Dear editor

We have read a recent article titled “The Association Between Frailty Evaluated by Clinical Frailty Scale and Mortality of Older Patients in the Emergency Department: A Prospective Cohort Study”, with great interest.¹ This study is significant, as it provides insights into the factors that predict adverse outcomes in older patients in the emergency department (ED). This article highlights the importance of the Clinical Frailty Scale (CFS) score. These findings have important implications for early prediction of unfavorable outcomes in the older ED population. While recognizing the value of this study, we would like to make the following comments.

Firstly, although the logistic regression model is widely acknowledged for its utility, it may inadvertently lead to risk overestimation in scenarios beset with potential competing risks. Consequently, for the mortality risk assessment explored in this paper, especially when systemic diseases (such as heart failure and pulmonary edema) are potentially interrelated, employing a competing risks model appears more fitting. Traditional survival analysis techniques might not adequately account for the influence of secondary events on the primary study outcome, whereas the competing risks model affords a more holistic analytical viewpoint.²

Secondly, the authors’ inclusion of important information such as demographic information and laboratory tests to adjust for potential covariates is commendable. However, we suggest a further expansion of the scope of covariates such as Charlson Comorbidity Index.³ Additionally, factors such as organ failure assessment and physical activity should also be considered for a more comprehensive assessment of the stability and reliability of the results.⁴

Last but not least, this article primarily explores the relationship between the CFS score and clinical outcome. Considering previous studies on the delays to admission from ED and in-hospital mortality, conducting subgroup analyses might enhance the applicability and universality of these findings.⁵ Furthermore, recommending stratified analyses for patients with complications could deepen the understanding and evaluate the impact of these factors on research outcomes.