Estimation of Arterial Carbon Dioxide Based on End-Tidal Gas Pressure and Oxygen Saturation

Post-COVID breathlessness: a mathematical model of respiratory processing in the brain

Breathlessness is among the most common post-COVID symptoms. In a considerable number of patients, severe breathlessness cannot be explained by peripheral organ impairment. Recent concepts have described how such persistent breathlessness could arise from dysfunctional processing of respiratory information in the brain. In this paper, we present a first quantitative and testable mathematical model of how processing of respiratory-related signals could lead to breathlessness perception. The model is based on recent theories that the brain holds an adaptive and dynamic internal representation of a respiratory state that is based on previous experiences and comprises gas exchange between environment, lung and tissue cells. Perceived breathlessness reflects the brain's estimate of this respiratory state signaling a potentially hazardous disequilibrium in gas exchange. The internal respiratory state evolves from the respiratory state of the last breath, is updated by a sensory measurement of CO2 concentration, and is dependent on the current activity context. To evaluate our model and thus test the assumed mechanism, we used data from an ongoing rebreathing experiment investigating breathlessness in patients with post-COVID without peripheral organ dysfunction (N = 5) and healthy control participants without complaints after COVID-19 (N = 5). Although the observed breathlessness patterns varied extensively between individual participants in the rebreathing experiment, our model shows good performance in replicating these individual, heterogeneous time courses. The model assumes the same underlying processes in the central nervous system in all individuals, i.e., also between patients and healthy control participants, and we hypothesize that differences in breathlessness are explained by different weighting and thus influence of these processes on the final percept. Our model could thus be applied in future studies to provide insight into where in the processing cascade of respiratory signals a deficit is located that leads to (post-COVID) breathlessness. A potential clinical application could be, e.g., the monitoring of effects of pulmonary rehabilitation on respiratory processing in the brain to improve the therapeutic strategies.

The PaCO2-ETCO2 gradient in pre-hospital intubations of all aetiologies from a single UK helicopter emergency medicine service 2015-2018

Background

Control of the arterial partial pressure of carbon dioxide (PaCO2) is important in the ventilated patient. End-tidal carbon dioxide (ETCO2) levels are often used as a proxy, but are clinically limited. The difference between the PaCO2 and ETCO2 has been suggested to be 0.5-1.0 kPa. However, this has not been consistently reflected in the physiologically unstable pre-hospital patient. This study aims to elucidate the PaCO2-ETCO2 gradient for pre-hospital intubated patients.

Methods

This was a retrospective, cohort study using data identified from the HEMSbase 2 database (Feb 2015-Nov 2018). Patients were included if they had documented ETCO2 and arterial PaCO2 measurements. Arterial PaCO2 data that could not be linked to within 5 minutes of ETCO2 were excluded. Bland-Altman plots were calculated to describe agreement.

Results

A total of 73 patients were identified. Aetiology was arranged into three categories: 13 (17.8%) medical, 22 (30.1%) traumatic and 38 (52.1%) out-of-hospital cardiac arrest (OHCA). The median PaCO2-ETCO2 gradient was 2.0 [1.3-3.1] kPa. A PaCO2-ETCO2 gradient of 0-1 kPa was seen for only 11 (15.1%) of total patients. The Bland-Altman agreement for all aetiologies was more than the accepted gradient of 0-1 kPa with the largest bias and widest limits of agreement seen for OHCA (-3.2 [0.3 - -6.8]).

Conclusion

The magnitude of the differences between the ETCO2 and PaCO2, levels of variation and inability to predict this suggest that ETCO2 is not a suitable surrogate upon which to base ventilatory settings in conditions where pH or PaCO2 require precise control.

Relationship between end-tidal carbon dioxide and arterial carbon dioxide in critically ill patients on mechanical ventilation: A cross-sectional study

