Abstract
Background
Frailty is an important predictor of surgical outcomes and has been quantified by several models. The modified frailty index (mFI) has recently been adapted from an 11-item index to a 5-item index and has promise to be a valuable risk assessment tool in orthopedic trauma patients. We perform a retrospective analysis of the 5-item mFI and evaluate its effectiveness in predicting outcomes in patients with long bone fractures.
Methods
The National Surgery Quality Improvement Program (NSQIP) 2006–2016 database was queried for surgical procedures in the treatment of long bone fractures by current procedural terminology (CPT) codes, excluding those performed on metacarpals and metatarsals. Cases were excluded if they were missing demographic, frailty, and variable data. The 5-item frailty index was calculated based on the sum of presence of 5 conditions: COPD/pneumonia, congestive heart failure, diabetes, hypertension, and impaired functional status. Chi square was used to determine variables significantly associated with each outcome. The significant variables were included in multivariate logistic regression along with the mFI. Significance was defined as p < 0.05.
Results
Of the 140,249 fixation procedures performed on long bone fractures in NSQIP, 109,423 cases remained after exclusion criteria were applied. The majority of patients were between the ages of 61 and 80 (34.0%), were female (65.6%) and Caucasian (86.3%). Multivariate analysis revealed that mFI scores ≥3 were predictive of unplanned reoperation (OR = 1.57), wound disruption (OR = 2.83), unplanned readmission (OR = 2.12), surgical site infection (OR = 1.90), major complications (OR = 3.04), and discharge destination (OR = 3.06).
Conclusions
Our study analyzed the relationship of frailty and postoperative complications in patients with long bone fractures. Patients had increased likelihood of morbidity, independent of other comorbidities and demographic factors. The mFI may have a role as a simple, easy to use risk assessment tool in cases of orthopedic trauma.
Keywords: Frailty, Long bone fracture, Complications, Outcomes, Readmission
1. Introduction
Long bone fractures are common injuries that comprise approximately 47.9% of all orthopedic fractures,1, 2, 3 most commonly from accidents and falls.2 Men tend to experience these fractures at a mean age of 36.7 years, while women experience them later in life at 61 years of age.2 While many comorbidities have been associated with increased risk of these fractures, osteoporosis and frailty are two conditions that have been implicated as important risk factors in the elderly population.1,4 This population is the fastest growing age group in the United States5,6 and as such, frailty is becoming increasingly recognized as an important predictor of surgical outcomes. Studies have shown increased likelihoods of postoperative complications, such as malunion, displacement, and morbidity after orthopaedic procedures in this population.7, 8, 9 Identifying perioperative risk factors at the time of initial surgery is imperative to mitigate morbidity and reduce hospital costs for these unplanned events.
Though age has been independently linked to adverse events, frailty may better highlight risk factors that can be modified in the clinical setting. It can be defined as a decrease of physiological reserve giving rise to vulnerability separate from the normal aging process resulting in a decreased ability to respond to stressors10 due to the accumulation of deficits.11 The modified frailty index (mFI), a derivative of the Canadian Study of Health and Aging Frailty Index (CSHA-FI),12 is a validated measure of medical, psychological, and functional capacity adapted by Velanovich et al.11 A new, 5-item modified frailty index (mFI) has since been adapted from this 11-item index and has been shown to be an effective risk assessment tool for orthopedic patients (Table 1).9,13, 14, 15, 16, 17., 18, 19, 20, 21, 22, 23, 24 This index takes into account five conditions from a patient’s history: history of chronic pulmonary obstructive disease, congestive heart failure, diabetes mellitus, hypertension, and functional status.14
Table 1.
| 5-Item Frailty Index Variables |
| COPDc |
| Congestive heart failure |
| Diabetes mellitus |
| Hypertension requiring medication |
| Functional status (totally or partially dependent) |
| Variables Included in 11-Item Frailty Index, not Included in 5-Item Index |
| Recent pneumonia |
| Transient ischemic attack or cerebrovascular accident |
| Cerebrovascular accident with neurological deficit |
| Impaired sensorium |
| Peripheral vascular disease or ischemic rest pain |
National Surgical Quality Improvement Program.
modified frailty index.
