Skip to main content
  • Original research
  • Open access
  • Published:

Monitoring tissue oxygenation index using near‐infrared spectroscopy during pre‐hospital resuscitation among out‐of‐hospital cardiac arrest patients: a pilot study



Tissue oxygenation index (TOI) using the near infrared spectroscopy (NIRS) has been demonstrated as a useful indicator to predict return of spontaneous circulation (ROSC) among out-of-hospital cardiac arrest (OHCA) patients in hospital setting. However, it has not been widely examined based on pre-hospital setting.


In this prospective observational study, we measured TOI in pre-hospital setting among OHCA patients receiving cardio-pulmonary resuscitation (CPR) during ambulance transportation between 2017 and 2018. Throughout the pre-hospital CPR procedure, TOI was continuously measured. The study population was divided into two subgroups: ROSC group and non-ROSC group.


Of the 81 patients included in the final analysis, 26 achieved ROSC and 55 did not achieve ROSC. Patients in the ROSC group were significantly younger, had higher ∆TOI (changes in TOI) (5.8 % vs. 1.3 %; p < 0.01), and were more likely to have shockable rhythms and event witnessed than patients in the non-ROSC group. ∆TOI cut-off value of 5 % had highest sensitivity (65.4 %) and specificity (89.3 %) for ROSC. Patients with a cut-off value ≤-2.0 % did not achieve ROSC and while all OHCA patient with a cut-off value ≥ 8.0 % achieved ROSC. In addition, ROSC group had stronger positive correlation between mean chest compression rate and ∆TOI (r = 0.82) than non-ROSC group (r = 0.50).


This study suggests that ∆ TOI could be a useful indicator to predict ROSC in a pre-hospital setting.


More than 100,000 people die from out-of-hospital cardiac arrest (OHCA) each year in Japan [1]. Although American Heart Association (AHA) guidelines for cardiopulmonary resuscitation (CPR) are regularly updated and the survival rate has been improved, the overall mortality of admitted OHCA patients still remain poor [2]. Survival rate after 1 month varied from 5.6 to 7.4 % regardless of the initial cardiac rhythm in Japan [1]. Brain is a vital organ with high metabolic activity and low energy storages and vulnerable to circulatory arrest [3, 4]. High-quality CPR according to the AHA guidelines for cardiopulmonary arrest (CPA) (with proper rate of 100–120 /min, proper depth of 5–6 cm, complete chest recoil and minimizing interruption of chest compressions measured by chest compression fraction and so on) can maintain cerebral blood flow only by 30–40 % of normal flow [5, 6]. Even if return of spontaneous circulation (ROSC) is obtained, brain injury remains as the leading cause of death after ROSC [7]. Some markers using neurological findings, imaging techniques and serum biomarkers, are known to evaluate the extent of brain injury and are only useful after ROSC [8]. However, there are no specific and reliable indicators to assess the cerebral blood flow directly to the response of CPR quality [9].

Near-infrared spectroscopy (NIRS) can provide information on oxygen saturation of brain tissue (StO2) non-invasively and continuously during CPR without a pulsating rhythm [10]. NIRS can measure StO2 from the ratio of oxygenated hemoglobin (O2Hb) to oxygenated and deoxygenated hemoglobin (HHb) in blood flow within venous, arterial and cerebral cortical tissue [7]. Several studies examined the correlation between StO2 and ROSC or neurological outcomes based on hospitalized patients [11, 12]. In our previous study which included 117 OHCA patients, we observed that ROSC patients had significantly higher initial StO2 than non-ROSC patients [13]. Other studies have also demonstrated that increase in StO2 (∆StO2) were associated with ROSC [12, 14]. In addition, usefulness of StO2 as a dynamic value rather than a single static value has also been emphasized [15]. Our previous study also showed that ∆StO2 could be more useful and accurate than a single initial StO2 when predicting ROSC [16]. Furthermore, in a recent meta-analysis, ∆StO2 demonstrated excellent predictive value for ROSC [15]. However, there is a dearth of well-designed studies which examined the association between high-quality CPR and level of StO2 during CPR, although a recent study based on small sample size demonstrated that high-quality CPR improved StO2 values [12].

