ABSTRACT
Background
End‐tidal carbon dioxide capnography (EtCO2) is widely used to guide intubated patients' ventilation. Current guidelines for prehospital advanced airway management assume a gradient of approximately 0.67 kPa between EtCO2 and arterial carbon dioxide partial pressure (PaCO2). However, concerns have been raised about the accuracy of capnography. We aimed to assess the agreement between EtCO2 and PaCO2 in different patient groups to see if EtCO2 could be accurate enough to guide ventilation during prehospital care.
Methods
We carried out a retrospective study of all anaesthetised and intubated patients with a measured PaCO2 by a single helicopter emergency medical services unit from 1 January 2014 to 31 December 2021. The patients were divided into five different patient groups based on clinical diagnosis: out‐of‐hospital cardiac arrest, trauma, intoxication, neurological conditions (including stroke) and other. The agreement between EtCO2 and PaCO2 in each group was assessed with Bland–Altman analysis. In addition, the percentage of patients with a higher PaCO2–EtCO2 gradient than 0.67 kPa was calculated.
Results
In Bland–Altman analysis, the overall bias was 1.7 kPa (standard deviation = 1.6) with limits of agreement of −1.4 to 4.8 kPa. Out of 670, the PaCO2–EtCO2 gradient was higher than 0.67 kPa in 530 (79.1% [75.9–82.0]) patients. 186/221 (84.2% [78.8–88.4]) in out‐of‐hospital cardiac arrest patients, 85/104 (81.7% [73.2–88.0]) in trauma patients, 146/197 (74.1% [67.6–97.7]) in neurological patients, 75/99 (75.8% [66.5–83.1]) in intoxicated patients and 38/49 (77.6% [64.1–87.0]) in other patients. The results are presented as n/nn (% [95% confidence interval]).
Conclusions
Our results show that for critically ill patients intubated in the prehospital setting, capnography is not accurate in guiding ventilation.
Editorial Comment
Capnography may be used to assess ventilation effect in intubated patients. In an intubated helicopter ambulance mixed‐diagnosis cohort end‐tidal carbon dioxide levers were assessed, compared to pCO2 results from a point of care blood gas analysis device. The proportion of larger gaps between these two values in this mixed cohort is presented.
Keywords: air ambulance, anaesthesia, capnography, critical care, emergency medical services, helicopter emergency medical services, intubation, prehospital, ventilation
1. Background
In patients undergoing prehospital emergency anaesthesia (PHEA), normoventilation is crucial in many patient groups. Especially for patients suffering from traumatic brain injury (TBI), as hypo‐ or hypercapnia may alter intracranial perfusion, causing possibly preventable secondary damage to the brain. Both hypocapnia and hypercapnia have been shown to be associated with increased mortality and worse neurological outcome in TBI [1, 2, 3, 4]. End‐tidal carbon dioxide capnography (EtCO2), which measures carbon dioxide from the exhaled gas at the end of expirium and displays a continuous waveform and a numerical value, is used to guide ventilation in the prehospital setting since it has been thought to underestimate arterial CO2 only slightly.
With its easy non‐invasive modality, EtCO2 proposes a tempting surrogate for arterial carbon dioxide partial pressure (PaCO2), and based on earlier studies, the PaCO2–EtCO2 gradient has been estimated to be approximately 0.5 kPa. However, this approximation is mainly based on healthy anaesthetised in‐hospital patients, and recent literature indicates that severely ill patients anaesthetised in a prehospital setting may have a greater and more unpredictable PaCO2–EtCO2 gradient than suggested [1, 5, 6, 7, 8, 9, 10]. Current guidelines for prehospital advanced airway management suggest that EtCO2 and PaCO2 should be between 4 and 6 kPa and 4.67 and 6.67 kPa, respectively, when managing critically ill intubated patients in the prehospital setting. However, tighter ventilation control, with EtCO2 between 4 and 4.67 kPa and PaCO2 between 4.67 and 5.33 kPa, is recommended for patients suffering from TBI. These recommendations assume a PaCO2–EtCO2 gradient of 0.67 kPa [11].
