Journal Club Summary
Methodology Score: 3.5/5
Usefulness Score: 3.5/5
Question and Methods: Retrospective observational cohort study to evaluate if the addition ACLS to BLS during EMS management for Out-of-Hospital cardiac arrest – OHCA (and for a predefined subgroup of potential ECMO candidates) improve rates of ROSC, survival to hospital discharge and delay from call to hospital arrival.
Findings: Prehospital ACLS is not associated with an improvement in survival to hospital discharge in patients suffering from OHCA or potential ECMO candidates and leads to a longer delay to hospital arrival, but with an improvement in prehospital ROSC (for 30 seconds or more)
Limitations and Interpretation: Lack of survival benefit from addition of ACLS for OHCA is already known. Future studies reporting the actual duration of ROSC and survival benefit among ECMO eligible candidates are needed before application to practice.
By: Dr. Erica Lee
We commonly evaluate the bivariate association (a.k.a. Univariate analysis) of groups of patients to a certain variable or outcome. E.g. what is the strength of association of age to syncope patients with and without arrhythmias? Since consecutive patients are enrolled, when we compare the age among patients with and without arrhythmia, usually there will be significant difference in age with older patients suffering arrhythmias. How will you compare two groups that could potentially be different at the outset (in the study by Cournoyer et al) it is likely that BLS and ACLS groups are likely to be very different. Then you can use effect size to evaluate the difference in their characteristics. (E.g. men are taller than women, the difference between the height of men and the height of women is known as the effect size)
Cohen’s d effect size
Cohen’s d was used for continuous variables and is the difference of two population means and it is divided by the standard deviation from the data. Values between 0 to 0.3 is a small effect size, if it is between 0.3 and 0.6 it is a moderate effect size, and an effect size bigger than 0.6 is a large effect size. Cramer’s V was used for categorical data uses Chi-square to measure the effect size for nominal data.