End-tidal carbon dioxide monitoring may be associated with a higher possibility of return of spontaneous circulation during out-of-hospital cardiac arrest: a population-based study

This study was initiated after its protocol was approved by the Institutional Review Board of Dalin Tzu Chi Hospital, Buddhist Tzu Chi Medical Foundation, Taiwan, and was conducted in conformity with the Declaration of Helsinki.

The National Health Insurance (NHI) program was implemented in Taiwan in 1995 and provides compulsory universal health insurance. It enrolls about 99 % of the Taiwanese population and contracts with 97 % of all the country’s medical providers [10, 11]. The database contains comprehensive information about all insured subjects, including sex, date of birth, residential or work area, dates of clinical visits, diagnoses identified by International Classification of Diseases (Ninth Revision) Clinical Modification (ICD-9-CM) diagnostic codes, details of prescribed medications and procedures administered, expenditure amounts, and outcome at hospital discharge (i.e., recovered, died, or transferred out) [12]. A random sample of 1,000,000 people who received health benefits from the NHI program was selected based on calendar-year 2005 reimbursement data and was considered representative of the entire population; according to the Taiwan National Health Research Institute, the group did not differ statistically from the larger cohort in age, sex, or health care costs [13, 14]. This sample was used as our study cohort.

The population sample was followed from 1 January 2003 to 31 December 2012 (a total of 10 years). For our study cohort, we first identified individuals who were still alive in 2005 and were older than 18 years at the time of OHCA. OHCA was defined by the ICD-9-CM codes ventricular fibrillation (427.4), cardiac arrest (427.5), and sudden death (798.0–798.9) in outpatient clinic records. To avoid including patients who had Do Not Attempt Resuscitation orders, no chance of survival, and coding errors, we excluded patients on whom no chest compression was attempted. ETCO₂ monitoring was defined by the charge record for continuous capnography during the visits. For each charge record of ETCO₂, the institutes could claim for about US$ 14 from the Bureau of National Health Insurance. After exclusions, we identified 83 patients who received ETCO₂ monitoring and 4958 who did not receive such monitoring. In order to identify sustained ROSC, each patient was tracked if he or she was hospitalized after OHCA (Fig. 1).

In order to investigate a significant influence on survival associated with ETCO₂-monitoring, we included several covariates in the analysis; age, sex, calendar year, urbanization level (i.e., urban, suburban, and rural), health care institutes, and socioeconomic status (SES). Income-related insurance payment amounts were used as a proxy measure of individual SES at follow-up. People were divided into 3 groups: (1) low SES: payment lower than US$571 per month (New Taiwan Dollars [NT$] 20,000); (2) moderate SES: payment between US$571 and US$1141 per month (NT$ 20,000–40,000); and (3) high SES: payment of US$1142 or more per month (NT$40,001 or more) [12]. The health care institutes visited by patients were classified into 4 levels (medical centers, regional hospitals, local hospitals, and clinics) based on hospital accreditation. Two additional covariates that may be related to sustained ROSC following resuscitation, the CPR duration and attempted defibrillation, were identified based on charge records. Finally, the prevalence of selected comorbid conditions (i.e., diabetes, hypertension, coronary artery disease, hyperlipidemia, malignancies, heart failure, atrial fibrillation, intracranial hemorrhage, ischemic stroke, chronic renal insufficiency, and liver cirrhosis) and the Charlson Comorbidity Index (CCI) score were determined using discharge diagnoses either during outpatient clinic visits or hospitalizations before 1 January 2005. The CCI is a scoring system that assigns weights to important concomitant diseases; it has been validated for use in studies that employ ICD-9-CM data [14, 15].

In this study, the propensity score was the conditional probability for using ETCO₂ monitoring in the presence of possible confounders. The prespecified covariates were added into a multivariable logistic regression model to predict the probability of ETCO₂ use. The predicted probability from the model was used as the propensity score for each patient. We then matched each patient in the ETCO₂ group to 20 patients in the untreated group with the closest propensity score using a standard greedy-matching algorithm [16] and compared the probability of survival benefits between these groups.

The SAS statistical package, version 9.4 (SAS Institute, Inc., Cary, NC, USA) was used for data analysis. All covariates were taken as categorical variables except age, calendar year, CPR duration, and propensity score, which were treated as continuous variables. Categorical variables were compared using Pearson’s chi-square test, and continuous variables were assessed using the t test to determine baseline heterogeneity in the 2 groups. Simple conditional logistic regression models were then used to calculate the ORs of sustained ROSC and survival to hospital discharge for patients with ETCO₂ use in the matched group.

In order to further assess the robustness of our results, we sampled another cohort by matching each patient in the ETCO₂ group to 4 patients in the untreated group using the same method (Fig. 1). We compared the crude ORs and risk differences of survival benefits among 2 matched cohorts and the original group to evaluate if the results are similar. We also evaluated the extent of the effect of a potentially unmeasured confounder in accounting for the results [17]. A two-tailed P value of 0.05 was considered significant.

First, our findings were generated from administrative data. The definitions of OHCA were based on ICD-9-CM codes, which are useful for insurance reimbursement but may not be exact substitutes for precise operative definitions. There is also lack of data whether ETCO₂ monitoring was performed during or after chest compressions. We validated the selection processes by analyzing 150 medical records of patients with OHCA randomly selected from the electronic database from 2010 to 2012 at Dalin Tzu Chi Hospital, Buddhist Tzu Chi Medical Foundation. All 118 patients who were charged with chest compression had documented CPR records. Among 69 patients with ETCO₂ charge records, the confirmation of use was found by documented CO₂ levels during resuscitations. All 42 patients with records of hospitalization were alive before admissions. These selection processes yielded positive predictive values of 100 %.

Second, the percentage of ETCO₂ monitoring in our study cohort was only 1.6 %, which may be an underestimate of the true number of patients who actually were monitored. In clinical practice in Taiwan, procedures are charged either by healthcare or administrative staff. In a busy situation such as resuscitation, staff may forget to charge for the ETCO₂ monitoring in some patients. Although the extent of crossover to the treatment category cannot be assessed in this study, according to the intention-to-treat analytical principle, the result in this study would show only a bias toward a null result, and the estimation of OR would be more conservative than the actual number reported.

Third, the overall percentage of sustained ROSC (13 %) is low compared with results reported in other publications [20, 21]. Results similar to ours were found in a study published by Huang et al. [22], and fewer shockable rhythms in that study may account for the similarity. Because of the limited number of cases, we failed to find benefits of ETCO₂ monitoring on patients’ survival to hospital discharge. Further study should be conducted to evaluate if chest compression monitoring by ETCO₂ can improve survival to hospital discharge and, perhaps, if it can be associated with better cerebral performance.

Fourth, for an observational study, confounding by indication may be a concern even after one applies propensity score matching to establish the comparability between groups that use ETCO₂ monitoring or do not. For example, in patients with a lower likelihood of survival, physicians may be less likely to use ETCO₂ monitoring during resuscitation. However, because the outcome of survival is not perfectly predictable beforehand, such an argument is not plausible. Moreover, because the baseline characteristics were similar between the ETCO₂ group and the untreated group even before matching, we believe that resuscitation teams would use ETCO₂ monitoring guidelines instead of clinical judgments.

Fifth, with the use of propensity score analyses to adjust for possible confounding, we also lost the ability to evaluate other possible covariates of survival. Finally, although we extensively adjusted for many possible covariates, unmeasured confounding and the possibility of overmatching may still exist. In our database, we were unable to obtain prehospital information such as bystander CPR, prehospital intubation, and duration of collapse.