Skip to main content
Download PDF

Comparing outcomes of out-of-hospital cardiac arrest patients with initial shockable rhythm in Singapore and Osaka using population-based databases

Critical Care volumeĀ 27, ArticleĀ number:Ā 479 (2023) Cite this article

Abstract

Background

Previous research indicated outcomes among refractory out-of-hospital cardiac arrest (OHCA) patients with initial shockable rhythm were different in Singapore and Osaka, Japan, possibly due to the differences in access to extracorporeal cardiopulmonary resuscitation. However, this previous study had a risk of selection bias. To address this concern, this study aimed to evaluate the outcomes between Singapore and Osaka for OHCA patients with initial shockable rhythm using only population-based databases.

Methods

This was a secondary analysis of two OHCA population-based databases in Osaka and Singapore, including adult OHCA patients with initial shockable rhythm. A machine-learning-based prediction model was derived from the Osaka data (nā€‰=ā€‰3088) and applied to the PAROS-SG data (nā€‰=ā€‰2905). We calculated the observed-expected ratio (OE ratio) for good neurological outcomes observed in Singapore and the expected derived from the data in Osaka by dividing subgroups with or without prehospital ROSC.

Results

The one-month good neurological outcomes in Osaka and Singapore among patients with prehospital ROSC were 70% (791/1,125) and 57% (440/773), and among patients without prehospital ROSC were 10% (196/1963) and 2.8% (60/2,132). After adjusting patient characteristics, the outcome in Singapore was slightly better than expected from Osaka in patients with ROSC (OE ratio, 1.067 [95%CI 1.012 to 1.125]), conversely, it was worse than expected in patients without prehospital ROSC (OE ratio, 0.238 [95%CI 0.173 to 0.294]).

Conclusion

This study showed the outcomes of OHCA patients without prehospital ROSC in Singapore were worse than expected derived from Osaka data even using population-based databases.

(249/250 words).

Introduction

Extracorporeal cardiopulmonary resuscitation (ECPR), which utilizes extracorporeal membrane oxygenation (ECMO) during cardiopulmonary resuscitation, is an advanced procedure designed for OHCA patients who are unresponsive to standard resuscitation, especially those presenting with initial shockable rhythms like ventricular fibrillation (VF) or pulseless ventricular tachycardia (VT) [1,2,3,4]. Our previous research indicated the frequency of ECPR differs greatly between Osaka, Japan, and Singapore for refractory OHCA patients with initial shockable rhythm, and, among such patients, observed neurologically favorable outcomes and survival in Singapore was less than expected compared to Osaka [2]. However, this previous study had a substantial risk of selection bias, as the reference population in Osaka included only patients who were selected and transferred to tertiary care hospitals. To address concerns about selection bias and to confirm the robustness of previous results, we investigated the reproducibility and validity using population-based data. We also estimated the additional number of patients, using population-based data, who might obtain favorable outcomes if the resuscitation strategy in Osaka was implemented in Singapore. This study aimed to evaluate the validity of the previous results indicating outcome differences among OHCA patients with initial shockable rhythm in Singapore and Osaka using population-based data.

Methods

This was a secondary analysis of two population-based OHCA databases in Singapore and Osaka, Japan, which were the All-Japan Utstein Registry and the Singapore Pan-Asian Resuscitation Outcomes Study (SG-PAROS) [5, 6]. Detailed information about these databases can be found in the Additional fileĀ 1 (S-Method 1). We extracted data on the Osaka Prefecture from this nationwide registry, with the aim of addressing the limitations of our previous study as mentioned in the background section. Similar to the previous study, [2] we included adult OHCA patients aged 18ā€“74 with initial shockable rhythms, and excluded those without prehospital records, those who didn't receive resuscitation, had external causes or weren't in arrest when paramedics arrived. A machine-learning-based prediction model derived and validated as mentioned below using data from the Osaka data (derivation data 2010ā€“2018, validation data 2019), was applied to the PAROS-SG data (2010ā€“2019). The primary outcome of this study was one-month survival with favorable neurological outcomes defined as Cerebral Performance Category (CPC) 1 or 2. The secondary outcome of this study was one-month survival [7].

