac Arrest Research Consortium (KoCARC) registry conformed to the principles of the Declaration of Helsinki (2013) and had been approved by the Institutional Review Board of each center (approval No.: 2015AN0269). Written informed consent was waived by the Institutional Review Board. Detailed protocols and further information regarding data collection for the KoCARC registry have been previously published.
Study population
Data were extracted from the KoCARC registry, a nationwide prospective registry of patients with OHCA in South Korea. The KoCARC registry is a self-funded, voluntary, hospital-based, collaborative research network that aims to provide evidence for OHCA and resuscitation (NCT03222999).8) From 64 tertiary hospitals across Korea, 7,577 patients were enrolled from October 2015 and December 2018.
Patients with OHCA who had been transported to the participating emergency departments via emergency medical services or from other hospitals were included. The exclusion criteria were: 1) terminal disease, 2) hospice or palliative care, 3) pregnancy, 4) documentation of a do-not-resuscitate order, 5) refusal to provide informed consent, and 6) non-cardiac origin of OHCA, such as drowning, hanging, poisoning, trauma, burns, or asphyxia. Of 7,577 patients with OHCA, 2,066 survivors admitted to the same hospital were included in the analysis (Figure 1).
Figure 1. Flowchart of the study.
KoCARC = Korean Cardiac Arrest Research Consortium; ROSC = return of spontaneous circulation; VA = ventricular arrhythmia.
Patient management and data collection
Patients were managed according to contemporary guidelines for cardiac arrest, as reported previously.9) The sequences usually included careful medical and family history-taking, repeated electrocardiography, transthoracic echocardiography, and coronary angiography for cardiological evaluation. Thrombolysis was performed based on contemporary guidelines, in the circumstances of ST-segment elevation myocardial infarction with primary percutaneous coronary intervention not available within 120 minutes of the first medical contact, onset of ischemic symptoms within 24 hours, and no absolute contraindications.10),11) For thrombolysis, fibrinolytic agents were infused or administered by bolus dose, and consequent coronary angiography was conducted either at the same hospital or at the transferred hospital.
Data were collected from the onset of OHCA until discharge from the following research fields: 1) epidemiology and preventive medicine, 2) community resuscitation, 3) emergency medical service resuscitation, 4) hospital resuscitation, 5) hypothermia and post-resuscitation care, 6) critical care resuscitation, and 7) pediatric resuscitation.
Outcome measurement and definition of variables
The primary outcome was in-hospital mortality during index admission. The secondary outcome was a good neurological outcome, defined as a grade-1 cerebral performance category (CPC) score at discharge. VA was defined as either ventricular fibrillation or pulseless, sustained ventricular tachycardia identified by electrocardiography. Depending on the presence of VA at cardiac arrest or during hospitalization, the patients were classified into 2 groups: VA and non-VA. For instance, if a patient presented VA at least once—that was detected by electrocardiography from 1) automated external defibrillator (either conducted by bystander or emergency medical service), 2) emergency department, or 3) during hospitalization—the patient was classified as VA group. The etiology of cardiac arrest was classified based on serial evaluation findings of 12-lead electrocardiography, echocardiography, coronary angiography or computed tomography angiography, laboratory markers, and brain imaging performed during hospitalization. Among the various diseases identified through clinical evaluation, the most probable cause of cardiac arrest was determined to be the etiology of OHCA.
Statistical analysis
Categorical variables are expressed as number (percentage), and continuous variables are expressed as mean ± standard deviation. Student’s t-test, Mann–Whitney U test, χ2 test, or Fisher’s exact test was used to compare variables, as indicated. The Kaplan–Meier analysis and log-rank tests were used to assess time-dependent variables. Logistic and Cox regression analyses were used to identify predictors of primary and secondary outcomes. Variables that were statistically significant in the univariable analysis (p<0.05) or had clinical significance were included in the multivariable analysis. For multivariable analysis, multicollinearity was assessed, and variables with variance inflation factor <10 were included. A backward elimination method was used for variable selection with a p value threshold of 0.05–0.20. To reduce biases related to a specific treatment, landmark analysis was done at 7 days after the day of arrival at emergency department. All tests were 2-tailed, and statistical significance was defined as a p value ≤0.05. All statistical analyses and model development were performed using SPSS version 26 (SPSS, Inc., Chicago, IL, USA) and R version 4.2.3 (R Foundation for Statistical Computing, Vienna, Austria).
