Abstract
Aim
To compare the prehospital outcomes of out-of-hospital cardiac arrest (OHCA) across different emergency medical service (EMS) system organizational models in China.
Methods
This is a retrospective analysis of data from the Baseline Investigation of Out-of-Hospital Cardiac Arrest (BASIC-OHCA) (August 1, 2019–December 31, 2020) in China. Two EMS models were compared. One is independent model, in which all stations are directly established and managed by an independent public agency. The other one is hospital-based model, in which the stations are affiliated to hospitals. The primary outcome was ROSC sustained until arrival at the emergency department and transfer of care (survived event). Association were analyzed using multivariable logistic regression.
Results
A total of 24,814 OHCA patients from 10 EMS systems were included. The overall survived event rate was 2.05%, which was higher in the independent model than the hospital-based model (2.23% vs. 1.40%; P < 0.001). After adjustment, the independent model remained associated with higher odds of survived event (adjusted odds ratio 2.32; 95% CI, 1.71–3.17). The independent model had longer EMS response times (median 12.00 vs. 10.00 min; P < 0.001) but higher rates of epinephrine administration (85.02% vs. 73.26%; P < 0.001) and advanced airway placement (49.24% vs. 21.48%; P < 0.001) than the hospital-based model.
Conclusions
In this study, the independent model was associated with a statistically higher survived event rate than the hospital-based model in China. However, considering the small absolute difference, future efforts should be taken to explore broader, system-level best practices to achieve more clinically meaningful improvements in OHCA outcomes.
Keywords: Out-of-hospital cardiac arrest, Emergency medical service, Organizational models, Survived event
Introduction
Survival after out-of-hospital cardiac arrest (OHCA) varies across regions even within the same country, despite broad adherence to resuscitation guidelines.1, 2, 3, 4 Prior evidence suggests that this variation cannot be fully explained by patient- and arrest-level characteristics alone, but is also influenced by system-level factors of emergency medical service (EMS) system.2, 4 Among these, the EMS system organizational structure may be an important foundational factor.4, 5 A critical dimension of organizational structure is whether ambulance stations are organized under an independent public agency or affiliated hospitals.6 In the former model, the EMS system has greater autonomy in station deployment and centralized management, which may facilitate system-wide quality control but requires dedicated investment in station construction and operation.7 In the latter model, ambulance stations are affiliated with hospitals and rely on existing hospital infrastructure and resources, but potentially leading to fragmented management and variable quality of prehospital care across stations.8 These organizational differences may affect both EMS response time and prehospital processes of care, both of which are closely associated with OHCA outcomes.9, 10, 11, 12
Comparing these models may help understand how EMS organizational structure influences outcomes after OHCA. However, isolating the impact remains challenging. In many countries, EMS system typically operates under a single dominant organizational model, limiting within-system variation and constraining meaningful internal comparisons.13 Where both models coexist, comparisons are often confounded by heterogeneity in other system-level characteristics, such as funding sources, prehospital care provider qualifications, and the number of EMS personnel dispatched to the scene.14 China provides a setting for addressing this challenge. Due to historical developments, both organizational models coexist across regions.15, 16 Meanwhile, national standards for prehospital emergency care have promoted greater consistency in dispatch and transport protocols, funding arrangements, infrastructure development, and basic EMS resource allocation-including EMS team composition, qualifications, and core equipment.17 The combination of local variation in organizational structure and national standardization creates an opportunity to examine the association between different EMS organizational models and OHCA outcomes, with mitigated confounding from other system-level factors.
Based on data from a prospective, multicenter, population-based OHCA registry in China, the Baseline Investigation of Out-of-Hospital Cardiac Arrest (BASIC-OHCA), this study aimed to assess the association between EMS organizational models and prehospital outcomes.18 Our findings may help identify system-level factors associated with early OHCA outcomes and provide evidence for EMS system optimization strategies to improve OHCA survival in China and other similar settings.
Methods
Ethical approval
This study was approved by Qilu Hospital of Shandong University ethics committee (authorization number: 2019012), and patient informed consent was waived. All EMS agencies contributing data to the BASIC-OHCA registry agreed to this ethical approval.
