In-hospital mortality and mode-specific risk factors in adults receiving emergency ECMO in three Chinese tertiary emergency centers: a multicenter retrospective cohort
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XiancongWang¹#1
JianSun¹#1
YueguoWang¹#1
JieQin²1
YanShi³1
HaichenYang³1
JunXu⁴1
ShushengZhou¹1
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KuiJin¹1✉
KuiJin1Email
1Department of Emergency Medicine, Division of Life Science and MedicineThe First Affiliated Hospital of USTC, University of Science and Technology of China230001HefeiChina
2Department of Emergency MedicineTaizhou People’s Hospital318000TaizhouChina
3Department of Emergency MedicineHuai’an Second People’s Hospital223002Huai’anChina
4Department of Emergency MedicinePeking Union Medical College Hospital100730BeijingChina
Xiancong Wang¹#, Jian Sun¹#, Yueguo Wang¹#, Jie Qin², Yan Shi³, Haichen Yang³, Jun Xu⁴, Shusheng Zhou¹, Kui Jin¹*
¹ Department of Emergency Medicine, The First Affiliated Hospital of USTC, Division of Life Science and Medicine, University of Science and Technology of China, Hefei 230001, China
² Department of Emergency Medicine, Taizhou People’s Hospital, Taizhou 318000, China
³ Department of Emergency Medicine, Huai’an Second People’s Hospital, Huai’an 223002, China
⁴ Department of Emergency Medicine, Peking Union Medical College Hospital, Beijing 100730, China
* Corresponding author: Kui Jin (Email: kuijin@ustc.edu.cn)
# These authors contributed equally to this work.
Abstract
Background
Emergency extracorporeal membrane oxygenation (ECMO) is increasingly applied for refractory cardiac or respiratory failure, yet in-hospital mortality remains substantial. Comprehensive multicenter evidence from departments of emergency medicine is limited.
Methods
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We conducted a retrospective, multicenter study of consecutive adult patients receiving ECMO between January 2019 and February 2025 at three tertiary emergency centers. Baseline characteristics, including demographics, comorbidities, therapeutic interventions, vital signs, and laboratory parameters, were collected. Independent predictors of in‑hospital mortality were determined using multivariable logistic regression, and model performance was evaluated by receiver operating characteristic (ROC) curve anal-ysis. Survival outcomes were analyzed using the Kaplan–Meier method and compared between the VA-ECMO and VV-ECMO subgroups.
Results
Among 304 included patients (VV-ECMO, 105 [34.5%]; VA-ECMO, 199 [65.5%]), overall in-hospital mortality was 36.2% (110/304), 41.3% with VA-ECMO and 26.2% with VV-ECMO. More than half of deaths (57.3%) occurred within 5 days after ECMO initiation. In the overall cohort, higher APACHE II scores (aOR 1.081, 95% CI 1.028–1.137; p = 0.002), systolic blood pressure (aOR 1.024,95% CI 1.003–1.046; p = 0.027), and lactate (aOR 1.108, 95% CI 1.019–1.205; p = 0.017) independently predicted in-hospital death, whereas diastolic blood pressure (aOR 0.948, 95% CI 0.916–0.980; p = 0.002) and albumin (aOR 0.951, 95% CI 0.905–1.000; p = 0.050) were protective. In subgroup models, APACHE II score, arterial pH, albumin, diastolic pressure, and white cell count were significant for VA-ECMO (AUC 0.845) and lactate and APTT for VV-ECMO (AUC 0.844). Kaplan–Meier curves showed significantly better survival with VV-ECMO than VA-ECMO (log-rank p < 0.001)
Conclusions
Over half of deaths occurred within 5 days after ECMO initiation. Mode-specific risk profiles support practical stratification and individualized management in emergency ECMO.
Keywords:
Extracorporeal membrane oxygenation
in-hospital mortality
APACHE II score
logistic regression
risk factors
ROC curve
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Introduction
Extracorporeal membrane oxygenation (ECMO) can partially substitute for cardiopulmonary function when conventional therapies fail, thereby providing critically ill patients with an opportunity for further definitive management [1]. With continuous advances in membrane oxygenators, centrifugal pumps, and anticoagulation strategies, the indications for ECMO have expanded, and its utilization has become increasingly frequent in emergency and critical care settings [2, 3]. The two major configurations are venovenous ECMO (VV-ECMO), for severe respiratory failure, and venoarterial ECMO (VA-ECMO), for cardiogenic shock or circulatory collapse. Accumulating evidence suggests that ECMO can improve short-term survival in selected patients and has become an important modality in the management of life-threatening critical illness [46].