RESULTS
Baseline characteristics
A total of 7,577 patients were enrolled from October 2015 to December 2018 (Figure 1), of whom 2,328 achieved ROSC and 2,066 were hospitalized. Finally, the cohort comprised 2,066 patients, including 1,505 (72.8%) men and 561 (27.2%) women, with a mean age of 59.6±17.4 years (Table 1). Witnessed cardiac arrest and ROSC on arrival occurred in 1,562 (75.6%) and 826 (40.0%) patients, respectively. The mean arrest time was 38.3±62.1 minutes, and 159 (7.7%) patients were placed on extracorporeal membrane oxygenation (ECMO). The mean left ventricular ejection fraction (LVEF) at the initial evaluation was 42.5±17.3%. Coronary reperfusion was performed in 644 (31.2%) patients with thrombolysis, percutaneous coronary intervention, or coronary artery bypass grafting. Thrombolysis was performed in 258 patients (12.5%), and consecutive 90 patients (4.4%) received further percutaneous coronary intervention after thrombolysis (Supplementary Table 1). The VA group comprised 915 patients, including 869 (95.0%) patients with VA as the initial rhythm. Among 915 patients, 856 (93.6%) were detected as VA before arriving at emergency department, either by emergency medical service or bystander (Supplementary Table 2). Compared to patients without VA, those with VA were younger and had higher proportions of men, current smokers, current drinkers, patients with OHCA, and patients with dyslipidemia. Further, they had a shorter arrest time, better Glasgow Coma Scale score on admission, lower lactate levels, higher blood pressure after ROSC, higher peak creatine kinase-myoglobin binding (CK-MB) and troponin-I levels, lower LVEF, and more frequent ECMO use.
Table 1. Baseline characteristics.
| Variables | Total (n=2,066) | No VA (n=1,151) | VA (n=915) | p value | ||
|---|---|---|---|---|---|---|
| Age (years) | 59.6±17.4 | 61.9±18.8 | 56.6±15.0 | <0.001 | ||
| Male sex | 1,505 (72.8) | 747 (64.9) | 758 (82.8) | <0.001 | ||
| Body mass index (kg/m2) | 23.2±4.6 | 22.9±4.6 | 23.6±4.6 | 0.038 | ||
| Family history of sudden cardiac death | 791 (38.4) | 443 (38.7) | 348 (38.0) | 0.808 | ||
| Family history of death from coronary artery disease | 828 (40.2) | 451 (39.4) | 377 (41.2) | 0.421 | ||
| Current smoker | 478 (30.9) | 220 (25.9) | 258 (37.0) | <0.001 | ||
| Current drinker | 742 (48.6) | 356 (41.8) | 386 (57.2) | <0.001 | ||
| Hypertension | 852 (41.2) | 502 (43.6) | 350 (38.3) | 0.016 | ||
| Diabetes mellitus | 535 (25.9) | 351 (30.5) | 184 (20.1) | <0.001 | ||
| Dyslipidemia | 122 (6.8) | 53 (5.3) | 69 (8.6) | 0.008 | ||
| Witnessed cardiac arrest | 1,562 (75.6) | 815 (70.8) | 747 (81.6) | <0.001 | ||
| After emergency department arrival | ||||||
| Ventricular arrhythmia at the initial rhythm | 869 (42.1) | 0 (0.0) | 869 (95.0) | <0.001 | ||
| Transferred from another hospital | 266 (12.9) | 135 (11.7) | 131 (14.3) | 0.093 | ||
| ROSC on arrival | 826 (40.0) | 272 (23.6) | 554 (60.5) | <0.001 | ||
| Total arrest time (minutes) | 38.3±62.1 | 42.6±69.0 | 32.8±51.6 | 0.001 | ||
| Total defibrillation count | 1.2±3.0 | 0.3±1.2 | 2.3±4.0 | <0.001 | ||
| Intensive care unit admission | 2,010 (98.0) | 1,128 (98.8) | 882 (97.1) | 0.012 | ||
| GCS score on admission | <0.001 | |||||
| GCS ≥14 | 227 (11.1) | 49 (4.3) | 178 (19.6) | |||
| 9≤ GCS ≤13 | 179 (8.8) | 67 (5.9) | 112 (12.3) | |||
| GCS ≤8 | 1,637 (80.1) | 1,018 (89.9) | 619 (68.1) | |||
| ECMO use | 159 (7.7) | 53 (4.6) | 106 (11.6) | <0.001 | ||
| Initial laboratory markers | ||||||
| Hemoglobin level (g/dL) | 12.6±2.8 | 11.7±2.9 | 13.6±2.4 | <0.001 | ||
| Platelet count (×103/µL) | 202.7±85.5 | 191.8±90.5 | 315.9±77.0 | <0.001 | ||
| Sodium level (mEq/L) | 138.8±6.5 | 138.1±7.5 | 139.5±5.0 | <0.001 | ||
