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Prehospital predicting factors using a decision tree model for patients with witnessed out-of-hospital cardiac arrest and an initial shockable rhythm

Tateishi, Kazuya 大阪大学

2023.12.01

概要

Title

Prehospital predicting factors using a decision
tree model for patients with witnessed out-ofhospital cardiac arrest and an initial shockable
rhythm

Author(s)

Tateishi, Kazuya; Saito, Yuichi; Yasufuku,
Yuichi et al.

Citation

Scientific Reports. 2023, 13(1), p. 16180

Version Type VoR
URL
rights

https://hdl.handle.net/11094/92832
This article is licensed under a Creative
Commons Attribution 4.0 International License.

Note

Osaka University Knowledge Archive : OUKA
https://ir.library.osaka-u.ac.jp/
Osaka University

www.nature.com/scientificreports

OPEN

Prehospital predicting factors using
a decision tree model for patients
with witnessed out‑of‑hospital
cardiac arrest and an initial
shockable rhythm
Kazuya Tateishi 1,8*, Yuichi Saito 1,8, Yuichi Yasufuku 2,8, Atsushi Nakagomi 1,8,
Hideki Kitahara 1, Yoshio Kobayashi 1, Yoshio Tahara 3, Naohiro Yonemoto 4, Takanori Ikeda 5,
Naoki Sato 6 & Hiroyuki Okura 7
The effect of prehospital factors on favorable neurological outcomes remains unclear in patients with
witnessed out-of-hospital cardiac arrest (OHCA) and a shockable rhythm. We developed a decision
tree model for these patients by using prehospital factors. Using a nationwide OHCA registry database
between 2005 and 2020, we retrospectively analyzed a cohort of 1,930,273 patients, of whom 86,495
with witnessed OHCA and an initial shockable rhythm were included. The primary endpoint was
defined as favorable neurological survival (cerebral performance category score of 1 or 2 at 1 month). A
decision tree model was developed from randomly selected 77,845 patients (development cohort) and
validated in 8650 patients (validation cohort). In the development cohort, the presence of prehospital
return of spontaneous circulation was the best predictor of favorable neurological survival, followed
by the absence of adrenaline administration and age. The patients were categorized into 9 groups
with probabilities of favorable neurological survival ranging from 5.7 to 70.8% (areas under the
receiver operating characteristic curve of 0.851 and 0.844 in the development and validation cohorts,
respectively). Our model is potentially helpful in stratifying the probability of favorable neurological
survival in patients with witnessed OHCA and an initial shockable rhythm.
The prognosis of patients with out-of-hospital cardiac arrest (OHCA) has improved with the development of
prehospital and postcardiac arrest care, but OHCA remains a health concern w
­ orldwide1,2. Recently, practical
predictive scoring systems have been developed for evaluating the return of spontaneous circulation (ROSC),
overall survival, and favorable neurological survival, providing stratification of OHCAs and facilitating decisionmaking3–8. Bystander witness and initial shockable rhythm are well-known good predictors of resuscitation and
favorable neurological ­survival9,10. In addition, early defibrillation plays a crucial role in achieving ROSC in
patients with OHCA and a shockable r­ hythm11, in whom prehospital predictors of favorable outcomes may be
different from those in the entire OHCA population. The type of patients who would benefit most from early
therapeutic strategies, such as defibrillation, in an OHCA setting remains controversial.
The decision tree model developed using recursive partitioning analysis can uniquely provide the probability of a favorable neurological survival and risk stratification of ­OHCAs12, which is readily available in clinical

