What Are the Mortality, Infection, and Nonunion Rates After Periprosthetic Femoral Fractures in the United States?

BACKGROUND: Periprosthetic femoral fractures are a serious complication that put a high burden on patients. However, comprehensive analyses of their incidence, mortality, and complication rates based on large-registry data are scarce. QUESTIONS/PURPOSES: In this large-database study, we asked: (1) What is the incidence of periprosthetic femoral fractures in patients 65 years and older in the United States? (2) What are the rates of mortality, infection, and nonunion, and what factors are associated with these outcomes? METHODS: In this retrospective, comparative, large-database study, periprosthetic femoral fractures occurring between January 1, 2010, and December 31, 2019, were identified from Medicare physician service records encompassing services rendered in medical offices, clinics, hospitals, emergency departments, skilled nursing facilities, and other healthcare institutions from approximately 2.5 million enrollees. These were grouped into proximal, distal, and shaft fractures after TKA and THA. We calculated the incidence of periprosthetic femur fractures by year. Incidence rate ratios (IRR) were calculated by dividing the incidence in 2019 by the incidence in 2010. The Kaplan-Meier method with Fine and Gray subdistribution adaptation was used to calculate the cumulative incidence rates of mortality, infection, and nonunion. Semiparametric Cox regression was applied with 23 measures as covariates to determine factors associated with these outcomes. RESULTS: From 2010 to 2019, the incidence of periprosthetic femoral fractures increased steeply (TKA for distal fractures: IRR 3.3 [95% CI 1 to 9]; p = 0.02; THA for proximal fractures: IRR 2.3 [95% CI 1 to 4]; p = 0.01). One-year mortality rates were 23% (95% CI 18% to 28%) for distal fractures treated with THA, 21% (95% CI 19% to 24%) for proximal fractures treated with THA, 22% (95% CI 19% to 26%) for shaft fractures treated with THA, 21% (95% CI 18% to 25%) for distal fractures treated with TKA , 22% (95% CI 17% to 28%) for proximal fractures treated with TKA, and 24% (95% CI 19% to 29%) for shaft fractures treated with TKA. The 5-year mortality rate was 63% (95% CI 54% to 70%) for distal fractures treated with THA, 57% (95% CI 54% to 62%) for proximal fractures treated with THA, 58% (95% CI 52% to 63%) for shaft fractures treated with THA, 57% (95% CI 52% to 62%) for distal fractures treated with TKA , 57% (95% CI 49% to 65%) for proximal fractures treated with TKA, and 57% (95% CI 49% to 64%) for shaft fractures treated with TKA. Age older than 75 years, male sex, chronic obstructive pulmonary disease (HR 1.48 [95% CI 1.32 to 1.67] after THA and HR 1.45 [95% CI 1.20 to 1.74] after TKA), cerebrovascular disease after THA, chronic kidney disease (HR 1.28 [95% CI 1.12 to 1.46] after THA and HR 1.50 [95% CI 1.24 to 1.82] after TKA), diabetes mellitus, morbid obesity, osteoporosis, and rheumatoid arthritis were clinical risk factors for an increased risk of mortality. Within the first 2 years, fracture-related infections occurred in 5% (95% CI 4% to 7%) of patients who had distal fractures treated with THA, 5% [95% CI 5% to 6%]) of patients who had proximal fractures treated with THA, 6% (95% CI 5% to 7%) of patients who had shaft fractures treated with THA, 6% (95% CI 5% to 7%) of patients who had distal fractures treated with TKA , 7% (95% CI 5% to 9%) of patients who had proximal fractures treated with TKA, and 6% (95% CI 4% to 8%) of patients who had shaft fractures treated with TKA. Nonunion or malunion occurred in 3% (95% CI 2% to 4%) of patients with distal fractures treated with THA, 1% (95% CI 1% to 2%) of patients who had proximal fractures treated with THA, 2% (95% CI 1% to 3%) of patients who had shaft fractures treated with THA, 4% (95% CI 3% to 5%) of those who had distal fractures treated with TKA, , 2% (95% CI 1% to 4%) of those who had proximal fractures treated with TKA, and 3% (95% CI 2% to 4%) of those who had shaft fractures treated with TKA. CONCLUSION: An increasing number of periprosthetic fractures were observed during the investigated period. At 1 and 5 years after periprosthetic femur fracture, there was a substantial death rate in patients with Medicare. Conditions including cerebrovascular illness, chronic kidney disease, diabetes mellitus, morbid obesity, osteoporosis, and rheumatoid arthritis are among the risk factors for increased mortality. After the surgical care of periprosthetic femur fractures, the rates of fracture-related infection and nonunion were high, resulting in a serious risk to affected patients. Patient well-being can be enhanced by an interdisciplinary team in geriatric traumatology and should be improved to lower the risk of postoperative death. Additionally, it is important to ensure that surgical measures to prevent fracture-related infections are followed diligently. Furthermore, there is a need to continue improving implants and surgical techniques to avoid often-fatal complications such as fracture-associated infections and nonunion, which should be addressed in further studies. LEVEL OF EVIDENCE: Level III, therapeutic study.