Chronic obstructive pulmonary disease.
Current studies of frailty in orthopaedics largely focus on injuries limited to specific bone fractures. As many patients with long bone fractures share similar characteristics, however, application of the mFI may be better suited for long bone fractures as a group. This would include fractures of the humerus, radius, ulna, clavicle, femur, tibia, and fibula. By combining groups of fractures with similar morphology (i.e., long bones), we investigate the predictive value of the mFI for rates of unplanned reoperation, wound disruption, and surgical site infection.
2. Methods
2.1. Data source and study population
The American College of Surgeons National Quality Improvement Program (NSQIP) is a database that has collected de-identified data up to 30 days postoperatively from voluntarily participating hospitals since 2005. NSQIP’s data has been validated with rigorous quality control measures and through numerous studies. We performed a retrospective analysis of the data in NSQIP from 2006 to 2016 and chose individuals who underwent surgical fixation of long bone fractures based on 121 Current Procedural Terminology (CPT) codes. Cases were included only if surgical fixation occurred as the principle procedure. Patients that were missing data for any of the 5 variables needed to calculate mFI were excluded. Cases were also excluded if they were missing any demographic data.
2.2. Modified frailty index (mFI)
The mFI was calculated by first creating a binary variable system. Each variable was ascribed with a value of 1, if present, or 0, if absent. These values were then added to determine each patient’s mFI score. Patients were grouped into one of four cohorts based off of their mFI score: 0, 1, 2, or ≥3. There were only 5333 patients with mFI = 3, 772 patients with mFI = 4, and 109 patients with mFI = 5. By comparison, there were 42,049 patients with mFI = 0, 39,216 patients with mFI = 1, and 22,094 patients with mFI = 2. Therefore, we combined mFI 3 through 5 into one cohort to make sizes more comparable for analysis, which is consistent with the precedence set by previous literature.9,14,25 This resulted in a cohort composed of patients with mFI scores ≥3, with a total of 6214 patients.
2.3. Outcomes of interest
Our primary outcome of interest was unplanned reoperation. In NSQIP, “unplanned reoperation” is defined as a surgical procedure related to the index or concurrent procedure performed within the 30-day postoperative period to any hospital or surgical facility.26 Secondary outcomes included discharge destination, major complications, wound disruption, and surgical site infection. Non-routine discharge was defined as discharge to a location other than the patient’s home (such as skilled care, acute care, rehabilitation facilities). Major complications were defined as presence of deep surgical site infection, sepsis, ventilator dependence >48 h, re-intubation, acute renal failure, deep vein thrombosis, pulmonary embolism, myocardial infarction, cardiac arrest, or cerebrovascular accident (Table 2).
Table 2.
Demographic data.
| Variable | Frequency (%) |
|---|---|
| Age | |
| ≤40 | 14,546 (13.3) |
| 41-60 | 22,087 (20.2) |
| 61-80 | 37,295 (34.0) |
| >81 | 35,645 (32.5) |
| Female | 71,867 (65.6) |
| Race | |
| Caucasian | 94,528 (86.3) |
| African American | 6961 (6.4) |
| Hispanic | 4106 (3.7) |
| Other | 3978 (3.6) |
| Complication | |
| Surgical Site Infection | 1250 (1.1) |
| Wound Dehiscence | 148 (0.1) |
| Unplanned Reoperation | 2536 (2.3) |
| Unplanned Readmission | 6248 (5.7) |
| Major Complication | 4848 (4.4) |
| Minor Complication | 6007 (5.5) |
| Discharge Destination Not Home | 48,148 (43.9) |
2.4. Statistical analysis
Bivariate analysis was conducted with Pearson’s chi square to determine the preoperative factors significantly associated with each outcome. Binary logistic regression models were then conducted to analyze the relationship between demographic and preoperative factors and frailty for each outcome. Only those preoperative factors that were significantly associated with each outcome were included in the regression models. Results were reported as an odds ratio (OR) with 95% confidence interval. Significance was defined as p < 0.05. Data were entered and analyzed using Statistical Package for the Social Science (SPSS) version 23 (International Business Machines, Corp., Armonk, NY).
