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Frailty indices based on routinely collected data: a scoping review.

Schenelle Dayna Dlima1, Danielle Harris1, Abodunrin Quadri Aminu2

  • 1School of Health Sciences, Faculty of Biology, Medicine and Health, The University of Manchester, Manchester, UK; National Institute for Health and Care Research, Applied Research Collaboration - Greater Manchester, School of Health Sciences, Faculty of Biology, Medicine and Health, The University of Manchester, Manchester, UK; National Institute for Health and Care Research Policy Research Unit in Healthy Ageing, School of Health Sciences, Faculty of Biology, Medicine and Health, The University of Manchester, Manchester, UK.

The Journal of Frailty & Aging
|May 4, 2025
PubMed
Summary

Routine data can calculate frailty indices (FI), offering a feasible risk stratification tool. However, research is limited to high-income nations, and current FIs focus on medical deficits.

Keywords:
Administrative dataDeficit accumulationElectronic health recordsFrailtyFrailty indexRoutine dataRoutinely collected data

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Area of Science:

  • Gerontology
  • Health Informatics
  • Public Health

Background:

  • Frailty Index (FI) quantifies health deficits.
  • Routine clinical and administrative data can automate FI calculation.
  • Current research on routine data-based FIs requires evaluation.

Purpose of the Study:

  • To conduct a scoping review of research on frailty indices constructed from routine data.
  • To evaluate the current research landscape and identify trends and gaps.

Main Methods:

  • Searched seven databases for literature published between 2013-2023.
  • Included original research articles on FIs using routine data with deficits in specified categories.
  • Screened 7526 publications, including 218 in the final review.

Main Results:

  • Studies were mainly from North America (47.7%) and community settings (35.3%).
  • Routine data-based FIs were primarily used for risk stratification (51.4%).
  • Identified 611 unique deficits, predominantly 'diseases' (34.4%) and 'symptoms/signs' (32.1%).

Conclusions:

  • Routine data-based FIs are feasible and valid for risk stratification.
  • Research is concentrated in high-income countries, with slow adoption and a medically-focused deficit approach.
  • Future research should explore LMIC applicability and leverage non-clinical data for proactive frailty management.