Strikingly High Rates of Periprosthetic Joint Infection Following Revision Surgery for Periprosthetic Fractures Regardless of Surgery Timing.

BACKGROUND: Periprosthetic fractures following total hip arthroplasty (THA) often occur in the early postoperative period. Recent data has indicated that early revisions are associated with higher complication rates, particularly periprosthetic joint infection (PJI). The purpose of this study was to assess the effect of timing of periprosthetic fracture surgery on complication rates. We hypothesized that complication rates would be significantly higher in revision surgeries performed within 3 months of the index THA. METHODS: The Medicare Part A claims database was queried from 2010 to 2017 to identify patients who underwent surgery for a periprosthetic fracture following primary THA. Patients were divided based on time between index and revision surgeries: <1, 1 to 2, 2 to 3, 3 to 6, 6 to 9, 9 to 12, and >12 months. Complication rates were compared between groups using multivariate analyses to adjust for demographics, comorbidities, and types of revision surgery. RESULTS: Of 492,340 THAs identified, 4,368 (0.9%) had a subsequent periprosthetic fracture requiring surgery: 1,725 (39.4%) at <1 month, 693 (15.9%) at 1 to 2 months, 202 (4.6%) at 2 to 3 months, 250 (5.7%) at 3 to 6 months, 134 (3.1%) at 6 to 9 months, 85 (19.4%) at 9 to12 months, and 1,279 (29.3%) at >12 months. The risk of PJI was 11.0% in the <1 month group, 11.1% at 1 to 2 months, 7.9% at 2 to 3 months, 6.8% at 3 to 6 months, 8.2% at 6 to 9 months, 9.4% at 9 to 12 months, and 8.5% at >12 months (P = .12). Adjusting for confounding factors, risk of PJI following periprosthetic fracture surgery was similar regardless of timing (P > .05). Rates of subsequent dislocation and aseptic loosening were also similar regardless of timing. CONCLUSIONS: The risk of PJI following repeat surgery for a periprosthetic fracture was strikingly high regardless of timing (6.8 to 11.1%), underscoring the high-risk of complications.

Revision Surgery for Instability After Total Hip Arthroplasty: Does Timing Matter?