3. Results
3.1. Demographics
Of the 140,249 fixation procedures performed on long bone fractures in NSQIP, 109,423 cases remained after exclusion criteria were applied. The majority of patients were between the ages of 61 and 80, female, and Caucasian. The most common complication was non-home discharge destination, while the least common complication was wound dehiscence (Table 2).
3.2. Unplanned reoperation
There were 2536 patients (2.3%) who underwent an unplanned reoperation. After accounting for age, sex, race, and other significant patient comorbidities, logistic regression revealed that the likelihood of unplanned reoperation increased with each increase in mFI score: OR = 1.34, 1.49, and 1.57 for mFI cohorts 1, 2, and ≤3, respectively (Table 3).
Table 3.
Predictors of major complication after logistic regression analysis.
| Variable | Wound Disruption |
Major Complication |
Unplanned Reoperation |
Unplanned Readmission |
Surgical Site Infection |
Non-Home Discharge |
|---|---|---|---|---|---|---|
| OR (95% CI) | OR (95% CI) | OR (95% CI) | OR (95% CI) | OR (95% CI) | OR (95% CI) | |
| Age >60 | 0.59 (0.39–0.88)* | 2.52 (2.27–2.79)* | 1.24 (1.11–1.38)* | 2.87 (2.21–3.73)* | 0.7 (0.61–0.81)* | 11.98 (11.46–12.53)* |
| Race | ||||||
| Caucasian (Reference) | ||||||
| African American | 1.34 (0.76–2.35) | 1.25 (1.11–1.41)* | 1.03 (0.87–1.21) | 0.8 (0.54–1.2) | 1.06 (0.86–1.31) | 0.8 (0.74–0.86)* |
| Hispanic | 0.77 (0.28–2.08) | 0.79 (0.66–0.95)* | 0.74 (0.58–0.95)* | 0.46 (0.3–0.72)* | 0.89 (0.65–1.21) | 0.55 (0.5–0.6)* |
| Other | 1.87 (0.95–3.69) | 0.86 (0.72–1.02) | 0.8 (0.63–1.02) | 1.41 (0.65–3.07) | 0.75 (0.53–1.07) | 0.61 (0.56–0.66) * |
| Female | 0.85 (0.61–1.2) | 0.73 (0.69–0.78)* | 0.84 (0.77–0.91)* | 0.89 (0.71–1.11) | 0.82 (0.73–0.92)* | 1.1 (1.06–1.14) * |
| Weight Loss | – | 1.4 (1.13–1.72) * | – | – | – | 1.79 (1.5–2.12) * |
| Preoperative Blood Transfusion | – | 1.45 (1.29–1.63) * | – | – | 1.09 (0.81–1.45) | – |
| Systemic Sepsis | – | 2.05 (1.9–2.22) * | 1.14 (1–1.3) | 1.43 (0.97–2.11) | 1.2 (1–1.45) | 2.33 (2.19–2.49) * |
| Corticosteroid Use | – | 1.32 (1.18–1.49) * | 1.62 (1.38–1.88)* | – | 1.48 (1.17–1.86)* | 1.4 (1.29–1.51) * |
| Open Wound/Wound Infection | 3.29 (2.06–5.26)* | 1.51 (1.35–1.68) * | 2.07 (1.81–2.38) * | – | 2.3 (1.9–2.77)* | 1.19 (1.1–1.29) * |
| On Dialysis | – | 1.8 (1.53–2.11) * | 2.16 (1.74–2.68) * | – | – | 2.49 (2.15–2.88) * |
| Emergency Services Used | – | 1.22 (1.14–1.31) * | 1.15 (1.04–1.26) * | 1.39 (1.05–1.85)* | – | 1.73 (1.67–1.8) * |
| Dyspnea | – | 1.51 (1.37–1.66) * | – | – | – | – |
| Bleeding Disorder | – | 1.34 (1.24–1.45) * | 1.16 (1.03–1.3) * | 1.57 (1.11–2.21)* | 1.24 (1.05–1.47) * | 2.2 (2.09–2.33) * |
| Ascites | – | 3.2 (2.2–4.65) * | 2.8 (1.68–4.64) * | – | – | 3.23 (2.06–5.07) * |
| Current Smoker | 1.51 (1.03–2.22)* | 0.81 (0.6–1.09) | 1.37 (1.24–1.52) * | 1.08 (0.82–1.43) | 1.4 (1.22–1.61) * | – |
| mFI Cohorts | ||||||
| 0 (Reference) | ||||||
| 1 | 2.49 (1.59–3.91)* | 1.55 (1.42–1.7) * | 1.34 (1.21–1.5)* | 1.44 (1.1–1.89)* | 1.41 (1.21–1.64) * | 2.06 (1.98–2.14) * |