| Potassium level (mEq/L) | 4.6±1.3 | 5.0±1.4 | 4.1±1.0 | <0.001 | ||
| Blood urea nitrogen level (mg/dL) | 24.6±19.8 | 27.9±23.0 | 20.6±14.0 | <0.001 | ||
| Creatinine level (mg/dL) | 1.8±2.0 | 2.0±2.2 | 1.6±1.6 | <0.001 | ||
| Aspartate transaminase level (U/L) | 286.1±750.9 | 375.6±915.4 | 177.1±458.2 | <0.001 | ||
| Alanine transaminase level (U/L) | 190.4±524.0 | 229.4±582.5 | 142.9±438.2 | 0.004 | ||
| Lactate level (mmol/L) | 12.1±14.4 | 13.2±15.4 | 10.8±12.8 | 0.006 | ||
| HbA1c (%) | 6.2±1.4 | 6.3±1.4 | 6.1±1.5 | 0.287 | ||
| High-density lipoprotein level (mg/dL) | 39.6±13.6 | 37.1±13.0 | 42.1±13.8 | 0.003 | ||
| Low-density lipoprotein level (mg/dL) | 94.1±40.3 | 79.4±31.8 | 103.0±42.3 | <0.001 | ||
| Triglyceride level (mg/dL) | 147.3±123.3 | 145.6±143.5 | 149.1±95.6 | 0.813 | ||
| After ROSC | ||||||
| Systolic blood pressure (mmHg) | 120.5±44.3 | 117.7±47.9 | 123.8±39.4 | 0.051 | ||
| Diastolic blood pressure (mmHg) | 73.7±27.3 | 68.9±27.9 | 79.1±25.5 | <0.001 | ||
| Heart rate (bpm) | 101.5±33.2 | 102.3±34.5 | 100.7±31.7 | 0.511 | ||
| Lactate level (mmol/L) | 14.4±23.6 | 15.2±24.2 | 13.3±22.9 | 0.109 | ||
| Peak CK-MB level (ng/mL) | 157.9±1317.7 | 92.0±279.3 | 219.8±1,812.9 | 0.061 | ||
| Peak troponin-I level (ng/mL) | 30.8±121.2 | 18.1±90.7 | 43.2±143.9 | 0.001 | ||
| NT-proBNP level (pg/dL) | 2,953.0±8,803.4 | 4,327.3±11,073.3 | 1,517.0±5,154.6 | <0.001 | ||
| LVEF at the initial examination (%) | 42.5±17.3 | 46.4±16.9 | 38.7±16.9 | 0.001 | ||
| Targeted temperature management | 640 (31.0) | 307 (26.7) | 333 (36.4) | <0.001 | ||
| Treatment | ||||||
| Coronary reperfusion | 644 (31.2) | 199 (17.3) | 445 (48.6) | <0.001 | ||
| Reperfusion within 24 hours | 553 (26.8) | 180 (15.6) | 373 (40.8) | <0.001 | ||
| Thrombolysis | 258 (12.5) | 97 (8.4) | 161 (17.6) | <0.001 | ||
| Percutaneous coronary intervention | 324 (15.7) | 87 (7.6) | 237 (25.9) | <0.001 | ||
| Coronary artery bypass grafting | 37 (1.8) | 6 (0.5) | 31 (3.4) | <0.001 | ||
| Pacemaker | 55 (2.7) | 20 (1.7) | 35 (3.8) | 0.005 | ||
| Implantable cardioverter defibrillator | 73 (3.5) | 5 (0.4) | 68 (7.4) | <0.001 | ||
Values are presented as mean ± standard deviation or number (%).
CK-MB = creatine kinase-myoglobin binding; ECMO = extracorporeal membrane oxygenation; GCS = Glasgow Coma Scale; HbA1c = glycated hemoglobin; LVEF = left ventricular ejection fraction; NT-proBNP = N-terminal pro–B-type natriuretic peptide; ROSC = return of spontaneous circulation; VA = ventricular arrhythmia.
Figure 2 shows the etiology of OHCA. In the total cohort, obstructive coronary artery disease (CAD) was the most common identifiable cause of OHCA (n=413, 20.0%), followed by coronary vasospasm (n=98, 4.7%) and noncardiac etiologies, such as cerebral hemorrhage or infarction (n=74, 3.6%; Figure 2A). Hypertrophic cardiomyopathy and inherited arrhythmia syndromes were relatively uncommon (0.7% and 0.3%, respectively). A total of 1,394 (67.5%) patients were diagnosed with sudden unexplained death syndrome (SUDS). Compared to patients without VA, those with VA had significantly higher proportions of obstructive CAD (34.1% vs. 8.8%, p<0.001) and coronary vasospasm (7.9% vs. 2.3%, p<0.001; Figure 2B and C). The prevalence of SUDS was significantly lower in patients with VA (48.3% vs. 82.7%, p<0.001). Etiology of cardiac arrest were further specified in patients who survived till discharge (n=956; Supplementary Figure 1). Compared to the total cohort, the proportion of SUDS was lower (n=541, 56.6%) in patients who survived till discharge.
Figure 2. Etiologies of cardiac arrest. Etiologies of cardiac arrest in the (A) overall cohort, (B) VA group, and (C) non-VA group.
VA = ventricular arrhythmia.
