Orthopedics

Feature Article Supplemental Data

Frailty Index Is Associated With Periprosthetic Fracture and Mortality After Total Knee Arthroplasty

Rebecca L. Johnson, MD; Matthew P. Abdel, MD; Ryan D. Frank, MS; Alanna M. Chamberlain, PhD; Elizabeth B. Habermann, PhD; Carlos B. Mantilla, MD, PhD

Abstract

This cohort study of adult (≥50 years) patients aimed to calculate a validated, preoperative frailty deficit index (FI) and used it to compare outcomes following total knee arthroplasty (TKA), primary and revision, from 2005 through 2016. Using multivariable logistic and Cox regression, the authors analyzed whether FI, adjusted for age, predicts outcomes prior to hospital discharge, within 90 days, and within 365 days. They classified 9818 patients undergoing TKA (7920 primary and 1898 revision; median age, 69 years) as frail (21%), vulnerable (39%), and non-frail (40%). Frail, relative to non-frail, patients were more often female with more systemic diseases (American Society of Anesthesiologists classification, ≥III). While in-hospital, frail patients were found to have increased odds of reoperation (odds ratio, 2.52) and wound complications/hematoma (odds ratio, 2.15). Within 90 days, there was increased risk for periprosthetic fracture (>4-fold) and mortality (>9-fold) following TKA after age adjustment. Within the first year, frail patients were at heightened risk for death (hazard ratio, 8.08), any patient infection (hazard ratio, 1.97), wound complications/hematoma (hazard ratio, 2.16), periprosthetic fracture (hazard ratio, 3.03), and reoperation (hazard ratio, 1.41). At no time point were significant associations found with arthrofibrosis, aseptic loosening, or patellar clunk syndrome. One-fifth of patients undergoing primary and revision TKAs are frail and at notable risk for complications. Calculating a preoperative FI should guide pre-habilitation efforts (eg, chronic disease management, wellness) before and postoperative surveillance after TKA. [Orthopedics. 2019; 42(6):335–343.]

Abstract

This cohort study of adult (≥50 years) patients aimed to calculate a validated, preoperative frailty deficit index (FI) and used it to compare outcomes following total knee arthroplasty (TKA), primary and revision, from 2005 through 2016. Using multivariable logistic and Cox regression, the authors analyzed whether FI, adjusted for age, predicts outcomes prior to hospital discharge, within 90 days, and within 365 days. They classified 9818 patients undergoing TKA (7920 primary and 1898 revision; median age, 69 years) as frail (21%), vulnerable (39%), and non-frail (40%). Frail, relative to non-frail, patients were more often female with more systemic diseases (American Society of Anesthesiologists classification, ≥III). While in-hospital, frail patients were found to have increased odds of reoperation (odds ratio, 2.52) and wound complications/hematoma (odds ratio, 2.15). Within 90 days, there was increased risk for periprosthetic fracture (>4-fold) and mortality (>9-fold) following TKA after age adjustment. Within the first year, frail patients were at heightened risk for death (hazard ratio, 8.08), any patient infection (hazard ratio, 1.97), wound complications/hematoma (hazard ratio, 2.16), periprosthetic fracture (hazard ratio, 3.03), and reoperation (hazard ratio, 1.41). At no time point were significant associations found with arthrofibrosis, aseptic loosening, or patellar clunk syndrome. One-fifth of patients undergoing primary and revision TKAs are frail and at notable risk for complications. Calculating a preoperative FI should guide pre-habilitation efforts (eg, chronic disease management, wellness) before and postoperative surveillance after TKA. [Orthopedics. 2019; 42(6):335–343.]

Primary and revision total knee arthroplasty (TKA) rates are expected to skyrocket in the future.1 The costs associated with caring for TKA patients are high, and the economic and health care burden is substantial.2,3 Reimbursement rates for TKA are decreasing,2 increasing pressure on hospitals to limit the cost associated with caring for TKA patients. In particular, institutions must be prepared to manage the TKA patient with frailty in order to administer complex medical care while containing costs. Frailty is often indiscernible from the observed disability resulting from arthritis that is prevalent in TKA patients. However, frailty may have an independent, large effect on postoperative outcomes.

Frailty is an age-related clinical syndrome linked with dysfunction throughout the body and vulnerability to adverse health outcomes. Among patients undergoing orthopedic surgery, frail patients display greater rates of long-term care dependency, disability, and death within the first postoperative year, even when accounting for the presence of comorbid disease.4–6 However, limited information is currently available regarding the impact of frailty measures on the risk for postoperative complications specifically in TKA patients.

The aim of this study was to determine if the presence of frailty, when calculated by the frailty deficit index (FI),7,8 impacts both immediate and 1-year outcomes after TKA. The authors hypothesized that greater baseline frailty would be associated with heightened risk for complications following TKA. Calculation of FI may thus inform case-mix decisions for surgery, allow for tailored perioperative optimization strategies, and perhaps guide postoperative surveillance.

Materials and Methods

This single-site sample was a retrospective cohort of adult (≥50 years) patients undergoing TKA (primary or revision) from January 1, 2005, through December 31, 2016. This study received institutional review board approval and was conducted using STrengthening the Reporting of OBservational studies in Epidemiology (STROBE) guidelines.9

Similar to prior methods,10 the authors leveraged validated institutional databases, including the Total Joint Registry11 and the Perioperative Data Mart,10,12 to abstract patient characteristics, anesthesia, and surgical records for the index arthroplasties. Patients receiving their first TKA and who had unilateral surgery were included. Patients who underwent unicompartmental arthroplasties were excluded. Patients who were missing more than 3 items necessary to determine the FI or who refused research authorization were also excluded.

Using validated methods previously described,7,8 the FI was calculated using information on 32 conditions abstracted from chart review in addition to information on self-reported functional abilities (Table A, available in the online version of this article). Information on activities of daily living was obtained from a questionnaire administered within 18 months of TKA. If multiple questionnaires were available, the questionnaire completed closest to the time of the procedure was used. The authors categorized frailty into 3 levels: non-frail (FI<0.11), vulnerable (FI=0.11–0.20), and frail (FI≥0.21).13–16

Calculation of the Frailty Deficit Index

Table A:

Calculation of the Frailty Deficit Index

The outcomes of importance for this study included death, infection, reoperation, wound complications or hematoma, instability, periprosthetic fracture, arthrofibrosis, and aseptic loosening. The Total Joint Registry contains prospectively ascertained outcomes data on all joint replacements performed at the authors' institution, with postoperative follow-up at 3 months, 1 year, 2 years, and 5-year intervals thereafter TKA. The authors evaluated each outcome at 3 time points: prior to hospital discharge, within 90 days of the index surgery, and within 365 days of the index surgery.

Statistical Analysis

The authors summarized their data using medians and interquartile ranges and counts and percentages for continuous and categorical variables, respectively. Pearson chi-square, Fisher exact, and Kruskal–Wallis tests, where appropriate, were used to compare data distributions across categorized FI. The authors performed similar comparisons across study inclusion. They used univariate linear regression models to examine the association between age and FI. Multivariable logistic regression was used to analyze inhospital outcomes.

The authors used cause-specific hazards regression models to analyze the association between FI and 90-day and 1-year outcomes. Duration of follow-up was the number of days from TKA to first occurrence of the event, death, or last joint examination in the Total Joint Registry. Death was modeled as a competing event for non-mortality outcomes,17 and patients were censored at last joint examination. For illustrative purposes, cumulative incidence figures were generated using competing event methods. Age was included as an adjustment term (as age does not contribute to FI) in all regression models. As a pre-specified sensitivity analysis, all regression models were repeated on primary only and revision only subgroups. P<.05 (2-sided) was considered significant. All of the analyses were performed using SAS software (SAS Institute, Cary, North Carolina).

Results

A total of 9818 unilateral TKAs performed in 9818 patients met study inclusion criteria (consisting of 80.7% primary and 19.3% revision). Patients excluded (n=467) because they were missing 3 or more FI items were more likely to be in the revision subgroup (23.3% vs 19.3%), be male (57.2% vs 43.2%), have slightly lower body mass index (median, 31 vs 32 kg/m2), and have hyperlipidemia (6.9% vs 4.4%).

The median (Q1, Q3) body mass index at TKA was 32 kg/m2 (28, 36), and the median age was 69 years (Table 1). The global median duration of surgery (in minutes) was 142. Primary TKA required less time (median, 140 minutes) than revision TKA (median, 165 minutes). Patients were nearly as likely to receive general anesthesia (51%) as neur-axial anesthesia (49%). Lack of racial diversity was notable, with more than 96% of the cohort identifying themselves as being non-Hispanic white.

Clinical and Demographic Characteristics by Categorized Frailty Indexa

Table 1:

Clinical and Demographic Characteristics by Categorized Frailty Index

The median (Q1, Q3) FI overall was 0.13 (0.08, 0.17). The non-frail, vulnerable, and frail levels comprised 40.4%, 39.1%, and 20.5% of the sample, respectively. When frail patients were compared with non-frail patients, frail patients tended to be female (67.4% vs 50.1%), had higher American Society of Anesthesiologists classification (59.8% classification III or higher vs 19.5%), more often received general anesthesia (64.9% vs 43.1%), and underwent longer procedures (median, 151 vs 140 minutes), especially in the case of revision surgery (median, 33 minutes longer). Also when frail patients were compared with non-frail patients, frail patients had a larger percentage of revision surgery (25.1% vs 16.8%) (Table 1). All components of frailty are presented by categorized FI in Table 2.

Comorbidities and Activities of Daily Living by Frailty IndexaComorbidities and Activities of Daily Living by Frailty Indexa

Table 2:

Comorbidities and Activities of Daily Living by Frailty Index

Unexpectedly, larger FI values were observed in older adult patients (Figure A, available in the online version of this article). On linear regression analyses, older age was significantly associated with increased FI (per 10-year mean increase, 0.009; 95% confidence interval, 0.007–0.010; P<.001), corresponding to 1 increased comorbidity per 34 years of age.

Boxplots of Frailty Index by 5-year categorizations of age

Figure A:

Boxplots of Frailty Index by 5-year categorizations of age

In-Hospital Outcomes

Frail, relative to non-frail, patients had high risk of in-hospital reoperation (odds ratio, 2.52; 95% confidence interval, 1.43–4.43; P=.004) and wound complications/hematoma (odds ratio, 2.15; 95% confidence interval. 1.44–3.21; overall P<.001) (Table 3). In-hospital deaths, instability, any patient infections, periprosthetic fractures, aseptic loosening, arthrofibrosis, and patellar clunk syndrome were not analyzed due to too few events.

Associations Between Frailty Index and Outcomes on Multivariable Logistic and Cause-Specific Hazards RegressionaAssociations Between Frailty Index and Outcomes on Multivariable Logistic and Cause-Specific Hazards Regressiona

Table 3:

Associations Between Frailty Index and Outcomes on Multivariable Logistic and Cause-Specific Hazards Regression

Outcomes Within 90 Days

Frail patients had increased rates of infections (hazard ratio, 2.17; 95% confidence interval, 1.46–3.24; overall P<.001), mortality (hazard ratio, 9.23; 95% confidence interval, 2.68–31.86; overall P<.001), and instability (hazard ratio, 2.95; 95% confidence interval, 1.20–7.25; overall P=.044). Similarly, frail patients had increased reoperation (hazard ratio, 1.44; 95% confidence interval, 1.23–1.68; P<.001) and wound complications/hematoma (hazard ratio, 2.13; 95% confidence interval, 1.68–2.69; overall P<.001) rates within 90 days compared with non-frail patients. Additionally, periprosthetic fracture risk was highest in frail patients (hazard ratio, 4.28; 95% confidence interval, 1.62–11.31; P=.005). There was no evidence of aseptic loosening, arthrofibrosis, or patellar clunk syndrome associated with FI.

Outcomes Within 1 Year

Within 1 year of TKA, frail patients had higher rates of infection (hazard ratio, 1.97; 95% confidence interval, 1.45–2.69; overall P<.001), mortality (hazard ratio, 8.08; 95% confidence interval, 4.21–15.51; overall P<.001), periprosthetic fracture (hazard ratio, 3.03; 95% confidence interval, 1.50–6.10; overall P=.007), wound complications/hematoma (hazard ratio, 2.16; 95% confidence interval, 1.72–2.72; overall P<.001), and reoperations (hazard ratio, 1.41; 95% confidence interval, 1.26–1.57; P<.001). Similarly, vulnerable patients, relative to non-frail patients, were at greater risk of mortality, reoperation, and periprosthetic fracture.

Cumulative incidence over time for the outcome of mortality at 1 year is displayed in Figure 1. Cumulative incidence over time for the outcomes of infection, periprosthetic fracture, reoperation, and wound complications/hematoma is displayed in Figure 2. Greater FI was not associated with arthrofibrosis, aseptic loosening, or patellar clunk syndrome.

Cumulative incidence of mortality within 1 year of total knee arthroplasty (TKA). Death was treated as a competing event when analyzing other outcomes.

Figure 1:

Cumulative incidence of mortality within 1 year of total knee arthroplasty (TKA). Death was treated as a competing event when analyzing other outcomes.

Cumulative incidence of infection, wound complications/hematoma, reoperation, and periprosthetic fracture within 1 year of total knee arthroplasty (TKA). Death was treated as a competing event when analyzing other outcomes.

Figure 2:

Cumulative incidence of infection, wound complications/hematoma, reoperation, and periprosthetic fracture within 1 year of total knee arthroplasty (TKA). Death was treated as a competing event when analyzing other outcomes.

The results were comparable in the revision and primary TKA subgroup sensitivity analyses. The significant associations in the overall results were also observed in the primary and revision subgroups. Therefore, the authors grouped the primary and revision TKAs together for the main analysis. Except for periprosthetic fracture (P=.43), due to limited instances, the within 1-year associations observed in the overall results were significant in revision TKAs.

Discussion

In this large-scale cohort study of 9818 TKAs (7920 primary and 1898 revision), more than half of the patients met the criteria for being vulnerable (39%) or frail (21%). Greater frailty was associated with heightened risk for adverse events after primary and revision TKA, which supported the authors' hypothesis. They found that within 1 year after TKA, frail patients were 8-fold more likely to die, 3-fold more likely to have a periprosthetic fracture, and approximately 2-fold more likely to have an infection, a wound complication, or a hematoma compared with those without frailty. The authors recommend that surgeons consider using FI as a clinical tool that has the ability to inform on the patient's preoperative disability status in addition to the presence of disease burden for use in shared decision-making and pre-habilitation. Calculating a FI using data freely available within the electronic medical record to identify those patients with higher frailty prior to consideration for surgery is not only useful and important but also necessary.

Despite TKA being generally considered safe and routine, adverse events have been associated with significant morbidity and mortality; thus, more objective information to identify patients at risk would enhance the informed consent process. Within the first year, even those patients considered vulnerable to frailty (FI≥0.11) were at higher risk for death, periprosthetic fracture, and reoperation. Although Abdel et al18 have shown periprosthetic fracture to be among the top 3 reasons why TKAs fail in contemporary practice, a clear correlation between increasing FI and periprosthetic fracture has not been previously reported. In addition, given the projected increase in demand for not only primary but also revision TKA and growing emphasis on cost-containment, better resource utilization will be important. Considering that periprosthetic fractures around TKAs pose a significant surgical challenge, the current findings have real potential for use within risk stratification models before surgical listing offers are made to patients considering TKA.

This single-center study builds on the findings of previous population-based studies6,19–21 that have demonstrated that frailty level is linked to adverse events after TKA. Unlike these prior works, the current study design allowed for the appraisal of details in complications that are unavailable within these administrative datasets. Uniquely, the current authors described when there should be heightened concern for patient mortality, periprosthetic fracture, reoperation, infection, wound complications, and hematoma immediately and throughout the first year after TKA. Further, the authors presented original data providing for no association between frailty and arthrofibrosis, aseptic loosening, and patellar clunk syndrome following TKA. Additionally, this study population allowed the authors to inform on results from primary and revision arthroplasty. Because the diagnosis and pathology may differ between primary and revision TKA, the authors believed that inclusion of both surgeries was imperative to study knee arthroplasty completely as it relates to frailty.

Not unlike prior authors, the current authors advocate for the routine use of preoperative frailty measurement.5,6,21 Shin et al6 studied the National Surgical Quality Improvement Program's modified frailty index to predict complications in patients after TKA. Recently, Runner et al21 showed that the National Surgical Quality Improvement Program's modified FI was a stronger predictor of reoperation than the American Society of Anesthesiologists score following TKA. Uniquely, and in contrast to these previous studies, the current authors are the first to provide predictive information beyond the 30-day limitations of National Surgical Quality Improvement Program data. Consequently, using the FI as the frailty measurement tool of choice over the modified FI provides for a clearer picture of complications (inclusive of 90-day and 1-year outcomes). The current authors' findings offer potential targets for preoperative optimization strategies such as pre-habilitation. Approximately 60% of the patients in the current cohort were classified as frail or vulnerable before surgery. Limitations in function that can be potentially restored by TKA include dependence on assistive devices for ambulation and difficulty with climbing stairs. Until now, granular information on activities of daily living has been unavailable. Surprisingly, three-fourths of those who were frail reported that they required assistance to perform daily life activities. Practically and with strong potential to inform on the value of TKA, a re-calculated FI after surgery may also be useful as a tool for postoperative surveillance and for providing important data for quality rankings.

This study had limitations. The results were not intended nor expected to reveal the impact of modifiable perioperative management strategies (eg, type of anesthesia) or potentially modifiable preoperative risk factors on postoperative outcomes. However, these may be logical next steps for clinical investigation. In this era of evolving artificial intelligence and enabling technology, information to generate the FI will be available in the electronic medical record and can be used, without any incremental work, to provide surgeons with a frailty measure to inform patients of perioperative risk. Surgeons can also choose to alter surgical plans (eg, using stemmed tibial and femoral components in primary TKAs). Accordingly, the authors think that there is a benefit to the FI, even if some of the factors in it are not directly modifiable. Next, FI calculation was dependent on available data. Therefore, others replicating this work may experience variance in comorbidities across medical record systems. Additionally, information on disability was obtained via questionnaire. Therefore, the authors cannot rule out possible inaccuracies in self-reported function (activities of daily living). Like similar retrospective analyses, reliability of the data may be in question. However, unlike inaccuracies common in administrative data, the Total Joint Registry has a notably high capture rate for both clinical and radiographic follow-up ascertained postoperatively at 3 months, 1 year, 2 years, and 5-year intervals thereafter. A small percentage (4.5%) of eligible TKAs were excluded because missing data prevented FI calculation, and this may have introduced bias. However, with only minimal differences between patients included and excluded, the authors expect any such bias to be minimal. Finally, these results have limited generalizability to other, different TKA populations. Specifically, this patient population was almost entirely white (96.7%).

Conclusion

As the prevalence of osteoarthritis and other indicators for TKA rises, adults with visible and subclinical frailty may be in the majority. Frailty, surprisingly common among patients undergoing primary and revision TKA, was significantly associated with both inferior immediate and within 1-year outcomes. Consequently, FI should be used for shared decision-making, to influence pre-habilitation/optimization, and potentially used for surveillance after TKA.

References

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Clinical and Demographic Characteristics by Categorized Frailty Indexa

CharacteristicNon-frail: Frailty Index<0.11 (N=3965)Vulnerable: Frailty Index=0.11–0.20 (N=3840)Frail: Frailty Index0.21 (N=2013)Total (N=9818)P
Type of first surgery, No.<.001b
  Primary knee3300 (83.2%)3113 (81.1%)1507 (74.9%)7920 (80.7%)
  Revision knee665 (16.8%)727 (18.9%)506 (25.1%)1898 (19.3%)
American Society of Anesthesiologists classification, No.<.001b
  I118 (3.0%)26 (0.7%)11 (0.5%)155 (1.6%)
  II3075 (77.6%)2360 (61.5%)797 (39.6%)6232 (63.5%)
  III770 (19.4%)1434 (37.3%)1168 (58.0%)3372 (34.3%)
  IV2 (0.1%)20 (0.5%)37 (1.8%)59 (0.6%)
Race/ethnicity, No.<.001b
  Missing605929148
  Non-Hispanic white3801 (97.3%)3645 (96.4%)1871 (94.3%)9317 (96.3%)
  Non-Hispanic black21 (0.5%)40 (1.1%)44 (2.2%)105 (1.1%)
  Any Hispanic24 (0.6%)30 (0.8%)17 (0.9%)71 (0.7%)
  Non-Hispanic Asian21 (0.5%)14 (0.4%)12 (0.6%)47 (0.5%)
  Other38 (1.0%)52 (1.4%)40 (2.0%)130 (1.3%)
Sex, No.<.001b
  Female1986 (50.1%)2234 (58.2%)1357 (67.4%)5577 (56.8%)
  Male1979 (49.9%)1606 (41.8%)656 (32.6%)4241 (43.2%)
Age, median (Q1, Q3), y68 (61, 74)69 (62, 76)70 (63, 77)69 (62, 75)<.001c
BMI, median (Q1, Q3), kg/m229 (26, 33)33 (29, 37)35 (30, 40)32 (28, 36)<.001c
Surgery time, overall, median (Q1, Q3), min140 (94, 176)142 (85, 181)151 (86, 200)142 (89, 182)<.001c
Surgery time, primary, median (Q1, Q3), min138 (98, 170)141 (92, 174)145 (85, 184)140 (93, 174).013c
Surgery time, revision, median (Q1, Q3), min155 (79, 211)155 (69, 219)188 (92, 242)165 (78, 221)<.001c
Type of anesthetic<.001b
  General anesthesia1708 (43.1%)1991 (51.8%)1307 (64.9%)5006 (51.0%)
  Regional block2257 (56.9%)1849 (48.2%)706 (35.1%)4812 (49.0%)

Comorbidities and Activities of Daily Living by Frailty Indexa

CharacteristicNo.

Non-frail: Frailty Index<0.11 (N=3965)Vulnerable: Frailty Index=0.11–0.20 (N=3840)Frail: Frailty Index0.21 (N=2013)Total (N=9818)
Frailty comorbidity
  Body mass index
    Missing997048217
    Underweight (<18.5 kg/m2)11 (0.3%)6 (0.2%)6 (0.3%)23 (0.2%)
    Normal (18.5–24.9 kg/m2)616 (15.9%)271 (7.2%)114 (5.8%)1001 (10.4%)
    Overweight (25.0–29.9 kg/m2)1499 (38.8%)910 (24.1%)380 (19.3%)2789 (29.0%)
    Obese (≥30 kg/m2)1740 (45.0%)2583 (68.5%)1465 (74.6%)5788 (60.3%)
  Hypertension1681 (42.4%)2829 (73.7%)1670 (83.0%)6180 (62.9%)
  Congestive heart failure15 (0.4%)108 (2.8%)213 (10.6%)336 (3.4%)
  Coronary artery disease12 (0.3%)116 (3.0%)184 (9.1%)312 (3.2%)
  Cardiac arrhythmia4 (0.1%)40 (1.0%)78 (3.9%)122 (1.2%)
  Hyperlipidemia32 (0.8%)187 (4.9%)215 (10.7%)434 (4.4%)
  Stroke3 (0.1%)16 (0.4%)31 (1.5%)50 (0.5%)
  Arthritis110 (2.8%)293 (7.6%)290 (14.4%)693 (7.1%)
  Asthma164 (4.1%)424 (11.0%)417 (20.7%)1005 (10.2%)
  Cancer487 (12.3%)924 (24.1%)588 (29.2%)1999 (20.4%)
  Renal disease55 (1.4%)251 (6.5%)366 (18.2%)672 (6.8%)
  Chronic obstructive pulmonary disease35 (0.9%)130 (3.4%)264 (13.1%)429 (4.4%)
  Dementia0 (0.0%)0 (0.0%)2 (0.1%)2 (0.0%)
  Depression7 (0.2%)46 (1.2%)56 (2.8%)109 (1.1%)
  Diabetes187 (4.7%)836 (21.8%)756 (37.6%)1779 (18.1%)
  Osteoporosis4 (0.1%)41 (1.1%)51 (2.5%)96 (1.0%)
  Schizophrenia0 (0.0%)0 (0.0%)2 (0.1%)2 (0.0%)
  Substance abuse20 (0.5%)72 (1.9%)80 (4.0%)172 (1.8%)
Activities of daily living (ADL)
  Difficulty eating by yourself1 (0.0%)7 (0.2%)61 (3.0%)69 (0.7%)
  Difficulty dressing yourself8 (0.2%)73 (1.9%)368 (18.3%)449 (4.6%)
  Difficulty using the toilet3 (0.1%)32 (0.8%)233 (11.6%)268 (2.7%)
  Difficulty bathing2 (0.1%)64 (1.7%)464 (23.1%)530 (5.4%)
  Difficulty walking227 (5.7%)1397 (36.4%)1503 (74.7%)3127 (31.8%)
  Difficulty housekeeping36 (0.9%)382 (9.9%)1003 (49.8%)1421 (14.5%)
  Difficulty climbing stairs467 (11.8%)1963 (51.1%)1702 (84.6%)4132 (42.1%)
  Difficulty with transportation10 (0.3%)77 (2.0%)396 (19.7%)483 (4.9%)
  Difficulty taking medications9 (0.2%)32 (0.8%)181 (9.0%)222 (2.3%)
  Difficulty preparing meals8 (0.2%)55 (1.4%)479 (23.8%)542 (5.5%)
  Difficulty getting in/out of bed6 (0.2%)79 (2.1%)375 (18.6%)460 (4.7%)
  Dependent on breathing device181 (4.6%)570 (14.8%)532 (26.4%)1283 (13.1%)
  Require assistive device for ADL353 (8.9%)1344 (35.0%)1528 (75.9%)3225 (32.8%)
  Climb two flights of stairs without rest
    Yes, with no difficulty2022 (51.0%)582 (15.2%)98 (4.9%)2702 (27.5%)
    Yes, with difficulty1629 (41.1%)2287 (59.6%)741 (36.8%)4657 (47.4%)
    No, cannot at all165 (4.2%)833 (21.7%)1126 (55.9%)2124 (21.6%)
    Unknown149 (3.8%)138 (3.6%)48 (2.4%)335 (3.4%)

Associations Between Frailty Index and Outcomes on Multivariable Logistic and Cause-Specific Hazards Regressiona

OutcomeTotal (N=9818)In HospitalbWithin 90 DayscWithin 1 Yearc



No. of Events (Row)Odds Ratio (95% CI)PNo. of Events (Row)Hazard Ratio (95% CI)PNo. of Events (Row)Hazard Ratio (95% CI)P
Mortality-<.001<.001
  Non-frail39650 (0.0%)-3 (0.1%)1.00 (reference)11 (0.3%)1.00 (reference)
  Vulnerable38401 (0.0%)7 (0.2%)2.22 (0.57–8.58)31 (0.8%)2.62 (1.32–5.22)d
  Frail20132 (0.1%)16 (0.8%)9.23 (2.68–31.86)e53 (2.6%)8.08 (4.21–15.51)e
Infection-<.001<.001
  Non-frail39650 (0.0%)-47 (1.2%)1.00 (reference)82 (2.1%)1.00 (reference)
  Vulnerable38403 (0.1%)55 (1.4%)1.22 (0.83–1.81)78 (2.0%)1.00 (0.73–1.36)
  Frail20131 (0.0%)50 (2.5%)2.17 (1.46–3.24)e78 (3.9%)1.97 (1.45–2.69)e
Wound complications/hematoma<.001<.001<.001
  Non-frail396546 (1.2%)1.00 (reference)136 (3.4%)1.00 (reference)140 (3.5%)1.00 (reference)
  Vulnerable384053 (1.4%)1.14 (0.77–1.70)157 (4.1%)1.18 (0.94–1.49)166 (4.3%)1.21 (0.97–1.52)
  Frail201353 (2.6%)2.15 (1.44–3.21)e146 (7.3%)2.13 (1.68–2.69)e152 (7.6%)2.16 (1.72–2.72)e
Reoperation.004<.001<.001
  Non-frail396521 (0.5%)1.00 (reference)385 (9.7%)1.00 (reference)784 (19.8%)1.00 (reference)
  Vulnerable384030 (0.8%)1.37 (0.78–2.41)413 (10.8%)1.13 (0.98–1.30)910 (23.7%)1.23 (1.11–1.35)e
  Frail201330 (1.5%)2.52 (1.43–4.43)d264 (13.1%)1.44 (1.23–1.68)e521 (25.9%)1.41 (1.26–1.57)e
Periprosthetic fracture-.005.007
  Non-frail39650 (0.0%)-6 (0.2%)1.00 (reference)13 (0.3%)1.00 (reference)
  Vulnerable38401 (0.0%)9 (0.2%)1.53 (0.55–4.31)29 (0.8%)2.26 (1.18–4.36)f
  Frail20130 (0.0%)13 (0.6%)4.28 (1.62–11.31)d20 (1.0%)3.03 (1.50–6.10)d
Aseptic loosening/loosening--.047
  Non-frail39650 (0.0%)-0 (0.0%)-1 (0.0%)1.00 (reference)
  Vulnerable38400 (0.0%)0 (0.0%)12 (0.3%)12.76 (1.66–98.20)f
  Frail20130 (0.0%)0 (0.0%)4 (0.2%)8.51 (0.95–76.23)
Instability-.044.091
  Non-frail39652 (0.1%)-8 (0.2%)1.00 (reference)32 (0.8%)1.00 (reference)
  Vulnerable38400 (0.0%)11 (0.3%)1.40 (0.56–3.49)34 (0.9%)1.10 (0.68–1.79)
  Frail20130 (0.0%)12 (0.6%)2.95 (1.20–7.25)f27 (1.3%)1.72 (1.03–2.88)f
Arthrofibrosis-.521.349
  Non-frail39650 (0.0%)-211 (5.3%)1.00 (reference)290 (7.3%)1.00 (reference)
  Vulnerable38400 (0.0%)186 (4.8%)0.93 (0.76–1.13)246 (6.4%)0.89 (0.75–1.05)
  Frail20132 (0.1%)108 (5.4%)1.06 (0.84–1.34)139 (6.9%)0.99 (0.81–1.21)
Patellar clunk syndrome-.859.549
  Non-frail39650 (0.0%)-6 (0.2%)1.00 (reference)38 (1.0%)1.00 (reference)
  Vulnerable38400 (0.0%)6 (0.2%)1.03 (0.33–3.21)35 (0.9%)0.96 (0.61–1.53)
  Frail20130 (0.0%)2 (0.1%)0.67 (0.13–3.33)13 (0.6%)0.71 (0.38–1.33)

Calculation of the Frailty Deficit Index

Variables in frailty deficit index (unless indicated: all Yes=1,No=0)
Comorbidities (abstracted from the electronic medical record)
body mass index†, hypertension, congestive heart failure, coronary artery disease, cardiac arrhythmia, hyperlipidemia, stroke, arthritis, asthma, cancer, chronic kidney disease, chronic obstructive pulmonary disease, dementia, depression, diabetes, osteoporosis, schizophrenia, substance abuse disorders (drug and alcohol) †Underweight (<18.5) or obese (≥30)=1, Overweight (25 to <30)=0.5, Normal (18.5 to <25)=0
Activities of Daily Living (14 validated questions self-reported by patients)
need help preparing meals?, need help feeding yourself?, need help dressing yourself?, need help using the toilet?, need help with housekeeping?, need help climbing stairs?, need help bathing?, need help walking?, need help using transportation?, need help getting in and out of bed?, need help managing medications?, depend on assistive devices (walker, cane, etc.) or other people to perform activities of daily life?, Dependent on a device for normal breathing?, climb 2 flights of stairs without rest‡? ‡No, can't do at all=1, Yes, with difficulty=0.5, Yes, with no difficulty=0
Authors

The authors are from the Department of Anesthesiology and Perioperative Medicine (RLJ, CBM), the Department of Orthopedic Surgery (MPA), the Division of Biomedical Statistics and Informatics (RDF), and the Department of Health Services Research (AMC, EBH), Mayo Clinic College of Medicine, Rochester, Minnesota.

Dr Johnson, Mr Frank, Dr Chamberlain, Dr Habermann, and Dr Mantilla have no relevant financial relationships to disclose. Dr Abdel is a paid consultant for and receives royalties from Stryker, but he reports no conflicts regarding this manuscript.

This study was supported by a Robert D. and Patricia E. Kern Center for the Science of Health Care Delivery award, a Mayo Clinic Department of Development Benefactor Gift, and the Department of Anesthesiology and Perioperative Medicine Small Grants Program.

The authors thank the Anesthesia Clinical Research Unit and the expert program analysts within the Health Sciences Research Departments, Mayo Clinic, Rochester, Minnesota, especially Ms Danette Bruns, Ms Amy Glasgow, Mr Andrew Hanson, Ms Barbara Abbott, Mr Gregory Houston, and Mr Timothy J. Weister, for their data extraction expertise; Darrell R. Schroeder, MS, for additional statistical support; Dr Sara Ikeda, Resident, Department of Anesthesiology, for assistance with data collection; Ms Youlonda Loechler and Dr Christopher Salib, Department of Orthopedic Surgery, for expert consultation for access to the Total Joint Registry; and Dr James Kirkland and Dr Nathan LeBrasseur, Directors, Robert and Arlene Kogod Center, Mayo Clinic, for mentorship on measures of frailty.

Correspondence should be addressed to: Rebecca L. Johnson, MD, Department of Anesthesiology and Perioperative Medicine, Mayo Clinic College of Medicine, 200 First St SW, Rochester, MN 55905 ( johnson.rebecca1@mayo.edu).

Received: March 09, 2019
Accepted: July 22, 2019
Posted Online: August 14, 2019

10.3928/01477447-20190812-05

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