In the statistical analysis, patient characteristics, pre-hospital information, in-hospital procedures, and outcomes were described as continuous variables with median and interquartile range (IQR), while categorical variables were reported as numbers and percentages. In the main analysis, similar to the previous study, [2] we developed and validated machine-learning predictive models (random forest) of outcomes using the Osaka derivation data. The prediction models incorporated the following covariates: sex, age, witnessed events, bystander CPR, bystander automated external defibrillator, prehospital advanced airway management, prehospital adrenaline administration, and time from call to the hospital. We assessed the model's performance using the Osaka validation data. Then, we applied the prediction model to the SG-PAROS data in order to calculate the expected probability of outcomes. Subsequently, we computed the observed-expected ratio (OE ratio) and the observed-expected difference (OE difference) between the observed outcomes and the expected probability derived from the Osaka data with 95% confidence intervals (CI). Moreover, we estimated the incremental number of patients with the outcome by multiplying the OE difference by the number of cases. We calculated these values separately for patients with and without prehospital ROSC, to evaluate the potential impact of resuscitation strategies such as ECPR after hospital arrival but before ROSC. All statistical analyses were performed using R software, version 4.1.2 (R Foundation for Statistical Computing). The other details of the methods are described in the Additional file 1 (S-Method 2ā€“4).

Results

Patient characteristics

For the Osaka Utstein Registry database, 3414 OHCA patients with an initial shockable rhythm were included in the analysis [the derivation cohort (nā€‰=ā€‰3,088) and the validation cohort (nā€‰=ā€‰326)]. Of the SG-PAROS database, 2905 patients with initial shockable rhythm were included in the analysis. The study flowchart is described in the Additional file 1 (S-Results 1). The median [IQR] age was 62 [51, 68] in the derivation cohort in Osaka, and 58 [50, 65] in the SG-PAROS. Good neurological outcomes were 32% (987/3,088) in Osaka and 17% (500/2,905) in Singapore. The prehospital ROSC were 36% (1,125/3,088) in Osaka and 27% (773/2,905) in Singapore, and among them, one-month good neurological outcomes were 70% (791/1,125) in Osaka and 57% (440/773), respectively. In comparison, among the patients without prehospital ROSC, good neurological outcomes were 10.0% (196/1,963) and 2.8% (60/2,132), respectively. The other details of characteristics, in-hospital information, and outcomes are described in Table 1 and the Additional file 1: (S-Results 2ā€“5). ECMO was rarely performed in both populations of Singapore (with prehospital ROSC, 0.9% and without 0.5%). The prediction model derived from the Osaka derivation data performed well in the validation data. The details of the prediction model are described in the Additional fileĀ 1 (S-Results 6ā€“7).

Table 1 Patient characteristics, in-hospital information, and outcomes
Full size table

The observed expected ratio, difference, and incremental number of patients

The one-month good neurological outcome in patients with prehospital ROSC in Singapore was the same or slightly better than expected compared to Osaka (OE ratio, 1.067 [95%CI 1.012 to 1.125] and OE difference, 0.036 [95%CI 0.006 to 0.066]). Conversely, the neurological outcome in patients without prehospital ROSC in Singapore was worse than expected compared to Osaka (OE ratio, 0.238 [95%CI 0.173 to 0.294] and OE difference, āˆ’Ā 0.09 [95%CI āˆ’Ā 0.096 to -0.083]), (Fig.Ā 1). Similarly, for one-month survival, the result was consistent as follows: with prehospital ROSC, OE ratio, 0.97 [95%CI 0.925 to 1.012] and OE difference, āˆ’Ā 0.021 [95%CI āˆ’Ā 0.051 to 0.008], and without prehospital ROSC, OE ratio 0.297 [95%CI 0.242 to 0.345] and OE difference, -0.128 [95%CI -0.138 to -0.119], (Fig.Ā 1).

