Can frailty be measured rapidly, accurately, and reliably enough to inform surgical decision making?
In this cohort study, the novel Risk Analysis Index (RAI) measures frailty prospectively (RAI-C) using a questionnaire or retrospectively (RAI-A) using variables from Veterans Affairs or American College of Surgeons National Surgical Quality Improvement Projects. The RAI-C proved feasible for system-wide screening with good predictive power (C statistic, 0.772; N = 6856). A subsample (n = 1021) demonstrated similar predictive power between the RAI-C (C = 0.797), RAI-A (C = 0.865), and modified Frailty Index (C = 0.811).
The RAI measures frailty with predictive ability on par with other frailty tools and has the additional advantage of rapid, real-time, preoperative assessment feasible for guiding clinical practice.
Growing consensus suggests that frailty-associated risks should inform shared surgical decision making. However, it is not clear how best to screen for frailty in preoperative surgical populations.
To develop and validate the Risk Analysis Index (RAI), a 14-item instrument used to measure surgical frailty. It can be calculated prospectively (RAI-C), using a clinical questionnaire, or retrospectively (RAI-A), using variables from the surgical quality improvement databases (Veterans Affairs or American College of Surgeons National Surgical Quality Improvement Projects).
Design, Setting, and Participants
Single-site, prospective cohort from July 2011 to September 2015 at the Veterans Affairs Nebraska-Western Iowa Heath Care System, a Level 1 Veterans Affairs Medical Center. The study included all patients presenting to the medical center for elective surgery.
We assessed the RAI-C for all patients scheduled for surgery, linking these scores to administrative and quality improvement data to calculate the RAI-A and the modified Frailty Index.
Main Outcomes and Measures
Receiver operator characteristics and C statistics for each measure predicting postoperative mortality and morbidity.
Of the participants, the mean (SD) age was 60.7 (13.9) years and 249 participants (3.6%) were women. We assessed the RAI-C 10 698 times, from which we linked 6856 unique patients to mortality data. The C statistic predicting 180-day mortality for the RAI-C was 0.772. Of these 6856 unique patients, we linked 2785 to local Veterans Affairs Surgeons National Surgical Quality Improvement Projects data and calculated the C statistic for both the RAI-A (0.823) and RAI-C (0.824), along with the correlation between the 2 scores (r = 0.478; P < .001). Of these 2785 patients, there was sufficient data to calculate the modified Frailty Index for 1021, in which the C statistics were 0.865 (RAI-A), 0.797 (RAI-C), and 0.811 (modified Frailty Index). The correlation between the RAI-A and RAI-C was 0.547, and the correlations of the modified Frailty Index to the RAI-A and RAI-C were 0.300 and 0.26, respectively (all P < .001). A cutoff of RAI-C of at least 21 classified 18.3% patients as “frail” with a sensitivity of 0.50 and specificity of 0.82, whereas the RAI-A was less sensitive (0.25) and more specific (0.97), classifying only 3.7% as “frail.”
Conclusions and Relevance
The RAI-C and RAI-A represent effective tools for measuring frailty in surgical populations with predictive ability on par with other frailty tools. Moderate correlation between the measures suggests convergent validity. The RAI-C offers the advantage of prospective, preoperative assessment that is proved feasible for large-scale screening in clinical practice. However, further efforts should be directed at determining the optimal components of preoperative frailty assessment.
Hall DE, Arya S, Schmid KK, Blaser C, Carlson MA, Bailey TL, Purviance G, Bockman T, Lynch TG, Johanning J. Development and Initial Validation of the Risk Analysis Index for Measuring Frailty in Surgical Populations. JAMA Surg. Published online November 23, 2016. doi:10.1001/jamasurg.2016.4202