The application of information theory for the estimation of old-age multimorbidity

Geroscience. 2017 Dec;39(5-6):551-556. doi: 10.1007/s11357-017-9996-4. Epub 2017 Aug 28.

Abstract

Elderly patients are commonly characterized by the presence of several chronic aging-related diseases at once, or old-age "multimorbidity," with critical implications for diagnosis and therapy. However, at the present there is no agreed or formal method to diagnose or even define "multimorbidity." There is also no formal quantitative method to evaluate the effects of individual or combined diagnostic parameters and therapeutic interventions on multimorbidity. The present work outlines a methodology to provide such a measurement and definition, using information theoretical measure of normalized mutual information. A cohort of geriatric patients, suffering from several age-related diseases (multimorbidity), including ischemic heart disease, COPD, and dementia, were evaluated by a variety of diagnostic parameters, including static as well as dynamic biochemical, functional-behavioral, immunological, and hematological parameters. Multimorbidity was formally coded and measured as a composite of several chronic age-related diseases. The normalized mutual information allowed establishing the exact informative value of particular parameters and their combinations about the multimorbidity value. With the currently intensifying attempts to reduce aging-related multimorbidity by therapeutic interventions into its underlying aging processes, the proposed method may outline a valuable direction toward the formal indication and evidence-based evaluation of effectiveness of such interventions.

Keywords: Aging; Aging-related diseases; Frailty; Multimorbidity; Normalized mutual information.

MeSH terms

  • Age Factors
  • Aged
  • Aged, 80 and over
  • Aging / genetics
  • Aging / physiology*
  • Female
  • Geriatric Assessment / methods
  • Humans
  • Information Theory*
  • Male
  • Middle Aged
  • Multimorbidity / trends*
  • Multiple Chronic Conditions / epidemiology*
  • Predictive Value of Tests
  • Risk Assessment
  • Sex Factors
  • Survival Analysis