We have recently described a prevalence of a high PaCO2–EtCO2 gradient in PHEA patients [7]. However, it is unknown what proportion of the patients falls within the PaCO2–EtCO2 gradient of 0.67 kPa, suggested by the recent quality indicators, or if the gradient differs between patient groups. Consequently, in the current study with an extended patient cohort in addition to the previous paper, we aimed to determine the bias and limits of agreement between PaCO2 and EtCO2 in five different patient groups based on the clinical diagnosis by a helicopter emergency medical services (HEMS) physician. We also aimed to determine the proportion of patients with PaCO2–EtCO2 gradients exceeding 0.67 kPa.
2. Methods
2.1. Study Design
We carried out a retrospective observational registry study of all patients anaesthetised and intubated by a single HEMS unit from 1 January 2014 to 31 December 2021.
Since this was a retrospective registry‐based study, ethical approval was not required according to Finnish legislation. Access to patient records was granted by the Helsinki University Hospital (HUS/305/2022). This study was reported according to Strengthening the Reporting of Observational studies in Epidemiology (STROBE) guidelines [12].
2.2. Setting
The HEMS unit, FinnHEMS 10, operates in the Southern Finland area and serves a population of approximately 1.3 million. It is one of Finland's seven nationally operated HEMS. In the operating area, it provides the majority of PHEAs, while a small minority of PHEAs are provided by locally operating ground‐based physician‐staffed units. In the HEMS unit, PHEA is highly standardised and regularly trained. Clinical governance procedures cover all PHEA cases. The protocol and its implementation have been recently described in detail [13, 14]. The unit is staffed by an on‐call physician specialised in anaesthesiology, with a crew consisting of a HEMS paramedic and a pilot. The unit responds to missions with critically ill or seriously traumatised patients with a ground ambulance team to provide prehospital critical care and, if appropriate, to transport the patient by helicopter.
Arterial blood gas (ABG) analyses are conducted at the on‐call physician's discretion using a point‐of‐care (POC) device, which provides the results on scene. POC devices used during the study period were either iSTAT 1 (Abbott, IL, USA) until January 2018 or iSTAT Alinity (Abbott) from January 2018 onwards. After analysis, the data is transferred to the electronic patient record system (Merlot Medi, CGI Suomi, Finland) manually.
Monitoring of vital signs and continuous EtCO2 is carried out with a monitor–defibrillator. Either a device of the HEMS unit or a ground ambulance is used. The devices used were either Lifepak 15 (Physio‐Control, WA, USA), CorPuls3 (Corpuls, Germany) or Zoll X series (Zoll Medical Corporation, MA, USA). The monitor–defibrillator data is automatically transferred to the electronic patient record via WiFi or Bluetooth. An Oxylog 3000 plus ventilator (Dräger, Germany) was used to ventilate intubated patients until December 2016, and a Hamilton‐T1 ventilator (Hamilton Medical, Switzerland) afterwards.
2.3. Participants
All patients who were anaesthetised and intubated by the HEMS unit during the study period were eligible for this study and, therefore, included in the dataset. We excluded patients without blood gas analysis results after the intubation and patients intubated during out‐of‐hospital cardiac arrest (OHCA). Patients intubated after return of spontaneous circulation (ROSC) were included in the study. In theory, a single patient may have been encountered and treated with PHEA by the HEMS unit during the study period more than once. These cases were not excluded, as the acute condition likely affects the PaCO2–EtCO2 gradient more than the underlying conditions. In cases of multiple arterial samples analysed during prehospital care, only the first one was included in the study. Power analysis or sample size calculations were not performed prior to data collection.
We divided the patient population into five subgroups based on the primary clinical diagnosis made by the on‐call physician at the scene. These included ‘OHCA’, ‘Trauma’, ‘Intoxication’, ‘Neurological conditions’ and ‘Other’. ‘OHCA’ included patients intubated after ROSC and ‘Trauma’ included all traumatised patients, as well as TBI patients. ‘Neurological conditions’ included all patients with acute neurological pathology such as stroke, status epilepticus and non‐traumatic intracranial haemorrhage. The classification was based on the international consensus‐based guidelines on data collection [15]. However, the smallest patient groups were combined as ‘Other’ for purposeful analysis and can include groups such as ‘breathing difficulties’, ‘myocardial infarction’ or ‘chest pain’, ‘obstetric’, ‘infection’, ‘asphyxiation’ or ‘anaphylaxis’.