So far, only a few studies have examined and confirmed the correlation between end-expiratory carbon dioxide partial pressure (PETCO2) and arterial carbon dioxide tension (PaCO2) during invasive mechanical ventilation in critically ill patients. This study aimed to observe the correlation between PaCO2 and PETCO2 in patients on invasive mechanical ventilation.This was a cross-sectional study of adult patients on invasive mechanical ventilation enrolled between June 2018 and March 2019. Patients requiring invasive mechanical ventilation underwent one of the following mechanical ventilation modes: assisted/controlled ventilation, synchronized intermittent mandatory ventilation, and spontaneous breathing. Subsequently, the difference and correlation between PETCO2 and PaCO2 were analyzed.A total of 184 patients with 298 pairs of PETCO2-PaCO2 data were included in the analysis. Without distinguishing the ventilator mode, there was significant positive correlation between PETCO2 and PaCO2. In different ventilator modes, the correlation coefficient was 0.81 for synchronized intermittent mandatory ventilation, 0.47 for assisted/controlled ventilation, and 0.55 for spontaneous breathing, respectively. In patients with chronic obstructive pulmonary disease (r = 0.80), multiple trauma (r = 0.64), severe pneumonia (r = 0.60), gastrointestinal surgery (r = 0.57), and cerebrovascular diseases (r = 0.53), PETCO2 and PaCO2 were positively correlated. For oxygenation index <200 mm Hg, correlation coefficient r = 0.69, P < .001; oxygenation index ≥200, r = 0.73, P < .001. Under different oxygenation indexes, there was no statistically significant difference between the 2 correlation coefficients. 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 pairsIn patients on invasive mechanical ventilation, there was a good correlation between PETCO2 and PaCO2 in different ventilator modes, different disease types, and different oxygenation indexes, especially in synchronized intermittent mandatory ventilation mode and chronic obstructive pulmonary disease patients.

Evaluating a novel formula for noninvasive estimation of arterial carbon dioxide during post-resuscitation care

BACKGROUND

Controlling arterial carbon dioxide is paramount in mechanically ventilated patients, and an accurate and continuous noninvasive monitoring method would optimize management in dynamic situations. In this study, we validated and further refined formulas for estimating partial pressure of carbon dioxide with respiratory gas and pulse oximetry data in mechanically ventilated cardiac arrest patients.

METHODS

A total of 4741 data sets were collected retrospectively from 233 resuscitated patients undergoing therapeutic hypothermia. The original formula used to analyze the data is PaCO₂ -est1 = PETCO₂  + k[(PIO₂  - PETCO₂ ) - PaO₂ ]. To achieve better accuracy, we further modified the formula to PaCO₂ -est2 = k₁ *PETCO₂  + k₂ *(PIO₂  - PETCO₂ ) + k₃ *(100-SpO₂ ). The coefficients were determined by identifying the minimal difference between the measured and calculated arterial carbon dioxide values in a development set. The accuracy of these two methods was compared with the estimation of the partial pressure of carbon dioxide using end-tidal carbon dioxide.

RESULTS

With PaCO₂ -est1, the mean difference between the partial pressure of carbon dioxide, and the estimated carbon dioxide was 0.08 kPa (SE ±0.003); with PaCO₂ -est2 the difference was 0.036 kPa (SE ±0.009). The mean difference between the partial pressure of carbon dioxide and end-tidal carbon dioxide was 0.72 kPa (SE ±0.01). In a mixed linear model, there was a significant difference between the estimation using end-tidal carbon dioxide and PaCO₂ -est1 (P < .001) and PaCO₂ -est2 (P < .001) respectively.

CONCLUSIONS

This novel formula appears to provide an accurate, continuous, and noninvasive estimation of arterial carbon dioxide.

Arterial Partial Pressures of Carbon Dioxide Estimation Using Non-Invasive Parameters in Mechanically Ventilated Children

OBJECTIVE

We aim to create a predictive model capable of giving a noninvasive, immediate and reliable estimate of the arterial partial pressure of carbon dioxide (PaCO₂) in mechanically ventilated children with a better reliability than its estimation from end-tidal CO₂ (PetCO₂) and minute ventilation volume (Vmin) evolution.

METHODS

We collected data from the Intensive Care Unit (ICU) database of Sainte-Justine University Hospital (Montreal, Canada) and used the multilayer perceptron (MLP) to estimate the PaCO₂. Input data were (1) Arterial blood gas (ABG) at a previous time to calibrate the model, (2) mechanical ventilator parameters and (3) pulse oximetry. The data were divided into four groups depending on the time gap between previous ABG and its prediction: [0 h, 2 h], [2 h, 6 h], [6 h, 12 h] and [12 h, 24 h].

RESULTS

We included 17,329 ABGs collected from 527 patients between May 2015 and October 2018. Median age was 6.7 months (interquartile range 1-60) and female proportion was 45%. Patients had a median of 13 ABGs per patient (IQR 5-34). The accuracy of the models in the four groups was 18%, 18%, 19% and 25% higher than the minute volume models and the PetCO2 models (4% to 11%, respectively).

CONCLUSION

Our model based on noninvasive parameters was able to better estimate the PaCO₂ in mechanically ventilated children when compared to the traditional techniques.

SIGNIFICANCE

ABG analysis is very important in ICU; it is the gold standard in respiratory and acid-base evaluation. ABG is invasive, painful and risky. Our approach, noninvasive and reliable, is an alternative for optimizing mechanical ventilator settings, thus providing better care for patients.