Fig. 1
figure 1

OE difference and the incremental number of patients with or without Prehospital ROSC. (Left) The Observed-Expected ratio and 95% confidence interval (CI). (Right) The Observed-Expected difference and 95% confidence interval (CI). Good Neuro One-month good neurological outcome, Survival One-month survival, ROSC, Return of spontaneous circulation

Full size image

The incremental one-month good neurological outcome in patients with prehospital ROSC was better than expected (27.8 [95% CI 4.9 to 50.7]), but was fewer than expected (āˆ’Ā 191.9 [95% CI āˆ’205.6 to āˆ’Ā 177.6]) in patients without prehospital ROSC) between 2010 and 2019. The incremental number of one-month survival cases had a similar trend to neurological outcomes (patients with pre-hospital ROSC: āˆ’Ā 16.3 [95%CI āˆ’Ā 39.1 to 5.9], patients without prehospital ROSC: āˆ’Ā 272.7 [95%CI āˆ’Ā 293.2 to āˆ’Ā 253.8]). The incremental one-month good neurological outcome in each year is described in the Additional fileĀ 1 (S-Results 8).

Discussion

This population-based observational study showed that the outcomes of OHCA patients without prehospital ROSC in Singapore were worse than expected derived from Osaka data even using population-based databases. These results were consistent with our previous research, mitigating previous selection bias concerns.

Similar to the previous study, we speculated that the outcome difference among patients without ROSC between Singapore and Osaka was related to the different availability of ECPR. First, outcomes were similar among patients with ROSC after adjusting the patientsā€™ characteristics, which suggests the quality of post-resuscitation care after ROSC was comparable between Singapore and Osaka. In contrast, for patients without prehospital ROSC, ECMO was rarely performed (0.5%, 11/2,132) and hospital admission was considerably low (20%, 435/2,132) in Singapore while the percentage of ECPR cases was 30ā€“60% and hospital admission was reported around 60ā€“80% in the tertiary care hospitals in Osaka. [2] Considering the reports from RCTs that the hospital admission of the patients treated with ECPR were remarkably higher than the conventional group [e.g., INCEPTION trial, ECPR group 81% (57/70) vs 36% (23/64)], we suggested that the difference of hospital admission may be mainly caused by the difference of frequency of ECPR. [8, 9] In addition, the OE differences in neurological outcomes among patients without ROSC was approximately 9ā€“10%, which is comparable to the effect magnitude of the ECPR strategy observed in a previous study in Japan (SAVE-J study) [12.3% (32/260) and 1.5% (3/194), with a risk difference of 10.8%], or the RCT in Prague, which also reported a risk difference of approximately 9.5%. [10, 11] Therefore, considering that the OE difference in our study closely aligns with the effect estimates from previous studies investigating aggressive ECPR strategies, we think it is reasonable to attribute the difference to ECPR strategies.

Several limitations should be acknowledged in this study, similar to the previous study. It didn't directly examine the link between ECPR policies and outcomes but instead highlighted potential differences in in-hospital care before ROSC between Osaka and Singapore. Outcomes might also be influenced by unmeasured factors like past medical history, frailty, and quality of resuscitation. A lack of detailed data limited our comparison of resuscitation specifics. Moreover, there are risks associated with the prediction model, such as overfitting and regularization bias. [12, 13].

Conclusion

This study using two population-based databases indicated that the outcomes of OHCA patients without prehospital ROSC in Singapore were worse than expected compared to Osaka. The results are consistent with our previous research. We speculate that the outcome difference among patients without ROSC between Singapore and Osaka was due to different availability of ECPR.

(1455/1500 words).

Availability of data and materials

This study's datasets and/or analyses are not publicly available because the ethics committee did not permit it.