2.4. Variables
The primary endpoint variable was bias and limits of agreement between PaCO2 and EtCO2 in every patient group. The secondary endpoint was the proportion of patients with PaCO2–EtCO2 gradients exceeding 0.67 kPa.
All the ABG analyses were obtained after intubation and before hospital arrival. The analyses were manually paired with the closest EtCO2 value in the prehospital patient record system. A maximum of a 5‐min difference between the time of ABG and EtCO2 was accepted to include the patient in the study.
2.5. Data Sources
We extracted data from the FinnHEMS database (FHDB), a national electronic database including detailed structured data from all Finnish HEMS missions. Most of the details in the FHDB are entered manually by the physician on‐call promptly after a HEMS mission. The data follows an international standardised template [16]. The blood gas results and concurrent capnography values were collected retrospectively through manual chart review of electronic prehospital records.
2.6. Statistical Methods
Continuous variables are presented as mean and standard deviation (SD) or median and first quartile and third quartile [IQR] based on the data distribution, assessed visually using histograms. Categorical data are presented as number and percentage (%). We calculated 95% confidence intervals (95% CIs) for proportions using the modified Wald method. To assess the agreement between PaCO2 and EtCO2, we constructed Bland–Altman plots of the variables. The results are presented as bias and limits of agreement. Bland–Altman analysis was chosen for its suitability for method comparison, providing an assessment of the agreement, systematic bias, and consistency between the two measurement methods. Analyses were performed using SPSS Statistics for Mac, version 28 (IBM Corp., Armonk, NY, USA) and GraphPad Prism for Mac, version 9 (Dotmatics, Boston, MA, USA).
3. Results
Out of 2203 patients who were anaesthetised and intubated by the HEMS unit during the study period, 670 (30.4%) had an ABG analysis performed and were included in the analysis (Figure 1). No exclusions were made based on patient characteristics or demographics. Patient characteristics are presented in Table 1.
FIGURE 1.
Patient selection flowchart. ABG, arterial blood gas analysis; GAP, PaCO2–EtCO2 gradient; HEMS, helicopter emergency medicine services; OHCA, out‐of‐hospital cardiac arrest.
TABLE 1.
Patient characteristics.
| Missing data, n | All patients (n = 670) | OHCA (n = 221) | Trauma (n = 104) | Neuro (n = 197) | Intoxication (n = 99) | Other (n = 49) | |
|---|---|---|---|---|---|---|---|
| Sex, male | 3 | 435 (65.2) | 166 (75.5) | 80 (76.9) | 112 (57.1) | 50 (51.0) | 27 (55.1) |
| Age, years | 0 | 58 (42–69) | 63 (52–71) | 52 (33–64) | 63 (50–71) | 39 (28–50) | 58 (35–70) |
| Vitals at HEMS arrival | |||||||
| Heart rate, bpm | 79 | 95 (76–118) | 104 (84–125) | 88 (68–110) | 93 (74–114) | 93 (69–113) | 110 (95–129) |
| Systolic blood pressure, mmHg | 96 | 134 (115–162) | 129 (108–150) | 131 (118–153) | 152 (129–190) | 120.5 (102–138) | 130 (100–162) |
| Oxygen saturation, % | 105 | 97 (93–99) | 97 (89–99) | 97 (93–98) | 97 (94–99) | 98 (96–100) | 93 (81–97) |
| Glasgow Coma Scale score | 9 | 3 (3–6) | 3 (3–3) | 5 (3–8) | 4 (3–6) | 3 (3–6) | 4 (3–8) |
Note: Continuous variables are presented as median and interquartile range (25th–75th percentile); categorical variables are presented as number and percentage (%).
Abbreviations: HEMS, helicopter emergency medical services; Neuro, neurological conditions; OHCA, out‐of‐hospital cardiac arrest.
In every patient group, the observed PaCO2–EtCO2 gradient exceeded 0.67 kPa in the majority of patients, with the bias ranging from 1.3 (1.2) to 2.2 (2.5) kPa. Bias was higher in groups ‘OHCA’ and ‘Other’, compared to other groups, and seemed to be higher along with increasing overall CO2. Limits of agreement were large across the board as well. The results of the Bland–Altman analyses are shown in Figure 2.
FIGURE 2.