In Japan, the average time from emergency medical service (EMS) call to hospital arrival was 39.4 minutes in 2016 and the time has been increasing every year [1]. Brain can reserve only limited energy, and inadequate cerebral blood flow within 5 minutes can lead to hypoxic brain injury [17]. For every minute without CPR and defibrillation, the chance of survival decreases by 7–10 % [18]. In order to improve the quality of pre-hospital CPR, evaluation of direct cerebral blood flow is necessary. Portable NIRS device equipped in ambulance, where only limited devices can be equipped, can help evaluate the cerebral blood perfusion [19, 20], especially oxygen delivery to the brain. Effective CPR might increase O2Hb and StO2 and through these mechanism ROSC rate can be improved. However, based on pre-hospital setting, the association between various types of StO2 and, ROSC and CPR according to the latest CPR guidelines has not been examined yet.

The objective of this study was to examine the association between ∆StO2 and ROSC as well as between ∆StO2 and the CPR quality.


Study design and setting

This single-center prospective and observational study was conducted at St. Marianna University School of Medicine, a 1200-bed tertiary hospital in Kawasaki, Japan. Enrollment for this study started from May 2017 and continued till March 2019. The research protocol received ethics committee Institutional Review Board (IRB) approval. In Japan, paramedic’s Advanced Life Support (ALS) team consists of three persons, under the direction of a physician while they are permitted to administer epinephrine and perform intubation. Additional devices are allowed to use only in the five EMS teams in northern Kawasaki medical area as all of these teams completed required training sessions before this study started.

Study intervention

All ≥ 18 years old OHCA patients with non-traumatic cardiac arrest transferred to our emergency department (ED) were included. Excluded patients were as follows: patients with traumatic cause, patients with core body temperature less than 30°Celsius and patients who had achieved ROSC before the placement of the device probe. When the patients met inclusion criteria, the probe was placed onto the patient’s forehead left-laterally above the eyebrow immediately after transportation to the ambulance. One of the 3 paramedics placed the probe to minimize interruption of CPR procedure according to the AHA guidelines 2015 [21]. Although the paramedics were not blinded, they have not received the explanation about the meaning of the values, and followed the latest AHA guidelines without considering the StO2 values. They were instructed to administer epinephrine and perform tracheal intubation. They did not use mechanical chest compressions but did only manual chest compressions.

We used CCR-1® (Hamamatsu Photonics, Hamamatsu-City, Shizuoka, Japan) which can non-invasively and continuously measures StO2, so called tissue oxygenation index (TOI) in CCR-1®. This device is portable and can be operated by battery for 2 hours which make it suitable for use in an ambulance. TOI monitoring continued throughout ambulance transportation. Initial ROSC was defined as the presence of a palpable carotid pulse after CPR discontinuation, and successful ROSC was defined as ROSC > 20 minutes after CPR [22].

Measurements and statistical analysis

The study population was divided into 2 groups according to outcome: ROSC group and non-ROSC group. We defined initial TOI as the TOI measured at the moment the probe was attached inside the ambulance and final TOI as the last recorded TOI value at the arrival of the patient to our ED. We evaluated the change of TOI, namely the ∆TOI (∆TOI = final TOI - initial TOI). In addition to these values, mean, maximum and minimum TOI during ambulance transportation were also assessed. This device can also calculate the chest compression (CC) rate per minute using the waveform of O2Hb and HHb. Additional data were extracted from pre-hospital and hospital records according to Utstein style [23]. Pre-hospital records included the information about sex, age, the initial cardiac rhythm of cardiopulmonary arrest (CPA), witness, bystander CPR and the time from the EMS call to the scene arrival and hospital arrival. The hospital records included the causes of CPA, the amount of epinephrine received during CPR procedure, laboratory data and the outcomes in ED.

Primary aim of the statistical analysis was to examine the association between ∆TOI and ROSC. Secondary aims were to examine the association between other TOI values such as initial, final, mean, maximum and minimum and ROSC. In addition, different cut-off values of ∆TOI were examined as predictors of ROSC. We also examined the correlation between mean CC rate and ∆TOI using spearman’s rank correlation coefficient (r).

Continuous variables were summarized as median with interquartile range (IQR) or mean with standard deviation (SD). Distribution of continuous variables was examined using Shapiro-Wilk test. When the variables were normally distributed, unpaired t-tests were conducted. On the other hand, when the variables were positively or negatively skewed, we used Mann-Whitney U-tests. Categorical variables were summarized using counts and percentages, compared using chi-square test. Multiple regression analysis was conducted to examine the association between ∆TOI and Utstein variables after controlling for the potential confounding effects. Receiver Operating Characteristic (ROC) analysis was also conducted to determine the specific cut-off values predictive of ROSC. A p-value < 0.05 was considered statistically significant. Statistical analyses were performed using SPSS, version 25 (SPSS Inc., Chicago, IL, USA) and R statistical software (V.1.0.143, R Foundation for Statistical Computing).


