INTRODUCTION
Stroke is a leading cause of mortality and disability worldwide, ranking second among causes of death (11.6%) and third in global disease burden, as measured by disability-adjusted life years, according to 2019 global estimates [
1]. Concerns have been raised that the burden of stroke will continue to increase due to population aging, accumulation of risk factors, and gaps in healthcare access. Moreover, recent forecasting studies have projected an increasing trend in the global age-standardized incidence rate of ischemic stroke by 2030 [
2]. These changes represent a major healthcare challenge that extends beyond a simple increase in patient volume and necessitate restructuring of the entire emergency medical system (EMS), including optimization of the 119 EMS, mitigation of emergency department (ED) overcrowding, and reduction of regional disparities in treatment capacity [
3–
5].
The cornerstone of acute stroke care is time-dependent therapy. Intravenous thrombolysis is highly time-sensitive, and prompt administration after symptom onset is a key determinant of clinical outcomes [
6,
7]. Although the therapeutic time window for endovascular treatment (mechanical thrombectomy) in patients with large vessel occlusion has been expanded [
8,
9], substantial regional and socioeconomic disparities in access to treatment persist in clinical practice [
10,
11]. Key contributors to this gap include inadequate symptom recognition and delayed decision-making by patients and caregivers, failure to activate EMS, the absence of effective prehospital triage and transport strategies, and time delays during direct transport or interhospital transfer to treatment-capable centers [
7,
12,
13]. These patterns demonstrate that advances in individual therapeutic techniques alone are insufficient to ensure improved outcomes; instead, a system-based approach that integrates the prehospital phase with in-hospital care and systematically monitors key performance metrics (e.g., door-to-imaging time, door-to-treatment time, and interhospital transfer delays) is essential [
7,
13].
In Korea, stroke imposes a substantial disease burden, and the Stroke Statistics in Korea reports have systematically summarized epidemiological indicators, including prevalence, incidence, and risk factors, as well as the current status of prehospital transport and in-hospital stroke care [
4]. Simultaneously, Korea’s 119-based prehospital EMS faces ongoing challenges related to the increasing number of older patients with multimorbidity and the need to establish quality measurement and feedback systems grounded in real-world clinical data [
5]. In addition, nationwide disparities in ED crowding and imbalances in healthcare resources have been identified as major structural factors that may undermine timely and safe care for critically ill emergency patients [
3]. The COVID-19 pandemic, which started in 2020, affected ED care through delays in patient presentation due to infection concerns, reduced bed availability, and changes in ED operations for screening and isolation [
14]. Therefore, a dedicated reassessment of nationwide ED-based stroke care during this period is warranted.
In this context, the National Emergency Department Information System (NEDIS), a nationwide database in Korea that aggregates ED clinical data, provides an important source of evidence reflecting real-world ED care for acute stroke. Accordingly, this study aimed to analyze ED-based epidemiological characteristics, hospital access pathways, modes of transport, hospitalization rates, mortality, and care trajectories by hospital type among patients with acute ischemic and hemorrhagic stroke in Korea using NEDIS data from 2020 to 2024. Through this analysis, we aimed to provide evidence to inform improvements in emergency stroke care systems, optimize resource allocation, and support the design of patient-centered care pathways.
METHODS
Ethics statement
This study was approved by the Institutional Review Board of the National Medical Center (No. NMC-2023-08-094). The requirement for informed consent was waived due to the use of deidentified data and the retrospective nature of the study.
Data sources and study population
This retrospective cross-sectional study was based on patient data registered in the NEDIS between January 1, 2020, and December 31, 2024. NEDIS is a nationwide, ED-based surveillance system operated by the National Emergency Medical Center that collects and aggregates standardized clinical information from emergency medical institutions across Korea in real time [
14]. The database includes demographic information (e.g., sex, age, and insurance type), prehospital variables (mode of transport and EMS use), initial ED assessment (level of consciousness and need for intensive care), and clinical outcomes (hospitalization status, length of hospital stay, and in-hospital mortality). We analyzed data from 2020 to 2024 to describe the clinical characteristics and outcomes of patients presenting to EDs with acute stroke at a national level. Stroke cases were identified using the International Statistical Classification of Diseases and Related Health Problems, 10th Revision (ICD-10) diagnostic codes recorded in the ED. Acute ischemic stroke was defined as ICD-10 code I63.x, whereas hemorrhagic stroke was defined as codes I60–I62.x. Transient ischemic attack (G45.x) and traumatic intracranial hemorrhage were excluded.