Despite substantial advances, ECMO continues to carry high in-hospital mortality and complex complications, and further investigation is needed into short- and long-term outcomes and their determinants. Most existing mortality risk models have been derived from single-center, small-sample cohorts predominantly conducted outside China, characterized by heterogeneous variable selection and limited external validity [7, 8]. Several studies have proposed multivariable prognostic models and identified predictors (e.g., APACHE II, lactate, albumin); however, variability in model specification and performance assessment has limited comparability and hindered a unified, widely applicable framework [4, 9]. Moreover, given the substantial differences between VA- and VV-ECMO in indications, interventions, and underlying pathophysiology, key risk factors may be diluted or obscured. High-quality, population-specific evidence in emergency ECMO is scarce, and rigorous epidemiologic data remain limited. Accordingly, large, multicenter studies are needed to evaluate in-hospital mortality among ECMO recipients, identify independent risk factors, and compare outcomes across ECMO modes. Using multicenter retrospective data from emergency ECMO cases in China, we assessed in-hospital mortality and its determinants to support precise risk stratification and individualized management.
Materials and Methods
Study population and design
This multicenter retrospective cohort study was based on the Chinese Emergency Triage and Treatment Database (CETAT 2.0) [9, 10]. Consecutive patients who received ECMO between January 2019 and February 2025 at three tertiary emergency centers: the First Affiliated Hospital of the University of Science and Technology of China, Huai’an Second People’s Hospital, and Taizhou People’s Hospital were included. All three centers serve as regional medical hubs and are classified as tertiary A (Class 3A) hospitals. Clinical staff at each site received standardized, regular training in emergency and critical care, including ECMO, and were required to pass competency assessments.
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Across centers, ECMO initiation criteria and related management were harmonized and aligned with Extracorporeal Life Support Organization (ELSO) guidelines, and the timing of ECMO initiation was generally comparable.
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This study was approved by the Ethics Committee of the First Affiliated Hospital of USTC (approval no. 2024-ky300).
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Subsequent data updates through February 2025 were conducted under the same approval, and the need for written informed consent was waived.
Inclusion criteria
(1) Age ≥ 18 years; (2) Underwent ECMO at one of the three participating centers during the study period; (3) In-hospital outcome data available.
Exclusion criteria
(1) Documented brain death before ECMO initiation (e.g., organ donors);
(2) ECMO initiated for planned short-term postoperative support, such as after lung transplantation;
(3) Underlying conditions likely to confound outcome assessment, including end-stage malignancy or severe trauma;
(4) Incomplete key clinical or laboratory data that preclude statistical analysis.
Data collection
(1)
Demographics: sex, age, height, and weight.
(2)
Illness severity and vital signs: APACHE II score; pre-ECMO systolic and diastolic blood pressure; and body temperature.
(3)
Primary etiology and comorbidities: acute respiratory distress syndrome (ARDS), cardiogenic shock, cardiac arrest, hypertension, diabetes mellitus, coronary artery disease, and chronic obstructive pulmonary disease (COPD).
(4)
Therapeutic interventions: ECMO mode (VV or VA); CRRT; use of corticosteroids; and vasoactive agents (vasopressors/inotropes).
(5)
Laboratory parameters: complete blood count (CBC); liver and renal function tests; coagulation profile; procalcitonin; and arterial blood gas (ABG) analysis (e.g., pH, PaO₂, PaCO₂, lactate).
All data were de-identified prior to extraction and were stored and managed in encrypted form to ensure security, confidentiality, and regulatory compliance.
Study groups and endpoints
Participants were categorized by in-hospital outcome (survivors vs non-survivors). Subgroup analyses were prespecified by ECMO mode (VV or VA). The primary endpoint was in-hospital all-cause mortality. The secondary endpoints included early mortality (≤ 5 days after ECMO initiation) and the difference in in-hospital mortality between ECMO modes.
Statistical analysis
Analyses were performed in Stata/IC 16.0 (StataCorp LLC); figures in GraphPad Prism 10.0 (Dotmatics). Normality was assessed with the Kolmogorov–Smirnov test. As all continuous variables were non-normal, data are reported as median (IQR) and compared with the Mann–Whitney U test; categorical variables are summarized as n (%) and compared with the χ² test.