Primary outcome: in-hospital mortality
Among 2,066 patients, 117 had an unknown primary outcome because of hospital transfer on admission or hospitalization during our investigation. Finally, the primary outcome was collected for 1,950 patients, of whom 994 (51.0%) did not survive until discharge. Compared to the VA group, the non-VA group had significantly higher in-hospital mortality (hazard ratio [HR], 0.369; 95% confidence interval [CI], 0.321–0.425; p<0.001; Figure 3). VA was identified as an independent predictor of in-hospital mortality (adjusted HR, 0.774; 95% CI, 0.633–0.946; p=0.012; Table 2). Other independent predictors included age, diabetes mellitus, witnessed cardiac arrest, ROSC on arrival, total arrest time, alertness on admission, ECMO use, targeted temperature management, and coronary reperfusion.
Figure 3. Kaplan–Meier curves for in-hospital mortality. Comparison of in-hospital mortality between the VA and non-VA groups.
HR = hazard ratio; VA = ventricular arrhythmia.
Table 2. Predictors of in-hospital mortality.
| Variables | Univariable | Multivariable | ||||
|---|---|---|---|---|---|---|
| HR | 95% CI | p value | HR | 95% CI | p value | |
| Age (per year) | 1.020 | 1.016–1.024 | <0.001 | 1.014 | 1.009–1.019 | <0.001 |
| Male sex | 0.744 | 0.651–0.851 | <0.001 | 0.941 | 0.807–1.098 | 0.440 |
| Family history of sudden cardiac death | 0.918 | 0.808–1.043 | 0.187 | 0.715 | 0.463–1.102 | 0.129 |
| Family history of death from ischemic heart disease | 0.966 | 0.851–1.097 | 0.596 | 1.282 | 0.835–1.968 | 0.257 |
| Hypertension | 1.139 | 1.002–1.296 | 0.047 | 0.893 | 0.765–1.043 | 0.152 |
| Diabetes mellitus | 1.514 | 1.322–1.734 | <0.001 | 1.188 | 1.012–1.394 | 0.036 |
| Dyslipidemia | 0.840 | 0.639–1.104 | 0.211 | 0.859 | 0.644–1.146 | 0.301 |
| Witnessed cardiac arrest | 0.766 | 0.666–0.880 | <0.001 | 0.807 | 0.687–0.949 | 0.009 |
| Ventricular arrhythmia | 0.355 | 0.308–0.410 | <0.001 | 0.774 | 0.633–0.946 | 0.012 |
| Transferred from another hospital | 0.719 | 0.588–0.878 | 0.001 | 0.985 | 0.650–1.493 | 0.944 |
| ROSC on arrival | 0.234 | 0.199–0.275 | <0.001 | 0.323 | 0.254–0.411 | <0.001 |
| Total arrest time (per minute) | 1.002 | 1.001–1.002 | <0.001 | 1.002 | 1.001–1.003 | <0.001 |
| Defibrillation once or twice | 0.707 | 0.595–0.848 | <0.001 | 0.852 | 0.670–1.082 | 0.189 |
| Intensive care unit admission | 2.220 | 1.152–4.281 | 0.017 | 0.921 | 0.452–1.874 | 0.820 |
| Alert on admission (GCS score ≥14) | 0.043 | 0.019–0.097 | <0.001 | 0.116 | 0.043–0.315 | <0.001 |
| ECMO use | 1.876 | 1.542–2.283 | <0.001 | 2.102 | 1.653–2.674 | <0.001 |
| Targeted temperature management | 0.662 | 0.576–0.761 | <0.001 | 0.593 | 0.505–0.696 | <0.001 |
| Reperfusion | 0.574 | 0.496–0.666 | <0.001 | 0.784 | 0.642–0.957 | 0.017 |
| Pacemaker | 0.632 | 0.406–0.985 | 0.043 | 1.473 | 0.903–2.403 | 0.121 |
| Implantable cardioverter defibrillator | 0.039 | 0.010–0.157 | <0.001 | 0.000 | - | 0.875 |
| Inherited arrhythmia syndrome | 0.237 | 0.033–1.684 | 0.150 | 0.844 | 0.118–6.049 | 0.866 |
| Obstructive coronary artery disease | 0.571 | 0.480–0.680 | <0.001 | 0.831 | 0.655–1.055 | 0.129 |
| Coronary vasospasm | 0.423 | 0.279–0.609 | <0.001 | 0.702 | 0.435–1.134 | 0.148 |
| Heart failure with reduced LVEF | 0.672 | 0.508–0.889 | 0.005 | 0.921 | 0.666–1.274 | 0.621 |
| Hypertrophic cardiomyopathy | 0.438 | 0.196–0.977 | 0.044 | 0.678 | 0.279–1.644 | 0.390 |
CI = confidence interval; ECMO = extracorporeal membrane oxygenation; GCS = Glasgow Coma Scale; HR = hazard ratio; LVEF = left ventricular ejection fraction; ROSC = return of spontaneous circulation.
