Relationship between end-tidal carbon dioxide and arterial... : Medicine (2024)

1 Introduction

End-expiratory carbon dioxide partial pressure (PETCO₂) monitoring has been widely used in intensive care units (ICU) for patients in need of mechanical ventilation. In healthy people, the correlation between PETCO₂ and arterial carbon dioxide tension (PaCO₂) is strong. In sick patients, however, the variation is large, which may be associated with many factors.^[1–3] PETCO₂ can exceed PaCO₂ or can be lower than PaCO₂.^[4] When ventilator settings are adjusted, the correlation can be very poor or even reversed in ventilated patients. The ventilation of emergency patients can only be adjusted according to values derived from an arterial blood gas analysis. PETCO₂ measurements cannot be regarded as absolutely accurate values, except maybe in patients without primary cardiorespiratory dysfunction.^[1]

So far, the relationship between PaCO₂ and PETCO₂ has been reported in several pre-clinical^[5–7] and clinical studies, including children/infants,^[8–11] or mechanically ventilated patients with a single disease (acute respiratory distress syndrome, neurosurgical, or postcardiac arrest patients).^[12–15] Only 1 large sample study^[16] analyzed the relationship between PaCO₂ and PETCO₂ in 219 arterial blood gases obtained from 87 patients. They found a good correlation between the mean of PETCO₂ and PaCO₂ when using synchronized intermittent mandatory ventilation (SIMV), continuous positive airway pressure, and T-Tube models; SIMV (r = 0.893, 0.841, and 0.923, respectively).^[16] However, they did not conduct stratified analysis according to the disease type and severity, and its clinical significance was limited. Thus, in this study, we further examined the correlation between PaCO₂ and PETCO₂ under different ventilator modes, different disease types, and different oxygenation indexes in mechanically ventilated patients with relatively stable conditions in ICU.

2 Materials and methods

2.1 Study design

This was a cross-sectional study that evaluated the patients on invasive mechanical ventilation admitted to ICU of a tertiary university hospital between June 2018 and March 2019.

2.2 Eligibility criteria

Inclusion criteria were as follows: all patients underwent endotracheal intubation or tracheotomy; continued application of ventilator ≥48 hours in ICU; no vasoactive drugs were prescribed; age >18 years.

Exclusion criteria: incomplete data or patient refusal to participate.

The Institutional Review Board of Harrison International Peace Hospital (2018-1-013) approved the study protocol. Informed consent was obtained from all individual participants included in the study.

2.3 Interventions and data collection

Patients received one of the following mechanical ventilation methods: assisted/controlled ventilation (A/C), SIMV, and spontaneous breathing (SPONT) mode. The mainstream PETCO₂ monitor (KMI605A, Beijing Jinjiaxing Co., Ltd., Beijing, China) was used by the same doctor (with 10 years of experience) to detect PETCO₂. The sampling sensor was directly connected to the Y-shaped pipe of the ventilator and the endotracheal intubation or tracheotomy catheter. Blood samples of radial artery or femoral artery were collected and the arterial blood gas analysis was completed at beside (ABL90, Leidu, Denmark). PETCO₂ and arterial blood gas analysis were completed within 5 minutes.

2.4 Outcomes

Primary outcome measures: PETCO₂ and PaCO₂ were collected as primary outcome variables. The difference and correlation between PETCO₂ and PaCO₂ were tested in all patients.

Secondary outcome measures: The following data were collected when PETCO₂ and PaCO₂ were recorded, including age, gender, body mass index, primary disease type, mean arterial pressure, heart rate, ventilator mode and oxygenation index, Charlson comorbidity index, acute physiology and chronic health evaluation scoring system II, sequential organ failure assessment and treatments.

2.5 Statistical analysis

Assuming a type 1 error of 5% (alpha of 0.05), a power of 90% and r = 0.50 in preliminary, this study would require a sample size of 40 patients. To account for dropouts and incomplete data, we aimed for a sample size of 100 patients. Sample size was calculated based on both primary outcomes and the larger of the 2 calculations was utilized.

The Shapiro–Wilk test was used to verify whether all recorded variables were normally distributed (P > .05). Continuous data are expressed as the mean ± standard deviation. Correlations among data with measurable outcomes were analyzed using the Pearson test if distributed normally, or as median (interquartile range) and with the Spearman test if non-normally distributed. When there was a quantitative relationship between the 2 variables, linear regression was used to explore the regression equation. A P value <.05 was considered statistically significant.

3 Results

3.1 Patients

A total of 184 patients with 298 pairs of PETCO₂-PaCO₂ data were included in the analysis. The mean age was 68.45 ± 16.50 years, and 126 patients (68.48%) were men. Main characteristics of the patients are shown in Table 1.

3.2 Correlation analysis of PETCO₂ and PaCO₂ under different ventilator modes

Without distinguishing the ventilator mode, there was a significant positive correlation between PETCO₂ and PaCO₂; the correlation coefficient was 0.72, the linear regression equation Y = 11.81 + 0.65x (Y: PETCO₂; x: PaCO₂) (Table 2 and Fig. 1). As shown in Figure 1, the majority of PETO₂ and PaCO₂ values are distributed in 20 to 60 mm Hg (black dotted frame).