To identify independent risk factors for in-hospital death, multivariable logistic regression was fitted for the overall cohort and for VA-ECMO and VV-ECMO subsets, using stepwise (bidirectional) selection (entry p < 0.05; removal p > 0.10). Candidate variables were those with p < 0.10 on univariable analyses plus clinically relevant covariates. Effect estimates were reported as adjusted odds ratios (aORs) with 95% confidence intervals (CIs). Model discrimination was quantified by the area under the receiver operating characteristic curve (AUC). Survival across ECMO modes was estimated by Kaplan–Meier curves and compared with the log-rank test. All tests were two-sided; p < 0.05 was considered statistically significant.
Results
Baseline characteristics and univariable analysis
A total of 304 patients were included: 105 (34.5%) received VV-ECMO and 199 (65.5%) received VA-ECMO. In-hospital mortality was 26.2% in the VV-ECMO group and 41.3% in the VA-ECMO group. Compared with survivors, non-survivors had higher APACHE II scores and greater height and weight, but lower pre-ECMO body temperature and lower systolic and diastolic blood pressure (all p < 0.05). Sex and age were similar between groups, as were distributions of primary diagnoses and comorbidities.
Non-survivors were more likely than survivors to receive VA-ECMO (75.5% vs 59.8%, p < 0.01). CRRT use was also more frequent among non-survivors (53.6% vs 37.6%, p < 0.01), whereas corticosteroid therapy was less common (33.6% vs 51.0%, p < 0.01). Use of vasoactive agents did not differ significantly between groups.
Regarding laboratory parameters, non-survivors had higher white blood cell and neutrophil counts, procalcitonin, lactate, creatinine, blood urea nitrogen, direct bilirubin, prothrombin time, and PaCO₂, along with lower hemoglobin, red blood cell and platelet counts, albumin, and arterial pH (all p < 0.05). Hospital length of stay was shorter among non-survivors (4 vs 21 days, p < 0.01; Table 1).
Table 1
Baseline characteristics of ECMO patients by in-hospital outcome (survivors vs non-survivors)
Variable
survivors
non-survivors
p
n = 194
n = 110
Demographics
   
Male, n (%)
135 (69.6%)
74 (67.3%)
0.58
Age (years)
53(38, 60)
56 (35, 64)
0.57
Height (cm)
168 (168, 170)
170 (170, 172)
< 0.01
Weight (kg)
62 (62, 64)
65 (65, 68)
< 0.01
Pre-ECMO vital signs
   
Temperature (°C)
37.0 (36.0, 37.0)
36.5 (36.0, 37.0)
0.02
Systolic BP (mmHg)
114 (99, 130)
109 (83, 124)
< 0.01
Diastolic BP (mmHg)
74 (61, 84)
62 (47, 76)
< 0.01
Heart rate (bpm)
105 (88, 118)
102 (86, 112)
0.82
APACHE II score
21 (18, 26)
28 (24, 33)
< 0.01
Primary diagnosis
   
ARDS, n (%)
20 (10.3%)
13 (11.8%)
0.68
Cardiac arrest, n (%)
30 (15.5%)
26 (23.6%)
0.08
Cardiogenic shock, n (%)
52 (26.8%)
32 (29.1%)
0.67
Comorbidities
   
Hypertension, n (%)
65 (33.5%)
45 (40.9%)
0.2
Diabetes mellitus, n (%)
31 (16.0%)
20 (18.2%)
0.62
Coronary artery disease, n (%)
22 (11.3%)
11 (10.0%)
0.72
COPD, n (%)
7 (3.6%)
4 (3.6%)
0.99
Pre-ECMO medications
   
Norepinephrine, n (%)
152 (78.4%)
87 (79.1%)
0.88
Epinephrine, n (%)
128 (66.0%)
64 (58.2%)
0.18
Dopamine, n (%)