In landmark analysis, targeted temperature management and coronary reperfusion were associated with lower in-hospital mortality within 7 days, but the difference in in-hospital mortality diminished after day 7 (Supplementary Figure 2).
Secondary outcome: grade-1 cerebral performance category score at discharge
A total of 1,884 patients were assessed for neurological outcomes at discharge, of whom 550 (29.2%) had a grade-1 CPC score. VA was independently associated with a better CPC score (OR, 2.822; 95% CI, 1.909–4.172; p<0.001; Table 3). Other independent predictors of secondary outcomes were generally consistent with predictors of the primary outcome, including age, diabetes mellitus, ROSC on arrival, defibrillation ≤2 times, total arrest time, alertness on admission, ECMO use, implantable cardioverter defibrillator implantation, obstructive CAD, and coronary vasospasm.
Table 3. Predictors of grade-1 cerebral performance category score.
| Variables | Univariable | Multivariable | ||||
|---|---|---|---|---|---|---|
| OR | 95% CI | p value | OR | 95% CI | p value | |
| Age (per year) | 0.969 | 0.963–0.975 | <0.001 | 0.970 | 0.961–0.979 | <0.001 |
| Male sex | 1.820 | 1.429–2.318 | <0.001 | 0.859 | 0.584–1.265 | 0.442 |
| Family history of sudden cardiac death | 1.250 | 1.014–1.539 | 0.036 | 1.648 | 0.732–3.708 | 0.228 |
| Family history of death from ischemic heart disease | 1.142 | 0.930–1.401 | 0.206 | 0.709 | 0.323–1.554 | 0.390 |
| Hypertension | 0.763 | 0.622–0.936 | 0.009 | 1.215 | 0.83–1.774 | 0.312 |
| Diabetes mellitus | 0.409 | 0.315–0.531 | <0.001 | 0.579 | 0.380–0.882 | 0.011 |
| Dyslipidemia | 1.312 | 0.883–1.952 | 0.179 | 1.190 | 0.613–2.310 | 0.607 |
| Witnessed cardiac arrest | 1.755 | 1.361–2.252 | <0.001 | 1.269 | 0.848–1.899 | 0.246 |
| Ventricular arrhythmia | 8.525 | 6.732–10.795 | <0.001 | 2.822 | 1.909–4.172 | <0.001 |
| Transferred from another hospital | 1.470 | 1.107–1.952 | 0.008 | 1.524 | 0.861–2.697 | 0.148 |
| ROSC on arrival | 11.696 | 9.196–14.876 | <0.001 | 4.850 | 3.318–7.088 | <0.001 |
| Total arrest time (per minute) | 0.959 | 0.952–0.966 | <0.001 | 0.994 | 0.987–0.999 | 0.029 |
| Defibrillation once or twice | 0.723 | 0.552–0.949 | 0.019 | 0.590 | 0.372–0.935 | 0.025 |
| Intensive care unit admission | 0.213 | 0.111–0.412 | <0.001 | 0.700 | 0.198–2.472 | 0.579 |
| Alert on admission (GCS score ≥14) | 31.475 | 19.938–49.686 | <0.001 | 13.018 | 6.742–25.134 | <0.001 |
| ECMO use | 0.236 | 0.125–0.413 | <0.001 | 0.156 | 0.073–0.333 | <0.001 |
| Targeted temperature management | 1.051 | 0.849–1.300 | 0.648 | 1.427 | 1.016–2.005 | 0.040 |
| Reperfusion | 2.984 | 2.424–3.675 | <0.001 | 1.273 | 0.849–1.909 | 0.243 |
| Pacemaker | 1.964 | 1.126–3.426 | 0.017 | 0.452 | 0.170–1.204 | 0.112 |
| Implantable cardioverter defibrillator | 11.979 | 6.507–22.053 | <0.001 | 4.925 | 1.959–12.380 | 0.001 |
| Inherited arrhythmia syndrome | 14.691 | 1.765–122.315 | 0.013 | 11.217 | 0.406–309.783 | 0.153 |
| Obstructive coronary artery disease | 3.138 | 2.501–3.938 | <0.001 | 2.612 | 1.766–3.861 | <0.001 |
| Coronary vasospasm | 4.296 | 2.843–6.490 | <0.001 | 4.279 | 2.176–8.414 | <0.001 |
| Heart failure with reduced LVEF | 1.511 | 1.033–2.210 | 0.034 | 1.007 | 0.562–1.803 | 0.982 |
| Hypertrophic cardiomyopathy | 0.726 | 0.199–2.649 | 0.628 | 0.773 | 0.117–5.103 | 0.789 |
CI = confidence interval; ECMO = extracorporeal membrane oxygenation; GCS = Glasgow Coma Scale; LVEF = left ventricular ejection fraction; OR = odds ratio; ROSC = return of spontaneous circulation.