References

  1. Inoue A, Hifumi T, Sakamoto T, Kuroda Y. Extracorporeal cardiopulmonary resuscitation for out-of-hospital cardiac arrest in adult patients. J Am Heart Assoc. 2020;9(7):e015291.

    ArticleĀ  PubMedĀ  PubMed CentralĀ  Google ScholarĀ 

  2. Okada Y, Shahidah N, Ng YY, Chia MYC, Gan HN, Leong BSH, et al. Outcome assessment for out-of-hospital cardiac arrest patients in Singapore and Japan with initial shockable rhythm. Crit Care. 2023;27(1):351.

    ArticleĀ  PubMedĀ  PubMed CentralĀ  Google ScholarĀ 

  3. Okada Y, Kiguchi T, Irisawa T, Yamada T, Yoshiya K, Park C, et al. Development and validation of a clinical score to predict neurological outcomes in patients with out-of-hospital cardiac arrest treated with extracorporeal cardiopulmonary resuscitation. JAMA Netw Open. 2020;3(11):e2022920.

    ArticleĀ  PubMedĀ  PubMed CentralĀ  Google ScholarĀ 

  4. Okada Y, Komukai S, Kitamura T, Kiguchi T, Irisawa T, Yamada T, et al. Clinical phenotyping of out-of-hospital cardiac arrest patients with shockable rhythmā€“machine learning-based unsupervised cluster analysis. Circ J. 2022;86(4):668ā€“76.

    ArticleĀ  PubMedĀ  Google ScholarĀ 

  5. Okada Y, Irisawa T, Yamada T, Yoshiya K, Park C, Nishimura T, et al. Clinical outcomes among out-of-hospital cardiac arrest patients treated by extracorporeal cardiopulmonary resuscitation: The CRITICAL study in Osaka. Resuscitation. 2022; Ireland: Elsevier.

  6. Doctor NE, Ahmad NS, Pek PP, Yap S, Ong ME. The Panā€“Asian resuscitation outcomes study (PAROS) clinical research network: what, where, why and how. Singapore Med J. 2017;58(7):456ā€“8.

    ArticleĀ  PubMedĀ  PubMed CentralĀ  Google ScholarĀ 

  7. Cummins RO, Chamberlain DA, Abramson NS, Allen M, Baskett PJ, Becker L, et al. Recommended guidelines for uniform reporting of data from out-of-hospital cardiac arrest: the Utstein style. A statement for health professionals from a task force of the American heart association, the European resuscitation council, the heart and stroke foundation of Canada, and the Australian resuscitation council. Circulation. 1991;84(2):960ā€“75.

    ArticleĀ  CASĀ  PubMedĀ  Google ScholarĀ 

  8. Suverein MM, Delnoij TSR, Lorusso R, Brandon Bravo Bruinsma GJ, Otterspoor L, Elzo Kraemer CV, et al. Early Extracorporeal CPR for refractory out-of-hospital cardiac arrest. New Eng J Med 2023; 388(4): 299ā€“309.

  9. Yannopoulos D, Bartos J, Raveendran G, Walser E, Connett J, Murray TA, et al. Advanced reperfusion strategies for patients with out-of-hospital cardiac arrest and refractory ventricular fibrillation (ARREST): a phase 2, single centre, open-label, randomised controlled trial. The Lancet. 2020;396(10265):1807ā€“16.

    ArticleĀ  Google ScholarĀ 

  10. Sakamoto T, Morimura N, Nagao K, Asai Y, Yokota H, Nara S, et al. Extracorporeal cardiopulmonary resuscitation versus conventional cardiopulmonary resuscitation in adults with out-of-hospital cardiac arrest: a prospective observational study. Resuscitation. 2014;85(6):762ā€“8.

    ArticleĀ  PubMedĀ  Google ScholarĀ 

  11. Belohlavek J, Smalcova J, Rob D, Franek O, Smid O, Pokorna M, et al. Effect of intra-arrest transport, extracorporeal cardiopulmonary resuscitation, and immediate invasive assessment and treatment on functional neurologic outcome in refractory out-of-hospital cardiac arrest: a randomized clinical trial. JAMA. 2022;327(8):737ā€“47.