EMS system in China
The EMS system in mainland China is a physician-based, single-tiered system that provides 24-h public service.19, 20, 21, 22 Each city generally operates a single EMS system responsible for prehospital services within its jurisdiction. This system is organized around an EMS center, which is funded and supervised by the government and typically consists of a centralized dispatch center and multiple ambulance stations. Dispatch centers are responsible for answering emergency calls (120) and dispatching ambulances to arrest scenes. Dispatcher-assisted cardiopulmonary resuscitation (DA-CPR) programs are not yet widespread in mainland China.23 Prehospital medical care is primarily provided by ground ambulances stationed at individual ambulance stations. Ambulances conform to the Health Industry Standard WS/T292-2008 of the People's Republic of China. Each ambulance team includes at least one physician, one nurse, and one paramedic-driver, all of whom can provide basic life support. Physicians and nurses are additionally trained to provide advanced life support, including advanced airway management and medication administration, in according with cardiopulmonary resuscitation (CPR) guidelines recommended by the American Heart Association.24 Physicians on the ambulance team are authorized to terminate resuscitation on arrest scene and issue a death certificate. Both dispatchers and EMS personnel are full-time employees.
Data sources
This study was a secondary analysis of data from the BASIC-OHCA collected between August 1, 2019, and December 31, 2020. BASIC-OHCA is a prospective, multicenter, population-based registry of OHCA in China. The protocol of the BASIC-OHCA has been published previously.18 Briefly, EMS-attended OHCA patients were enrolled from 31 participating EMS systems across seven geographic regions of mainland China, covering approximately 9% of the national population. Data were collected following the Utstein template, including EMS system description, dispatch characteristics, demographic characteristics, arrest information, EMS interventions, and outcomes. This observational study was reported in accordance with the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) reporting guideline.
Classification of EMS organizational models
The exposure in this study was the organizational model of the participating EMS system. Based on the establishment and management of ambulance stations within each jurisdiction, the 31 participating EMS systems were classified into three structural models: independent, hospital-based, or mixed. (1) The independent model was defined as the system in which all ambulance stations were uniformly established and managed by the local EMS center. (2) The hospital-based model was defined as the system in which all ambulance stations were established and managed by their affiliated hospitals. (3) The mixed model was defined as the system in which some ambulance stations were established and managed by the local EMS center, and others were established and managed by affiliated hospitals.
Because the mixed model included two types of ambulance stations, it did not represent a distinct single EMS organizational model. Therefore, only participating EMS system operating under either an independent or hospital-based model were included in the primary analysis to ensure a clear and interpretable comparison of the association between EMS organizational models and OHCA outcomes. The key organizational characteristics of independent and hospital-based EMS organizational models are summarized in Table 1.
Table 1.
Key organizational characteristics of two EMS system organizational models.
| Independent model | Hospital-based model | |
|---|---|---|
| Main entity in EMS center construction | Independent public agency | Independent public agency |
| Main entity for ambulance station establishment and management | EMS center | Hospitals |
| Strengths | Centralized management; System-wide quality control |
Rapid stations expansion with lower initial investment for EMS center |
| Limitations | High financial burden of stations expansion for EMS center | Fragmented oversight; Inconsistent quality across all stations |
EMS, emergency medical service.
Study population
For the primary analysis, we included adult (≥18 years) EMS-treated OHCA patients, regardless of causes from 10 participating EMS systems operating under either an independent model (n = 6) or a hospital-based model (n = 4). Patients with EMS-witnessed arrests, and those with missing data for the primary outcome were excluded from the primary analysis. Patients were assigned to either the independent model or hospital-based model group according to the EMS organizational model of the system delivering their prehospital care.
Study variables
The following variables were included: (1) EMS system characteristics: population density, gross domestic product(GDP) per capita, proportion of the population aged 65 and above, number of ambulances per 100,000 inhabitants, and annual EMS calls; (2) patient baseline characteristics: age, gender, cause of arrest, location of arrest, initial rhythm, witness to collapse, and bystander CPR; (3) prehospital processes of care: EMS response time, epinephrine administration, and advanced airway placement.
Study endpoint
The primary outcome of this study was the survived event. This was defined as return of spontaneous circulation (ROSC) sustained until arrival at the hospital emergency department and transfer of care to medical staff at the receiving hospital.
Statistical methods
Descriptive analyses of all study variables were stratified by EMS organizational model. Continuous variables are presented as mean ± standard deviation for normally distributed data and median (interquartile range) for non-normally distributed data. Between-group comparisons were performed using the independent-samples t test or the Mann-Whitney U test, as appropriate. Categorical variables are reported as frequencies (percentages), with differences assessed using the chi-square test or Fisher’s exact test.