DISCUSSION
Based on a large-scale prospective registry of OHCA, we investigated the etiologies and cardiovascular risk factors of in-hospital mortality and neurological outcomes at discharge. The key findings of this study warrant discussion. First, a significant proportion of OHCA cases were classified as SUDS, even after a thorough evaluation of the cardiac arrest etiologies. Second, VA was significantly associated with lower in-hospital mortality rates. Finally, favorable outcomes in this population suggest that active evaluation and management in the early period of resuscitation. The KoCARC is a nationwide OHCA registry that reflects the contemporary health burden and outcomes of OHCA in South Korea and includes 64 tertiary hospitals across the country. This study was based on the largest OHCA database in South Korea, capturing recent clinical characteristics and outcomes of OHCA.
Although treatment strategies in various fields of the prehospitalization (emergency medical services and community training for bystander cardiopulmonary resuscitation) and hospitalization (secondary and primary prevention) phases have evolved over the decades, OHCA survival rates from OHCA has not significantly improved in the past 3 decades.4) In patients with OHCA, survival to hospital discharge has been consistently low (6.7–8.4%) across various populations. Our cohort showed an improved proportion of survival to discharge, in which 956 of 7,577 (12.6%) patients survived until discharge. This may be supported by the increased proportion of witnessed cardiac arrests with increased alertness to OHCA in the general population and earlier detection and treatment of CAD in the contemporary era.12),13),14)
Obstructive CAD is the most common cause of OHCA, predisposing 50–70% of patients to OHCA.15),16) In the present study, although obstructive CAD was identified as the major cause of OHCAs in Korea (20.0% in the overall cohort and 34.1% in the VA group), the prevalence was lower than reported. This finding may be attributable to the lower prevalence of CAD in Asian OHCA cases and to the earlier detection and treatment of CAD.
Contrary to the high prevalence of SUDS in our cohort, a previous study utilizing the French OHCA registry revealed that SUDS accounted for 2.4% of cases.16) In addition, inherited arrhythmia syndrome accounted for 0.3% of our cohort, which is significantly lower than that in the French registry (3.2% of all OHCA cases and 5.2% of identifiable cardiac causes). Inherited arrhythmia syndrome predisposes 1–5% of OHCA cases in Western countries, whereas its prevalence is higher in Asian populations (10% in Japan and 13% in Korea).13),17),18),19) Jiménez-Jáimez et al.20) reported that inherited arrhythmia syndrome was the cause of OHCA in half of the patients with SUDS. In addition, a recent report on cardiac autopsies of adolescents with sudden cardiac death in the United Kingdom revealed that 63% had structurally normal hearts, indicating the predominance of sudden arrhythmic death syndrome in young individuals without a structural substrate.21) Accordingly, many patients with SUDS may have an inherited arrhythmia syndrome.22) To elucidate the dynamic electrical changes in inherited arrhythmia syndrome influenced by the autonomic tone, further studies are required to differentiate inherited arrhythmia syndrome from SUDS. Next-generation sequencing has recently emerged as a genetic testing technique and could provide a higher diagnostic yield.23),24) Further studies on next-generation sequencing of patients with unidentified cause of OHCA are warranted to elucidate the causes of SUDS.
Predictors of survival or neurologic outcomes in OHCA have been established in the past decades and are categorized into patient-related, event- or system-related, and treatment-related factors.1),25) Most predictors identified in our study were consistent with previously identified risk factors for mortality: age, arrest time, early ROSC, and shockable rhythm.
Targeted temperature management has been identified as a favorable marker of in-hospital mortality, whereas no independent association was found in landmark analysis after day 7. Targeted temperature management was previously reported to have no benefit in reducing short-term mortality in patients with OHCA.26) The favorable outcomes in patients undergoing targeted temperature management may be explained by the possibility of survivor bias, i.e., choosing patients with a better chance of survival for targeted temperature management and ineligibility for target temperature management of those who died in the early period.
VA was significantly associated with better outcomes at discharge. Patients with VA were younger, had shorter arrest times, and better neurologic outcomes at ROSC. Conversely, they had a lower LVEF; higher levels of CK-MB, troponin, and N-terminal pro–B-type natriuretic peptide; and more frequent ECMO use. However, the overall outcome was significantly better in the VA group, as explained by the underlying cardiovascular conditions in the VA group that could be reversed by rapid reperfusion and medical treatment. Similarly, a previous study utilizing a French registry revealed better survival outcomes in patients with cardiac causes than in those with noncardiac causes.16) In the present study, obstructive CAD and coronary vasospasm were favorable markers for good neurological outcomes at discharge. In other words, CAD or coronary vasospasm may indicate better outcomes, regardless of the presence/absence of VA. Therefore, prompt and active evaluation of patients with VA may reverse the clinical outcomes, and a thorough evaluation of obstructive CAD or vasospasm should be performed regardless of the initial rhythm.
This study has several limitations. First, the KoCARC is an observational registry that contains objective data at the time of cardiac arrest. Accordingly, objective findings of cardiac and noncardiac evaluations were included; however, the direct relationship between cardiovascular conditions and cardiac arrest was unclear. For instance, obstructive CAD on coronary angiography may not have directly caused the arrest. Also, detailed baseline comorbidities or cardiovascular risk factors were not specified. Nonetheless, established cardiovascular risk factors for cardiac arrest—that were not considered in our analysis—should not be undervalued. Second, noncardiac causes of cardiac arrest were not of interest. Although patients were evaluated for noncardiac causes of cardiac arrest that are routinely performed in the emergency department (i.e., blood gas analysis, radiography, laboratory markers, and brain imaging), noncardiac causes were not specified and may have been underestimated. Third, the KoCARC registry focuses on the early phase of arrest, including the prehospitalization (emergency medical service resuscitation) and resuscitation phases at the emergency department. Therefore, nonischemic cardiac causes, such as cardiomyopathy, valvular heart disease, and inherited arrhythmia syndrome, may have been underestimated and are not usually evaluated at the initial phase of cardiac arrest. Also, selection bias may lie in predictors such as targeted temperature management or implantable cardioverter defibrillator implantation—pursuing such clinical intervention may highly be skewed toward patients with favorable outcome, rather than it directly affected clinical outcome. Further landmark analysis revealed that subsequent treatments such as targeted temperature management or coronary reperfusion were not associated with lower in-hospital morality after 7 days. Consequently, treatment-related predictors should be interpreted as an association with outcome, rather than a direct cause-and-effect relationship. Finally, the data were obtained exclusively from the Korean population, and generalization to other ethnicities may be limited.
In the present study, SUDS was common in patients who achieved ROSC after OHCA. VA was strongly associated with lower in-hospital mortality and favorable neurological outcomes at discharge. Additionally, reversible cardiovascular conditions were associated with improved survival. Early clinical intervention for diagnostic evaluation and treatment may improve short-term outcomes in OHCA survivors.
ACKNOWLEDGMENTS
We would like to acknowledge and thank to investigators from all participating hospitals of KoCARC:
Sang Kuk Han, Phil Cho Choi (Kangbuk Samsung Medical Center), Young Hwan Lee, Sang O Park (Konkuk University Medical Center), Jong Seok Lee, Ki Young Jeong (Kyung Hee University Hospital), Sung Hyuk Choi, Young Hoon Yoon (Korea University Guro Hospital), Su Jin Kim, Kap Su Han (Korea University Anam Hospital), Min Seob Sim, Gun Tak Lee (Samsung Medical Center), Youn Jung Kim (Asan Medical Center), Jong Whan Shin, Hui Jai Lee (SMG-SNU Boramae Medical Center), Keun Hong Park, Hahn Bom Kim (Seoul Medical Center), Yoo Seok Park, Arom Choi (Yonsei University Severance Hospital), Tae Young Kong, Hyuna Hwang (Yonsei University Gangnam Severance Hospital, Youngsuk Cho (Hallym University Kangdong Sacred Heart Hospital), Gu Hyun Kang, Yong Soo Jang (Hallym University Kangnam Sacred Heart Hospital), Jae Hoon Oh, Jun Cheol Lee (Hanyang University Seoul Hospital), Sung Wook Park, Wook Tae Yang (Pusan National University Hospital), Hyun Wook Ryu, Jae Yun Ahn (Kyungpook National University Hospital), Hyuk Jun Yang, Jae-hyug Woo (Gachon University Gil Medical Center), Sung Hyun Yun, Chong Sun Kim (Catholic Kwandong University International St. Mary's Hospital), Sun Pyo Kim (Chosun University Hospital), Jin Woong Lee, Wonjoon Jeong (Chungnam National University Hospital), Sung Soo Park, Jae Kwang Lee (Konyang University Hospital), Ryeok Ahn, Wook Jin Choi (Ulsan University Hospital), Bang Shill Rhee (Ajou University Hospital), You Hwan Jo, Sung Min Park (Seoul National University Bundang Hospital), In Byung Kim, Ki Ok Ahn (Myongji Hospital), Se Joong Ahn (Korea University Ansan Hospital), Seung Cheol Lee, Sang Hun Lee, Kyeong Min Lee (Dongguk University Ilsan Hospital), Young Sik Kim (Bundang Jesaeng Hospital), Jin Sik Park, Myung Hee Park (Sejong Hospital), Dai Han Wi (Wonkwang University Sanbon Hospital), Jin Kun Bae, Yong Hee Lee (Cha University