| 2 | 2.83 (1.71–4.68)* | 2.04 (1.86–2.24) * | 1.49 (1.33–1.69) * | 1.87 (1.37–2.56)* | 1.77 (1.49–2.09) * | 2.63 (2.51–2.74) * |
| ≥3 | 3.08 (1.55–6.12)* | 3.04 (2.71–3.41) * | 1.57 (1.33–1.85) * | 2.12 (1.36–3.32)* | 1.90 (1.5–2.41) * | 3.06 (2.84–3.31) * |
* significance defined as p < 0.05, - not included due to failure to reach significance with chi square testing.
3.3. Wound dehiscence
There were 148 patients (0.1%) who experienced wound dehiscence. After accounting for age, sex, race, and other significant patient comorbidities, logistic regression revealed that the likelihood of experiencing wound dehiscence increased with each increase in mFI score: OR = 2.49, 2.83, and 3.08 for mFI cohorts 1, 2, and ≤3, respectively (Table 3).
3.4. Unplanned readmission
There were 6248 patients (5.7%) who experienced wound dehiscence. After accounting for age, sex, race, and other significant patient comorbidities, logistic regression revealed that the likelihood of having an unplanned readmission within 30 days increased with each increase in mFI score: OR = 1.44, 1.87, and 2.12 for mFI cohorts 1, 2, and ≤3, respectively (Table 3).
3.5. Surgical site infection
There were 1250 patients (1.1%) who experienced a surgical site infection. After accounting for age, sex, race, and other significant patient comorbidities, logistic regression revealed that the likelihood of having a surgical site infection within 30 days increased with each increase in mFI score: OR = 1.41, 1.77, and 1.90 for mFI cohorts 1, 2, and ≤3, respectively (Table 3).
3.6. Major complications
There were 4848 patients (4.4%) who experienced a major complication. After accounting for age, sex, race, and other significant patient comorbidities, logistic regression revealed that the likelihood of having a major complication within 30 days increased with each increase in mFI score: OR = 1.55, 2.04, and 3.04 for mFI cohorts 1, 2, and ≤3, respectively (Table 3).
3.7. Non-home discharge destination
There were 48,148 patients (43.9%) who were discharged to a location other than home. After accounting for age, sex, race, and other significant patient comorbidities, logistic regression revealed that the likelihood of being discharged to a location other than home increased with each increase in mFI score: OR = 2.06, 2.63, and 3.06 for mFI cohorts 1, 2, and ≤3, respectively (Table 3).
4. Discussion
The goal of our study was to determine the predictive value of the modified frailty index (mFI) on patient outcomes after fixation of long bone fractures. The mFI measures five easily obtained variables and may be applied clinically to stratify patients into high or low risk categories to allow for better-informed preoperative planning. After accounting for other variables, including age, race, and gender, patients with a frailty score of ≥3 are at increased risk of wound disruption, unplanned reoperation, unplanned readmission, surgical site infection, major complications, and discharge destination.