Bland–Altman analyses. Patient groups are presented as separate panels. (A) Whole patient population; (B) OHCA (out‐of‐hospital cardiac arrest); (C) Trauma; (D) neurological conditions (including stroke); (E) intoxication; (F) other. Dotted lines present 95% limits of agreement. Continuous line presents bias. PaCO2 and EtCO2 in kPa.
In addition, the PaCO2–EtCO2 gradient was larger than 0.67 kPa in 530 (79.1%) patients. Table 2 presents the PaCO2 and EtCO2 values, along with limits of agreement. Overall, the bias was 1.7 (1.6) kPa and the limits of agreement between PaCO2 and EtCO2 ranged from −1.4 to 4.9 kPa.
TABLE 2.
End‐tidal (EtCO2) and arterial (PaCO2) CO2 partial pressures, and bias and 95% agreement between the measurement methods, during prehospital anaesthesia.
| All patients (n = 670) | OHCA (n = 221) | Trauma (n = 104) | Neuro (n = 197) | Intoxication (n = 99) | Other (n = 49) | |
|---|---|---|---|---|---|---|
| EtCO2, kPa | 4.72 (1.04) | 4.83 (1.06) | 4.41 (0.84) | 4.57 (1.00) | 4.95 (0.84) | 5.01 (1.52) |
| PaCO2, kPa | 6.42 (1.83) | 7.01 (2.11) | 5.82 (1.09) | 5.92 (1.48) | 6.32 (1.03) | 7.22 (2.95) |
| > 0.67 GAP, n (%) | 530 (79.1 [82.0–75.9]) | 186 (84.2 [78.8–88.4]) | 85 (81.7 [73.2–88.0]) | 146 (74.1 [67.6–79.7]) | 75 (75.8 [66.5–83.1]) | 38 (77.6 [64.1–87.0]) |
| Bias, kPa | 1.7 (1.6) | 2.2 (1.8) | 1.4 (1.2) | 1.3 (1.2) | 1.4 (1.0) | 2.2 (2.5) |
| 95% Limits of agreement, kPa | −1.4 to 4.8 | −1.4 to 5.8 | −0.9 to 3.7 | −1.1 to 3.8 | −0.7 to 3.4 | −2.7 to 7.1 |
Note: The data are presented as mean (SD) and n (% [95% confidence interval]).
Abbreviations: Neuro, neurological conditions; OHCA, out‐of‐hospital cardiac arrest.
4. Discussion
4.1. Main Findings
In this retrospective study of prehospital anaesthetised patients, we found that the bias between PaCO2 and EtCO2 exceeded 1.2 kPa and limits of agreement were substantially wide in all patient groups, with the vast majority of patients having a higher PaCO2–EtCO2 gradient than proposed by the prehospital anaesthesia quality indicators [11]. Bias increasing with overall CO2 shows that the PaCO2–EtCO2 gradient is not constant across different EtCO2 values. The findings strongly suggest that normoventilation is not achievable using solely EtCO2 to guide ventilation in any patient group undergoing PHEA.
4.2. Clinical Relevance
Our results show that clinical diagnosis has limited predictive value when assessing the need for ABG analyses in adjusting ventilation, and end‐tidal capnography alone may expose patients to hypo‐ or hypercapnia. Hypocapnia reduces cerebral blood flow (CBF) and thus might cause ischemia, resulting in secondary brain injury in neurocritical patients. Hypercapnia, on the other hand, increases CBF, leading to higher ICP, possibly worsening existing haemorrhage or causing herniation [17].
This phenomenon is especially important in critically ill patients undergoing PHEA, since they might suffer from primary brain injury and brain oedema from inadequate oxygenation or trauma [18]. Even relatively small deviations from normocapnia might have major consequences when intracranial pressure is already elevated. In patients with TBI, worse outcomes have been demonstrated in both hypoventilated and hyperventilated patients compared to normoventilated patients. This association is observed only in intubated patients but not in patients breathing spontaneously [2]. The association between prehospital normoventilation and better outcomes of patients intubated in a prehospital setting has been confirmed in multiple studies [1, 2, 3, 9], which, along with the CO2 reactivity of CBF, provide pathophysiologic support for maintaining normoventilation to limit secondary brain injury. The current evidence relies mainly on EtCO2 levels, and accurate blood CO2 levels by ABG analyses during the prehospital phase have not been studied. As EtCO2 is quite unreliable during prehospital care, more evidence of ventilation guided by PaCO2 is needed.