To evaluate the association between EMS organizational models and survived event, we performed multivariable logistic regression analyses adjusting for EMS system characteristics and patient baseline characteristics. Adjusted odds ratios (aORs) with 95% confidence interval (CIs) were reported.
To address sample size imbalance between the two groups while retaining the full analytic cohort, we performed stabilized inverse probability of treatment weighting (IPTW) as a sensitivity analysis. Covariate balance before and after weighting was assessed using standardized mean differences, with an absolute value < 0.10 considered acceptable. After weighting, the association was examined using weighted logistic regression, additionally adjusting for EMS system characteristics. Results are reported as weighted aORs with 95% CIs.
All hypothesis tests were two-sided, with a P value < 0.05 considered statistically significant. Data analysis was performed using Stata 17.0 and R 4.3.1.
Results
During the study period, a total of 27,681 OHCA patients treated by 10 EMS systems were identified. We excluded 436 patients aged <18 years, 3 patients whose cardiac arrest was due to other causes, and 2428 EMS-witnessed cases. The final analytic cohort therefore included 24,814 patients. No missing data were identified for the variables included in the final analysis. Of these patients, 19,312 (77.83%) were treated under the independent model and 5502 (22.17%) were treated under the hospital-based model (Fig. 1).
Fig. 1.
Patient flow.
EMS, emergency medical service.
Across the 10 EMS systems, the crude survived event rates ranged from 0.49% to 5.44% (Supplementary Fig. S1). The crude survived event rate was higher among patients treated under independent model than among those treated under hospital-based model (2.23% vs. 1.40%; P < 0.001).
Characteristics of EMS systems are summarized in Table 2. Compared with hospital-based model, the independent model served areas with higher population density, proportion of the population aged 65 years or older, ambulance density and annual EMS call volume, but lower GDP per capita. None of these differences in EMS system characteristics was statistically significant.
Table 2.
EMS system characteristics grouped by EMS organizational model.
| All | Independent model | Hospital-based model | P value | |
|---|---|---|---|---|
| EMS system (n, %) | 10 (100.00) | 6 (60.00) | 4 (40.00) | |
| Population densitya (people per km2) | 2792.64 (1962.23, 3825.05) |
3375.59 (2787.00, 4164.85) |
1627.26 (1115.50, 2378.91) |
0.11 |
| GDP per capitab (¥) | 80,892.00 (72,836.25, 99,528.75) |
76,753.50 (72,836.25, 95,943.75) |
88,062.00 (74,242.25, 98,853.50) |
0.76 |
| Proportion of population aged 65 and abovec (%) | 14.11 (12.30, 14.73) |
14.4 (13.51, 14.73) |
13.02 (11.22, 14.61) |
0.61 |
| Ambulances per 100,000 inhabitantsd (vehicle) | 2.28 (1.55, 2.55) |
2.48 (1.63, 2.71) |
2.12 (1.86, 2.25) |
0.48 |
| Annual EMS callse (Events) | 351,000.00 (240,100.00, 527,217.00) |
351,000.00 (328,750.00, 915,964.00) |
319,924.00 (207,118.00, 461,105.00) |
0.76 |
EMS, emergency medical services; GDP, gross domestic product.
Density = Population served/area served.
Per capita GDP data extracted from the 2020 statistical yearbooks of each EMS system's host city.
Proportion of population aged 65 and above is sourced from the 2020 Seventh National Population Census data of each EMS system's host city.
Ambulances per 100,000 inhabitants = Population served/the number of ambulances owned by the EMS system × 100,000.
The number of emergency calls received by each EMS system in 2020.
Patient baseline characteristics and prehospital processes of care are summarized in Table 3. Compared with patients treated under hospital-based model, those treated under independent model were older (median 69 vs. 66 years) and had higher proportions of presumed cardiac arrests (82.55% vs. 80.03%), home/residence arrests (84.70% vs. 74.37%), shockable rhythms (6.99% vs. 4.31%) and bystander CPR (21.47% vs. 16.76%). Regarding prehospital care processes, the independent model had longer EMS response times (median 12.00 vs. 10.00 min) but higher rates of epinephrine administration (85.02% vs. 73.26%) and advanced airway placement (49.24% vs. 21.48%). All of these differences in patient baseline characteristics and prehospital processes of care were statistically significant.
Table 3.
