key: cord-0021809-rq9wom0z
authors: Davis, Ryan; Jewell, Elizabeth; Engoren, Milo; Maile, Michael
title: Difference between arterial and end-tidal carbon dioxide and adverse events after non-cardiac surgery: a historical cohort study
date: 2021-10-06
journal: Can J Anaesth
DOI: 10.1007/s12630-021-02118-8
sha: f384a4a65ffdbb136a4603acbbba52c9dee9aba9
doc_id: 21809
cord_uid: rq9wom0z

PURPOSE: The difference between arterial and end-tidal partial pressure of carbon dioxide (ΔCO(2)) is a measure of alveolar dead space, commonly evaluated intraoperatively. Given its relationship to ventilation and perfusion, ΔCO(2) may provide prognostic information and guide clinical decisions. We hypothesized that higher ΔCO(2) values are associated with occurrence of a composite outcome of re-intubation, postoperative mechanical ventilation, or 30-day mortality in patients undergoing non-cardiac surgery. METHODS: We conducted a historical cohort study of adult patients undergoing non-cardiac surgery with an arterial line at a single tertiary care medical centre. The composite outcome, identified from electronic health records, was re-intubation, postoperative mechanical ventilation, or 30-day mortality. Student’s t test and Chi-squared test were used for univariable analysis. Logistic regression was used for multivariable analysis of the relationship of ΔCO(2) with the composite outcome. RESULTS: A total of 19,425 patients were included in the final study population. Univariable analysis showed an association between higher mean (standard deviation [SD]) intraoperative ΔCO(2) values and the composite outcome (6.1 [5.3] vs 5.7 [4.5] mm Hg; P = 0.002). After adjusting for baseline subject characteristics, every 5-mm Hg increase in the ΔCO(2) was associated with a nearly 20% increased odds of the composite outcome (odds ratio, 1.20; 95% confidence interval, 1.12 to 1.28; P < 0.001). CONCLUSIONS: In this patient population, increased intraoperative ΔCO(2) was associated with an increased odds of the composite outcome of postoperative mechanical ventilation, re-intubation, or 30-day mortality that was independent of its relationship with pre-existing pulmonary disease. Future studies are needed to determine if ΔCO(2) can be used to guide patient management and improve patient outcomes.

nearly 20% increased odds of the composite outcome (odds ratio, 1.20; 95% confidence interval, 1.12 to 1.28; P \ 0.001).

Conclusions In this patient population, increased intraoperative DCO 2 was associated with an increased odds of the composite outcome of postoperative mechanical ventilation, re-intubation, or 30-day mortality that was independent of its relationship with pre-existing pulmonary disease. Future studies are needed to determine if DCO 2 can be used to guide patient management and improve patient outcomes.

Objectif La diffe´rence entre la pression partielle arte´rielle et te´le´-expiratoire en dioxyde de carbone (DCO 2 ) est une mesure de l'espace mort alve´olaire couramment e´value´e en période perope´ratoire. Compte tenu de sa relation avec la ventilation et la perfusion, la DCO 2 pourrait fournir des informations pronostiques et guider les de´cisions cliniques. Nous avons e´mis l'hypothe`se que des valeurs de DCO 2 plus e´leve´es seraient associe´es al 'apparition d'un re´sultat composite de re´intubation, de ventilation me´canique postope´ratoire ou de mortalite´a`30 jours chez les patients be´ne´ficiant d'une chirurgie non cardiaque. Méthode Nous avons mene´une e´tude de cohorte historique de patients adultes be´ne´ficiant d'une chirurgie non cardiaque dans un seul centre me´dical de soins tertiaires et chez lesquels une canule arte´rielle e´tait installe´e. Le re´sultat composite, identifie´a`partir des dossiers de sante´e´lectroniques, e´tait la re´intubation, la ventilation me´canique postope´ratoire ou la mortalite´a`30 jours. Le test t de Student et le test du chi carre´ont e´teŔ utilise´s pour l'analyse univarie´e. La re´gression logistique a e´te´utilise´e pour l'analyse multivarie´e de la relation entre la DCO 2 et le re´sultat composite. Résultats Au total, 19 425 patients ont e´te´inclus dans la population finale a`l'e´tude. L'analyse univarie´e a montreú ne association entre des valeurs perope´ratoires moyennes plus e´leve´es (e´cart type [ET]) de DCO 2 et le re´sultat composite (6,1 [5,3] vs 5,7 [4, 5] mmHg; P = 0,002). Apre`s ajustement pour tenir compte des caracte´ristiques de base des sujets, chaque augmentation de 5 mmHg de la DCO 2 a e´te´associe´e à une augmentation de pre`s de 20 % de la probabilite´du re´sultat composite (rapport de cotes, 1,20; intervalle de confiance a`95 %, 1,12 a`1,28; P \ 0,001). Conclusion Dans cette population de patients, une augmentation perope´ratoire de la DCO 2 e´tait associe´e à une probabilite´accrue du re´sultat composite de ventilation me´canique postope´ratoire, de re´intubation ou de mortaliteá`3 0 jours, inde´pendamment de sa relation avec une maladie pulmonaire pre´existante. D'autres e´tudes sont ne´cessaires a`l'avenir pour de´terminer si la DCO 2 peut eˆtre utilise´e pour guider la prise en charge des patients et ame´liorer les devenirs des patients.

