Comparative performance of the risk analysis index versus traditional frailty measures in predicting outcomes following revision total hip arthroplasty for mechanical prosthetic complications
Cameron J. Sabet, Bhav Jain, Jad Lawand, Bill Young, Dang Nguyen, Bara M. Hammadeh, Mohammad D. AlfawarehBackground:
Frailty assessment has become increasingly important in predicting surgical outcomes, particularly in complex revision procedures. Among patients undergoing revision total hip arthroplasty (rTHA) for mechanical prosthetic complications, optimal frailty stratification remains poorly defined. This study compared the Risk Analysis Index (RAI) and the modified 5-item frailty index (mFI-5) in predicting postoperative outcomes within this high-risk population.
Methods:
We conducted a retrospective cohort study of 5631 adult patients undergoing rTHA for mechanical prosthetic complications using data from the 2015–2021 American College of Surgeons National Surgical Quality Improvement Program (ACS-NSQIP). Frailty was assessed using RAI and mFI-5, calculated via published algorithms. Primary outcomes were 30-day all-cause mortality and non-home discharge. Secondary outcomes included major complications, minor complications, unplanned readmission, reoperation, and extended length of stay (LOS >4 days). Discriminative performance was evaluated using receiver operating characteristic curves and area under the curve (AUC) analysis, with bootstrap resampling (100 iterations) for internal validation.
Results:
Mean patient age was 66.80 ± 11.57 years with 57.61% female. Overall, 30-day mortality was 0.4% with major complications occurring in 2.4%. RAI demonstrated superior discrimination for mortality (AUC 0.822) compared to mFI-5 (0.688,
Conclusion:
The Risk Analysis Index significantly outperforms the mFI-5 across key outcomes following revision total hip arthroplasty for mechanical prosthetic complications. Its multidomain structure, which captures age, functional dependence, comorbidities, and acute physiological stress, makes it a superior tool for surgical risk stratification, discharge planning, and identifying high-risk patients who may benefit from targeted perioperative optimisation.