Patient baseline characteristics and prehospital processes of care grouped by EMS organizational model.
| All | Independent model | Hospital-based model | P value | |
|---|---|---|---|---|
| Patients (n, %) | 24,814 (100) | 19,312 (77.83) | 5502 (22.17) | |
| Patient demographics | ||||
| Age, years | 69.00 (57.00, 80.00) | 69.00 (59.00, 80.00) | 66.00 (55.00, 78.00) | <0.001 |
| Male (n, %) | 16,638 (67.05) | 12,873 (66.66) | 3765 (68.43) | 0.014 |
| Presumed cause (n, %) | <0.001 | |||
| Cardiac | 20,345 (81.99) | 15,942 (82.55) | 4403 (80.03) | |
| Non-cardiac | 2928 (11.80) | 2417 (12.52) | 511 (9.29) | |
| Trauma | 701 (2.83) | 417 (2.16) | 284 (5.16) | |
| Drug overdose/poisoning | 167 (0.67) | 107 (0.55) | 60 (1.09) | |
| Drowning | 72 (0.29) | 53 (0.27) | 19 (0.35) | |
| Electrocution | 82 (0.33) | 45 (0.23) | 37 (0.67) | |
| Asphyxial | 519 (2.09) | 331 (1.71) | 188 (3.42) | |
| Arrest location (n, %) | <0.001 | |||
| Home/residence | 20,449 (82.41) | 16,357 (84.70) | 4092 (74.37) | |
| Public locations | 3704 (14.93) | 2470 (12.79) | 1234 (22.43) | |
| Health-care facilities | 661 (2.66) | 485 (2.51) | 176 (3.20) | |
| Initial rhythm (n, %) | <0.001 | |||
| Shockable | 1587 (6.40) | 1350 (6.99) | 237 (4.31) | |
| Nonshockable | 22,986 (92.63) | 17,884 (92.61) | 5102 (92.73) | |
| Not recorded | 241 (0.97) | 78 (0.40) | 163 (2.96) | |
| Witnessed by bystander (n, %) | 13,343 (53.77) | 10,108 (52.34) | 3235 (58.80) | <0.001 |
| Bystander CPR (n, %) | 5069 (20.43) | 4147 (21.47) | 922 (16.76) | <0.001 |
| Prehospital care | ||||
| EMS response time, minutes | 11.00 (8.00, 16.00) | 12.00 (9.00, 16.00) | 10.00 (8.00, 14.00) | <0.001 |
| Epinephrine administration (n, %) | 20,451 (82.42) | 16,420 (85.02) | 4031 (73.26) | <0.001 |
| Advanced airway placement (n, %) | 10,691 (43.08) | 9509 (49.24) | 1182 (21.48) | <0.001 |
CPR, cardiopulmonary resuscitation; EMS, emergency medical service.
In the multivariable logistic regression analysis, the independent model was significantly associated with a higher odd of survived event compared with the hospital-based model (aOR, 2.32; 95% CI, 1.71–3.17) (Table 4). This association remained robust in sensitivity analysis (Supplementary Fig. S2, Table S1).
Table 4.
Multivariate logistic regression analysis of survived event in OHCA patients.
| aOR (95%CI) | P value | |
|---|---|---|
| EMS organizational model | ||
| Independent model | 2.32 (1.71–3.17) | <0.001 |
| Hospital-based model | Reference | |
| EMS system characteristics | ||
| Population density (people per km2) | ||
| ≥2792.64 | 0.94 (0.65–1.36) | 0.760 |
| <2792.64 | Reference | |
| GDP per capita | ||
| ≥¥80,892.00 | 0.38 (0.26–0.55) | <0.001 |
| <¥80,892.00 | Reference | |
| Percentage of population aged 65 and above (%) | ||
| ≥14.11 | 0.16 (0.12–0.21) | <0.001 |
| <14.11 | Reference | |
| Ambulances per 100,000 population (vehicle) | ||
| ≥2.28 | 4.06 (3.01–5.53) | <0.001 |
| <2.28 | Reference | |
| Annual EMS calls (events) | ||
| ≥351,000 | 0.86 (0.54–1.35) | 0.514 |
| <351,000 | Reference | |
| Patient demographics | ||
| Age, per 1 additional year | 0.99 (0.98–0.99) | <0.001 |
| Gender | ||
| Male | Reference | |
| Female | 1.48 (1.22–1.80) | <0.001 |
| Cardiac arrest event | ||
| Presumed cause | ||
| Cardiac | Reference | |
| Non-Cardiac | 1.05 (0.77–1.39) | 0.769 |
| Trauma | 1.13 (0.69–1.78) | 0.616 |
| Drug overdose/poisoning | 2.42 (0.99–5.05) | 0.031 |
| Drowning | 1.01 (0.15–3.66) | 0.991 |
| Electrocution | 0.79 (0.19–2.26) | 0.706 |
| Asphyxial | 2.53 (1.56–3.90) | <0.001 |
| Arrest location | ||
| Home/residence | Reference | |
| Public locations | 1.39 (1.09–1.75) | 0.007 |
| Health-care facilities | 1.69 (0.95–2.79) | 0.056 |
| Initial rhythm | ||
| Shockable | 4.24 (3.36–5.32) | <0.001 |
| Nonshockable | Reference | |
| Not recorded | 6.42 (3.74–10.51) | <0.001 |
| Witnessed arrest | ||
| Yes | 2.19 (1.79–2.70) | <0.001 |
| None | Reference | |
| Bystander CPR | ||
| Yes | 1.39 (1.11–1.73) | 0.003 |
| No | Reference | |
aOR, adjusted odds ratios; CI, confidence interval; CPR, cardiopulmonary resuscitation; EMS, emergency medical service; GDP, gross domestic product; OHCA, out-of-hospital cardiac arrest.