Keywords dead space Á arterial to end-tidal carbon dioxide difference Á end-tidal carbon dioxide (CO 2 ) Á delta carbon dioxide (CO 2 ) Postoperative respiratory complications are common and significant postoperative adverse events. Patients affected by complications such as pulmonary edema, postoperative respiratory failure, and pneumonia tend to have longer hospital stays, higher costs, and increased 30-day mortality. [1] [2] [3] [4] Recent studies indicate that ventilator settings contribute to postoperative pulmonary complications, even in healthy patients. [5] [6] [7] [8] These studies have focused primarily on the impact of inspiratory pressures and tidal volume (TV), with little data collected on ventilation efficiency. Dead space ventilation (VD) is affected by both lung function and perfusion, 9 and its intraoperative measurement may provide prognostic information and aid in optimizing ventilation management during surgery.

Dead space is the portion of TV that does not participate in gas exchange. Total dead space (VD phys ) is composed of anatomic (VD ana ) and alveolar dead space (VD alv ), represented by the equation VD phys = VD ana ? VD alv . The division between VD ana and VD alv is not a strictly anatomic division, but depends on the interface between inspired air and alveolar gas. Dead space associated with conducting airways (endotracheal tube, trachea, proximal bronchi) is described as VD ana and dead space associated with non-perfused alveoli (distal airways) is described as VD alv . 10 [11] [12] [13] variables, many of which are monitored and modified under general anesthesia.

Evaluation of VD phys is useful in assessment and management in some clinical settings. An elevated VD phys predicts an increased risk for mortality in acute respiratory disease syndrome (ARDS). [14] [15] [16] Through its relationship to atelectasis and ventilation/perfusion (V/Q) matching, VD phys is also useful for choosing the optimal level of PEEP in mechanically ventilated patients. [17] [18] [19] As VD ana is often a relatively fixed component, evaluation and management based on VD phys or VD alv , might be useful in other patient populations undergoing mechanical ventilation.

There are multiple ways to calculate or estimate VD phys , VD ana , and VD alv , but these are often not available in many clinical settings. The difference between arterial partial pressure of carbon dioxide (PaCO 2 ) and partial pressure of end-tidal CO 2 (PetCO 2 ), DCO 2 , is one of the most readily available methods to estimate VD alv as the difference between these two values is attributed primarily to VD alv.

13,20-28 Increased DCO 2 has been associated with poor outcomes in trauma 29 and critically ill patients requiring major surgery. 13 The objective of this study was to evaluate if DCO 2 is independently associated with worse outcomes in patients undergoing non-cardiac surgery. We hypothesized that, in patients undergoing general anesthesia, greater DCO 2 is associated with an increased odds of the postoperative composite outcome of postoperative mechanical ventilation, re-intubation, or 30-day mortality.

Following approval from the Institutional Review Board, we conducted a historical cohort study at University of Michigan Medical Center (Ann Arbor, MI, USA); the requirement for patient consent was waived. We queried the electronic anesthesia records for all adult cases from 23 July 2009 to 31 January 2019 for which general endotracheal anesthesia was performed and at least one arterial blood gas (ABG) was recorded during the procedure. Exclusion criteria were an American Society of Anesthesiology (ASA) Physical Status classification of VI, preoperative mechanical ventilation, sodium bicarbonate administration within 15 min prior to ABG measurement, cardiopulmonary bypass or extracorporeal membrane oxygenation, absent PetCO 2 or PaCO 2 data, or intraoperative one-lung ventilation.