1

Department of Cardiovascular Medicine, Chiba University Graduate School of Medicine, 1‑8‑1 Inohana,
Chuo‑ku, Chiba, Chiba  260‑8677, Japan. 2Department of Biostatistics and Data Science, Graduate School
of Medicine, Osaka University, Osaka, Japan. 3Department of Cardiovascular Medicine, National Cerebral
and Cardiovascular Center, Osaka, Japan. 4Department of Public Health, Juntendo University School of
Medicine Tokyo, Tokyo, Japan. 5Department of Cardiovascular Medicine, Faculty of Medicine, Toho University,
Tokyo, Japan. 6Cardiovascular Medicine, Kawaguchi Cardiovascular and Respiratory Hospital, Saitama,
Japan. 7Department of Cardiology, Gifu University Graduate School of Medicine, Gifu, Japan. 8These authors
contributed equally: Kazuya Tateishi, Yuichi Saito, Yuichi Yasufuku and Atsushi Nakagomi. *email: kxtateishi@
gmail.com
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practice. Therefore, we aimed to identify the prehospital factors that would affect favorable neurological survival
in patients with witnessed OHCA and an initial shockable rhythm using the decision tree model.

Methods

Study design and population (data source)

In this retrospective observational study, we used prospectively collected nationwide data from patients with
OHCA in Japan based on the Utstein-style t­ emplate13,14. We identified patients aged ≥ 18 years who were transported to a hospital by emergency medical services (EMS) due to OHCA between January 2005 and December
2020. Patients were excluded based on the following criteria: (1) absence of a witness; (2) absence of an initial
shockable rhythm; and (3) unknown variable information (Fig. 1). The missing rates of the variables are shown
in Table S1.
This study complied with the Declaration of Helsinki regarding human investigations. The Ethics Committee
of Chiba University approved this study (unique identifier: #M10316). The requirement for written informed
consent has been waived by the Ethics Committee of Chiba University because the data were anonymized.

Emergency medical service system in Japan

Japan has approximately 800 fire stations with dispatch centers in 47 prefectures. The EMS system is under
the supervision of the Fire and Disaster Management Agency (FDMA). EMS personnel, in cooperation with
physicians, record data on OHCA patients using a Utstein-style template. The data are then integrated into the
National Registry System on the FDMA database server and are checked by the computer system. If any problems
are detected, data are sent back to the corresponding fire stations for correction. We utilized anonymous data
from the registry, including age, sex, witness, type of initial rhythm, type of bystander, public access automated
external defibrillator (AED), number of defibrillation attempts, waveforms of the defibrillator (i.e., monophasic
or biphasic), type of airway management device, and adrenaline use. Furthermore, prehospital ROSC, etiology
of cardiac arrest, 1-month survival, and neurological function were assessed using the cerebral performance
category (CPC) score at 1 month. In addition, information on the time course of collapse, initiation of cardiopulmonary resuscitation (CPR), prehospital ROSC, and arrival at the hospital was obtained.
According to Japanese g­ uidelines15, out-of-hospital EMS providers are not allowed to terminate resuscitation in the field. Therefore, all patients with OHCA treated by EMS providers are transported to a hospital. EMS
personnel are permitted to perform general medical treatments including the use of AED, basic airway adjuncts,

Figure 1.  Study flow. EMS, emergency medical services; PEA, pulseless electrical activity; VF, ventricular
fibrillation; VT, ventricular tachycardia.

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and peripheral intravenous catheters. Furthermore, the insertion of a tracheal tube and the administration of
intravenous adrenaline are allowed only under the instructions of a physician in the command center.

Definition and endpoints

The primary endpoint was a favorable neurological survival at 1 month, which was defined as survival with a
CPC score of 1 or 2­ 16. We also developed a decision tree model to predict survival at 1 month. Daytime admission was defined as admission to the hospital between 6:00 AM and 5:59 PM. Weekend/holiday admission was
defined as admission on a Saturday, Sunday, or Japanese national holiday.