Of 104 patients that were transported to our ED, TOI was measured in 81 (77.8 %) patients and 23 were excluded. The reasons for exclusion were; 19 patients were due to apparatus dysfunction during initial CPR procedure (i.e.: attachment failure of probes and start-up delay), 3 patients achieved ROSC before arrival at ED and one patient had CPA due to trauma. Among those who were included in this study (n = 81), 26 (32.1 %) achieved ROSC (ROSC group) and 55 (67.9 %) did not achieve ROSC (non-ROSC group) (Fig. 1).

Fig. 1
figure 1

Flow chart of patient inclusion. Abbreviations: OHCA Out-of-hospital cardiac arrest, TOI Tissue oxygenation index, ROSC Return of spontaneous circulation, CPA Cardiopulmonary arrest.

Patients’ demographics and key characteristics according to Utstein style were compared between ROSC and non-ROSC group (Table 1). Patients in ROSC group were younger and were more likely to have their cardiac event witnessed. Furthermore, patients in this group exhibited higher shockable initial rhythm and suspected cardiac cause of CPA. Blood gas analysis showed that patients in ROSC group had significantly lower lactate concentration and higher PaO2 than patients in non-ROSC group.

Table 1 Patient characteristics

Primary outcome measurement

∆TOI was significantly higher in ROSC group (median 5.8 % [IQR3.2 to14.6 %]) than non-ROSC group (median 1.3 % [IQR-1.1 to -1.3 %]) (p < 0.01) (Fig. 2).

Fig. 2
figure 2

Distributions of ∆TOI for patients with out-of-hospital cardiac arrest (OHCA) by ROSC status***: p < 0.01 . Abbreviations: ROSC Return of spontaneous circulation, TOI Tissue oxygenation index

Secondary outcome measurement

Initial, final, minimum, maximum and mean TOI values were also significantly higher in ROSC group than that in non-ROSC group (Table 2).

Table 2 Various type of tissue oxygenation index (TOI) and outcomes

Table 3 shows crude and adjusted odds ratio of achieving ROSC based on logistic regression analysis. Among different TOI values, ∆TOI had highest odds ratio for predicting ROSC based on bivariate logistic regression analysis. Even after adjusted by witness status and shockable rhythm, the association between ∆TOI and ROSC was statistically significant, although other TOI values were not. (Table 3).

Table 3 Odd ratio of ROSC prediction for each TOI and ∆ TOI after adjustment for baseline variables

Figure 3 shows correlation between CC rate and ∆TOI during ambulance transportation. Overall, there was statistically significant positive correlation between CC rate and ∆TOI (r = 0.65). ROSC group had stronger positive correlation between CC rate and ∆TOI (r = 0.82) than non-ROSC group (r = 0.50).

Fig. 3
figure 3

Correlation between ∆TOI and mean CC rate

The shaded region indicated 95 % CI. Abbreviations: ROSC Return of spontaneous circulation, TOI Tissue oxygenation index, CC Chest compression, CI Confident interval

ROC analysis showed that ∆TOI cut-off value 5 % had the highest sensitivity and specificity to predict ROSC (65.4 and 89.3 %, respectively). The area under the ROC curve (AUC) was 0.82 (95 % confidence interval, 0.72–0.93) (Fig. 4). Patients with OHCA whose ∆TOI was ≤-2.0 % did not achieve ROSC, whereas patients with OHCA whose ∆TOI was ≥ 8.0 % achieved ROSC (Fig. 5).

Fig. 4
figure 4

ROC curve with ∆TOI as a predictor of ROSC. Abbreviation: AUC Area under the curve

Fig. 5
figure 5

∆ tissue oxygenation index (TOI) of each patient. Abbreviation: ROSC Return of spontaneous circulation


To the best of our knowledge, this is the first study conducted in Japan which demonstrated that ∆TOI is a significant predictor of ROSC even after adjusting for Utstein variables in a pre-hospital setting. Several other studies also reported regional cerebral oxygen saturation (rSO2) as a correlate of ROSC status in a hospital setting [24]. Together, these studies emphasize that initial rSO2 as well as increase in rSO2 (∆rSO2) could be regarded as a useful parameter to assess ROSC in hospital and pre-hospital settings [19, 20].