    ArticleĀ  PubMedĀ  PubMed CentralĀ  Google ScholarĀ 

  12. KĆ¼hl N, Schemmer M, Goutier M, Satzger G. Artificial intelligence and machine learning. Electron Mark. 2022;32(4):2235ā€“44.

    ArticleĀ  Google ScholarĀ 

  13. James G, Witten D, Hastie T, Tibshirani R, Service S. An Introduction to Statistical Learning: with Applications in R. New York: Springer; 2013.

    Google ScholarĀ 

Download references

Acknowledgements

The authors are deeply grateful to all EMS personnel for collecting the Utstein data. The authors would also like to thank Ms. Maeve Pek and the late Ms. Susan Yap from the Department of Emergency Medicine, Singapore General Hospital; Ms Noor Azuin and Ms. Nurul Asyikin from Unit for Prehospital Emergency Care, Singapore General Hospital; Ms Woo Kai Lee from National University Heart Centre Singapore for their contributions and support to the Singapore PAROS registry, and all other Singapore PAROS investigators (Ling Tiah, Changi General Hospital, Singapore; Si Oon Cheah, Urgent Care Clinic International, Singapore; Shun Yee Low, Sengkang General Hospital, Singapore; Wei Ling Tay, Ng Teng Fong General Hospital, Singapore; Shir Lynn Lim, National University Heart Centre Singapore, Singapore) that contributed to the study. The authors thank the JSPS Overseas Research Fellowships, the FUKUDA Foundation for Medical Technology, and the International Medical Research Foundation for their support.

Funding

This study was supported by a scientific research grant from the JSPS KAKENHI of Japan (JP22K21143 to YO), the ZOLL Foundation (YO), National Medical Research Council, Clinician Scientist Award, Singapore (NMRC/CSA/024/2010, NMRC/CSA/0049/2013 and NMRC/CSASI/014/2017) to MEHO, and Ministry of Health, Health Services Research Grant, Singapore (HSRG/0021/2012) to MEHO. These organizations have no role in conducting this research.

Author information

Authors and Affiliations

  1. Duke-NUS Medical School, Health Services and Systems Research, Singapore, Singapore

    Yohei OkadaĀ &Ā Marcus Eng Hock Ong

  2. Department of Preventive Services, Graduate School of Medicine, Kyoto University, Kyoto, Japan

    Yohei Okada,Ā Takeyuki Kiguchi,Ā Norihiro NishiokaĀ &Ā Taku Iwami

  3. Department of Emergency Medicine, Singapore General Hospital, Singapore, Singapore

    Nur ShahidahĀ &Ā Marcus Eng Hock Ong

  4. Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore, Singapore

    Yih Yng Ng

  5. Department of Preventive and Population Medicine, Tan Tock Seng Hospital, Singapore, Singapore

    Yih Yng Ng

  6. Emergency Department, Tan Tock Seng Hospital, Singapore, Singapore

    Michael Y. C. Chia

  7. Accident and Emergency, Changi General Hospital, Singapore, Singapore

    Han Nee Gan

  8. Emergency Medicine Department, National University Hospital, Singapore, Singapore

    Benjamin S. H. Leong

  9. Department of Acute and Emergency Care, Khoo Teck Puat Hospital, Singapore, Singapore

    Desmond R. Mao

  10. Emergency Medicine Department, Ng Teng Fong General Hospital, Singapore, Singapore

    Wei Ming Ng

  11. Department of Emergency Medicine, Sengkang General Hospital, Singapore, Singapore

    Nausheen Edwin

  12. Division of Environmental Medicine and Population Sciences, Department of Social and Environmental Medicine, Graduate School of Medicine, Osaka University, Osaka, Japan