Recent literature suggests that the mFI is used to predict mortality, postoperative complications, and unplanned 30-day readmissions in a variety of surgical specialties.16 One study used the 5-item mFI to stratify risk in patients with surgically managed distal radius fractures, finding that patients with higher scores were significantly more likely to suffer a postoperative complication.9 Other studies reported similar increased risk in patients with higher mFI scores undergoing kyphoplasty vertebral augmentation,14 in the evaluation of geriatric hip fractures,17 total shoulder arthroplasty,25 and distal radius fractures.9 Similar trends have been found in other types of surgery, lending to its increased utilization across many disciplines and patient populations.20,27,28 Our data are consistent with the work of others in orthopaedic surgery, but is the first to describe the use of the mFI in patient with long bone fractures. While information regarding preoperative risk with fixation of individual bones is clinically useful, many risk factors are shared among patients with long bone fractures,29 thus warranting organization of these fractures into a collective group. Furthermore, given the inherent limitations of large database studies, namely the lack of detailed, granular data, classifying patients into larger groups reduces the risk of selection bias due to the inability to correct for various patient factors.
Our analysis shows that the mFI is predictive of postoperative complications after long bone fracture fixation. The previous mFI-11 stratifies patients based on eleven different factors, however, multiple studies have shown that the mFI-5 has similar predictive value.16,24 By using this tool during surgical planning, physicians and patients may make better informed decisions when it comes to discussing treatment and prognosis. In particular, it can provide clinically useful information about post-operative adverse events and play a significant role in determining discharge sequences from the hospital. Therefore, in frail patients, close observation is needed during the postoperative period, as is a tailored plan for follow-up after discharge.
There is an increased risk of experiencing post-operative surgical site infections and wound dehiscence in patients with higher mFI scores. In such patients, anticipating these outcomes may lead to appropriate implementation of preventative measures rather than retrospectively treating unexpected morbidity. It may also better inform patients of their risks for complications, allowing patients to take an active role in their health care, but this must be evaluated with future prospective studies.
Our study has several limitations. The data collected from the NSQIP database is retrospective in nature, which prevents any conclusions of cause and effect to be drawn; all of the results pertaining to mFI data are correlational. Further, there is an unavoidable sampling bias in that we are limited to the surgical outcomes recorded within the database. Long term sequelae of long bone fracture surgery like nonunion may not be reported because the database only includes complications and readmissions up to 30 days after the surgery, which could result in under-reporting. Furthermore, the mFI does not take into account the effect of socioeconomic factors, which has been shown to have an impact on postoperative outcomes of various fractures.19 However, the use of database information to assess postoperative risk through logistic regression analyses has been proven to yield valuable performance statistics.30 While our study highlights the possibility of using the mFI to identify high risk patients, it does not necessarily offer treatment options targeting potentially actionable comorbidities, as many of the factors involved in calculating mFI involve chronic medical conditions, such as a history of diabetes mellitus, hypertension, and chronic obstructive pulmonary disease. These are often not addressable in acute care settings, preventing clinicians from reducing preoperative risk and mitigating postoperative morbidity and mortality.
Using mFI as a tool to assess postoperative risks after long bone fracture surgery may be beneficial for orthopaedic surgeons to counsel patients and anticipate adverse events. The mFI can be rapidly and easily implemented by using information presented in the patient history, which may serve as a benefit in acute or time-sensitive situations. Therefore, it may have potential in serving as a convenient risk assessment tool in orthopaedic surgery.
Grants and funding
None.
Declaration of competing interest
The authors have declared that they have no conflicts of interest for this project. This manuscript has not been submitted to, nor is under review at, another journal or other publishing venue. The authors have no affiliation with any organization with a direct or indirect financial interest in the subject matter discussed in the manuscript.
Acknowledgements
None.
Appendix 1. Major and Minor Complication Definitions
| Grade | Definition and Variables |
|---|---|
| Major | Deep SSIa, sepsis, ventilator dependence ≥48 h, re-intubation, acute renal failure, deep vein thrombosis, pulmonary embolism, myocardial infarction, cardiac arrest, and cerebrovascular accident. |
| Minor | Superficial SSIa, wound disruption, pneumonia, urinary tract infection, and renal insufficiency. |
a = Surgical site infection.
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