Patient and perioperative data Demographic information was collected from the preoperative history and physical documentation. This comprised age, sex, height, weight (actual and ideal), body mass index (BMI), ASA Physical Status score, smoking status (current or former), chronic obstructive pulmonary disease (COPD), and asthma. Preoperative values for albumin, blood urea nitrogen, creatinine, and peripheral oxygen saturation (SpO 2 ) were obtained. Creatinine clearance was calculated using the Cockcroft-Gault formula. Procedure variables included (i) type, (ii) duration, and (iii) emergent vs elective procedure. Other intraoperative variables were the total transfusion requirement including volume of packed red blood cells, fresh frozen plasma, platelets, and cryoprecipitate; estimated blood loss; total crystalloid administration; and grams of albumin administered.

Blood gas, ventilator, and hemodynamic data For all patients, the first ABG obtained in the operating room after induction of general anesthesia was utilized in our analysis and pH, PaCO 2 , arterial partial pressure of oxygen (PaO 2 ), and arterial oxygen saturation (SaO 2 ) were collected. Ventilator parameters (TV, RR, minute ventilation, pressure support, peak airway pressure, PEEP, and fraction of inspired oxygen), hemodynamic data (HR and MAP), and PetCO 2 were collected for the ten-minute epoch surrounding the ABG time and the median values were utilized in the analysis.

Our study outcome was a composite of the postoperative complications, postoperative mechanical ventilation, reintubation, or mortality, as identified from the electronic health record (EHR). Postoperative mechanical ventilation was defined as any patient who required mechanical ventilation after the procedure for any duration not associated with another procedure. We defined reintubation as a period of time in the EHR without receipt of mechanical ventilation subsequently followed by the documented presence of mechanical ventilation. Mortality was included as an outcome since it would act to censor future pulmonary complications. Patients were assumed to be alive at 30 days unless noted to be deceased in the medical record.

We calculated descriptive statistics for each variable. Continuous variables that were normally distributed were presented as mean (standard deviation [SD] ). Continuous variables that were non-normally distributed were summarized as median [interquartile range (IQR)]. Categorical variables were summarized using counts and proportions.

Preoperative characteristics and DCO 2 were compared between those with and without the composite outcome using Chi square tests for categorical variables and t tests or Mann-Whitney U tests for continuous variables. Multivariable logistic regression models were generated to evaluate whether DCO 2 was associated with the composite outcome while adjusting for other patient and surgical factors based on clinical and statistical significance. For entry into the model, we chose clinically relevant variables that were selected by two anesthesiologists based on the literature. Additionally, we considered variables with standardized differences greater than 0.2 using Cohen's d. We also collapsed several variables into one variable, or chose a representative variable from a group, when the groups of variables were similar or highly correlated (q [ 0.8). Upon constructing the multivariable model, we removed, collapsed, or grouped variables with variance inflation factors [ 2.5.

For each of the continuous variables included in the model (age, creatinine clearance, preoperative albumin, total albumin, case duration, total volume blood products, percent inspired oxygen, TV, RR, MAP, HR, and DCO 2 ), the following potential transformations were considered: X -2 , X -1 , X -0.5 , ln(X), X 0.5 , X 1 , and X 2 . The continuous variables were binned and plotted as the proportion within each bin with the outcome. Variables with values of zero were shifted by one or one plus their minimum to use all the listed transformations. Univariate regressions with the composite outcome as the outcome and each transformation of each continuous variable were then used to predict values at the median of the bins and plot the predicted outcome for the transformation. These transformations were inspected visually and used to determine that no non-linear terms were necessary.

Additionally, all variables that were significant predictors in the initial multivariable model were also considered for interactions with the primary predictor, DCO 2 . Each significantly associated variable was centred and an interaction between it and DCO 2 was added individually to the initial full regression. All interactions that were significant individually were used together in a full regression. Interactions that were not significant at a level of P\ 0.05 in this new full regression were removed. Even though multiple interactions remaining in the model had P.05, most interaction terms had a small magnitude of association with the outcome, none changed the direction of the relationship between DCO 2 and the primary outcome, and none of the variables were conceptually thought to have a multiplicative effect for any of the variables with the DCO 2 . Based on this, none of the interactions were added to the final, clinically relevant model.

Akaike Information Criteria, Bayes Information Criteria, -2 log likelihood, and area under the receiver operating characteristic curve (95% confidence interval [CI]) were used to evaluate the model. P values .05 and CIs excluding 1 were considered statistically significant. Analyses were performed in R version 3.6.2 (R Core Team, R Foundation for Statistical Computing, Vienna, Austria).

