Functional Age Predicted by Electronic Short Physical Performance Battery Can Detect Frailty Status in Older Adults - Jung HW (Dove Medical Press, 2020)
Hee-Won Jung, 1 Taeyang Jin, 1 Ji Yeon Baek, 1 Seongjun Yoon, 2 Eunjoo Lee, 1 Jack M Guralnik, 3 Il-Young Jang 1 1Division of Geriatrics, Department of Internal Medicine, Asan Medical Center, Seoul, Republic of Korea; 2Dyphi Research Institute, Dyphi Inc., Daejeon, Republic of Korea; 3Division of Gerontology, Department of Epidemiology and Public Health, University of Maryland School of Medicine, Baltimore, MD, USACorrespondence: Il-Young JangDivision of Geriatrics, Department of Internal Medicine, Asan Medical Center, University of Ulsan College of Medicine, 88 Olympic-ro 43-gil, Songpa-gu, Seoul 05505, Republic of KoreaTel +82-2-3010-1658Email onezero2@gmail.comPurpose: The importance of evaluating frailty status of older adults in clinical practice has been gaining attention with cumulative evidence showing its relevance in clinical outcomes and decision-making. We aimed to develop and validate whether the functional age predicted by an electronic continuous short physical performance battery (eSPPB) could predict frailty status.Patients and Methods: We reviewed medical records of outpatients (N=834) of Asan Medical Center, aged 51– 95 years. We used the eSPPB data of 717 patients as a development cohort, and that of 117 patients, who also underwent comprehensive geriatric assessments, as a validation cohort. Frailty index was calculated by counting deficits of 45 geriatric items including comorbidities, daily functions, mobility, mood, and cognition. For functional age, we used balance score (0– 4), gait speed (m/s), and stand-up time (s) measured 5 times in the chair rise test.Results: From the development cohort, we established a functional age using the formula (83.61 − 1.98*[balance score] − 5.21*[gait speed] + 0.23*[stand-up time]), by multivariate linear regression analysis with chronological age as a dependent variable (R 2 = 0.233). In the validation cohort, the functional age positively correlated with frailty index (p < 0.001). C-statistics classifying frailty (defined as frailty index ≥ 0.25) was higher (p < 0.001) with functional age (0.912) than that with chronological age (0.637). A cut-off functional age of ≥ 77.2 years maximized Youden’s J when screening for frailty, with sensitivity of 94.4% and specificity of 80.8%.Conclusion: A newly developed functional age predictor using eSPPB parameters can predict the frailty status as defined by the deficit accumulation method and may serve as a physical biomarker of human aging.Keywords: frailty, biomarker, physical performance, diagnosis