Statistical analysis

Statistical analysis was performed using the Stata statistical software package version 15.1 (StataCorp LLC, Texas,
USA). Continuous variables are expressed as mean ± standard deviation and were compared using Student’s t-test.
Categorical data are presented as absolute numbers and percentages and were compared using the chi-square
test. Differences were considered statistically significant at p < 0.05.
The following 16 prehospital variables were selected for developing a prediction model: age (years old),
male (yes or no), collapse witnessed by EMS personnel (yes or no), bystander CPR by citizen (yes or no), chest
compression by citizen (yes or no), rescue breathing by citizen (yes or no), AED by citizen (yes or no), biphasic
defibrillator (yes or no), the number of defibrillation attempts (times), prehospital use of adrenaline (yes or no),
prehospital ROSC (yes or no), collapse-to-CPR time interval (min), collapse-to-first shock time interval (min),
collapse-to-hospital arrival time interval (min), daytime admission (yes or no), and weekend/holiday admission
(yes or no). Variables with “yes or no” were considered dichotomous.
To develop a decision tree model for the outcomes, we conducted a recursive partitioning analysis using the
Gini ­index17,18. Recursive partitioning analysis can provide a branching decision tree by dividing the patient
population into subgroups based on the analysis results of the relationship between outcomes and prehospital
­variables19. We initially randomly divided all patients into the validation and development cohorts (a ratio of
1:9). Using the development cohort, tenfold cross-validation was then performed to generate a classification and
regression tree. Finally, the predictive ability of the classification and regression tree model was assessed in the
development and validation cohorts. ...

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参考文献

1. Berdowski, J., Berg, R. A., Tijssen, J. G. & Koster, R. W. Global incidences of out-of-hospital cardiac arrest and survival rates:

Systematic review of 67 prospective studies. Resuscitation 81, 1479–1487 (2010).

2. Nichol, G. et al. Regional variation in out-of-hospital cardiac arrest incidence and outcome. JAMA 300, 1423–1431 (2008).

3. Gräsner, J. T. et al. ROSC after cardiac arrest–the RACA score to predict outcome after out-of-hospital cardiac arrest. Eur. Heart

J. 32, 1649–1656 (2011).

4. Baldi, E. et al. An Utstein-based model score to predict survival to hospital admission: the UB-ROSC score. Int. J. Cardiol. 308,

84–89 (2020).

5. Liu, N. et al. Development and validation of an interpretable prehospital return of spontaneous circulation (P-ROSC) score for

patients with out-of-hospital cardiac arrest using machine learning: A retrospective study. eClinicalMedicine 48, 101422 (2022).

6. Gue, Y. X. et al. Out-of-hospital cardiac arrest: A systematic review of current risk scores to predict survival. Am. Heart J. 234,

31–41 (2021).

7. Wong, X. Y. et al. Development and validation of the SARICA score to predict survival after return of spontaneous circulation in

out of hospital cardiac arrest using an interpretable machine learning framework. Resuscitation 170, 126–133 (2022).

8. Wu, W., Chopra, A., Ziegler, C., McLeod, S. L. & Lin, S. Predictive value of hospital discharge neurological outcome scores for

long-term neurological status following out-of-hospital cardiac arrest: A systematic review. Resuscitation 151, 139–144 (2020).

9. Sasson, C., Rogers, M. A., Dahl, J. & Kellermann, A. L. Predictors of survival from out-of-hospital cardiac arrest: A systematic

review and meta-analysis. Circ. Cardiovasc. Qual. Outcomes 3, 63–81 (2010).

10. Weisfeldt, M. L. & Becker, L. B. Resuscitation after cardiac arrest: A 3-phase time-sensitive model. JAMA 288, 3035–3038 (2002).

11. Nakahara, S. et al. Association of bystander interventions with neurologically intact survival among patients with bystanderwitnessed out-of-hospital cardiac arrest in Japan. JAMA 314, 247–254 (2015).

12. Goto, Y., Maeda, T. & Goto, Y. Decision-tree model for predicting outcomes after out-of-hospital cardiac arrest in the emergency

department. Crit. Care 17, R133 (2013).