The literature is mainly focused on TBI patients, but other critically ill patients might suffer from inadequate ventilation as well. Since most of the patients prior to intubation in the prehospital setting might have insufficient perfusion to the brain, inadequate physiologic regulation of CBF or elevated ICP, they are susceptible to secondary brain injury regardless of the clinical diagnosis. In addition, hypercapnia might worsen acidosis in critically ill patients. Thus, even without a TBI, the deviation from normocapnia should be avoided. Similarly to our results, other studies have also reported high PaCO2–EtCO2 gradients with moderate correlation at best, which makes ventilation based on capnography questionable [1, 7]. Studies have found a large PaCO2–EtCO2 gradient at hospital arrival when treating patients with TBI [8, 9]. In OHCA patients achieving ROSC, similar results have been reported [19, 20].
Several factors may contribute to the difference between PaCO2 and EtCO2. This difference is primarily caused by ventilation‐perfusion mismatch, where circulating blood does not reach ventilated alveoli for gas exchange, or reaches alveoli that are not ventilated. In healthy individuals, this mismatch, and therefore the PaCO2–EtCO2 gradient, should be small. However, it can be increased by hypotension, as blood pressure may not be sufficient to maintain adequate pulmonary circulation, resulting in reduced gas exchange [21]. Another cause might be inadequate ventilation and atelectasis, as blood circulation perfuses closed, unventilated alveoli. Higher tidal volumes and lower ventilatory frequency might lower mismatch between pulmonary ventilation and circulation, thereby lowering the PaCO2–EtCO2 gradient [6].
The reasons behind the higher bias in the ‘OHCA’ and ‘Other’ groups in our Bland–Altman analyses are not certain. One can speculate that patients suffering OHCA may have been hypoventilated during chest compressions, and aspiration is likely more common in this group. The ‘Other’ group includes patients with respiratory failure as the main problem among many other heterogeneous preliminary diagnoses, which do not allow for a more precise explanation based on the current data.
The PaCO2–EtCO2 gradient is, however, impossible to estimate accurately without an ABG analysis, and PaCO2 could be adjusted with ventilation optimisation if an ABG analysis was available to guide treatment. The fact that arterial cannulation doesn't seem to delay definitive treatment further encourages obtaining an ABG during prehospital treatment [22].
4.3. Limitations
Our study has several limitations that need consideration. First, it was retrospective and was limited to the extent of data already gathered. The PaCO2 from the ABG analysis was not compared with the capnography at the exact same time, thus possibly resulting in measurement error in the PaCO2–EtCO2 gradient. But as we limited the maximum time difference to 5 min and the vital signs are automatically recorded, we estimate this error to be minimal.
Second, the time between intubation and the ABG analysis is not reliably available from the database. This could lead to a falsely high PaCO2–EtCO2 gradient since it might be higher immediately after intubation compared to a period of mechanical ventilation and haemodynamic optimisation. However, the extent of this is unclear, since our results are in line with other studies that have measured ABGs at hospital arrival [8, 9, 19, 20].
Third, since ABG analysis is not performed on every patient, there is a risk of selection bias towards a more morbid population of patients with possibly higher PaCO2–EtCO2 gradients. However, 30% of all anaesthetised patients during the study period were included in the study, and almost 80% of these had a higher PaCO2–EtCO2 gradient than 0.67 kPa. This indicates that despite the possible selection bias, the results represent a significant proportion of all anaesthetised patients.
Finally, the patients included in the study represent a highly heterogeneous group. The classification of the patients into groups by the prehospital physician is not fully standardised and may lead to some patients being placed in the wrong category based on the initial evaluation, which could cause bias. However, we estimate this to be of no major relevance, as a previous study on the reliability of the database showed that the inter‐rater reliability of the classification appears to be rather high [23]. The data collection period was several years, during which different monitor–defibrillators were used. The influence of this on measured EtCO2 values cannot be ruled out.
5. Conclusions
The gradient between EtCO2 and PaCO2 is large and unpredictable during anaesthesia of critically ill prehospital patients. Consequently, relying exclusively on EtCO2 to achieve normoventilation is likely to be unreliable.