When comparing different ventilator modes, only the SIMV mode showed a significant correlation (r = 0.81, P < .001). In both A/C and SPONT mode, the correlation was relatively weak (correlation coefficient r = 0.47 and 0.55, respectively).

3.3 Correlation analysis of PETCO₂ and PaCO₂ of different disease types

In patients with chronic obstructive pulmonary disease, multiple injuries, severe pneumonia, gastrointestinal surgery, and cerebrovascular diseases, PETCO₂ and PaCO₂ were positively correlated (the correlation coefficients were 0.80, 0.64, 0.60, 0.57, and 0.53 respectively; Table 3). For other diseases (including malignant tumors and cardiovascular disease), no correlation was found (r = 0.46, P = .06).

3.4 Correlation analysis of PETCO₂ and PaCO₂ with different oxygenation indexes

Oxygenation index <200 mm Hg, correlation coefficient r = 0.69, P < .001; oxygenation index ≥200 correlation coefficient r = 0.73, P < .001 (Table 4). Under different oxygenation indexes, there was no statistically significant difference between the 2 correlation coefficients (Z = 0.67, P = .50). Among 116 pairs of data with oxygenation index <200 mm Hg, the difference of PaCO₂-PETCO₂ ≥10 mm Hg was found in 25 pairs (21.55%); in 182 pairs of data with oxygenation index ≥200 mm Hg, the difference of PaCO₂-PETCO₂ ≥10 mm Hg was found in 26 pairs (14.29%) (χ2 = 2.64, P = .19).

4 Discussion

This study showed that there was a significant positive correlation between PETCO₂ and PaCO₂ on invasive mechanical ventilation admitted to ICU, especially in SIMV mode, chronic obstructive pulmonary disease patients. Under different oxygenation indexes, the correlation remained strong.

In healthy people, the difference between PETCO₂ and PaCO₂ is generally 2 to 5 mm Hg.^[17] PaCO₂, dead space, lung perfusion, and sampling points affect PETCO₂. When the dead space is large, the alveolar CO₂ (PACO₂) evacuation is not uniform, so PETCO₂ is more likely to have a lower value compared with PaCO₂. When the ventilation/perfusion ratio is low, the effect of shunt on PETCO₂ is small, and cannot easily degrade PETCO₂. The difference between PETCO₂ and PaCO₂ in patients with respiratory failure is large, and the difference is closely related to ventilation/perfusion. PETCO₂ should not be used to evaluate PaCO₂. In severely ill patients, pulmonary organic diseases cause increased pulmonary shunts; this mixed blood flow into the arterial system results in an increased gradient of PETCO₂-PaCO₂ difference.^[4] In severe lung diseases or systemic diseases, this difference is as high as 20 mm Hg. In other words, PETCO₂ underestimates PaCO₂ levels. Sivan et al^[18] found that the average difference is 3.4 ± 6.6 mm Hg. When the PaCO₂/PACO₂ ratio is lower than 0.3, the difference begins to increase, reaching 7.8 ± 7.3 mm Hg; when the PaCO₂/PACO₂ ratio is greater than 0.3, the difference is only 0 ± 3.4 mm Hg. This study did not distinguish ventilator mode and disease type, the difference was 2.75 ± 8.38 mm Hg, but stratified analysis was not based on PaCO₂/PACO₂.

4.1 Ventilator mode and PETCO₂

Weinger and Brimm^[19] found a good correlation between PaCO₂ and PETCO₂ in 25 adult patients with lung disease or extrapulmonary disease using a SIMV mode; the difference between PaCO₂ and PETCO₂ was 4.24 ± 4.42 mm Hg. In patients with non-pulmonary diseases, who underwent mechanical ventilation or automatic ventilation through tracheal intubation the difference between PaCO₂ and PETCO₂ was 0.8 to 3.5 mm Hg.^[20] A recent study suggested a strong correlation between PaCO₂ and PETCO₂ under the conditions of SIMV, continuous positive airway pressure mode, and T-tube,^[16] which was consistent with our findings. However, there was only a weak correlation between PaCO₂ and PETCO₂ in A/C mode. This might be due to small sample size; only 30 sets with A/C mode were analyzed. In addition, the A/C mode in this study was mostly used for surgical postoperative or severe pneumonia patients, which requires deep sedation, analgesia, and complete control of ventilation, and alveolar minute ventilation and exhaled tidal volume are basically in a constant state, so PETCO₂ variation is small in this case. Even if PaCO₂ increases or decreases, it is difficult to stimulate the central or surrounding receptors, change the breathing frequency and tidal volume, so there is weak correlation between PaCO₂ and PETCO₂.