53 (27.3%)
21 (19.1%)
0.11
Corticosteroids, n (%)
99 (51.0%)
37 (33.6%)
< 0.01
CRRT, n (%)
73 (37.6%)
59 (53.6%)
< 0.01
VA-ECMO, n (%)
116 (59.8%)
83 (75.5%)
< 0.01
Laboratory tests
   
PLT (×10^9/L)
173 (127, 223)
154 (97, 206)
0.03
Hb (g/L)
123 (109, 139)
116 (97, 131)
< 0.01
WBC (×109/L)
10.3 (8.4, 13.3)
11.3 (10.0, 11.6)
0.02
RBC (×1012/L)
4.1 (3.8, 4.6)
3.8 (3.7, 4.0)
< 0.01
NEU (×109/L)
8.4 (6.6, 11.7)
9.6 (9.0, 10.1)
< 0.01
CRP (mg/L)
31.8 (10.0, 87.2)
52.4 (7.7, 74.2)
0.51
PCT (ng/mL)
0.3 (0.1, 0.8)
1.1 (0.2, 1.1)
< 0.01
Cr (µmol/L)
81 (63, 138)
111 (89, 156)
< 0.01
BUN (mmol/L)
7.2 (5.5, 10.2)
8.1 (6.7, 10.9)
< 0.01
TBIL (µmol/L)
16.1 (10.5, 21.2)
14.8 (12.4, 22.2)
0.44
DBIL (µmol/L)
6.5 (4.8, 7.8)
8.2 (6.9, 9.5)
< 0.01
ALB (g/L)
33.4 (28.9, 36.4)
27.1 (26.2, 29.4)
< 0.01
Lac (mmol/L)
2.9 (2.2, 5.4)
5.5 (5.5, 14.8)
< 0.01
pH
7.4 (7.3, 7.4)
7.3 (7.2, 7.4)
< 0.01
PCO2 (mmHg)
33 (29, 42)
36 (31, 51)
0.02
PO2 (mmHg)
74 (56, 146)
67 (53, 149)
0.48
HCO3 (mmol/L)
20 (18, 23)
21 (15, 22)
0.76
APTT (s)
37 (28, 50)
35 (31, 52)
0.75
PT (s)
15 (13, 18)
16 (15, 20)
0.02
TT (s)
18 (16, 22)
19 (17, 23)
0.54
D-dimer (mg/L)
6.1 (2.6, 14.9)
6.2 (2.8, 20.0)
0.18
Fg (g/L)
2.9 (1.8, 4.9)
2.9 (1.7, 4.6)
0.59
Hospital stay (days)
21 (10, 32)
4 (1, 10)
< 0.01
Abbreviations: ARDS, acute respiratory distress syndrome; COPD, chronic obstructive pulmonary disease; CRRT, continuous renal replacement therapy; VA-ECMO, venoarterial extracorporeal membrane oxygenation; APACHE II, Acute Physiology and Chronic Health Evaluation II; PLT, platelets; Hb, hemoglobin; WBC, white blood cells; RBC, red blood cells; NEU, neutrophils; CRP, C-reactive protein; PCT, procalcitonin; Cr, creatinine; BUN, blood urea nitrogen; TBIL, total bilirubin; DBIL, direct bilirubin; ALB, albumin; Lac, lactate; p values compare survivors and non-survivors.
Multivariable analysis and model performance
In the overall cohort, higher APACHE II score (aOR 1.081; 95% CI, 1.028–1.137; p = 0.002), systolic blood pressure (aOR 1.024; 95% CI, 1.003–1.046; p = 0.027), and lactate (aOR 1.108; 95% CI, 1.019–1.205; p = 0.017) were independently associated with increased in-hospital mortality, whereas diastolic blood pressure (aOR 0.948; 95% CI, 0.916–0.980; p = 0.002) and albumin (aOR 0.951; 95% CI, 0.905–1.000; p = 0.050) were protective. The model demonstrated good discrimination (AUC 0.820; Fig. 1).
Fig. 1
Multivariable logistic regression and ROC curve for in-hospital mortality among all ECMO patients
Click here to Correct
Note
DM, diabetes mellitus; DA use, dopamine administration; HTN, hypertension; ANC, absolute neutrophil count (×10⁹/L); Lac, serum lactate (mmol/L); SBP, systolic blood pressure (mmHg); DBP, diastolic blood pressure (mmHg); Alb, serum albumin (g/L); APACHE II, Acute Physiology and Chronic Health Evaluation II score. Points and horizontal lines represent adjusted odds ratios (ORs) with 95% confidence intervals (CIs) from multivariable logistic regression. p < 0.05 indicates statistical significance. AUC, area under the ROC curve. Quotation marks (↔) denote that the OR or its 95% CI extends beyond the plotted axis range.
