<p>Functional Age Predicted by Electronic Short Physical Performance Battery Can Detect Frailty Status in Older Adults</p>

Abstract

Purpose 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.

Validity of the Clinical Frailty Scale in Korean older patients at a geriatric clinic

“…For instance, the question about loss of body weight to screen the risk of malnutrition can be only effective if the person is in the course of losing weight, and it might not be able to capture an advanced frailty status with already decreased lean mass. Although physical performance measures can be assessed objec-tively and are excellent in determining the frailty status with high prediction ability for adverse outcomes in older adults [31], issues exist due to the dynamic range of tools and feasibilities in applying them in varying circumstances of real-world clinical practice. For example, usual gait speed and the timed up and go test are excellent to screen the vulnerable population in primary care or community setting [32][33][34]; however, these examinations are less feasible in acutely ill patients with tethers (e.g., intravenous access lines, urinary catheters, and endotracheal tubes) or unstable vital signs.…”

Four different frailty models predict health outcomes in older patients with stable chronic obstructive pulmonary disease

“…The components of SPPB, which is a functional test evaluating standing balance, walking speed, and chair rise test, assess overlapping parameters with FFP. The recent study suggested that SPPB correlates with two widely used models of the FFP and FI [ 18 ], and can be effective to identify frailty both by the phenotype and deficit accumulation models in geriatric outpatients [ 19 ]. SPPB also has been suggested as a preferred measure in clinical trials to characterize baseline frailty by European Medicines Agency, given its prognostic value, validation status, and clinical feasibility [ 20 ].…”

Comparison of Human Interpretation and a Rule-Based Algorithm for Instrumented Sit-to-Stand Test

“…While eSPPB using these sensors has been in clinical use since 2019, raw weight-time and distance-time data other than the eSPPB score were not stored in the hardware until a software update in December 2020; the updated software allowed the current post-hoc analysis for 5STS using stored raw eSPPB data. Similar to a previous study using continuous parameters of eSPPB for classifying frailty, 22) explorations of human kinet- ic features with newer algorithms using already installed sensors may identify additional physical biomarkers related to sarcopenia and frailty. The present study had some limitations.…”

“… 21 ) In addition, continuous parameters derived from eSPPB were used to classify frailty in geriatric outpatients. 22 ) The eSPPB protocol used two sensors, i.e., a loadcell and LiDAR, as well as a rule-based algorithm, to determine the sit and stand maneuvers. 21 ) Although the correlation between the instrumented 5STS in eSPPB and manual 5STS was assessed semi-quantitatively (score ranging from 0 to 4), the validity of the rule-based algorithm has not yet been proven.…”