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Published on: February 2, 2020
Developing a self-reported comorbidity index to predict mortality of community-dwelling older adults
Md Yuzaiful Md Yusof1, Michael Arthur Horan, Maureen Jones
1Department of Clinical Gerontology, School of Translational Medicine, University of Manchester, Salford Hospital Foundation Trust, Stott Lane, Salford M6 8HD, United Kingdom. yuzaifulyusof@doctors.org.uk
Insights
A simple count of medications and age effectively predict mortality in older adults, outperforming complex comorbidity indices. This resource-efficient method aids survival prediction in community-dwelling seniors.
Area of Science:
- Gerontology
- Epidemiology
- Biostatistics
Background:
- Current comorbidity measures show limited predictive accuracy for mortality in community-dwelling older adults.
- There is a need for simpler, resource-efficient methods to predict survival in this population.
Purpose of the Study:
- To develop and validate a simpler, self-reported comorbidity index for predicting mortality in older adults.
- To compare the predictive validity of a medication count and age against established indices like Charlson Comorbidity Index (CCI) and Cornell Medical Index (CMI).
Main Methods:
- A cohort of 113 older adults in Greater Manchester, UK, was studied over 7 years.
- Data collected included Charlson Comorbidity Index (CCI) components, Cornell Medical Index (CMI) questionnaire responses, and medication counts.
- Cox-regression and Kaplan-Meier analysis were used to assess the predictive ability of various factors for mortality.
Main Results:
- Individually, CCI, CMI, and sex were not significant predictors of mortality.
- Forward stepwise Cox-regression identified the number of medications (p=0.011) and age (p=0.037) as significant predictors of mortality.
- A simple count of prescribed medications demonstrated higher predictive accuracy for 7-year mortality than CMI or CCI.
Conclusions:
- The number of medications and age are significant, simple predictors of mortality in community-dwelling older adults.
- This approach offers a more accurate and resource-efficient alternative to complex comorbidity indices for survival prediction.
- Larger-scale studies are recommended to validate these findings for epidemiological use.
Abstract:
Current common comorbidity measures have poor to moderate predictive validity of mortality of community-dwelling older adults. Hence, our aim is to develop a simpler resource-efficient self-reported comorbidity index in the prediction of survival. 113 older adults in Greater Manchester, United Kingdom attended a routine medical examination whereby information gathered was used to construct Charlson Comorbidity Index (CCI). They completed the Cornell Medical Index (CMI) questionnaire and reported the number of medication prescribed to them. We compared the ability of CCI, CMI, number of medication, age and sex to predict mortality of the sample over 7-year period using Cox-regression and Kaplan-Meier plot and rank test. None of the variables individually was significant when tested using either Cox-regression via ENTER method or Kaplan-Meier test. Remarkably, by means of forward step-wise Cox-regression, two variables emerged significant: (i) number of medicine (beta coefficient=0.229, SE=0.090 and p=0.011) and (ii) age (beta coefficient=0.106, SE=0.051 and p=0.037). We demonstrated that simple count of medication predicted mortality of community-dwelling older adults over the next 7 years more accurately than CMI or CCI. Further works involving a larger scale of subjects is needed for use in epidemiological study of survival where cost and resources are concerned.