13. Cummins, R. O. 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 84, 960–975 (1991).

14. Jacobs, I. et al. Cardiac arrest and cardiopulmonary resuscitation outcome reports: Update and simplification of the Utstein templates for resuscitation registries: A statement for healthcare professionals from a task force of the International Liaison Committee

on Resuscitation (American Heart Association, European Resuscitation Council, Australian Resuscitation Council, New Zealand

Resuscitation Council, Heart and Stroke Foundation of Canada, InterAmerican Heart Foundation, Resuscitation Councils of

Southern Africa). Circulation 110, 3385–3397 (2004).

15. Nagao, K. et al. Duration of prehospital resuscitation efforts after out-of-hospital cardiac arrest. Circulation 133, 1386–1396 (2016).

16. Perkins, G. D. et al. Cardiac arrest and cardiopulmonary resuscitation outcome reports: update of the Utstein Resuscitation Registry Templates for Out-of-Hospital Cardiac Arrest: A statement for healthcare professionals from a task force of the International

Liaison Committee on Resuscitation (American Heart Association, European Resuscitation Council, Australian and New Zealand

Council on Resuscitation, Heart and Stroke Foundation of Canada, InterAmerican Heart Foundation, Resuscitation Council of

Southern Africa, Resuscitation Council of Asia); and the American Heart Association Emergency Cardiovascular Care Committee

and the Council on Cardiopulmonary, Critical Care, Perioperative and Resuscitation. Circulation 132, 1286–1300 (2015).

17. Mingers, J. An empirical comparison of selection measures for decision-tree induction. Mach. Learn. 3, 319–342 (1989).

18. Farris, F. The Gini index and measures of inequality. Am. Math Month 117, 336–339 (2010).

19. Hess, E. P. et al. Development of a clinical prediction rule for 30-day cardiac events in emergency department patients with chest

pain and possible acute coronary syndrome. Ann. Emerg. Med. 59, 115–25.e1 (2012).

20. Sandroni, C., Cronberg, T. & Sekhon, M. Brain injury after cardiac arrest: Pathophysiology, treatment, and prognosis. Intensive

Care Med. 47, 1393–1414 (2021).

21. Thomassen, A. & Wernberg, M. Prevalence and prognostic significance of coma after cardiac arrest outside intensive care and

coronary units. Acta Anaesthesiol. Scand. 23, 143–148 (1979).

22. Dragancea, I., Rundgren, M., Englund, E., Friberg, H. & Cronberg, T. The influence of induced hypothermia and delayed prognostication on the mode of death after cardiac arrest. Resuscitation 84, 337–342 (2013).

23. Adrie, C. et al. Predicting survival with good neurological recovery at hospital admission after successful resuscitation of out-ofhospital cardiac arrest: The OHCA score. Eur. Heart J. 27, 2840–2845 (2006).

24. Seewald, S. et al. CaRdiac Arrest Survival Score (CRASS)—a tool to predict good neurological outcome after out-of-hospital cardiac

arrest. Resuscitation 146, 66–73 (2020).

25. Fonarow, G. C. et al. Risk stratification for in-hospital mortality in acutely decompensated heart failure: Classification and regression tree analysis. JAMA 293, 572–580 (2005).

26. Hirano, Y., Kondo, Y., Sueyoshi, K., Okamoto, K. & Tanaka, H. Early outcome prediction for out-of-hospital cardiac arrest with

initial shockable rhythm using machine learning models. Resuscitation 158, 49–56 (2021).

27. Panchal, A. R. et al. Part 3: Adult basic and advanced life support: 2020 American Heart Association guidelines for cardiopulmonary

resuscitation and emergency cardiovascular care. Circulation 142(16_suppl_2), S366–S468 (2020).

28. Perkins, G. D. et al. A randomized trial of epinephrine in out-of-hospital cardiac arrest. N. Engl. J. Med. 379, 711–721 (2018).

29. Vargas, M., Buonanno, P., Iacovazzo, C. & Servillo, G. Epinephrine for out of hospital cardiac arrest: A systematic review and metaanalysis of randomized controlled trials. Resuscitation 136, 54–60. https://​doi.​org/​10.​1016/j.​resus​citat​ion.​2019.​01.​016 (2019).

