Geriatric Syndromes and Their Relationship with Mortality in a Population of Mexican Older Adults Aged 65 and Over, Admitted to the Emergency Department of a Second-Level Care Hospital.
José Juan Gómez-RamosMelissa González-GuerraIngrid Patricia Dávalos-RodríguezMaría Eloísa Pérez-RuízEmiliano Peña-DuránAlejandro Marín-MedinaPublished in: Healthcare (Basel, Switzerland) (2024)
The main objective of this study was to analyze the relationship between Geriatric Syndromes (GSs) and in-hospital mortality in adults aged 65 and older admitted to the Emergency Department (ED). The study included 202 Older Adults (OAs) who met the inclusion criteria. We conducted a Comprehensive Geriatric Assessment and collected clinical and demographic data. A univariate analysis was carried out for each of the GSs analyzed. Those variables with p < 0.05 were entered into a multiple logistic regression using the backward stepwise entry method to analyze the independent predictor variables. The average number of GSs per individual was 4.65 (±2.76). Frailty syndrome was the most prevalent (70.2% of patients). Our study found an association between mortality and some GSs, such as frailty ( p = 0.042), risk of falls ( p = 0.010), delirium, cognitive impairment, dependence, and risk of ulcers ( p < 0.001). We found that cognitive impairment (adjusted OR, 6.88; 95% CI, 1.41-33.5; p = 0.017) and dependence (adjusted OR, 7.52; 95% CI, 1.95-29.98; p = 0.003) were independent predictors associated with mortality in our population. It is necessary to develop new care strategies in the ED that respond to the needs of aging societies, including the use of new technologies and personnel with experience in gerontology.
Keyphrases
- emergency department
- cognitive impairment
- healthcare
- physical activity
- palliative care
- end stage renal disease
- community dwelling
- type diabetes
- risk factors
- chronic kidney disease
- hip fracture
- cardiovascular disease
- cardiac surgery
- machine learning
- pain management
- electronic health record
- coronary artery disease
- peritoneal dialysis
- prognostic factors
- artificial intelligence
- data analysis
- big data
- drug induced