After excluding patients with missing data or data concerning for errant manual entry, 19,425 patients remained for analysis (Fig. 1) . The mean (SD) time from anesthesia start to ABG was 73 (26) min. In our patient population, 15% (n = 2,830) had COPD, 10% (n = 1,951) had asthma, and 7% (n = 1,274) were on supplemental oxygen preoperatively ( Table 1 ). The mean values for PaCO 2 and PetCO 2 were 40.2 (5.8) mm Hg and 34.3 (4.0) mm Hg, respectively, with a mean DCO 2 of 5.8 (4.6) mm Hg. A total of 3,006 patients (16%) had the composite outcome: 85 (0.4%) required postoperative re-intubation, 2,769 (14%) required postoperative mechanical ventilation, and 452 (2%) had 30-day mortality.

Many of the evaluated variables were significantly different between groups of patients who did and did not experience the composite outcome (Table 2) . Notably, patients who had the composite outcome were more likely to use preoperative supplemental oxygen (19% vs 4%; P \ 0.001) and have COPD (17% vs 14%; P \ 0.001), and less likely to have asthma (8% vs 10%; P \ 0.001). Similar smoking rates (55% vs 55%; P = 0.386) were present in each cohort. Patients who had the composite outcome had a significantly higher mean (SD) DCO 2 (6.1 [5.3] 

After adjusting for other factors, DCO 2 was associated with an increased occurrence of re-intubation, postoperative mechanical ventilation, or 30-day mortality ( (Fig. 2) .

Several other variables were independently associated with the composite outcome ( Table 3 ). The use of preoperative supplemental oxygen was associated with the composite outcome (OR, 2.37; 95% CI, 1.97 to 2.84; P \ 0.001), while higher albumin levels were associated with a decrease in the incidence of the composite outcome (OR, 0.53; 95% CI, 0.48 to 0.58; P \ 0.001 per gÁdL -1 ). Chronic obstructive pulmonary disease was not associated with re-intubation, postoperative mechanical ventilation, or 30-day mortality (OR, 1.16; 95% CI, 0.99 to 1.36; P = 0.06). The model had good discrimination (C statistic, 0.87; 95% CI, 0.86 to 0.88).

In this historical cohort study, we found that increased DCO 2 was associated with an increase in the composite outcome of postoperative mechanical ventilation, reintubation, or 30-day mortality in patients undergoing general anesthesia for a non-cardiac surgical procedure. This relationship remained significant even when we adjusted for other relevant pre-existing clinical factors, including COPD, preoperative oxygen use, dialysis, and age. Since the magnitude of association for a 5 mm Hg of change is nearly 20%, even relatively small changes in DCO 2 produce clinically important changes to the risk of the composite outcome. For a patient with a DCO 2 of 2-4 mm Hg, consistent with normal healthy levels, the median The magnitude of DCO 2 is affected by both ventilated but unperfused alveoli (true dead space) and alveoli with high ventilation/perfusion ratios (V/Q inequality). Shunt (perfusion without ventilation) will cause a small increase in DCO 2 by raising the PaCO 2 as mixed venous blood, which is higher in PCO 2 , crosses the pulmonary circulation and mixes with the systemic arterial circulation. True dead space and V/Q inequality are markers of disease severity and increase with COPD severity or low CO states. DCO 2 is also influenced by dynamic, modifiable, and clinically important variables of ventilation (RR, TV, PEEP), perfusion (HR, MAP, CO, volume status, patient positioning), and metabolism (CO 2 production). [11] [12] [13] 30 Some of these are under the control of the anesthesiologist, and studies should be conducted to determine how anesthesiologists' manipulation of these parameters affects both DCO 2 , and more importantly, patient outcome.

Clinically, DCO 2 has been utilized as a surrogate for VD alv , primarily in the intensive care unit, where levels correlate with the amount of VD alv . 31 In addition, DCO 2 has been shown to correlate with disease severity in ARDS patients, with a higher DCO 2 being associated with more severe ARDS 32 and with death. 33 Similarly, elevated DCO 2 in patients who have survived cardiac arrest is associated with high hospital mortality. 34 Nevertheless, use of DCO 2 as an intraoperative prognostic measure is less common but is supported by our findings.