Scientific Reports |

(2023) 13:16180 |

https://doi.org/10.1038/s41598-023-43106-w

Vol.:(0123456789)

www.nature.com/scientificreports/

30. Hagihara, A. et al. Prehospital epinephrine use and survival among patients with out-of-hospital cardiac arrest. JAMA 307,

1161–1168 (2012).

31. Kitamura, T. et al. Nationwide improvements in survival from out-of-hospital cardiac arrest in Japan. Circulation 126, 2834–2843

(2012).

32. Larsen, M. P., Eisenberg, M. S., Cummins, R. O. & Hallstrom, A. P. Predicting survival from out-of-hospital cardiac arrest: A

graphic model. Ann. Emerg. Med. 22, 1652–1658 (1993).

33. Swor, R. A. et al. Bystander CPR, ventricular fibrillation, and survival in witnessed, unmonitored out-of-hospital cardiac arrest.

Ann. Emerg. Med. 25, 780–784 (1995).

34. Iwami, T., Kitamura, T., Kiyohara, K. & Kawamura, T. Dissemination of chest compression-only cardiopulmonary resuscitation

and survival after out-of-hospital cardiac arrest. Circulation 132, 415–422 (2015).

35. Kitamura, T. et al. Bystander-initiated rescue breathing for out-of-hospital cardiac arrests of noncardiac origin. Circulation 122,

293–299 (2010).

36. Ogawa, T. et al. Outcomes of chest compression only CPR versus conventional CPR conducted by lay people in patients with out

of hospital cardiopulmonary arrest witnessed by bystanders: Nationwide population based observational study. BMJ 342, c7106

(2011).

37. Takei, Y. et al. Recruitments of trained citizen volunteering for conventional cardiopulmonary resuscitation are necessary to

improve the outcome after out-of-hospital cardiac arrests in remote time-distance area: A nationwide population-based study.

Resuscitation 105, 100–108 (2016).

38. Geri, G. et al. Does transport time of out-of-hospital cardiac arrest patients matter? A systematic review and meta-analysis. Resuscitation 115, 96–101 (2017).

39. Spaite, D. W. et al. The impact of prehospital transport interval on survival in out-of-hospital cardiac arrest: Implications for

regionalization of post-resuscitation care. Resuscitation 79, 61–66 (2008).

40. Spaite, D. W. et al. Effect of transport interval on out-of-hospital cardiac arrest survival in the OPALS study: Implications for

triaging patients to specialized cardiac arrest centers. Ann. Emerg. Med. 54, 248–255 (2009).

Acknowledgements

We thank all the EMS personnel, staff of the Fire and Disaster Management Agency, and staff of the Institute for

Fire Safety and Disaster Preparedness of Japan for their cooperation in establishing and maintaining the Utstein

database. The JCS-ReSS Group comprise Naohiro Yonemoto, Yoshio Tahara, and Takanori Ikeda.

Author contributions

K.T.: Conceptualization, Methodology, Investigation, Writing-Original Draft. Y.S.: Conceptualization, Methodology, Writing-Review & Editing. Y.Y.: Formal analysis. A.N.: Conceptualization, Methodology, Formal analysis,

Writing-Review & Editing. H.K.: Conceptualization, Writing-Review & Editing, Supervision. Y.K.: WritingReview & Editing, Supervision. Y.T.: Writing-Review & Editing, Supervision. N.Y.: Formal analysis, Data Curation, Writing-Review & Editing, Supervision. T.I.: Writing-Review & Editing, Supervision. N.S.: Writing-Review

& Editing, Supervision. H.O.: Writing-Review & Editing, Supervision.

Funding

The authors received no financial support for the research, authorship, or publication of this manuscript.

Competing interests The authors declare no competing interests.

Additional information

Supplementary Information The online version contains supplementary material available at https://​doi.​org/​

10.​1038/​s41598-​023-​43106-w.

Correspondence and requests for materials should be addressed to K.T.

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