In the VA-ECMO subgroup, higher APACHE II score (aOR 1.053; 95% CI, 1.006–1.102; p = 0.025) was independently associated with increased in-hospital mortality. In contrast, higher levels of arterial pH (aOR 0.046; 95% CI, 0.005–0.407; p = 0.006), albumin (aOR 0.854; 95% CI, 0.796–0.916; p < 0.001), diastolic blood pressure (aOR 0.956; 95% CI, 0.927–0.986; p = 0.004), and white blood cell count (aOR 0.939; 95% CI, 0.889–0.991; p = 0.022) were associated with lower odds of in-hospital mortality. The model demonstrated good discrimination (AUC 0.845; Fig. 2).
Fig. 2
Multivariable logistic regression and ROC curve for in-hospital mortality among VA-ECMO patients
Click here to Correct
Note
AUC, area under the ROC curve; OR, odds ratio; CI, confidence interval; APACHE II, Acute Physiology and Chronic Health Evaluation II. p < 0.05 indicates statistical significance. AUC, area under the ROC curve. Quotation marks (↔) denote that the OR or its 95% CI extends beyond the plotted axis range.
In the VV-ECMO subgroup, higher lactate (aOR 1.247; 95% CI, 1.005–1.547; p = 0.045) was independently associated with increased in-hospital mortality, whereas a longer APTT (aOR 0.931; 95% CI, 0.875–0.989; p = 0.021) was protective. Other variables, including APACHE II score and albumin, were not independently associated with mortality. The model showed good discrimination (AUC 0.844; Fig. 3).
Fig. 3
Multivariable logistic regression and ROC curve for in-hospital mortality among VV-ECMO patients
Click here to Correct
Note
AUC, area under the ROC curve; OR, odds ratio; CI, confidence interval; APACHE II, Acute Physiology and Chronic Health Evaluation II; CRRT, continuous renal replacement therapy; aPTT, activated partial thromboplastin time. Horizontal lines represent adjusted odds ratios (ORs) with 95% confidence intervals (CIs) from multivariable logistic regression. p < 0.05 indicates statistical significance. AUC, area under the ROC curve. Quotation marks (↔) denote that the OR or its 95% CI extends beyond the plotted axis range.
Kaplan–Meier survival comparison (VA-ECMO vs VV-ECMO)
Kaplan–Meier curves showed higher survival in the VV-ECMO group than in the VA-ECMO group (log-rank test, p < 0.001), indicating greater early mortality risk among VA-ECMO patients. Overall, 57.3% of deaths occurred within the first 5 days after ECMO initiation, after which the mortality risk declined (Fig. 4).
Fig. 4
Distribution of in-hospital deaths by ECMO mode and Kaplan–Meier survival among VA-ECMO and VV-ECMO patients
Click here to Correct
Note
Bars show the number of in-hospital deaths within hospital-stay intervals (0–5, 6–10, 11–15, 16–20 days); values above bars denote counts. The dashed lines map the in-hospital mortality rate (%) to the right-hand y-axis. The Kaplan–Meier curve (right panel) compares survival between VA-ECMO and VV-ECMO (log-rank p < 0.001). Day 0 is ECMO initiation; patients are censored at discharge.
Discussion
This multicenter study included 304 ECMO recipients from three emergency ECMO centers, constituting one of the largest cohorts reported in China. The overall in-hospital mortality was 36.2%, and more than half of the deaths occurred within the first 5 days after ECMO initiation. Multivariable analyses identified higher APACHE II score, lactate, and systolic blood pressure as independent predictors of in-hospital mortality, whereas higher diastolic blood pressure and albumin were protective. Subgroup analyses demonstrated significantly poorer survival among patients receiving VA-ECMO than those treated with VV-ECMO. These findings underscore the importance of meticulous patient selection and early-phase management in emergency ECMO. Although outcomes in this population have improved compared with earlier domestic reports, mortality remains substantial, highlighting the need for further optimization of treatment strategies and providing new evidence on mode-specific prognostic differences.
The in-hospital mortality in our cohort (36.2%) was higher than that reported in a multicenter Chinese study by Cheng et al. (29.6%) [11] but lower than in several international cohorts [4, 12, 13]. Several factors may account for these differences. First, the case mix differed: VA-ECMO accounted for 65% of our cohort—higher than in some prior Chinese series—and, given its indication for circulatory failure and higher mortality than VV-ECMO, this likely increased the overall mortality rate [11, 13]. Second, baseline illness severity was high among emergency ECMO candidates. APACHE II is an independent predictor of mortality in ECMO recipients [4, 14]. In our cohort, the median APACHE II exceeded 20; among non-survivors, the median was 28 and 25% had scores > 33—consistent with greater baseline severity and plausibly contributing to higher mortality. Third, CRRT exposure was substantial. Prior studies report ~ 3.7-fold higher in-hospital mortality among ECMO patients receiving CRRT, with mortality exceeding 60% [15]. Mechanistically, concomitant CRRT may worsen hemocompatibility-related cellular injury and amplify systemic inflammation [16, 17]. In our cohort, ~ 50% received CRRT, which was associated with a significantly higher risk of death. Finally, ECMO center practices have become increasingly standardized. About 15 years ago, a Taiwanese cohort reported overall mortality approaching 60% [18]. More recently, the establishment of specialized ECMO teams and progressively standardized protocols for patient selection, anticoagulation, and complication management across centers—domestically and internationally—has markedly improved care quality, which has been closely linked to better outcomes [5, 1113].