Author Contributions
J.T. and J.P. prepared the first version of the manuscript. J.N., J.L. and H.L. gathered, and all authors interpreted the data. H.L. and J.N. performed the statistical analyses. J.T. and H.L. prepared Tables 1 and 2. J.N. prepared Figure 2. J.P. and J.N. supervised the study process. All authors made significant contributions to the concept and design of the study as well as to the revision and editing of the manuscript. All authors have read and approved the final manuscript.
Funding
This work was supported by Helsingin ja Uudenmaan Sairaanhoitopiiri (VTR TYH2022320).
Ethics Statement
The authors have nothing to report.
Consent
The authors have nothing to report.
Conflicts of Interest
The authors declare no conflicts of interest.
Acknowledgements
The authors would like to thank Rosemarie Hartman for her unyielding help during manuscript preparation. Open access publishing facilitated by Helsingin yliopisto, as part of the Wiley ‐ FinELib agreement.
Data Availability Statement
An anonymised dataset can be made available upon a reasonable request from the corresponding author.
References
- 1. Knapp J., Doppmann P., Huber M., et al., “Pre‐Hospital Endotracheal Intubation in Severe Traumatic Brain Injury: Ventilation Targets and Mortality—A Retrospective Analysis of 308 Patients,” Scandinavian Journal of Trauma, Resuscitation and Emergency Medicine 31, no. 1 (2023): 46. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 2. Davis D. P., Idris A. H., Sise M. J., et al., “Early Ventilation and Outcome in Patients With Moderate to Severe Traumatic Brain Injury,” Critical Care Medicine 34, no. 4 (2006): 1202–1208. [DOI] [PubMed] [Google Scholar]
- 3. Bossers S. M., Mansvelder F., Loer S. A., et al., “Association Between Prehospital End‐Tidal Carbon Dioxide Levels and Mortality in Patients With Suspected Severe Traumatic Brain Injury,” Intensive Care Medicine 49, no. 5 (2023): 491–504. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 4. Bouma G. J. and Muizelaar J. P., “Cerebral Blood Flow, Cerebral Blood Volume, and Cerebrovascular Reactivity After Severe Head Injury,” Journal of Neurotrauma 9 (1992): S333–S348. [PubMed] [Google Scholar]
- 5. Nunn J. F. and Hill D. W., “Respiratory Dead Space and Arterial to End‐Tidal Carbon Dioxide Tension Difference in Anesthetized Man,” Journal of Applied Physiology 15 (1960): 383–389. [DOI] [PubMed] [Google Scholar]
- 6. Fletcher R. and Jonson B., “Deadspace and the Single Breath Test for Carbon Dioxide During Anaesthesia and Artificial Ventilation. Effects of Tidal Volume and Frequency of Respiration,” British Journal of Anaesthesia 56, no. 2 (1984): 109–119. [DOI] [PubMed] [Google Scholar]
- 7. Harve‐Rytsala H., Angerman S., Kirves H., and Nurmi J., “Arterial and End‐Tidal Carbon Dioxide Partial Pressure Difference During Prehospital Anaesthesia in Critically Ill Patients,” Acta Anaesthesiologica Scandinavica 65 (2021): 534–539, 10.1111/aas.13751. [DOI] [PubMed] [Google Scholar]
- 8. Price J., Sandbach D. D., Ercole A., Wilson A., and Barnard E. B. G., “End‐Tidal and Arterial Carbon Dioxide Gradient in Serious Traumatic Brain Injury After Prehospital Emergency Anaesthesia: A Retrospective Observational Study,” Emergency Medicine Journal 37, no. 11 (2020): 674–679. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 9. Doppmann P., Meuli L., Sollid S. J. M., et al., “End‐Tidal to Arterial Carbon Dioxide Gradient Is Associated With Increased Mortality in Patients With Traumatic Brain Injury: A Retrospective Observational Study,” Scientific Reports 11, no. 1 (2021): 10391. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 10. Hiller J., Silvers A., McIlroy D. R., Niggemeyer L., and White S., “A Retrospective Observational Study Examining the Admission Arterial to End‐Tidal Carbon Dioxide Gradient in Intubated Major Trauma Patients,” Anaesthesia and Intensive Care 38, no. 2 (2010): 302–306. [DOI] [PubMed] [Google Scholar]