Utstein variables are widely used to determine the predictive indicators associated with ROSC [23]. Among these variables, initial cardiac rhythm, witness, bystander CPR, time from EMS call to scene arrival and cardiac cause are especially known as core Utstein variables [24]. Similar to our previous study based on hospital setting, we also observed in this study that adding witness status and initial shockable rhythm to ∆TOI in pre-hospital setting increased the accuracy of ROSC prediction [13]. Other study with larger sample size also demonstrated that witness and shockable rhythm had significant association with ROSC [20]. We also observed stronger correlation between CC rate and ∆TOI among patients in the ROSC group than in non-ROSC group. Thus, ∆TOI might also be considered as an indicator of high-quality CPR in addition to its effectiveness as a ROSC predictor.

Determining the cut-off values might suggest that TOI could potentially replace pulse checks during CPR, which could reduce hands-off time. TOI increases when CPR delivers O2Hb to the brain. ∆TOI as a dynamic value might reflect the quality of CPR. ∆rSO2 ≥ 15 % during CPR procedure showed higher chance of achieving ROSC in a previous study[20]. In our study, we observed that ∆TOI cut-off value 5.0 % could predict ROSC. This discrepancy was due to difference in using the parameter as predictors in the respective study (∆rSO2 v.s. ∆TOI) as well as differences in the calculation method[13]. Different cut-off values of ∆TOI generated in this study to predict the probability of ROSC (∆TOI ≤-2 % did not achieve ROSC ≥ 8 % achieved ROSC) are study-specific values only. Although these values are based on the findings of our study, future studies might shed more light on appropriate cut-off values.

CCR-1® can measure mean CC rate from the waveform of O2Hb and HHb. ∆TOI and CC rate showed significant positive correlation in this study with stronger correlation in ROSC group than non-ROSC group. Appropriate CC rate based on CPR guidelines is 100–120 per minute [5]. Surprisingly, 23 out of 81 people (28.4 %) could not comply with the latest CPR guidelines in this study. As in the narrow space of the ambulance, chest compressions are not always performed according to the guidelines, visual NIRS monitoring might replace the evaluation of CPR quality.

∆TOI as a dynamic value was more specific indicator to predict ROSC than other static TOI values. These results are similar to other studies based on NIRS monitoring in pre-hospital setting [19, 20]. TOI is expressed as the ratio of O2Hb and HHb, and it increases with O2Hb level. Blood gas analysis showed that ROSC group had higher PaO2 and lower PaCO2 than non-ROSC group. Experimental animal CPR model also showed that rSO2 was lower with 50 % oxygen than 100 % oxygen [25]. Together, these studies support the use of TOI as a dynamic value to predict ROSC in both pre-hospital and hospital settings.

Our study has several limitations. This study was conducted in a single center, and the sample size was small because only 5 EMS teams could be equipped with portable NIRS device. There were many date errors attached to probe performance. Also, we had only a few subjects with neurological event, therefore we could not evaluate the association between TOI values and neurological outcomes. Portable NIRS device could not measure the depth of CC during CPR and we could only evaluate the correlation between mean CC rate and ∆TOI. Finally, we did not have laboratory data and, therefore, could not evaluate the change in PaO2 during ambulance transportation.


In this pilot study, we demonstrated the feasibility of ∆TOI as a dynamic value rather than single static value among OHCA patients in a pre-hospital setting. ∆TOI can be considered as a predictor of ROSC and can guide CC rate. Other findings, such as, an absolute increase of 8 % or higher in TOI during pre-hospital CPR procedure is associated with ROSC and absolute decrease of 2 % or lower from the baseline is associated with non-ROSC, would be helpful to generate future cut-off values in this regard.

Availability of data and materials

The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.



Tissue oxygenation index


Near infrared spectroscopy


Return of spontaneous circulation


Out-of-hospital cardiac arrest


Cardio-pulmonary resuscitation


American Heart Association


Cardiopulmonary arrest

StO2 :

Oxygen saturation of brain tissue


Oxygenated hemoglobin


Deoxygenated hemoglobin

∆StO2 :

Increase in StO2


Emergency medical service


Institutional Review Board


Advanced Life Support


Emergency department


Tissue oxygenation index


Chest compression


Interquartile range


Standard deviation


Receiver Operating Characteristic


Regional cerebral oxygen saturation


  1. Fire and Disaster Management Agency MoIAaC. The current state of emergency and rescue, 2013 edition in Tokyo. Japanese edition. 2013.

