Frailty as a Predictor of Outcomes in Subarachnoid Hemorrhage: A Systematic Review and Meta-Analysis.
Michael P FortunatoFangyi LinAnaz UddinGaladu SubahRohan PatelEric FeldsteinAiden LuiJose DominguezMatthew MercklingPatricia XuMatthew McIntyreChirag GandhiFawaz Al-MuftiPublished in: Brain sciences (2023)
Frailty is an emerging concept in clinical practice used to predict outcomes and dictate treatment algorithms. Frail patients, especially older adults, are at higher risk for adverse outcomes. Aneurysmal subarachnoid hemorrhage (aSAH) is a neurosurgical emergency associated with high morbidity and mortality rates that have previously been shown to correlate with frailty. However, the relationship between treatment selection and post-treatment outcomes in frail aSAH patients is not established. We conducted a meta-analysis of the relevant literature in accordance with PRISMA guidelines. We searched PubMed, Embase, Web of Science, and Google Scholar using "Subarachnoid hemorrhage AND frailty" and "subarachnoid hemorrhage AND frail" as search terms. Data on cohort age, frailty measurements, clinical grading systems, and post-treatment outcomes were extracted. Of 74 studies identified, four studies were included, with a total of 64,668 patients. Percent frailty was 30.4% under a random-effects model in all aSAH patients ( p < 0.001). Overall mortality rate of aSAH patients was 11.7% when using a random-effects model ( p < 0.001). There was no significant difference in mortality rate between frail and non-frail aSAH patients, but this analysis only included two studies and should be interpreted cautiously. Age and clinical grading, rather than frailty, independently predicted outcomes and mortality in aSAH patients.
Keyphrases
- end stage renal disease
- subarachnoid hemorrhage
- chronic kidney disease
- ejection fraction
- peritoneal dialysis
- brain injury
- cardiovascular disease
- randomized controlled trial
- physical activity
- type diabetes
- emergency department
- machine learning
- healthcare
- adipose tissue
- deep learning
- cerebral ischemia
- artificial intelligence
- big data
- weight loss
- blood brain barrier