Abstract
Introduction
As the number of patients requiring total hip arthroplasty (THA) continues to increase, it is critical to develop tools that predict postoperative periprosthetic fracture (PPF). One way to risk stratify patients undergoing THA is using a frailty score. Recently, the Risk Analysis Index (RAI) has emerged as a potentially more accurate marker of frailty and predictor of postoperative complications following spine surgery. Our study aims to assess the association between frailty, as rated by RAI, and PPF within 30 days following primary THA.
Methods
The ACS NSQIP database was queried from the years 2015–2020 analyzing patients who underwent primary THA. The RAI scored patients on a scale from 0 to 81 and grouped them into four categories based on their frailty score: robust, normal, frail, and severely frail. Multivariate analysis was performed, controlling for age, female sex, American Society of Anesthesiologists (ASA) score, and tobacco smoking, with the primary outcome being PPF within 30 days of surgery.
Results
A total of 186,756 patients were included in this study– 372 (0.2 %) of which had PPFs within the 30-day postoperative period. Increasing frailty, as measured by RAI, was a significant predictor of PPF following primary THA in normal, frail, and severely frail patients. Female sex and ASA status also showed increased risk of PPF. Furthermore, each additional unit on the RAI scale increased the odds of PPF by 7.5 %.
Discussion
Frailty has been identified as a predictor of adverse postoperative outcomes in patients undergoing THA, however, this study is the first to find a significant association between frailty and PPF within 30 days of THA. Incorporating frailty assessment into preoperative discussions may aid surgeons in risk stratification, inform surgical decision-making, and enhance the effectiveness of postoperative monitoring following THA.
Keywords: Total hip arthroplasty, Periprosthetic fracture, Frailty, Risk Analysis Index
Highlights
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Frailty, as measured by the RAI, is a significant predictor of periprosthetic fractures following THA.
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This is the first study to find that frailty is associated with PPFs within 30 days of THA.
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We are also the first study to show that frailty, as scored by RAI, is an independent risk factor for PPF following THA.
1. Introduction
The utilization of total hip arthroplasty (THA) has increased in recent years, particularly among older adults.1 THA is one of the most successful orthopedic surgeries, but it is well known that age is an independent risk factor for complications.2,3 One particularly challenging complication is periprosthetic fracture (PPF), which has an incidence of 0.4–3.5 % after primary THA and accounts for 18 % of THA revisions.4,5 Established risk factors for PPF include medical factors: advanced age, female sex, osteoarthritis, osteoporosis, neuromuscular diseases (e.g., Parkinson's disease, epilepsy, ataxia), and cognitive disorders; as well as surgical factors such as stem design, approach, and technique (cementing vs. non-cementing).4,6, 7, 8, 9, 10 Due to the rising incidence and the expansion of bundled payments, improving preoperative risk stratification tools can better help us identify risk factors that may increase the rate of complications and expenditures.11 Frailty is an independent risk factor that may be useful for identifying patients at risk for PPF following primary THA.
Frailty is receiving greater attention within the medical community as the proportion of older adults rises globally.12 Defined as a decline across multiple physiological systems, frailty leads to increased vulnerability to stressors and is a well-known risk factor for adverse outcomes following surgery.13,14 While numerous studies have investigated frailty indices to predict postoperative outcomes after primary THA, there is a poor understanding of the direct association between quantifiable measures of frailty and PPF.15, 16, 17, 18, 19 Several indices quantifying frailty or comorbidity have been developed, such as the Clinical Frailty Scale, the Charlson Comorbidity Index (CCI), Elixhauser Comorbidity Index (ECI), and 5-factor Modified Frailty Index. The Clinical Frailty Scale is a tool that clinicians use to assess frailty based on their own clinical judgment, while the CCI and ECI primarily focus on comorbidities.20,21 The mFI-5 also calculates frailty based on comorbidities, but additionally adds functional dependence as a component.22,23 These scales are often used to predict perioperative outcomes following orthopedic surgery procedures.17,20,21 However, many of these indices are not consistent with the main domains of frailty because they do not assess physical function or physiological reserve.24 More recently, the Risk Analysis Index (RAI) emerged, providing a more comprehensive measurement by incorporating five domains of frailty, including demographics, nutrition, functionality, cognitive, and physical.25 The RAI is also a weighted model, as some deficits do not carry the same level of significance.25 Yet, the RAI has still not been used to predict PPFs. The objective of our study is to assess the association between frailty, as rated by the RAI, and PPF within 30 days following primary THA.
2. Methods
2.1. Data source and calculation of frailty using RAI
The American College of Surgeons National Surgical Quality Improvement Program (NSQIP) was queried from the years 2015–20. The NSQIP is a national database with risk-adjusted surgical outcomes data in the 30-day postoperative period. Patients included in this study were identified using the Current Procedural Terminology (CPT) code 27130 for primary THA. Patients with missing procedure or comorbidity data were excluded. The RAI was developed by Hall et al. as a more comprehensive measure of frailty utilizing 11 weighted variables which encompass multiple domains of frailty.25,26 Patients are scored on a scale from 0 to 81, with the 90th percentile of RAI scores representing severely frail patients. Patients were grouped into four categories based on their frailty score (<10th percentile = robust, >10th percentile and <50th percentile = normal, >50th percentile and <90th percentile = frail, >90th percentile = severely frail) (Supplemental Tables 1 and 2).
2.2. Demographic and clinical characteristics
Descriptive variables regarding demographic and clinical characteristics were collected for the total cohort and for each tiered frailty category. Demographic variables included age, sex, race, ethnicity, and body mass index (BMI). Preoperative variables included American Society of Anesthesiologists (ASA) score, hypertension requiring medication, diabetes requiring oral medication or insulin, history of severe chronic obstructive pulmonary disease (COPD), diagnosis of congestive heart failure within 30-days prior to surgery, functional health status (independent, partially or totally dependent), and diagnosis of cancer. Continuous variables were reported as median values with interquartile range (IQR). Preoperative comorbidities and 30-day outcome variables were reported as counts with percentages.
2.3. Statistical analysis
Patients were stratified according to frailty status and categorical variables were presented as counts with percentages and continuous variables were presented as medians with interquartile ranges. We initially conducted univariate regression for each variable and the binary outcomes. Variables that were significantly associated with outcomes (p < 0.05) were used in a binary logistic regression. Backwards stepwise p-value removal was used to build the final model. Only variables that were significant at or less than 0.05 were utilized in the final model. Multivariate analysis was performed controlling for age, female sex, ASA, and tobacco smoking within 30 days preoperatively. The primary outcome was PPF within 30 days. Results of multivariate logistic regression are represented as odds ratios (OR) with 95 % confidence intervals (95% CI). All statistical analysis was performed using Statistical Package for Social Sciences (Version 29; IBM, Inc., Armonk, NY).
3. Results
3.1. Patient demographics
There were a total of 186,756 patients from 2015 to 2020 who underwent THA. Of these, there were 372 (0.2 %) patients who developed PPFs within the 30 day postoperative period. In regard to frailty status as scored by the RAI, 2.2 % of patients were robust, 43.7 % were normal, 51.2 % were frail, and 2.9 % were severely frail. The median age by RAI frailty category was 35 years old (IQR: 11) for robust patients, 58 years old (IQR: 11) for normal patients, 72 years old (IQR: 10) for frail patients, and 84 years old (IQR: 10) for severely frail patients. The majority of patients were female (54.7 %) and of white race (71.8 %). The most common comorbidity was hypertension (54.9 %). The median BMI for each cohort was 29.01 kg/m2 (IQR: 9.95) for robust patients, 29.02 kg/m2 (IQR: 8.93) for normal patients, 29.12 kg/m2 (IQR: 7.42) for frail patients, and 27.29 kg/m2 (IQR: 7.12) for severely frail patients. The most common 30-day postoperative complications were unplanned readmission (3.4 %), minor complication (2.1 %), and reoperation (1.9 %). Other 30-day postoperative outcomes included mortality (0.2 %), deep vein thrombosis (DVT) (0.4 %), and wound complication (0.9 %). The median hospital length of stay was 2 days for robust (IQR: 2), normal (IQR: 2) and frail patients (IQR: 2), while it was 3 days for severely frail (IQR: 3) patients. Discharge destinations also varied across frailty groups. Among robust patients, 94.5 % were discharged home, 2.2 % were sent to skilled care, and 0.3 % were sent to rehabilitation. In the normal group, 90.9 % were discharged home, while 5.2 % required skilled care, and 3.3 % were sent to rehabilitation. For frail patients, 81.1 % went home, 11.7 % were discharged to skilled care, and 6.2 % were discharged to rehabilitation. In the severely frail group, only 52.1 % were discharged home, 26.4 % required skilled care, and 15.9 % were discharged to rehabilitation. The median operation time was 98 min (IQR: 51) for robust, 87 min (IQR: 42) for normal, 82 min (IQR: 38) for frail, and 84 min (IQR: 46) for severely frail (Table 1).
Table 1.
Baseline patient demographics and characteristics.
| Robust (N = 4025) | Normal (N = 81621) | Frail (N = 95689) | Severely Frail (N = 5421) | |
|---|---|---|---|---|
| Periprosthetic Fracture | 2 (0.05 %) | 130 (0.2 %) | 223 (0.2 %) | 17 (0.3 %) |
| Race | ||||
| White | 2424 (60.2 %) | 58242 (71.4 %) | 69672 (72.8 %) | 3842 (70.9 %) |
| Black | 742 (18.4 %) | 8299 (10.2 %) | 5510 (5.8 %) | 305 (5.6 %) |
| Female | 2973 (73.9 %) | 54537 (66.8 %) | 42764 (44.7 %) | 1937 (35.7 %) |
| Discharge Destination | ||||
| Home | 3802 (94.5 %) | 74197 (90.9 %) | 77634 (81.1 %) | 2822 (52.1 %) |
| Skilled Care | 87 (2.2 %) | 4255 (5.2 %) | 11179 (11.7 %) | 1431 (26.4 %) |
| Rehab | 11 (0.3 %) | 2677 (3.3 %) | 5929 (6.2 %) | 863 (15.9 %) |
| Diabetes Mellitus | ||||
| Insulin Dependent | 44 (1.1 %) | 1932 (2.4 %) | 3236 (3.4 %) | 348 (6.4 %) |
| Non-Insulin Dependent | 79 (2.0 %) | 6199 (7.6 %) | 10655 (11.1 %) | 616 (11.4 %) |
| Current Smoker | 1010 (25.1 %) | 14271 (17.5 %) | 7872 (8.2 %) | 314 (5.8 %) |
| Dyspnea | ||||
| At rest | 3 (0.1 %) | 105 (0.1 %) | 189 (0.2 %) | 43 (0.8 %) |
| Moderate Exertion | 30 (0.7 %) | 1076 (1.3 %) | 5736 (6.0 %) | 1121 (20.7 %) |
| Functional Health Status | ||||
| Partially Dependent | 0 (0 %) | 134 (0.2 %) | 1360 (1.4 %) | 1629 (30.0 %) |
| Totally Dependent | 0 (0 %) | 2 (0.002 %) | 17(0.02 %) | 149 (2.7 %) |
| Ventilator dependent | 0 (0 %) | 10 (0.01 %) | 6 (0.006 %) | 3 (0.1 %) |
| Severe Chronic Obstructive Pulmonary Disease | 22 (0.5 %) | 2137 (2.6 %) | 4583 (4.8 %) | 591 (10.9 %) |
| Ascites | 0 (0 %) | 14 (0.02 %) | 23 (0.02 %) | 12 (0.2 %) |
| Congestive Heart Failure | 0 (0 %) | 25 (0.03 %) | 307 (0.3 %) | 375 (6.9 %) |
| Hypertension | 612 (15.2 %) | 36341 (44.5 %) | 61704 (64.5 %) | 3875 (71.5 %) |
| Acute Renal Failure (postop) | 1 (0.02 %) | 26 (0.03 %) | 53 (0.1 %) | 36 (0.7 %) |
| On Dialysis | 2 (0.05 %) | 38 (0.05 %) | 217 (0.2 %) | 222 (4.1 %) |
| Disseminated Cancer | 0 (0 %) | 0 (0 %) | 24 (0.03 %) | 763 (14.1 %) |
| Open wound/wound infection | 10 (0.2 %) | 156 (0.2 %) | 284 (0.3 %) | 122 (2.3 %) |
| Steroid Use | 400 (9.9 %) | 2918 (3.6 %) | 3352 (3.5 %) | 387 (7.1 %) |
| >10 % loss in body weight in last 6 months | 0 (0 %) | 14 (0.02 %) | 179 (0.2 %) | 218 (4.0 %) |
| Obese | 2016 (50.1 %) | 47047 (57.6 %) | 48806 (51.0 %) | 2032 (37.5 %) |
| Elderly | 0 (0 %) | 18221 (22.3 %) | 78929 (82.5 %) | 5036 (92.9 %) |
| Wound Complication | 39 (1.0 %) | 772 (0.9 %) | 867 (0.9 %) | 73 (1.3 %) |
| Deep Vein Thrombosis | 8 (0.2 %) | 203 (0.2 %) | 414 (0.4 %) | 43 (0.8 %) |
| Mortality | 2 (0.05 %) | 38 (0.05 %) | 165 (0.2 %) | 103 (1.9 %) |
| Minor Complication | 69 (1.7 %) | 1272 (1.6 %) | 2335 (2.4 %) | 297 (5.5 %) |
| Readmission | 137 (3.4 %) | 2060 (2.5 %) | 3598 (3.8 %) | 495 (9.1 %) |
| Reoperation | 80 (2.0 %) | 1434 (1.8 %) | 1862 (1.9 %) | 197 (3.6 %) |
| BMI - Median (IQR) | 29.0056 (9.95) | 29.0223 (8.93) | 29.1243 (7.42) | 27.2880 (7.12) |
| Age - Median (IQR) | 35.00 (11) | 58.00 (11) | 72.00 (10) | 84.00 (10) |
| Total Operation Time - Median (IQR) | 98.00 (51) | 87.00 (42) | 82.00 (38) | 84.00 (46) |
| Length of Stay - Median (IQR) | 2.00 (2) | 2.00 (2) | 2.00 (2) | 3.00 (3) |
3.2. Multivariate regression
A multivariate regression analysis was performed to understand the value of frailty as a predictor of PPFs following THA. After controlling for BMI, total operation time, current smoker status, and LOS, increasing frailty, as measured by the RAI, was a significant predictor of greater odds of PPF following primary THA (normal OR: 1.884 [95 % CI: 1.143–3.106] p < 0.013, frail OR: 3.274 [95 % CI: 1.998–5.364] p < 0.001, and severely frail OR: 3.388 [95 % CI: 1.884–6.091] p < 0.001, respectively). Female sex (OR: 3.691 [95 % CI: 2.858–4.766] p < 0.001) and ASA status (OR: 1.342 [95 % CI: 1.117–1.613] p = 0.002) were also associated with increased risk of 30 day postoperative periprosthetic fractures following THA (Table 2). A multivariate regression analysis was conducted to assess how each unit increase in the RAI affected the risk of PPF. The results indicated that with each additional unit on the RAI scale, the likelihood of PPF increased by 7.5 % (OR: 1.075 [95 % CI: 1.052–1.099] p < 0.001) (Table 3).
Table 2.
Multivariate for periprosthetic fracture.
| Variable | Odds Ratio (95 % CI) | p-value |
|---|---|---|
| Normal | 1.884 (1.143–3.106) | 0.013 |
| Frail | 3.274 (1.998–5.364) | <0.001 |
| Severe Frail | 3.388 (1.884–6.091) | <0.001 |
| BMI | 1.013 (0.998–1.028) | 0.101 |
| American Society of Anesthesiologists (ASA) Score | 1.342 (1.117–1.613) | 0.002 |
| Current Smoker | 0.721 (0.534–0.974) | 0.033 |
| Female | 3.691 (2.858–4.766) | <0.001 |
| Total Operation Time | 1.002 (1.000–1.004) | 0.103 |
| Length of Stay (LOS) | 1.016 (0.989–1.045) | 0.248 |
Table 3.
Multivariate for periprosthetic fracture by continuous units of RAI.
| Variable | Odds Ratio (95 % CI) | p-value |
|---|---|---|
| RAI Continuous Units | 1.075 (1.052–1.099) | <0.001 |
| ASA | 1.282 (1.066–1.542) | 0.008 |
| BMI | 1.015 (1.000–1.031) | 0.048 |
| Current Smoker | 0.712 (0.526–0.962) | 0.027 |
| Female Sex | 3.690 (2.860–4.760) | <0.001 |
| Hospital Length of Stay | 1.010 (0.983–1.038) | 0.480 |
| Total Operation Time | 1.002 (0.999–1.004) | 0.141 |
4. Discussion
As the United States population ages, the utilization of THA is increasing, and with it, the risk of THA-related complications such as PPF.1,27 PPFs are one of the most common causes of failure following primary THA, representing around 18% of revision THAs, and are associated with significant morbidity and financial cost.5,28 Examining 30-day PPFs is particularly important, as evidence suggests an inverse relationship between the risk of PPF and the time elapsed since uncemented implant placement and stem osseointegration.29 Additionally, research indicates that among PPFs occurring within the first year post-surgery, the average time to fracture is approximately 9 weeks, ranging from as early as 2 days postoperative.30 Thus, it is critical to identify preoperative risk factors that predispose patients to 30-day PPFs following THA. One tool that has been used to predict postoperative complications following primary THA is frailty, however, it has not been used to predict PPF. Our large scale study of 186,756 patients using the NSQIP database found that frailty, as measured by the RAI, is associated with increased risk of 30-day postoperative PPF.
Frailty is characterized by a decline across multiple physiological systems, resulting in an increased vulnerability to stressors and is a well known risk factor for adverse postoperative outcomes following various orthopedic procedures.13 Numerous studies have identified frailty as a significant predictor of wound complications, reoperation, readmission, pneumonia, adverse discharge, and mortality in patients undergoing THA.15,17,19 However, our study is the first to find a significant association between frailty and PPF within 30 days of THA. Specifically, for each unit increase in the RAI score, the odds of PPF were elevated by 7.5 % (Table 3). This contrasts with a previous study by Johnson et al. who found no association between a frailty deficit index and PPFs within one year of THA.15 A plausible explanation for our findings is that the RAI encompasses many of the demographic risk factors for PPFs, including advanced age and female sex, while the frailty deficit index only includes comorbidities, activities of daily living, and BMI. Johnson et al. controlled for age to exclude it as a factor in their assessment of frailty.15 In contrast, the RAI accounts for age as it is a significant contributor to frailty.
Various indices have been used to assess frailty in surgical patients outcomes across multiple surgical subspecialties.15, 16, 17,22 The RAI has been praised for its ease of use, being calculated from readily available medical records, allowing clinicians to quickly identify patients at higher risk without needing specialized equipment or additional testing.15,17,26,31 It is also a more effective measure of frailty because in addition to comorbidities, it incorporates five domains of frailty, including demographics, nutrition, functionality, cognitive, and physical (comorbidity).25 The RAI is also a weighted model, as some deficits do not carry the same level of significance.25 This distinguishes the RAI from other frailty indices that primarily focus on comorbidities. For example, the mFI-5 characterizes a patient's frailty based on whether they have comorbidities, such as diabetes mellitus, congestive heart failure, hypertension requiring medication, a history of COPD or pneumonia, and functional independence.22 Our study is the first to demonstrate that the RAI, in particular, is a predictor of PPF following THA, with our data showing that normal, frail and severe frail patients are at increased risk. Our findings are explained by existing literature on the impact of physiologic frailty on risk of fragility fractures. Frailty often reflects reduced muscle mass and strength, diminished bone quality, and compromised physiological reserves, all of which contribute to an increased likelihood of falls and fractures.32,33 Thus, the RAI frailty score may be another method for capturing preoperative risk in this vulnerable patient population.
In addition to frailty, several other risk factors for periprosthetic fractures have been identified in the literature, including female sex, older age, preexisting osteoporosis, and revision THA.34,35 Our findings are consistent with the current literature, as we found that female sex, along with ASA classification was associated with increased odds of periprosthetic fracture. We also found that even after controlling for these well established risk factors for PPF, RAI remained an independent predictor of PPF.34,36 Our findings suggest that there may be a role for frailty assessment during preoperative risk determination. Identifying frail patients early on can help identify patients who may be at unique risk of complications including PPF. This can assist surgeons in their preoperative risk discussions with patients, surgical decision making, and enhance efficacy of postoperative monitoring following THA.37, 38, 39 For example, the RAI can help surgeons select specific types of implants to reduce the likelihood of PPFs in high-risk patients. However, future research is needed to explore whether these interventions aimed at mitigating frailty could specifically reduce the incidence of periprosthetic fractures.
There are several limitations to our study that should be noted. First, while the RAI is a widely used and validated measure of frailty, it may not capture all aspects of a patient's overall health status. Next, some of the PPFs that were found may have been intraoperative fractures that were found shortly after operation. Additionally, our study utilized data from the NSQIP database, which does not capture certain important clinical factors, such as bone quality, detailed imaging findings, or surgeon-specific variables that could influence the risk of periprosthetic fractures such as stem design or surgical technique. Another potential limitation is the generalizability of our findings, as the NSQIP database captures data from a specific cohort of hospitals, which may not be fully representative of all institutions performing THA, particularly those with different patient demographics or surgical practices. Additionally, the NSQIP database only records postoperative complications within 30 days, limiting our ability to assess longer-term outcomes, such as fractures that occur beyond this period. Finally, the reliance on administrative data for frailty measures may result in inaccuracies due to coding variations or incomplete reporting of relevant clinical details. Despite these limitations, our study provides valuable insight into the relationship between frailty, as measured by the RAI, and periprosthetic fractures.
5. Conclusion
Frailty, as measured by the RAI, is a significant predictor of periprosthetic fractures following THA. To our knowledge, this is the first study to find that frailty is associated with PPFs within 30 days of THA. We are also the first study to show that frailty, as scored by RAI, is an independent risk factor for PPF following THA. Our study suggests that RAI scoring may be included in preoperative management in patients considering THA. By addressing frailty preoperatively, there is potential to improve both short- and long-term outcomes for this growing patient population.
CRediT authorship contribution statement
Jared Sasaki: Conceptualization, Methodology, Software, Validation, Investigation, Resources, Writing – original draft, Writing – review & editing, Visualization, Supervision, Project administration. Victor Koltenyuk: Conceptualization, Methodology, Software, Validation, Formal analysis, Investigation, Resources, Data curation, Writing – original draft, Writing – review & editing, Visualization, Supervision, Project administration. Staton Albert: Conceptualization, Methodology, Software, Validation, Investigation, Writing – original draft, Writing – review & editing, Visualization, Project administration. Hirmand Salehi: Conceptualization, Methodology, Software, Validation, Investigation, Writing – original draft, Writing – review & editing, Visualization, Project administration. Tanmaya D. Sambare: Conceptualization, Methodology, Software, Validation, Investigation, Writing – original draft, Writing – review & editing, Visualization, Supervision. John Andrawis: Conceptualization, Methodology, Software, Validation, Investigation, Writing – original draft, Writing – review & editing, Visualization, Supervision.
Funding statement
This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.
Guardian/Patient consent was not needed for this study.
Acknowledgement
Bowers Neurosurgical Frailty and Outcomes Data Science Lab, Flint, MI, USA.
Institutional Ethical Committee Approval was not needed for this study. However, we upheld publishing ethics as our duty as authors.
Footnotes
Supplementary data to this article can be found online at https://doi.org/10.1016/j.jor.2025.03.018.
Appendix A. Supplementary data
The following is the Supplementary data to this article.
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