  2. Koenig MA. Brain resuscitation and prognosis after cardiac arrest. Crit Care Clin. 2014;30(4):765–83.

    Article  Google Scholar 

  3. Cournoyer A, Iseppon M, Chauny JM, Denault A, Cossette S, Notebaert E. Near-infrared Spectroscopy Monitoring During Cardiac Arrest: A Systematic Review and Meta-analysis. Acad Emerg Med. 2016;23(8):851–62.

    Article  Google Scholar 

  4. Deakin CD, Yang J, Nguyen R, Zhu J, Brett SJ, Nolan JP, et al. Effects of epinephrine on cerebral oxygenation during cardiopulmonary resuscitation: A prospective cohort study. Resuscitation. 2016;109:138–44.

    Article  Google Scholar 

  5. Perkins GD, Travers AH, Berg RA, Castren M, Considine J, Escalante R, et al. Part 3: Adult basic life support and automated external defibrillation: 2015 International Consensus on Cardiopulmonary Resuscitation and Emergency Cardiovascular Care Science with Treatment Recommendations. Resuscitation. 2015;95:e43–69.

    Article  Google Scholar 

  6. Meaney PA, Bobrow BJ, Mancini ME, Christenson J, de Caen AR, Bhanji F, et al. Cardiopulmonary resuscitation quality: [corrected] improving cardiac resuscitation outcomes both inside and outside the hospital: a consensus statement from the American Heart Association. Circulation. 2013;128(4):417–35.

    Article  Google Scholar 

  7. Sinha N, Parnia S. Monitoring the Brain After Cardiac Arrest: a New Era. Curr Neurol Neurosci Rep. 2017;17(8):62.

    Article  Google Scholar 

  8. Sandroni C, D’Arrigo S, Nolan JP. Prognostication after cardiac arrest. Crit Care. 2018;22(1):150.

    Article  Google Scholar 

  9. Nosrati R, Lin S, Ramadeen A, Monjazebi D, Dorian P, Toronov V. Cerebral Hemodynamics and Metabolism During Cardiac Arrest and Cardiopulmonary Resuscitation Using Hyperspectral Near Infrared Spectroscopy. Circulation journal: official journal of the Japanese Circulation Society. 2017;81(6):879–87.

    Article  CAS  Google Scholar 

  10. Steppan J, Hogue CW. Jr. Cerebral and tissue oximetry. Best Pract Res Clin Anaesthesiol. 2014;28(4):429–39.

    Article  Google Scholar 

  11. Asim K, Gokhan E, Ozlem B, Ozcan Y, Deniz O, Kamil K, et al. Near infrared spectrophotometry (cerebral oximetry) in predicting the return of spontaneous circulation in out-of-hospital cardiac arrest. Am J Emerg Med. 2014;32(1):14–7.

    Article  Google Scholar 

  12. Ahn A, Nasir A, Malik H, D’Orazi F, Parnia S. A pilot study examining the role of regional cerebral oxygen saturation monitoring as a marker of return of spontaneous circulation in shockable (VF/VT) and non-shockable (PEA/Asystole) causes of cardiac arrest. Resuscitation. 2013;84(12):1713–6.

    Article  Google Scholar 

  13. Tsukuda J, Fujitani S, Morisawa K, Shimozawa N, Lohman BD, Okamoto K, et al. Near-infrared spectroscopy monitoring during out-of-hospital cardiac arrest: can the initial cerebral tissue oxygenation index predict ROSC? Emerg Med J. 2018.

  14. Ehara N, Hirose T, Shiozaki T, Wakai A, Nishimura T, Mori N, et al. The relationship between cerebral regional oxygen saturation during extracorporeal cardiopulmonary resuscitation and the neurological outcome in a retrospective analysis of 16 cases. J Intensive Care. 2017;5:20.

    Article  Google Scholar 

  15. Schnaubelt S, Sulzgruber P, Menger J, Skhirtladze-Dworschak K, Sterz F, Dworschak M. Regional cerebral oxygen saturation during cardiopulmonary resuscitation as a predictor of return of spontaneous circulation and favourable neurological outcome - A review of the current literature. Resuscitation. 2018;125:39–47.

    Article  CAS  Google Scholar 

  16. Koyama Y, Wada T, Lohman BD, Takamatsu Y, Matsumoto J, Fujitani S, et al. A new method to detect cerebral blood flow waveform in synchrony with chest compression by near-infrared spectroscopy during CPR. Am J Emerg Med. 2013;31(10):1504–8.