Discussion
Based on China’s first prospective, multicenter, population-based OHCA registry, we found that OHCA patients treated under the independent model was associated with a higher survived event rate than those treated under the hospital-based model. However, given the absolute difference of less than 1%, its clinical meaningfulness should be interpreted cautiously.
These finding may be associated with discrepancies in the management of ambulance stations across different models.16 In the independent model, the EMS center integrates dispatching, resource allocation, and EMS performance evaluations. This model provides the center with direct means to implement quality control; consequently, stations are consistently equipped and personnel are more likely to strictly adhere to resuscitation protocols. By contrast, the role of the EMS center in the hospital-based model is primarily limited to dispatch. The actual management of resources and personnel assessment vary significantly between individual hospitals. Because the EMS center lacks direct management at the station level, this model has few effective means to ensure uniform quality of care across all stations. In our data, the potential management strengths of the independent model were reflected in two main process measures. First, key prehospital interventions were more frequently delivered. We observed higher rates of epinephrine administration and advanced airway placement in the independent model. This may suggest more consistency in the resource availability and implementation of standardized resuscitation protocols across stations under this model. Second, documentation appeared to be more complete. Direct management may be associated with more rigorous recording, resulting in fewer missing initial rhythm records. Our data showed that the proportion of cases with an unrecorded initial rhythm was lower in the independent model, suggesting more thorough assessment and documentation of initial rhythm. This difference in documentation quality may have influenced the observed distribution of initial rhythms, including the higher proportion classified as shockable. Overall, these findings are consistent with international evidence suggesting that strengthening prehospital management may be an important target for improving OHCA care.11, 12, 25
Although the independent model may have strengths in station management, it faces inherent challenges in station expansion.26 In our study, from the perspective of EMS centers, expanding stations under the independent model depends entirely on the financial resources of EMS centers, which may restrict geographic coverage in resource-constrained settings.9 In contrast, the hospital-based model can directly leverage the existing infrastructure, personnel, and equipment of host hospitals. Consequently, from the perspective of EMS centers, the hospital-based model may reduce the initial investment required for station expansion and facilitate broader geographic coverage. However, it is worth noting that although EMS response times were longer in the independent model than in the hospital-based model, they were delayed in both groups, far exceeding the critical window for OHCA resuscitation.27 Moreover, the absolute difference was only approximately two minutes, suggesting that this variation may not translate into clinically meaningful differences in survived event.28
Furthermore, the overall survived event rate in our study remains relatively low compared with that reported in many other countries and regions.29 This discrepancy is likely associated with broader system-level barriers within China’s Chain of Survival, particularly early links that are strongly associated with survived event, such as bystander CPR and early defibrillation.30 Compared with established international registries such as EuReCa-THREE, our study shows a lower rate of bystander CPR (20.43% vs. 52.5%) and a smaller proportion of patients presenting with an initial shockable rhythm (6.40% vs. 20.6%).27 These discrepancies may partly explain the low overall survived event rate observed in our study.
Although the difference in the survived event rate between the two models reached statistical significance due to the large sample size of this cohort, its clinical significance warrants cautious interpretation. On one hand, from a public health perspective, given China’s population of approximately 1.4 billion and a crude EMS-assessed OHCA incidence of 95.7 per 100,000,30 even a modest increment in the survived event rate may potentially translate into a substantial number of additional survivors annually. On the other hand, although EMS organizational structure may be a potentially modifiable system-level factor in China, the low baseline survival and the minimal divergence between the two models suggest that optimizing the EMS organizational structure alone may have a limited impact on improving prehospital outcomes. Achieving more clinically meaningful improvements would likely require exploring broader system-level best practices across the Chain of Survival, particularly early links such as early recognition, bystander CPR, DA-CPR and public-access defibrillation.31
Limitations
This study has several limitations. First, although the multicenter design provides preliminary evidence for EMS system optimization, only 10 EMS systems from the BASIC-OHCA registry were included, which may limit the generalizability of the findings to all EMS systems in China. Second, due to registry constraints and the inherent complexity of EMS systems, several important clinical and system-level factors, such as CPR quality, defibrillation parameters, and prehospital-to-hospital coordination, were unavailable for analysis and may have introduced residual confounding. Third, the study period overlapped with the COVID-19 pandemic, which represents an important limitation. Although the pandemic did not change the EMS organizational models in China, it may have differentially affected their capacity to maintain pre-pandemic ambulance team composition and consistency in resuscitation protocol implementation. These pandemic-related effects may have influenced the observed differences in prehospital care processes and survived event rate.32 Since we were unable to adjust for these pandemic-related effects, residual confounding related to COVID-19 cannot be excluded.
Conclusions
In this multicenter registry study, the independent EMS model was associated with a statistically significantly higher survived event rate than the hospital-based model. However, the absolute difference between the two organizational models was less than 1%, suggesting that the clinical significance of this difference should be interpreted with caution. While these findings suggest EMS organizational structure may be a potentially modifiable system-level factor in China, optimizing the EMS organizational structure alone may have a limited impact on improving prehospital outcomes. To achieve more clinically meaningful improvements in OHCA outcomes, future efforts should be taken to explore broader, system-level best practices across the Chain of Survival in China.
Funding sources
This study was supported by the National Natural Science Foundation of China (82325031), Noncommunicable Chronic Diseases-National Science and Technology Major Project (2023ZD0505504).
CRediT authorship contribution statement
Xi Xie: Writing – review & editing, Writing – original draft, Methodology, Formal analysis, Conceptualization. Yanan Peng: Writing – review & editing, Writing – original draft, Formal analysis. Qiang Gao: Writing – review & editing, Resources, Investigation, Data curation. Bin Li: Writing – review & editing, Resources, Investigation, Data curation. Guolin Jiang: Writing – review & editing, Resources, Investigation, Data curation. Nan Wang: Writing – review & editing, Resources, Investigation, Data curation. Ruixue Song: Writing – review & editing, Methodology, Formal analysis. Jingjing Ma: Writing – review & editing, Project administration, Data curation. Kai Cheng: Writing – review & editing, Project administration, Data curation. Jianbo Zhang: Writing – review & editing, Project administration, Data curation. Chunyi Wang: Writing – review & editing, Project administration, Data curation. Yuan Bian: Writing – review & editing, Project administration, Data curation. Rugang Liu: Writing – review & editing, Project administration, Data curation. Chang Pan: Writing – review & editing, Project administration, Data curation. Rui Shang: Writing – review & editing, Project administration, Data curation. Marcus Eng Hock Ong: Writing – review & editing, Supervision. Wen Zheng: Writing – review & editing, Supervision, Methodology, Conceptualization. Feng Xu: Writing – review & editing, Supervision, Methodology, Funding acquisition, Conceptualization.
Declaration of competing interest
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
Acknowledgment
We thank all Coordinators and Investigators for their contributions to the BASIC-OHCA.
Footnotes
Supplementary material to this article can be found online at https://doi.org/10.1016/j.resplu.2026.101431.
Contributor Information
Wen Zheng, Email: wen.zheng@sdu.edu.cn.
Feng Xu, Email: xufengsdu@126.com, xufengsdu@email.sdu.edu.cn.
Appendix A. Supplementary material
The following are the Supplementary material to this article:
Supplementary Figures S1–S2 and Supplementary Table S1.
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Supplementary Materials
Supplementary Figures S1–S2 and Supplementary Table S1.