Original StudyProspective Comparison of 6 Comorbidity Indices as Predictors of 1-Year Post-Hospital Discharge Institutionalization, Readmission, and Mortality in Elderly Individuals
Section snippets
Patients and Data Collection
We carried out a prospective study in a 300–acute bed geriatric hospital (HOGER) where 22.7% were direct admission from the community, 54.0% were referred by the emergency unit, and 23.3% were transferred from other divisions of Geneva University Hospitals, Switzerland. Patients and data collection have been described elsewhere.16, 17, 18 Briefly, a representative sample of all patients aged 75 years and older, consecutively admitted between January 2004 and December 2005 were selected by
Characteristics of Participants
Of the 1854 eligible patients, 556 were randomized, 523 were successfully enrolled, and 496 survived to hospital discharge (27 died during the hospitalization). The 1-year follow-up was not done in 52 patients (10.5%): 12 patients had moved abroad from Switzerland and the assessment was refused in 30 cases by the patient and in 10 cases by the family. Then 444 patients were successfully followed for 12 months and had full data for these analyses (mean age 85.3 ± 6.7; 74% women). Table 2
Discussion
This article examines the relationship between a variety of comorbidity indices and a number of health outcomes (rehospitalization, institutionalization, and death) in hospitalized elderly. Of the 6 indices, the GIC explained the greatest amount of variability of 2 of the studied outcomes in univariate analysis, and its scores alone were the only ones associated with institutionalization. One of the main strengths of this study was the prospective, comprehensive assessment of the presence and
Acknowledgments
We thank the teams of Mrs. O. Baumer, L. Humblot, and M. Cos for technical assistance.
References (39)
- et al.
Causes and consequences of comorbidity: A review
J Clin Epidemiol
(2001) - et al.
A prognostic model for 1-year mortality in older adults after hospital discharge
Am J Med
(2007) - et al.
Geriatrics index of comorbidity was the most accurate predictor of death in geriatric hospital among six comorbidity scores
J Clin Epidemiol
(2010) - et al.
How to measure comorbidity: A critical review of available methods
J Clin Epidemiol
(2003) - et al.
How to measure comorbidity in elderly persons
J Clin Epidemiol
(2004) - et al.
A new method of classifying prognostic comorbidity in longitudinal studies: Development and validation
J Chronic Dis
(1987) - et al.
The importance of classifying initial co-morbidity in evaluating the outcome of diabetes mellitus
J Chronic Dis
(1974) - et al.
A chronic disease score from automated pharmacy data
J Clin Epidemiol
(1992) - et al.
Long-term physical functioning in persons with knee osteoarthritis from NHANES. I: Effects of comorbid medical conditions
J Clin Epidemiol
(1994) - et al.
Aging, comorbidity, and reduced rates of drug treatment for diabetes mellitus
J Clin Epidemiol
(1999)
Outcomes at 12 months in a population of elderly patients discharged from a rehabilitation unit
J Am Med Dir Assoc
A user’s guide to selecting a comorbidity index for clinical research
J Clin Epidemiol
Loss of independence in activities of daily living in older adults hospitalized with medical illnesses: Increased vulnerability with age
J Am Geriatr Soc
Patterns of functional decline at the end of life
JAMA
Death Foretold: Prophecy and Prognosis in Medical Care
Serving patients who may die soon and their families: The role of hospice and other services
JAMA
Accurate prognostications of death: Opportunities and challenges for clinicians
West J Med
Prognosis in lung cancer: Physicians’ opinions compared with outcome and a predictive model
Thorax
Survival prediction of terminally ill cancer patients by clinical symptoms: Development of a simple indicator
J Clin Oncol
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This work was supported by grants from the Swiss National Science Foundation (SNF) (3200B0–102069) and from the Swiss Foundation for Ageing Research (AETAS).