4.2 Disease type and PETCO₂

Kerr et al^[21] reported a good correlation between PaCO₂ and PETCO₂ in adult patients with traumatic brain injury without pulmonary disease (positive end-expiratory pressure <5 cmH₂O). Another study found that PaCO₂ and PETCO₂ had a strong correlation regardless of the disease, using a ventilator, or SPONT; but the correlation coefficients were different among different diseases.^[22] Barton et al reported that in non-intubated patients with different conditions in the emergency room, PaCO₂ and PETCO₂ also had a strong correlation. PETCO₂ monitoring may be sufficient to represent PaCO₂ and avoid repeated arterial blood gas analysis.^[22] Tobias and Meyer^[23] found that percutaneous CO₂ monitoring is more accurate than PETCO₂ in predicting PaCO₂ in infants and young children. The difference between percutaneous CO₂ and PaCO₂ is smaller than the deviation between PaCO₂ and PETCO₂ (2.3 ± 1.3 mm Hg and 6.8 ± 5.1 mm Hg, respectively). Continuous monitoring of PETCO₂ and finger oxygen saturation is safe and effective for patients after coronary artery bypass grafting. Moreover, PETCO₂ can predict PaCO₂ (r = 0.76), can easily detect hypercapnia, and has a sensitivity of 95%.^[24] Consistently, in this study, we found a good correlation between PaCO₂ and PETCO₂ in patients with COPD, multiple injuries, and severe pneumonia. A correlation coefficient of 0.57 and 0.53 for gastrointestinal surgery and cerebrovascular diagnosis is weak, and clinically irrelevant, even though statistically significant.

4.3 Oxygenation index and PETCO₂

Previous studies have argued that the relationship between PETCO₂ and PaCO₂ in different clinical settings is controversial. McDonald et al^[25] suggested a good correlation between PETCO₂ and PaCO₂ in 129 critically ill patients who received invasive mechanical ventilation through tracheal intubation. The statistical analysis of 1708 paired data showed a higher PETCO₂ (39.9 ± 12.7 mm Hg) compared to PaCO₂ (45.5 ± 14.1 mm Hg); PETCO₂-PaCO₂ difference was ≤5 mm Hg in 54%, and ≤10 mm Hg in 80% paired data. The presence of lung disease had a negative impact on the correlation between the two. In the data of 640 groups with oxygenation index <200 mm Hg, the difference of PETCO₂-PaCO₂ in 223 groups (35%) was >10 mm Hg. However, among the 1068 data sets with an oxygenation index >200 mm Hg, only 111 groups (10%) had a difference >10 mm Hg. This trend suggests that the lower the oxygenation index, the greater the difference between the two.^[25] In this study, among 116 pairs of data with oxygenation index <200 mm Hg, the difference of PaCO₂-PETCO₂ ≥10 mm Hg was found in 25 pairs (21.55%); in 182 pairs of data with oxygenation index ≥200 mm Hg, the difference of PaCO₂-PETCO₂ ≥10 mm Hg was found in 26 pairs. These data suggest that the oxygenation index of adult patients was negatively correlated with the PaCO₂-PETCO₂ difference.

This study had several limitations. First of all, the PETCO₂ sampling sensor was directly connected to the Y-shaped pipe of the ventilator. It is necessary to ensure that the exhaled gas will not leak due to insufficient tube cuff pressure, which may be ignored by the researcher during the research process, resulting in measurement error. However, in all patients during mechanical ventilation, manually re-measure the cuff pressure every 6 to 8 hours, and the pressure is always maintained at 25 to 30 cmH₂O to minimize the possibility of air leakage. Second, although PETCO₂ was measured only once without the average of multiple measurements, but when recording the value, ensure that the PETCO₂ is in a steady state (the fluctuation range is <±2 mm Hg within 5 minutes), which may reduce the error. Finally, with the increasing accuracy of the measuring instruments, PETCO₂ has become clinically applied as a substitute for PaCO₂. However, caution is required for its application, and use without knowing the advantages and disadvantages of this method may result in erroneous results and improper clinical interpretation. Further studies are needed to assess their suitability in different diseases and clinical situations.

5 Conclusions

In patients receiving invasive mechanical ventilation, PETCO₂ and PaCO₂ showed a good correlation in different ventilator modes, different disease types and different oxygenation indexes, especially in SIMV mode and chronic obstructive pulmonary disease patients.

Author contributions

Conceptualization: Jinrong Wang, Jianjun Zhang, Zhaobo Cui.

Data curation: Jianjun Zhang, Yajing Liu, Huimian Shang, Li Peng, Zhaobo Cui.

Formal analysis: Jinrong Wang, Jianjun Zhang, Yajing Liu, Huimian Shang, Li Peng, Zhaobo Cui.

Funding acquisition: Jinrong Wang, Zhaobo Cui.

Investigation: Jianjun Zhang, Zhaobo Cui.

Methodology: Jianjun Zhang, Zhaobo Cui.

Project administration: Jinrong Wang, Zhaobo Cui.

Writing – original draft: Jianjun Zhang, Zhaobo Cui.

Writing – review & editing: Jinrong Wang, Yajing Liu, Huimian Shang, Li Peng.

References