Defining aging phenotypes and related outcomes: Clues to recognize frailty in hospitalized older patients

Maura Marcucci, Carlotta Franchi, Alessandro Nobili, Pier Mannuccio Mannucci, Ilaria Ardoino

Research output: Contribution to journalArticle

14 Citations (Scopus)

Abstract

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.

Original languageEnglish
Pages (from-to)395-402
Number of pages8
JournalJournals of Gerontology - Series A Biological Sciences and Medical Sciences
Volume72
Issue number3
DOIs
Publication statusPublished - 2017

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Phenotype
Odds Ratio
Confidence Intervals
Hospital Mortality
Mortality
Internal Medicine
Geriatrics
Osteoporosis
Arthritis
Cluster Analysis
Registries
Comorbidity
Logistic Models

Keywords

  • Aging phenotypes
  • Cluster analysis
  • Frailty
  • Internal medicine and geriatric wards
  • Outcomes

ASJC Scopus subject areas

  • Ageing
  • Geriatrics and Gerontology

Cite this

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title = "Defining aging phenotypes and related outcomes: Clues to recognize frailty in hospitalized older patients",
abstract = "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.",
keywords = "Aging phenotypes, Cluster analysis, Frailty, Internal medicine and geriatric wards, Outcomes",
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TY - JOUR

T1 - Defining aging phenotypes and related outcomes

T2 - Clues to recognize frailty in hospitalized older patients

AU - Marcucci, Maura

AU - Franchi, Carlotta

AU - Nobili, Alessandro

AU - Mannucci, Pier Mannuccio

AU - Ardoino, Ilaria

PY - 2017

Y1 - 2017

N2 - 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.

AB - 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.

KW - Aging phenotypes

KW - Cluster analysis

KW - Frailty

KW - Internal medicine and geriatric wards

KW - Outcomes

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DO - 10.1093/gerona/glw188

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VL - 72

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JO - Journals of Gerontology - Series A Biological Sciences and Medical Sciences

JF - Journals of Gerontology - Series A Biological Sciences and Medical Sciences

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