Scaling up frailty: psychometric validation of the functional limitations and geriatric syndromes frailty questionnaire-a new tool for uniformly classifying vulnerable hospital patients

Abstract

Background: Comprehensive psychometric validation is essential to obtain a common metric for reliable diagnostic and prognostic decision-making in frailty. In this study, we used a single-factor approach to derive and psychometrically validate a standardized frailty measure from 23 reflective items (eight functional limitations and 15 geriatric syndromes) from a new, multidomain, questionnaire. We used confirmatory factor analysis (CFA) and item response theory (IRT) to achieve this goal.

Methods: This single-centre, cross-sectional study included a convenience sample of 900 community-dwelling patients (median age: 73.4 years; IQR: 67.0-81.6; 59.7% male) undergoing elective surgery (n = 568, 63.1%) or admitted to the internal medicine unit for acute illnesses (n = 332, 36.9%). Of the elective patients, 50.4% completed the questionnaire via a web platform. The rest completed the questionnaire during a face-to-face interview at their preoperative visit or within 48 h of admission.

Results: The CFA validated the single-factor solution for 16 of the 23 items in the questionnaire and confirmed the good internal consistency of the construct. IRT analyses showed that the 16 items of the Functional Limitation and Geriatric Syndrome Frailty Questionnaire (FLIGS-FQ-16) have good discriminatory power, satisfactory threshold parameters, and equal function for men and women. The FLIGS-FQ-16 total score provides reliable information on the severity of frailty, ranging from 0.18 standard deviations below the population mean ("not frail") to 2.7 standard deviations above the population mean ("severely frail"). Applying the standardized FLIGS-FQ-16 threshold scores to our sample, we found an overall prevalence of frailty of 40.9%, with a significant difference between acute patients (75.3%) and elective patients (20.8%, p < 0.001). Among acute patients, 37.6% were moderately or severely frail. Among elective patients, 19.0% were moderately frail and 1.8% were severely frail.

Conclusion: The five functional limitations and 11 geriatric syndromes of the FLIGS-FQ-16 aggregate into a robust single-factor construct with adequate psychometric properties that uniformly measure frailty up to the most severe levels. In addition to serving as a screening tool, the FLIGS-FQ-16 is useful for making individualized decisions and developing personalized treatment plans in perioperative medicine and the management of hospitalised older adults because it is based on treatable risk factors.

Keywords: confirmatory factor analysis; frailty; integrated care pathways; item response theory; patient care planning; psychometric.

Figure 1

FLIGS-FQ-16 single-factor frailty model. The graph confirms the original hypothesis that frailty (F), conceptualized as a single continuous latent variable, is adequately represented by a set of items reflecting it (the rectangles). The directional arrow indicates the standardized frailty load for each item. The goodness-of-fit indices show the high reliability of the internal consistency of the construct.

Figure 2

Item characteristic curves of the FLIGS-FQ-16. The curves illustrate the probability of each item occurring as a function of frailty severity (shifting to the right). The red dashed line indicates the 50% threshold and corresponds to the difficulty parameter of the listed items. Some curves overlap due to their very similar difficulties. The high common value of discrimination explains why the curves are so steep.

Figure 3

FLIGS-FQ-16 test reliability. (A) Shows the accuracy of the information from the entire test (blue line), which is obtained by adding the probabilities of the individual items together. The vertical dashed lines indicate the boundaries of the information. The greater the distance from the information peak in either direction, the greater the standard error (red line) and the less information the instrument provides about a person’s frailty. (B) Illustrates the strong calibration between the predicted frailty theta score (blue line) and the FLIGS-FQ-16 scores (red dots). The vertical dashed lines indicate the FLIGS-FQ-16 score threshold for standardized patient classification. The cut-off value of 3 separates progressively more frail patients from non-frail patients. VS, very severe.

Figure 4

FLIGS-FQ-16 score and frailty categories among elective surgical and acute medical patients. (A) Shows the median value and interquartile range in the boxes, and the whiskers show the 5th and 95th percentile values. The dots represent outliers. The vertical dashed lines indicate the threshold values for uniformly classifying frailty. (B) Shows the resulting frailty categories.