In both the overall cohort and the VA-ECMO subgroup, APACHE II remained an independent predictor of in-hospital mortality, consistent with Cui et al [4], supporting its continued prognostic utility. By contrast, APACHE II showed limited discrimination in the VV-ECMO subgroup. plausibly because VV-ECMO candidates typically have isolated respiratory failure and relatively preserved circulation, whereas APACHE II weights hemodynamic and neurologic components. Accordingly, ELSO guidance recommends tools tailored to respiratory ECMO (e.g., RESP) to improve survival estimation in VV-ECMO [19]. Regarding lactate, prior studies highlight its early-warning value [20, 21]. In our data, lactate predicted adverse outcomes in the overall cohort and in VV-ECMO, but not in VA-ECMO, likely because hyperlactatemia in VA-ECMO reflects not only hypoperfusion but also vasoactive support and impaired clearance, reducing specificity. Consistent with prior reports, lactate clearance or trajectories appear more informative than single time points [20, 22]. Blood-pressure indices also diverged: higher systolic pressure correlated with worse outcomes–suggesting “pseudo-stability” under vasopressors amid low cardiac output and microcirculatory dysfunction–whereas higher diastolic pressure was protective, in line with its role in coronary/renal perfusion and with reports of diastolic pressure–guided resuscitation benefiting VA-ECMO patients [23, 24].
Limitations
Although this study is among the larger multicenter cohorts in China, several limitations warrant consideration. First, its retrospective design may have introduced missing data and inter-center variability in documentation (e.g., absent prehospital information). Regional differences in ECMO initiation timing, case mix, and ascertainment of cardiac arrest etiology could reduce cohort homogeneity and limit external generalizability, underscoring the need for larger prospective studies with standardized workflows. Second, we analyzed only static pre-ECMO variables and lacked key process-of-care data (e.g., lactate clearance, time-varying hemodynamics, complications), constraining characterization of disease trajectories and treatment response. Third, the primary endpoint was restricted to in-hospital mortality; longer-term outcomes (post-discharge survival, neurologic recovery, duration of organ support) were not assessed. Future work should adopt prospective designs with multi–time-point monitoring and long-term follow-up to strengthen prognostic inference. In summary, despite more than two decades of ECMO development, in-hospital mortality in this emergency cohort still exceeded one third and remains higher with VA-ECMO than VV-ECMO. Risk profiles differ by ECMO mode, supporting mode-specific risk stratification and individualized management in emergency ECMO.
Declarations
Ethics approval and consent to participate
This study was approved by the Ethics Committee of the First Affiliated Hospital of USTC (approval no. 2024-ky300). Subsequent data updates through February 2025 were conducted under the same approval, and the need for written informed consent was waived.
Consent for publication
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.
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Data Availability
Due to patient privacy and institutional policies, the dataset is not publicly available. De-identified data may be made available by the corresponding author upon reasonable request, subject to approval by the Ethics Committee of the First Affiliated Hospital of the University of Science and Technology of China (Approval No. 2024-ky300) and the execution of a data-use agreement.
Competing interests
The authors declare no competing interests.
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Funding
This study was supported by the Natural Science Foundation of Anhui Province (2208085MH235).
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Author Contribution
XW and KJ contributed to the conception and design of the study. XW, JS, YW, JQ, YS, HY, JX, and SZ contributed to data acquisition. XW, JS and YW performed data analysis. XW and KJ drafted the manuscript. XW and KJ approved the final version of the manuscript.
Acknowledgements
We thank the clinical teams across the participating centers for their support.
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Total words in MS: 3166
Total words in Title: 21
Total words in Abstract: 263
Total Keyword count: 6
Total Images in MS: 8
Total Tables in MS: 1
Total Reference count: 24