- 11. Kottmann A., Kruger A. J., Sunde G. A., et al., “Establishing Quality Indicators for Pre‐Hospital Advanced Airway Management: A Modified Nominal Group Technique Consensus Process,” British Journal of Anaesthesia 128, no. 2 (2022): e143–e150. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 12. von Elm E., Altman D. G., Egger M., et al., “The Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) Statement: Guidelines for Reporting Observational Studies,” International Journal of Surgery 370, no. 9596 (2007): 1453–1457. [DOI] [PubMed] [Google Scholar]
- 13. Ångerman S., Kirves H., and Nurmi J., “A Before‐and‐After Observational Study of a Protocol for Use of the C‐MAC Videolaryngoscope With a Frova Introducer in Pre‐Hospital Rapid Sequence Intubation,” Anaesthesia 73, no. 3 (2018): 348–355. [DOI] [PubMed] [Google Scholar]
- 14. Angerman S., Kirves H., and Nurmi J., “Multifaceted Implementation and Sustainability of a Protocol for Prehospital Anaesthesia: A Retrospective Analysis of 2115 Patients From Helicopter Emergency Medical Services,” Scandinavian Journal of Trauma, Resuscitation and Emergency Medicine 31, no. 1 (2023): 21. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 15. Tønsager K., Kruger A. J., Ringdal K. G., Rehn M., and P‐EMS Template Collaborating Group , “Template for Documenting and Reporting Data in Physician‐Staffed Pre‐Hospital Services: A Consensus‐Based Update,” Scandinavian Journal of Trauma, Resuscitation and Emergency Medicine 28, no. 1 (2020): 25. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 16. Heino A., Iirola T., Raatiniemi L., et al., “The Reliability and Accuracy of Operational System Data in a Nationwide Helicopter Emergency Medical Services Mission Database,” BMC Emergency Medicine 19, no. 1 (2019): 53. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 17. Lassen N. A. and Christensen M. S., “Physiology of Cerebral Blood Flow,” British Journal of Anaesthesia 48, no. 8 (1976): 719–734. [DOI] [PubMed] [Google Scholar]
- 18. Ko N. U. and Engelhard K., “Neurocritical Care,” in Miller's Anesthesia, ed. Gropper M. A., Eriksson L. I., Fleisher L. A., Cohen N. H., Leslie K., and Johnson‐Akeju O. (Elsevier, 2024), 2570–2595. [Google Scholar]
- 19. Eichlseder M., Eichinger M., Pichler A., et al., “Out‐of‐Hospital Arterial to End‐Tidal Carbon Dioxide Gradient in Patients With Return of Spontaneous Circulation After Out‐of‐Hospital Cardiac Arrest: A Retrospective Study,” Annals of Emergency Medicine 82 (2023): 558–563, 10.1016/j.annemergmed.2023.03.001. [DOI] [PubMed] [Google Scholar]
- 20. Abrahamowicz A. A., Counts C. R., Danielson K. R., et al., “The Association Between Arterial‐End‐Tidal Carbon Dioxide Difference and Outcomes After Out‐of‐Hospital Cardiac Arrest,” Resuscitation 181 (2022): 3–9. [DOI] [PubMed] [Google Scholar]
- 21. Askrog V. F., Pender J. W., and Eckenhoff J. E., “Changes in Physiological Dead Space During Deliberate Hypotension,” Anesthesiology 25 (1964): 744–751. [DOI] [PubMed] [Google Scholar]
- 22. Eichlseder M., Labenbacher S., Pichler A., et al., “Is Time to First CT Scan in Patients With Isolated Severe Traumatic Brain Injury Prolonged When Prehospital Arterial Cannulation Is Performed? A Retrospective Non‐Inferiority Study,” Scandinavian Journal of Trauma, Resuscitation and Emergency Medicine 32, no. 1 (2024): 81. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 23. Koivulahti O., Tommila M., and Haavisto E., “The Accuracy of Preliminary Diagnoses Made by Paramedics—A Cross‐Sectional Comparative Study,” Scandinavian Journal of Trauma, Resuscitation and Emergency Medicine 28, no. 1 (2020): 70. [DOI] [PMC free article] [PubMed] [Google Scholar]
Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Data Availability Statement
An anonymised dataset can be made available upon a reasonable request from the corresponding author.