RESULTS
Characteristics of acute stroke in the ED
A total of 340,324 ED visits for acute ischemic stroke and 125,874 visits for acute hemorrhagic stroke were identified (
Tables 1,
2). The mean age was 70.5 years for ischemic stroke and 64.5 years for hemorrhagic stroke. Among older adults (≥65 years), the mean age was approximately 77–78 years in both groups, and this age group accounted for 68.8% of ischemic stroke cases (234,175 of 340,324) and 52.0% of hemorrhagic stroke cases (65,484 of 125,874). These findings indicate that ED-based stroke care is predominantly centered on older and clinically vulnerable patients. Accordingly, the allocation of ED, inpatient bed, and intensive care resources should be designed with older patients with stroke as a central consideration.
Among patients with acute ischemic stroke, the median time from symptom onset to ED arrival was 2.8 hours (interquartile range [IQR], 1.0–7.9 hours). This finding indicates that although a substantial proportion of patients arrived within the therapeutic time window, prehospital delay remained considerable and heterogeneous, as reflected by the wide IQR. Because prehospital delay is a major barrier to treatment access in ischemic stroke, improvement in this phase requires enhanced public education on symptom recognition, increased use of the 119 ambulance service (51.7% in the present study), and stronger prehospital strategies, including prenotification and severity-based destination selection (e.g., direct transport to thrombectomy-capable centers) [
7,
12,
13].
Given the importance of the sequence from symptom recognition to EMS activation and transport to treatment-capable centers in the management of acute ischemic stroke [
5,
7,
12,
13], these findings suggest that this process may not yet be functioning optimally. Factors contributing to prehospital delay have repeatedly been identified as inadequate symptom recognition, nonuse of EMS, inefficiencies in hospital-EMS coordination, and socioeconomic factors [
11–
13]. In Korea, structural and quality-management issues within the 119 system have also been consistently highlighted as areas requiring ongoing improvement [
5]. Therefore, to improve the timely delivery of reperfusion therapy, it is essential to strengthen symptom-recognition education for the general public and high-risk populations, establish prehospital stroke screening and direct-transport protocols to appropriate treatment-capable centers, and operate regional treatment networks that minimize decision-to-transport time during interhospital transfer [
5,
13]. Among patients with ischemic stroke, the median ED length of stay was 3.8 hours (IQR, 2.4–6.4 hours), with an admission rate of 82.3% and an intensive care unit (ICU) admission rate of 27.6%. Although these indicators indirectly reflect linkage to definitive treatment, more precise evaluation of clinical performance requires systematic monitoring of key performance indicators, including total time from symptom onset to treatment, door-to-needle time, door-to-groin puncture time, and reperfusion therapy rates [
7,
13].
Hemorrhagic stroke carries a high risk of rapid clinical deterioration during the early hours after onset because of hematoma expansion and increased intracranial pressure, and timely interventions such as blood pressure control, reversal of anticoagulant effects, neurosurgical intervention, and intensive care management are directly associated with patient outcomes [
15]. Among patients with hemorrhagic stroke, the median time from symptom onset to ED arrival was 1.5 hours (IQR, 0.8–4.1 hours), which was shorter than that observed for ischemic stroke, and the utilization rate of the 119 ambulance service was also higher at 61.2%. This finding supports the interpretation that the more dramatic presentation of hemorrhagic stroke, including decreased consciousness, severe headache, and vomiting, is more likely to prompt rapid requests for emergency medical care [
15]. In addition, a relatively high proportion of patients with hemorrhagic stroke arrived via transfer from other hospitals (22.9%), suggesting that transfer for appropriate care frequently occurs because of regional disparities in neurosurgical capacity and ICU resources even after initial diagnosis [
3,
15]. In terms of final disposition, 83.3% of patients with hemorrhagic stroke were admitted, and 66.7% were admitted to the ICU, indicating a high level of clinical severity. However, the concurrently high rates of transfer (38.0%), ED death (1.5%), and in-hospital mortality (16.7%) suggest that capacity constraints in specific regions or periods, particularly the availability of neuro-ICUs and the emergency neurosurgical workforce, may act as bottlenecks within the care system. Such disparities in healthcare resources and ED crowding are major structural factors that negatively affect outcomes in critically ill emergency patients and warrant reevaluation, particularly in the context of system-wide changes during the pandemic period [
3,
14]. In hemorrhagic stroke, time represents not merely a treatment window but a golden time for intervention before deterioration, including blood pressure management and surgical decision-making [
15]. Therefore, policy-level efforts should strengthen systematic management by incorporating indicators such as interhospital transfer time, transfer failure rates, and ICU waiting time.
Fig. 1 illustrates annual trends in age- and sex-standardized ED visit rates per 100,000 population for ischemic and hemorrhagic stroke from 2020 to 2024. ED visit rates for ischemic stroke remained relatively stable over time, with no clear monotonic increase or decrease, and trend analysis showed no statistically significant change (P for trend=0.429). Similarly, no statistically significant trend was observed for hemorrhagic stroke (P for trend=0.066), although a modest declining tendency was noted in certain subgroups.
This apparent plateau in visit rates may be interpreted in two ways. First, despite increasing incidence pressure driven by population aging and the accumulation of risk factors, national-level prevention strategies and improved management of major risk factors such as hypertension and diabetes may have partially offset any increase in stroke incidence [
1,
2,
4]. Second, because this indicator is based on ED visit data, changes in healthcare-seeking behavior during the COVID-19 pandemic, including delayed presentation due to infection concerns, redistribution of ED utilization, and limited bed availability, may have created a discrepancy between true incidence trends and observed ED visit trends [
14]. In particular, because recent projection studies have forecast an increase in the global age-standardized incidence of ischemic stroke through 2030 [
2], further investigation is needed to determine whether the observed plateau in ED visit rates in Korea reflects true stabilization of incidence or instead results from structural changes in the prehospital phase, including 119 ambulance use and interhospital transport and transfer systems [
5,
13].
Fig. 2 shows annual changes in age- and sex-standardized mortality rates per 100,000 population. No significant trends were observed for either ischemic stroke (P for trend=0.133) or hemorrhagic stroke (P for trend=0.610). These findings reflect a complex clinical reality shaped by two concurrent forces: improvements in outcomes driven by the dissemination of advanced treatment technologies and structural bottlenecks arising from population aging and strain on the EMS.
For ischemic stroke, the benefit of early intravenous thrombolysis has been well established [
6–
9]. In addition, because the therapeutic time window for endovascular treatment in patients with large vessel occlusion has been extended to up to 24 hours [
9], more effective coordination between prehospital transport systems and in-hospital stroke care pathways may contribute not only to reduced mortality but also to improved long-term functional outcomes [
7,
13]. In contrast, ED crowding and regional disparities in healthcare resources can lead to refusal or inability to accept critically ill patients in the ED, delays in timely hospitalization, and bottlenecks in emergency procedures and ICU access, thereby limiting treatment outcomes [
3]. The absence of a consistent increase in standardized mortality rates observed in this study suggests that the effects of partial adaptation and recovery of the EMS after the initial disruption of the early pandemic phase may be reflected in these outcomes [
14]. However, standardized mortality rates alone have limitations in comprehensively evaluating the quality of stroke care because they do not capture functional recovery, as measured by the modified Rankin Scale, or treatment timeliness, such as door-to-needle and door-to-groin puncture time [
7,
13]. Therefore, future efforts should adopt a systematic approach that monitors mortality indicators together with detailed treatment-process and functional outcome measures.
Fig. 3 presents annual changes in in-hospital mortality rates. During the study period, in-hospital mortality showed no significant trend for ischemic stroke (P for trend=0.435). For hemorrhagic stroke, no statistically significant increase was observed in the overall population, although an increasing trend was noted (P for trend=0.056). In contrast, a significant increasing trend was identified in the older adult subgroup (P for trend=0.030). In the context of relatively stable ED visit rates (
Fig. 1) and age- and sex-standardized mortality rates (
Fig. 2), the observed increase in in-hospital mortality for hemorrhagic stroke suggests that factors other than changes in patient volume, such as limitations in in-hospital treatment capacity or system-level bottlenecks, particularly those related to ICU and neurosurgical resource availability, may have influenced clinical outcomes.
DISCUSSION
Hemorrhagic stroke is characterized by a high risk of rapid hematoma expansion and increased intracranial pressure in the early phase, making prompt blood pressure control, reversal of anticoagulation, timely neurosurgical intervention, and access to intensive care critical determinants of survival [
15]. Accordingly, when constraints such as limited ICU bed availability, shortages in the neurosurgical workforce, prolonged waits for emergency surgery, and transfer delays accumulate, they may contribute to increased in-hospital mortality [
3,
15]. In addition, the potential adverse effects of variability in bed availability and pandemic-related operational changes, including ED screening, triage, and isolation measures, on in-hospital care pathways for critically ill emergency patients should be considered [
14].
In conclusion, the golden time in hemorrhagic stroke care should encompass not only the time to hospital arrival but also the interval from arrival to appropriate treatment decision-making and resource allocation [
15]. If deterioration in this composite time metric is observed, integrated healthcare strategies should be considered simultaneously, including regional optimization of neuro-ICU resources, strengthening of interhospital transfer networks, direct transport to centers with advanced neurosurgical and neuro-intensive care capabilities when necessary, and system-level interventions to alleviate ED crowding [
3,
5,
13,
14].
NOTES
-
Author contributions
Conceptualization: Sangsoo Han; Data curation: EK, So-hyun Han, HJK, HC; Formal analysis: EK, HJK, Sangsoo Han; Investigation: SN, EK, So-hyun Han, HJK, HC; Methodology: EK, Sangsoo Han; Visualization: Sangsoo Han; Writing–original draft: SN, Sangsoo Han; Writing–review & editing: all authors.
-
Conflicts of interest
The authors have no conflicts of interest to declare.
-
Funding
The authors received no financial support for this study.
-
Data availability
Data analyzed in this study were obtained from the National Emergency Medical Center (NEMC) under the Korean Ministry of Health and Welfare, and were used under license for the current study. Although the data are not publicly accessible, they are available from the corresponding author upon reasonable request with permission from the NEMC.
Fig. 1.Age- and sex-standardized emergency department (ED) visit rates per 100,000 population. (A) Acute ischemic stroke. (B) Acute hemorrhagic stroke. *P<0.05 (statistically significant difference between 2023 and 2024).
Fig. 2.Age- and sex-standardized mortality rates per 100,000 population. (A) Acute ischemic stroke. (B) Acute hemorrhagic stroke. *P<0.05 (statistically significant difference between 2023 and 2024).
Fig. 3.In-hospital mortality. (A) Acute ischemic stroke. (B) Acute hemorrhagic stroke. *P<0.05 (statistically significant difference between 2023 and 2024).
Table 1.Demographics, ED visit characteristics, and outcomes of patients with acute ischemic stroke
Table 1.
|
Variable |
Total (n=340,324) |
Pediatric group (<18 yr) (n=688) |
Adult group (18–64 yr) (n=105,459) |
Older adult group (≥65 yr) (n=234,175) |
|
Age (yr) |
70.5±13.9 |
9.6±5.8 |
54.2±8.9 |
78.0±7.6 |
|
Sex |
|
|
|
|
|
Male |
193,619 (56.9) |
366 (53.2) |
73,345 (69.5) |
119,907 (51.2) |
|
Female |
146,705 (43.1) |
322 (46.8) |
32,114 (30.5) |
114,268 (48.8) |
|
Time from symptom onset to ED arrival (hr) |
2.8 (1.0–7.9) |
2.5 (1.0–7.7) |
2.7 (1.0–7.9) |
2.9 (1.1–7.9) |
|
Type of ED |
|
|
|
|
|
Level I |
138,718 (40.8) |
379 (55.1) |
43,398 (41.2) |
94,940 (40.5) |
|
Level II |
201,606 (59.2) |
309 (44.9) |
62,061 (58.8) |
139,235 (59.5) |
|
Level III |
0 (0) |
0 (0) |
0 (0) |
0 (0) |
|
Route of arrival |
|
|
|
|
|
Direct visit |
276,073 (81.1) |
539 (78.3) |
87,032 (82.5) |
188,500 (80.5) |
|
Transfer from other hospital |
56,920 (16.7) |
132 (19.2) |
16,087 (15.3) |
40,701 (17.4) |
|
Referral from outpatient clinic |
7,263 (2.1) |
17 (2.5) |
2,330 (2.2) |
4,916 (2.1) |
|
Other |
63 (0.0) |
0 (0) |
9 (0.0) |
54 (0.0) |
|
Unknown |
5 (0.0) |
0 (0) |
1 (0.0) |
4 (0.0) |
|
Transport |
|
|
|
|
|
119 Ambulance |
175,845 (51.7) |
224 (32.6) |
47,607 (45.1) |
128,012 (54.7) |
|
Other medical institution ambulance |
7,209 (2.1) |
18 (2.6) |
1,723 (1.6) |
5,468 (2.3) |
|
Other ambulance |
30,364 (8.9) |
49 (7.1) |
7,355 (7.0) |
22,960 (9.8) |
|
Police or official transport |
232 (0.1) |
0 (0) |
152 (0.1) |
80 (0) |
|
Air transport |
552 (0.2) |
0 (0) |
135 (0.1) |
417 (0.2) |
|
Other transport |
123,511 (36.3) |
380 (55.2) |
47,535 (45.1) |
75,596 (32.3) |
|
Walk-in |
2,092 (0.6) |
15 (2.2) |
813 (0.8) |
1,264 (0.5) |
|
Other/unknown |
519 (0.2) |
2 (0.3) |
139 (0.1) |
378 (0.2) |
|
Length of stay (hr) |
|
|
|
|
|
Mean±SD |
5.8±6.8 |
5.8±5.1 |
5.4±6.4 |
6.0±7.0 |
|
Median (IQR) |
3.8 (2.4–6.4) |
4.3 (2.5–7.6) |
3.6 (2.3–5.9) |
3.9 (2.5–6.6) |
|
0–6 |
246,734 (72.5) |
454 (66.0) |
79,450 (75.3) |
166,829 (71.2) |
|
6–12 |
60,946 (17.9) |
163 (23.7) |
17,197 (16.3) |
43,585 (18.6) |
|
12–24 |
24,540 (7.2) |
62 (9.0) |
6,861 (6.5) |
17,617 (7.5) |
|
≥24 |
8,100 (2.4) |
9 (1.3) |
1,949 (1.8) |
6,142 (2.6) |
|
Unknown |
4 (0.0) |
0 (0) |
2 (0.0) |
2 (0.0) |
|
ED disposition |
|
|
|
|
|
Discharge |
47,047 (13.8) |
174 (25.3) |
17,916 (17.0) |
28,957 (12.4) |
|
Admissiona)
|
280,065 (82.3) |
485 (70.5) |
83,851 (79.5) |
195,727 (83.6) |
|
General ward |
182,399 (53.6) |
338 (49.1) |
56,120 (53.2) |
125,940 (53.8) |
|
Intensive care unit |
94,057 (27.6) |
146 (21.2) |
26,729 (25.3) |
67,181 (28.7) |
|
Transfer |
12,067 (3.5) |
29 (4.2) |
3,478 (3.3) |
8,560 (3.7) |
|
Comfort care discharge |
87 (0.0) |
0 (0) |
8 (0.0) |
79 (0.0) |
|
Death |
827 (0.2) |
0 (0) |
109 (0.1) |
718 (0.3) |
|
Other/unknown |
231 (0.1) |
0 (0) |
97 (0.1) |
134 (0.1) |
|
Hospital disposition |
|
|
|
|
|
Discharge |
240,394 (70.6) |
584 (84.9) |
82,551 (78.3) |
157,258 (67.2) |
|
Transfer |
78,085 (22.9) |
94 (13.7) |
19,177 (18.2) |
58,814 (25.1) |
|
Comfort care discharge |
286 (0.1) |
0 (0) |
39 (0.0) |
247 (0.1) |
|
Death |
18,469 (5.4) |
10 (1.5) |
2,792 (2.6) |
15,666 (6.7) |
|
Other/unknown |
3,090 (0.9) |
0 (0) |
900 (0.9) |
2,190 (0.9) |
Table 2.Demographics, ED visit characteristics, and outcomes of patients with acute hemorrhagic stroke
Table 2.
|
Variable |
Total (n=125,874) |
Pediatric group (<18 yr) (n=1,249) |
Adult group (18–64 yr) (n=59,140) |
Older adult group (≥65 yr) (n=65,484) |
|
Age (yr) |
64.5±16.2 |
8.5±5.9 |
51.9±9.7 |
77.0±7.6 |
|
Sex |
|
|
|
|
|
Male |
65,130 (51.7) |
719 (57.6) |
34,992 (59.2) |
29,418 (44.9) |
|
Female |
60,744 (48.3) |
530 (42.4) |
24,148 (40.8) |
36,066 (55.1) |
|
Time from symptom onset to ED arrival (hr) |
1.5 (0.8–4.1) |
2.0 (0.8–6.1) |
1.3 (0.7–3.7) |
1.7 (0.8–4.6) |
|
Type of ED |
|
|
|
|
|
Level I |
51,120 (40.6) |
598 (47.9) |
24,102 (40.8) |
26,420 (40.3) |
|
Level II |
74,754 (59.4) |
651 (52.1) |
35,038 (59.2) |
39,064 (59.7) |
|
Level III |
0 (0) |
0 (0) |
0 (0) |
0 (0) |
|
Route of arrival |
|
|
|
|
|
Direct visit |
95,440 (75.8) |
936 (74.9) |
44,610 (75.4) |
49,893 (76.2) |
|
Transfer from other hospital |
28,859 (22.9) |
297 (23.8) |
13,832 (23.4) |
14,730 (22.5) |
|
Referral from outpatient clinic |
1,544 (1.2) |
16 (1.3) |
684 (1.2) |
844 (1.3) |
|
Other |
25 (0.0) |
0 (0) |
10 (0) |
15 (0.0) |
|
Unknown |
6 (0.0) |
0 (0) |
4 (0.0) |
2 (0.0) |
|
Transport |
|
|
|
|
|
119 Ambulance |
77,038 (61.2) |
482 (38.6) |
35,076 (59.3) |
41,479 (63.3) |
|
Other medical institution ambulance |
4,187 (3.3) |
42 (3.4) |
1,980 (3.3) |
2,165 (3.3) |
|
Other ambulance |
19,899 (15.8) |
156 (12.5) |
9,468 (16.0) |
10,275 (15.7) |
|
Police or official transport |
135 (0.1) |
3 (0.2) |
98 (0.2) |
34 (0.1) |
|
Air transport |
461 (0.4) |
0 (0) |
220 (0.4) |
241 (0.4) |
|
Other transport |
23,621 (18.8) |
550 (44.0) |
12,040 (20.4) |
11,031 (16.8) |
|
Walk-in |
357 (0.3) |
12 (1.0) |
186 (0.3) |
159 (0.2) |
|
Other/unknown |
176 (0.1) |
4 (0.3) |
72 (0.1) |
100 (0.2) |
|
Length of stay (hr) |
|
|
|
|
|
Mean±SD |
4.8±6.9 |
4.6±4.6 |
4.4±6.6 |
5.1±7.2 |
|
Median (IQR) |
2.8 (1.8–4.8) |
3.0 (1.8–5.8) |
2.6 (1.7–4.4) |
3.0 (1.9–5.2) |
|
0–6 |
102,645 (81.5) |
954 (76.4) |
49,601 (83.9) |
52,090 (79.5) |
|
6–12 |
13,992 (11.1) |
202 (16.2) |
5,715 (9.7) |
8,074 (12.3) |
|
12–24 |
6,468 (5.1) |
80 (6.4) |
2,711 (4.6) |
3,677 (5.6) |
|
≥24 |
2,766 (2.2) |
13 (1.0) |
1,112 (1.9) |
1,641 (2.5) |
|
Unknown |
3 (0.0) |
0 (0) |
1 (0.0) |
2 (0.0) |
|
ED disposition |
|
|
|
|
|
Discharge |
6,248 (5.0) |
192 (15.4) |
2,736 (4.6) |
3,320 (5.1) |
|
Admissiona)
|
104,807 (83.3) |
911 (72.9) |
49,773 (84.2) |
54,122 (82.6) |
|
General ward |
20,670 (16.4) |
350 (28.0) |
8,247 (13.9) |
12,072 (18.4) |
|
Intensive care unit |
84,001 (66.7) |
561 (44.9) |
41,467 (70.1) |
41,973 (64.1) |
|
Transfer |
12,625 (10.0) |
142 (11.4) |
5,907 (10.0) |
6,576 (10.0) |
|
Comfort care discharge |
291 (0.2) |
0 (0) |
65 (0.1) |
226 (0.3) |
|
Death |
1,867 (1.5) |
3 (0.2) |
642 (1.1) |
1,222 (1.9) |
|
Other/unknown |
36 (0.0) |
1 (0.1) |
17 (0.0) |
18 (0.0) |
|
Hospital disposition |
|
|
|
|
|
Discharge |
54,551 (43.3) |
854 (68.4) |
28,458 (48.1) |
25,239 (38.5) |
|
Transfer |
47,880 (38.0) |
273 (21.9) |
21,603 (36.5) |
26,004 (39.7) |
|
Comfort care discharge |
448 (0.4) |
1 (0.1) |
134 (0.2) |
313 (0.5) |
|
Death |
20,999 (16.7) |
112 (9.0) |
8,030 (13.6) |
12,856 (19.6) |
|
Other/unknown |
1,996 (1.6) |
9 (0.7) |
915 (1.5) |
1,072 (1.6) |
REFERENCES
- 1. GBD 2019 Stroke Collaborators. Global, regional, and national burden of stroke and its risk factors, 1990-2019: a systematic analysis for the Global Burden of Disease Study 2019. Lancet Neurol 2021;20:795-820.
- 2. Pu L, Wang L, Zhang R, Zhao T, Jiang Y, Han L. Projected global trends in ischemic stroke incidence, deaths and disability-adjusted life years from 2020 to 2030. Stroke 2023;54:1330-9.
- 3. Cha WC, Ahn KO, Shin SD, Park JH, Cho JS. Emergency department crowding disparity: a nationwide cross-sectional study. J Korean Med Sci 2016;31:1331-6.
- 4. Kim JY, Kang K, Kang J, et al. Executive summary of stroke statistics in Korea 2018: a report from the Epidemiology Research Council of the Korean Stroke Society. J Stroke 2019;21:42-59.
- 5. Park JH, Song KJ, Shin SD. The prehospital emergency medical service system in Korea: its current status and future direction. Clin Exp Emerg Med 2023;10:251-4.
- 6. National Institute of Neurological Disorders and Stroke rt-PA Stroke Study Group. Tissue plasminogen activator for acute ischemic stroke. N Engl J Med 1995;333:1581-7.
- 7. Powers WJ, Rabinstein AA, Ackerson T, et al. Guidelines for the early management of patients with acute ischemic stroke: 2019 update to the 2018 guidelines for the early management of acute ischemic stroke: a guideline for healthcare professionals from the American Heart Association/American Stroke Association. Stroke 2019;50:e344-418.
- 8. Albers GW, Marks MP, Kemp S, et al. Thrombectomy for stroke at 6 to 16 hours with selection by perfusion imaging. N Engl J Med 2018;378:708-18.
- 9. Nogueira RG, Jadhav AP, Haussen DC, et al. Thrombectomy 6 to 24 hours after stroke with a mismatch between deficit and infarct. N Engl J Med 2018;378:11-21.
- 10. Saini V, Guada L, Yavagal DR. Global epidemiology of stroke and access to acute ischemic stroke interventions. Neurology 2021;97(20 Suppl 2):S6-16.
- 11. Kim KH, Ro YS, Shin SD, Kim SJ. Association between neighborhood socioeconomic status and mechanical thrombectomy for acute ischemic stroke: a nationwide multilevel observational study. Acad Emerg Med 2023;30:918-26.
- 12. Jiang B, Ru X, Sun H, et al. Pre-hospital delay and its associated factors in first-ever stroke registered in communities from three cities in China. Sci Rep 2016;6:29795.
- 13. Zachrison KS, Nielsen VM, de la Ossa NP, et al. Prehospital stroke care part 1: emergency medical services and the stroke systems of care. Stroke 2023;54:1138-47.
- 14. Pak YS, Ro YS, Kim SH, et al. Effects of emergency care-related health policies during the COVID-19 pandemic in Korea: a quasi-experimental study. J Korean Med Sci 2021;36:e121.
- 15. Greenberg SM, Ziai WC, Cordonnier C, et al. 2022 Guideline for the management of patients with spontaneous intracerebral hemorrhage: a guideline from the American Heart Association/American Stroke Association. Stroke 2022;53:e282-361.
