Nicholas M Pajewski1,2, Kristin Lenoir1,2,3, Brian J Wells1,3, Jeff D Williamson2,4, Kathryn E Callahan2,4. 1. Department of Biostatistics and Data Science, Division of Public Health Sciences, Winston-Salem, North Carolina. 2. Center for Health Care Innovation, Winston-Salem, North Carolina. 3. Clinical and Translational Science Institute, Winston-Salem, North Carolina. 4. Section on Gerontology and Geriatric Medicine, Department of Internal Medicine, Wake Forest School of Medicine, Winston-Salem, North Carolina.
Abstract
BACKGROUND: The accumulation of deficits model for frailty has been used to develop an electronic health record (EHR) frailty index (eFI) that has been incorporated into British guidelines for frailty management. However, there have been limited applications of EHR-based approaches in the United States. METHODS: We constructed an adapted eFI for patients in our Medicare Accountable Care Organization (ACO, N = 12,798) using encounter, diagnosis code, laboratory, medication, and Medicare Annual Wellness Visit (AWV) data from the EHR. We examined the association of the eFI with mortality, health care utilization, and injurious falls. RESULTS: The overall cohort was 55.7% female, 85.7% white, with a mean age of 74.9 (SD = 7.3) years. In the prior 2 years, 32.1% had AWV data. The eFI could be calculated for 9,013 (70.4%) ACO patients. Of these, 46.5% were classified as prefrail (0.10 < eFI ≤ 0.21) and 40.1% frail (eFI > 0.21). Accounting for age, comorbidity, and prior health care utilization, the eFI independently predicted all-cause mortality, inpatient hospitalizations, emergency department visits, and injurious falls (all p < .001). Having at least one functional deficit captured from the AWV was independently associated with an increased risk of hospitalizations and injurious falls, controlling for other components of the eFI. CONCLUSIONS: Construction of an eFI from the EHR, within the context of a managed care population, is feasible and can help to identify vulnerable older adults. Future work is needed to integrate the eFI with claims-based approaches and test whether it can be used to effectively target interventions tailored to the health needs of frail patients.
BACKGROUND: The accumulation of deficits model for frailty has been used to develop an electronic health record (EHR) frailty index (eFI) that has been incorporated into British guidelines for frailty management. However, there have been limited applications of EHR-based approaches in the United States. METHODS: We constructed an adapted eFI for patients in our Medicare Accountable Care Organization (ACO, N = 12,798) using encounter, diagnosis code, laboratory, medication, and Medicare Annual Wellness Visit (AWV) data from the EHR. We examined the association of the eFI with mortality, health care utilization, and injurious falls. RESULTS: The overall cohort was 55.7% female, 85.7% white, with a mean age of 74.9 (SD = 7.3) years. In the prior 2 years, 32.1% had AWV data. The eFI could be calculated for 9,013 (70.4%) ACO patients. Of these, 46.5% were classified as prefrail (0.10 < eFI ≤ 0.21) and 40.1% frail (eFI > 0.21). Accounting for age, comorbidity, and prior health care utilization, the eFI independently predicted all-cause mortality, inpatient hospitalizations, emergency department visits, and injurious falls (all p < .001). Having at least one functional deficit captured from the AWV was independently associated with an increased risk of hospitalizations and injurious falls, controlling for other components of the eFI. CONCLUSIONS: Construction of an eFI from the EHR, within the context of a managed care population, is feasible and can help to identify vulnerable older adults. Future work is needed to integrate the eFI with claims-based approaches and test whether it can be used to effectively target interventions tailored to the health needs of frail patients.
Authors: Karen Bandeen-Roche; Christopher L Seplaki; Jin Huang; Brian Buta; Rita R Kalyani; Ravi Varadhan; Qian-Li Xue; Jeremy D Walston; Judith D Kasper Journal: J Gerontol A Biol Sci Med Sci Date: 2015-08-21 Impact factor: 6.053
Authors: Kenneth Rockwood; Xiaowei Song; Chris MacKnight; Howard Bergman; David B Hogan; Ian McDowell; Arnold Mitnitski Journal: CMAJ Date: 2005-08-30 Impact factor: 8.262
Authors: Thomas M Gill; Dorothy I Baker; Margaret Gottschalk; Peter N Peduzzi; Heather Allore; Amy Byers Journal: N Engl J Med Date: 2002-10-03 Impact factor: 91.245
Authors: Gloria A Aguayo; Anne-Françoise Donneau; Michel T Vaillant; Anna Schritz; Oscar H Franco; Saverio Stranges; Laurent Malisoux; Michèle Guillaume; Daniel R Witte Journal: Am J Epidemiol Date: 2017-08-15 Impact factor: 4.897
Authors: Olga Theou; Alexandra M van der Valk; Judith Godin; Melissa K Andrew; Janet E McElhaney; Shelly A McNeil; Kenneth Rockwood Journal: J Gerontol A Biol Sci Med Sci Date: 2020-09-25 Impact factor: 6.053
Authors: Rupen Shah; Jeffrey D Borrebach; Jacob C Hodges; Patrick R Varley; Mary Kay Wisniewski; Myrick C Shinall; Shipra Arya; Jonas Johnson; Joel B Nelson; Ada Youk; Nader N Massarweh; Jason M Johanning; Daniel E Hall Journal: J Am Geriatr Soc Date: 2020-04-20 Impact factor: 5.562
Authors: Kathryn E Callahan; Clancy J Clark; Angela F Edwards; Timothy N Harwood; Jeff D Williamson; Adam W Moses; James J Willard; Joseph A Cristiano; Kellice Meadows; Justin Hurie; Kevin P High; J Wayne Meredith; Nicholas M Pajewski Journal: J Am Geriatr Soc Date: 2021-01-19 Impact factor: 5.562
Authors: Jared Rejeski; Ted Xiao; William Wheless; Nicholas M Pajewski; Elizabeth Jensen; Kathryn E Callahan Journal: J Am Geriatr Soc Date: 2021-10-30 Impact factor: 5.562
Authors: Brianne L Olivieri-Mui; Sandra M Shi; Ellen P McCarthy; Daniel Habtemariam; Dae H Kim Journal: J Am Geriatr Soc Date: 2020-11-25 Impact factor: 5.562