Bundang Medical Center), Sang Ook Ha, Won Seok Yang (Hallym University Pyeongchon Sacred Heart Hospital), Ju Ok Park, Hang A Park (Hallym University Dongtan Sacred Heart Hospital), Kyoung Chul Cha, Woo Jin Jung (Wonju Severance Christian Hospital), Taek Geun Ohk, Myoung Cheol Shin (Kangwon National University Hospital), An Mu Eob, Kyung Sook Park (Hallym University Chuncheon Sacred Heart Hospital), Sang Chul Kim, Gwan Jin Park (Chungbuk National University Hospital), Han Joo Choi, Yong Oh Kim (Dankook University Hospital), Tae Oh Jung, Jae Chol Yoon (Chonbuk National University Hospital), Young Tae Park, Ju Taek Lee (Dongguk University Gyeongju Hospital), Jin Hee Jeong, Sang Bong Lee (Gyeongsang National University Hospital), Won Kim, Yi Sang Moon (Cheju Halla General Hospital), Sung Wook Song, Seo Young Ko (Jeju National University Hospital), Joon-myoung Kwon, Eui Hyuk Kang (Mediplex Sejong Hospital), Sang Chan Jin, Tae-kwon Kim (Keimyung University Dongsan Medical Center), Chang Sun Kim, Hyun Goo Shin (Hanyang University Guri Hospital), Dong Sun Choi (Uijeongbu Eulji Medical Center, Eulji University), Chul Min Ha (Hanil General Hospital), Jai Woog Ko, Yun Jeong Hwang (Yongin Severance Christian Hospital)
To steering committee, comprised of following individuals:
Sung Phil Chung (Chair, Yonsei University Gangnam Severance Hospital), Kyung Jun Song (Chair of Steering Committee, SMG-SNU Boramae Medical Center), Sang Hoon Na (Advisory Committee, Seoul National University Hospital), Gyu Chong Cho (Data Safety and Management Board, Hallym University Kangdong Sacred Heart Hospital), Seung Sik Hwang (Security and Monitoring Board, Seoul National University), Sung Oh Hwang (Past Chair, Wonju Severance Christian Hospital), Sang Do Shin (Past executives of Steering Committee, Seoul National University hospital), Hyuk Jun Yang (Past executives of Advisory Committee, Gachon University Gil hospital), Jeong Ho Park (Secretariat, Seoul National University Hospital), Jong Hak Park (Community and Prehospital Committee, Korea University Ansan Hospital), Won Young Kim(Hospital and Post-Resuscitation Committee, Asan Medical Center), Jin Hee Jung (Pediatric Resuscitation Committee, SMG-SNU Boramae Medical Center), Kyoung Chul Cha (Prospective Research Committee, Wonju Severance Christian Hospital), Ji Hoon Kim (Data Science Committee, Yonsei University Severance Hospital)
To member of Secretariat:
Yeongho Choi (Seoul National University Bundang Hospital), Seulki Choi (Seoul National University Hospital), Se Jin Lee (Seoul National University Hospital), Hye Jee Joo (Seoul National University Hospital)
To National Fire Agency for providing prehospital EMS data.
And to Korean Association of Cardiopulmonary Resuscitation (KACPR) for support.
Footnotes
Funding: This work was supported by a Korea University Grant (J-I.C.) and a grant from the Korea University Anam Hospital, Seoul, Republic of Korea (J-I.C.). The funders had no role in data collection, analysis, interpretation, trial design, patient recruitment, or any aspect pertinent to the study.
Conflict of Interest: The authors have no financial conflicts of interest.
Data Sharing Statement: The data generated in this study is available from the corresponding authors upon reasonable request.
- Conceptualization: Jeong JH, Kim SJ, Choi JI.
- Data curation: Jeong JH, Min K, Kim SJ, Roh SY, Han KS, Song J, Lee SW, Kim YH.
- Formal analysis: Jeong JH, Kim SJ, Choi JI.
- Funding acquisition: Choi JI.
- Investigation: Choi JI.
- Project administration: Choi JI.
- Supervision: Choi JI.
- Writing - original draft: Jeong JH, Min K, Kim SJ, Choi JI.
- Writing - review & editing: Jeong JH, Min K, Kim SJ, Roh SY, Han KS, Song J, Lee SW, Kim YH, Choi JI.
SUPPLEMENTARY MATERIALS
Distribution of patients with ventricular arrhythmia
Distribution of patients with VA
Etiologies of cardiac arrest in patients that survived till discharge. Etiologies of cardiac arrest were specified in patients who survived till discharge. (A) Overall cohort, (B) VA group, and (C) non-VA group.
Kaplan–Meier curves for in-hospital mortality: landmark analysis.
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
Distribution of patients with ventricular arrhythmia
Distribution of patients with VA
Etiologies of cardiac arrest in patients that survived till discharge. Etiologies of cardiac arrest were specified in patients who survived till discharge. (A) Overall cohort, (B) VA group, and (C) non-VA group.
Kaplan–Meier curves for in-hospital mortality: landmark analysis.