    Tetsuhisa Kitamura

  13. Department of Emergency and Critical Care, Osaka General Medical Center, Osaka, Japan

    Takeyuki Kiguchi

Authors
  1. Yohei Okada
    View author publications

    You can also search for this author in PubMedĀ Google Scholar

  2. Nur Shahidah
    View author publications

    You can also search for this author in PubMedĀ Google Scholar

  3. Yih Yng Ng
    View author publications

    You can also search for this author in PubMedĀ Google Scholar

  4. Michael Y. C. Chia
    View author publications

    You can also search for this author in PubMedĀ Google Scholar

  5. Han Nee Gan
    View author publications

    You can also search for this author in PubMedĀ Google Scholar

  6. Benjamin S. H. Leong
    View author publications

    You can also search for this author in PubMedĀ Google Scholar

  7. Desmond R. Mao
    View author publications

    You can also search for this author in PubMedĀ Google Scholar

  8. Wei Ming Ng
    View author publications

    You can also search for this author in PubMedĀ Google Scholar

  9. Nausheen Edwin
    View author publications

    You can also search for this author in PubMedĀ Google Scholar

  10. Takeyuki Kiguchi
    View author publications

    You can also search for this author in PubMedĀ Google Scholar

  11. Norihiro Nishioka
    View author publications

    You can also search for this author in PubMedĀ Google Scholar

  12. Tetsuhisa Kitamura
    View author publications

    You can also search for this author in PubMedĀ Google Scholar

  13. Taku Iwami
    View author publications

    You can also search for this author in PubMedĀ Google Scholar

  14. Marcus Eng Hock Ong
    View author publications

    You can also search for this author in PubMedĀ Google Scholar

Contributions

Conceptualization, YO, MO. Data curation, NS, YYN, MYCC, HNG, BSHL, DRM, NED, WMN, TK, TI, MEHO. Methodology, YO, MO. Analysis, YO, MO. Writingā€“original draft, YO, MO. Writingā€“review & editing, NS, YYN, BSHL, TK, NN, TK, TI. Project administration, NS, TK. Funding Acquisition, YO, MEHO. All authors agree to be accountable for all aspects of the work.

Corresponding author

Correspondence to Yohei Okada.

Ethics declarations

Ethical approval and consent participate

This study was conducted according to the Declaration of Helsinki. This study was approved by the Centralized Institutional Review Board and Domain Specific Review Board in Singapore granted approval for the SG-PAROS database (ref no: 2013/604/C, 2013/00929 and 2018/2937) and Domain Specific Review Board (ref no: C/10/545 and 2013/00929) and the ethics committee of Kyoto University (R-1538). Informed consent was waived due to the nature of the observational study. Informed consent was waived due to the nature of the observational study.

Consent for publication

Not applicable.

Competing interests

YO has received a research grant from the ZOLL Foundation and an overseas scholarship from the FUKUDA Foundation for Medical Technology and the International Medical Research Foundation. MEHO reports grants from the Laerdal Foundation, Laerdal Medical, and Ramsey Social Justice Foundation for funding of the Pan-Asian Resuscitation Outcomes Study an advisory relationship with Global Healthcare SG, a commercial entity that manufactures cooling devices; and funding from Laerdal Medical on an observation program to their Community CPR Training Centre Research Program in Norway.Ā MEHO is a Scientific Advisor to TIIM Healthcare SG and Global Healthcare SG. These organizations have no role in conducting this research.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary Information

Additional file 1.

Supplementary material.

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Okada, Y., Shahidah, N., Ng, Y.Y. et al. Comparing outcomes of out-of-hospital cardiac arrest patients with initial shockable rhythm in Singapore and Osaka using population-based databases. Crit Care 27, 479 (2023). https://0-doi-org.brum.beds.ac.uk/10.1186/s13054-023-04771-5

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://0-doi-org.brum.beds.ac.uk/10.1186/s13054-023-04771-5

Critical Care

ISSN: 1364-8535