Tyburski et al. studied 501 trauma patients undergoing emergency surgery. In patients who died (n = 147 [29%]), DCO 2 was higher at all three time points evaluated (initial, post-resuscitation, and final). In those who died, lower values were predictive of longer survival with a final mean (SD) DCO 2 of 22.25 (14.32) mm Hg in patients who died intraoperatively, 19 .96 (14.91) mm Hg in patients who died within 24 hr after surgery, and 10.90 (10.13) mm Hg in patients who died after 24 hr but before hospital discharge. Survivors had a DCO 2 of 7.37 (6.30) mm Hg (all P.006 compared with non-survivors). 29 In another study of critically ill or injured patients requiring major surgery with 41% mortality, Domsky et al. found highest mortality rates in patient with the highest DCO 2 , and highest estimated VD alv fraction. 13 Our study is in agreement with these two studies. While these studies are limited to trauma and critically ill surgery patients, our study is more generalizable as it includes a large variety of non-cardiac operations. To our knowledge, our study is the first large study showing that increased DCO 2 is associated with our There are a few important limitations to this study. As a single-centre study, it might not be generalizable to other centres with different intraoperative ventilator strategies or practice surrounding PaCO 2 measurement indications or timing. At our institution, 97.7% of patients who had an arterial line placed for their procedure had an ABG measured with a mean (SD) time from anesthesia start to ABG measurement of 73 (26) min. This limits our findings to patients and procedures for which arterial lines are utilized and likely explains the wider DCO 2 found in this study compared with previous studies that excluded ASA III-V patients or patients with ''respiratory or cardiac abnormalities.'' 35, 36 Second, PaCO 2 and PetCO 2 are dynamic as patients undergo hours of anesthesia. The first ABG was selected to facilitate comparability of patients undergoing anesthesia as a baseline since many patients had only one ABG at the beginning of the case. Selecting the first ABG allowed for analysis of a large patient population; it also minimized bias. Nevertheless, this measure was more representative of the initial clinical ABG = arterial blood gas; ASA = American Society of Anesthesiologist ; BMI = body mass index; BUN = blood urea nitrogen; COPD = chronic obstructive pulmonary disease; Cr = creatinine; FFP = fresh frozen plasma; IQR = interquartile range; PaCO 2 = arterial partial pressure of carbon dioxide; PaO 2 = arterial partial pressure of oxygen; PEEP = positive end expiratory pressure; PetCO 2 = end-tidal partial pressure of carbon dioxide; pRBC = packed red blood cells; SD = standard deviation; SpO2 = peripheral oxygen saturation.

condition, rather than the final clinical condition, of patients undergoing anesthesia. Furthermore, we did not evaluate whether DCO 2 changes over the course of surgery and how that is associated with outcomes. Evaluating these changes and whether they can predict outcome needs further investigation. Previous studies have investigated risk factors for postoperative respiratory failure and have developed clinically predictive models. 37 Some of these clinically significant variables were not included in our analysis because they were not collected in our EHR. For example, New York Heart Association heart failure class has been shown to be a risk factor for postoperative pulmonary complications, 37 but this variable is not present in our anesthesia preoperative history. Where a previous study found that lower preoperative SpO 2 levels were associated with postoperative pulmonary complications, 38 our patients with low values had been placed on supplemental oxygen. Additionally, risk scores developed on a more general surgical population may not be applicable to our unique patient population, specifically patients with an arterial catheter who underwent non-cardiac surgery. Despite these limitations, our findings support the need for future research on the relationship between DCO 2 and postoperative adverse events. This study was retrospective and must be evaluated prospectively in patients undergoing non-cardiac surgery with a study design to address some of the limitations encountered in this patient population. Next, additional patient populations, such as those undergoing anesthesia without mechanical ventilation, pediatric patients, or those undergoing one-lung ventilation should be evaluated. Patients who require one-lung ventilation might represent a specific population risk of ventilation efficiency and where DCO 2 might have clinical predictive value.

In addition to evaluating the DCO 2 in other patient populations, additional research is needed to determine if specific therapies are beneficial when elevated DCO 2 is detected. For example, one animal study of ARDS found that the PEEP value that minimized capnography-measured VD alv also maximized CO and mixed venous oxygen saturation. 39 Further study is needed to determine if individualizing care, through ventilator adjustments or manipulation of hemodynamics, based on minimizing DCO 2 reduces adverse events.

Our study shows an independent association between increases in DCO 2 and a composite outcome of reintubation, postoperative mechanical ventilation, or 30-day mortality in patients undergoing non-cardiac surgery. Future research is needed to determine if the DCO 2 can be used for risk stratification or as a target for the optimization of intraoperative ventilator and hemodynamic management.

Author contributions Ryan Davis, Milo Engoren, and Michael Maile contributed to all aspects of this manuscript, including study conception and design; acquisition, analysis, and interpretation of data; and drafting the article. Elizabeth Jewell contributed to all aspects of this manuscript, including study design; acquisition, analysis, and interpretation of data; and drafting the article Disclosure Michael D. Maile was supported by KL2TR002241.

Funding None.

Editorial responsibility This submission was handled by Dr. Sheila Riazi, Associate Editor, Canadian Journal of Anesthesia/Journal canadien d'anesthe´sie. 

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