Frailty Screening Using the Risk Analysis Index: A User Guide

Abstract

The Risk Analysis Index (RAI) has emerged as the most thoroughly validated and flexible assessment of surgical frailty, proven feasible for at-scale bedside screening and available in a suite of tools, that effectively risk stratifies patients across a wide variety of clinical contexts and data sources. This user guide provides a definitive summary of the RAI's theoretical model, historical development, validation, statistical performance, and clinical interpretation, placing the RAI in context with other frailty assessments and emphasizing some of its advantages. Detailed instructions are provided for each RAI variant, along with a systematic review of existing RAI-related literature.

Figure 1:

As people age (x-axis), they expend physiologic reserve (y-axis) until it is exhausted at the time of death. Although the average person expends their reserve at a set rate (black line), the slope of this line can vary between individuals, with lower slopes indicating more robust patients (green) and higher slopes indicating more frail patients (yellow). This can explain why the stress of a similar surgical procedure (red lines) may overwhelm the reserves of a younger frail patient even as an older robust patient is able to recover.

Figure 2:

Panels A to C represent three validated frailty assessments: physical frailty, cumulative deficit frailty, and the Risk Analysis Index (RAI). Each assessment includes five domains, which are color coded to aid comparison across tools. Physical frailty includes functional performance measures (green), weight loss and nutrition (orange), and physical activity (blue). Cumulative deficit frailty adds cognitive decline (purple), and disability (red). The RAI adds demographics and living location (pink). CHF, congestive heart failure; CKD, chronic kidney disease.

Figure 3:

These graphs show the relative importance of Risk Analysis Index (RAI) compared to other predictors of post-operative outcomes according to multiple machine learning techniques for predicting (A) postoperative mortality and (B) Desirability of Outcome Ranking (DOOR) score. “Case Status” reflects urgent, emergent, or elective surgery; “Gagne Score” is a measure of comorbidity; “Specialty” denotes the surgical specialty of the operating physician. PASC, preoperative acute serious condition; DNR, do not resuscitate order; BMI, body mass index; OSS, Operative Stress Score; ADI, Area Deprivation Index.