    Article  Google Scholar 

  17. Lee JM, Grabb MC, Zipfel GJ, Choi DW. Brain tissue responses to ischemia. J Clin Investig. 2000;106(6):723–31.

    Article  CAS  Google Scholar 

  18. Larsen MP, Eisenberg MS, Cummins RO, Hallstrom AP. Predicting survival from out-of-hospital cardiac arrest: a graphic model. Ann Emerg Med. 1993;22(11):1652–8.

    Article  CAS  Google Scholar 

  19. Prosen G, Strnad M, Doniger SJ, Markota A, Stozer A, Borovnik-Lesjak V, et al. Cerebral tissue oximetry levels during prehospital management of cardiac arrest - A prospective observational study. Resuscitation. 2018;129:141–5.

    Article  Google Scholar 

  20. Genbrugge C, De Deyne C, Eertmans W, Anseeuw K, Voet D, Mertens I, et al. Cerebral saturation in cardiac arrest patients measured with near-infrared technology during pre-hospital advanced life support. Results from Copernicus I cohort study. Resuscitation. 2018;129:107–13.

    Article  Google Scholar 

  21. Neumar RW, Shuster M, Callaway CW, Gent LM, Atkins DL, Bhanji F, et al. Part 1: Executive Summary: 2015 American Heart Association Guidelines Update for Cardiopulmonary Resuscitation and Emergency Cardiovascular Care. Circulation. 2015;132(18 Suppl 2):315-67.

    Article  Google Scholar 

  22. Goldberger ZD, Chan PS, Berg RA, Kronick SL, Cooke CR, Lu M, et al. Duration of resuscitation efforts and survival after in-hospital cardiac arrest: an observational study. Lancet. 2012;380(9852):1473–81.

    Article  Google Scholar 

  23. Perkins GD, Jacobs IG, Nadkarni VM, Berg RA, Bhanji F, Biarent 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. Resuscitation. 2015;96:328 – 40.

  24. Weng TI, Huang CH, Ma MH, Chang WT, Liu SC, Wang TD, et al. Improving the rate of return of spontaneous circulation for out-of-hospital cardiac arrests with a formal, structured emergency resuscitation team. Resuscitation. 2004;60(2):137–42.

    Article  Google Scholar 

  25. Nelskyla A, Nurmi J, Jousi M, Schramko A, Mervaala E, Ristagno G, et al. The effect of 50 % compared to 100 % inspired oxygen fraction on brain oxygenation and post cardiac arrest mitochondrial function in experimental cardiac arrest. Resuscitation. 2017;116:1–7.

    Article  Google Scholar 

Download references


We would like to thank Kawasaki City Fire Department especially Tama and Miyamae Fire Department for collecting the pre-hospital data. This work was supported by JSPS KAKENHI Grant Number JP18K16524 and Public Trust Foundation of Marumo ER Medicine Research Institute.


Yasuhiko Taira reports having received Public Trust Foundation of Marumo ER Medicine Research Institute (2016). Jumpei Tsukuda reports having JSPS KAKENHI Grant Number JP18K16524 (2018).

Author information

Authors and Affiliations



JT, SF and YT conceived the research idea and designed the study. JT, KM and TK supervised the study and collected the data. JT and MR provided statistical advice on study design and analyzed the data. SF chaired the data oversight committee. JT, SF and MR drafted the first version of the manuscript, and all authors contributed substantially to the subsequent version and revisions. SF takes public responsibility of the contents of this paper. The author(s) read and approved the final manuscript.

Corresponding author

Correspondence to Shigeki Fujitani.

Ethics declarations

Ethics approval and consent to participate

This study was approved by the institutional review board of St. Marianna University School of Medicine (IRB: 3022). Informed consent from the patients was waived because this study contains de-identified information, which does not affect the rights and welfare of the patients.

Consent for publication

Not applicable

Competing interests

The authors declare that they have no competing interests.

Additional information

Publisher’s Note

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

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 The Creative Commons Public Domain Dedication waiver ( 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

Tsukuda, J., Fujitani, S., Rahman, M. et al. Monitoring tissue oxygenation index using near‐infrared spectroscopy during pre‐hospital resuscitation among out‐of‐hospital cardiac arrest patients: a pilot study. Scand J Trauma Resusc Emerg Med 29, 42 (2021).

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: