Defining aging phenotypes and related outcomes: Clues to recognize frailty in hospitalized older patients
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Background: Because frailty is a complex phenomenon associated with poor outcomes, the identification of patient profiles with different care needs might be of greater practical help than to look for a unifying definition. This study aimed at identifying aging phenotypes and their related outcomes in order to recognize frailty in hospitalized older patients. Methods: Patients aged 65 or older enrolled in internal medicine and geriatric wards participating in the REPOSI registry. Relationships among variables associated to sociodemographic, physical, cognitive, functional, and medical status were explored using a multiple correspondence analysis. The hierarchical cluster analysis was then performed to identify possible patient profiles. Multivariable logistic regression was used to verify the association between clusters and outcomes (in-hospital mortality and 3-month postdischarge mortality and rehospitalization). Results: 2,841 patients were included in the statistical analyses. Four clusters were identified: the healthiest (I) those with multimorbidity (II) the functionally independent women with osteoporosis and arthritis (III) and the functionally dependent oldest old patients with cognitive impairment (IV). There was a significantly higher in-hospital mortality in Cluster II (odds ratio [OR] = 2.27, 95% confidence interval [CI] = 1.15-4.46) and Cluster IV (OR = 5.15, 95% CI = 2.58-10.26) and a higher 3-month mortality in Cluster II (OR = 1.66, 95% CI = 1.13-2.44) and Cluster IV (OR = 1.86, 95% CI = 1.15-3.00) than in Cluster I. Conclusions: Using alternative analytical techniques among hospitalized older patients, we could distinguish different frailty phenotypes, differently associated with adverse events. The identification of different patient profiles can help defining the best care strategy according to specific patient needs Male Aging Osteoporosis we could distinguish different frailty phenotypes 030204 cardiovascular system & hematology Logistic regression 0302 clinical medicine Internal medicine and geriatric wards Aging phenotype 80 and over Medicine Cluster analysi LS4_4 95% CI = 1.15-3.00) than in Cluster I. Conclusions: Using alternative analytical techniques among hospitalized older patients 030212 general & internal medicine Hospital Mortality Prospective Studies Prospective cohort study physical Outcome 95% CI = 2.58-10.26) and a higher 3-month mortality in Cluster II (OR = 1.66 Aged, 80 and over Frailty 95% confidence interval [CI] = 1.15-4.46) and Cluster IV (OR = 5.15 Cognition Hospitalization Phenotype aging phenotypes cluster analysis frailty internal medicine and geriatric wards outcomes Female 841 patients were included in the statistical analyses. Four clusters were identified: the healthiest (I) Human medicine.medical_specialty the identification of patient profiles with different care needs might be of greater practical help than to look for a unifying definition. This study aimed at identifying aging phenotypes and their related outcomes in order to recognize frailty in hospitalized older patients. Methods: Patients aged 65 or older enrolled in internal medicine and geriatric wards participating in the REPOSI registry. Relationships among variables associated to sociodemographic Frail Elderly Socio-culturale Aging phenotypes Cluster analysis Outcomes Aged Humans Geriatric Assessment cognitive 03 medical and health sciences Multiple correspondence analysis Internal medicine differently associated with adverse events. The identification of different patient profiles can help defining the best care strategy according to specific patient needs Adverse effect Background: Because frailty is a complex phenomenon associated with poor outcomes Aging phenotypes, Cluster analysis, Frailty, Internal medicine and geriatric wards, Outcomes 95% CI = 1.13-2.44) and Cluster IV (OR = 1.86 business.industry and the functionally dependent oldest old patients with cognitive impairment (IV). There was a significantly higher in-hospital mortality in Cluster II (odds ratio [OR] = 2.27 Settore MED/09 - MEDICINA INTERNA and medical status were explored using a multiple correspondence analysis. The hierarchical cluster analysis was then performed to identify possible patient profiles. Multivariable logistic regression was used to verify the association between clusters and outcomes (in-hospital mortality and 3-month postdischarge mortality and rehospitalization). Results: 2 Odds ratio medicine.disease Confidence interval Internal medicine and geriatric ward Prospective Studie functional Physical therapy Geriatrics and Gerontology business - Language
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