CHEST Guidelines-Response_chest

Response_chest - 1

Pdf Summary

In a recent review of a study on the predictive capacity of frailty for survival in ICU patients, Dr. Xu Ma highlights shortcomings in the model, which relies solely on baseline frailty. The study suggests that baseline frailty is linked with survival outcomes but admits its predictive accuracy is low (area under the curve = 0.65), making it unsuitable for clinical risk stratification. The study is critiqued for using low-granularity dataset and not considering the post-ICU frailty, which captures changes in patient conditions during ICU stays and could improve model accuracy if included.<br /><br />In response, the study's authors clarify their intent was not to create a prognostic model but to investigate if pre-admission frailty can stratify mortality risk among home care clients. They acknowledge the potential value of including post-ICU frailty measures but note data access limitations for such detailed assessments. The authors defend their use of interRAI home care assessments, emphasizing their depth, which they argue is beyond what's deemed "low-granularity." These assessments were completed by trained clinicians, providing comprehensive data to evaluate various frailty indices. However, they concede that including post-ICU assessments could help understand health changes post-critical care.<br /><br />The exchange concludes with discussions about modeling improvements and data accessibility challenges, advocating for better integration of comprehensive health assessments to refine survival models and enhance decision-making in critical care settings. Both the critique and the response underline the complexity of using frailty in predictive models and the need for more comprehensive and dynamic data incorporation in future research and practice.

Keywords

frailty

ICU survival

predictive model

baseline frailty

post-ICU assessment

data granularity

interRAI assessments

mortality risk

critical care

health data integration