BACKGROUND: Instability is a common reason for revision surgery after total hip arthroplasty (THA). Recent studies suggest that revisions performed in the early postoperative period are associated with higher complication rates. The purpose of this study is to assess the effect of timing of revision for instability on subsequent complication rates. METHODS: The Medicare Part A claims database was queried from 2010 to 2017 to identify revision THAs for instability. Patients were divided based on time between index and revision surgeries: <1, 1-2, 2-3, 3-6, 6-9, 9-12, and >12 months. Complication rates were compared between groups using multivariate analyses to adjust for demographics and comorbidities. RESULTS: Of 445,499 THAs identified, 9298 (2.1%) underwent revision for instability. Revision THA within 3 months had the highest rate of periprosthetic joint infection (PJI): 14.7% at <1 month, 12.7% at 1-2 months, and 10.6% at 2-3 months vs 6.9% at >12 months (P < .001). Adjusting for confounding factors, PJI risk remained elevated at earlier periods: <1 month (adjusted odds ratio [aOR]: 1.84, 95% confidence interval [CI]: 1.51-2.23, P < .001), 1-2 months (aOR: 1.45, 95% CI: 1.16-1.82, P = .001), 2-3 months (aOR: 1.35, 95% CI: 1.02-1.78, P = .036). However, revisions performed within 9 months of index surgery had lower rates of subsequent instability than revisions performed >12 months (aOR: 0.67-0.85, P < .050), which may be due to lower rates of acetabular revision and higher rates of head-liner exchange in this later group. CONCLUSION: When dislocation occurs in the early postoperative period, delaying revision surgery beyond 3 months from the index procedure may be warranted to reduce risk of PJI.

Smoking, air pollution, and lung cancer risk in the Nurses' Health Study cohort: time-dependent confounding and effect modification.

The proportional hazards (PH) model is commonly used in epidemiology despite the stringent assumption of proportionality of hazards over time. We previously showed, using detailed simulation data, that the impact of a modest risk factor cannot be estimated reliably using the PH model in the presence of confounding by a strong, time-dependent risk factor. Here, we examine the same and related issues using a real dataset. Among 97,303 women in the prospective Nurses' Health Study cohort from 1994 through 2010, we used PH regression to investigate how effect estimates for cigarette smoking are affected by increasingly detailed specification of time-dependent exposure characteristics. We also examined how effect estimates for fine particulate matter (PM2.5), a modest risk factor, are affected by finer control for time-dependent confounding by smoking. The objective of this analysis is not to present a credible estimate of the impact of PM2.5 on lung cancer risk, but to show that estimates based on the PH model are inherently unreliable. The best-fitting model for cigarette smoking and lung cancer included pack-years, duration, time since cessation, and an age-by-pack-years interaction, indicating that the hazard ratio (HR) for pack-years was significantly modified by age. In the fully adjusted best-fitting model for smoking including pack-years, the HR per 10-µg/m3 increase in PM2.5 was 1.06 (95% confidence interval (CI) = 0.90, 1.25); the HR for PM2.5 in the full cohort ranged between 1.02 and 1.10 in models with other smoking adjustments, indicating a residual confounding effect of smoking. The HR for PM2.5 was statistically significant only among former smokers when adjusting for smoking pack-years (HR = 1.35, 95% CI = 1.00, 1.82 in the best-fitting smoking model), but not in models adjusting for smoking duration and average packs (pack-years divided by duration). The association between cumulative smoking and lung cancer is modified by age, and improved model fit is obtained by including multiple time-varying components of smoking history. The association with PM2.5 is residually confounded by smoking and modified by smoking status. These findings underscore limitations of the PH model and emphasize the advantages of directly estimating hazard functions to characterize time-varying exposure and risk. The hazard function, not the relative hazard, is the fundamental measure of risk in a population. As a consequence, the use of time-dependent PH models does not address crucial issues introduced by temporal factors in epidemiological data.

Corrosion Concerns? Trends in Metal-on-Polyethylene Total Hip Arthroplasty Revision Rates and Comparisons Against Ceramic-on-Polyethylene up to 10 Years of Follow-Up.

BACKGROUND: We compared the revision risk between metal-on-polyethylene (MOP) and ceramic-on-polyethylene (COP) total hip arthroplasty patients and evaluated temporal changes in short-term revision risks for MOP patients. METHODS: Primary MOP (n = 9480) and COP (n = 3620) total hip arthroplasties were evaluated from the Medicare data set (October 2005 to December 2015) for revision risk, with up to 10 years of follow-up using multivariate analysis. Temporal change in the short-term revision risk for MOP was evaluated (log-rank and Wilcoxon tests). RESULTS: Revision incidence was 3.8% for COP and 4.3% for MOP. MOP short-term revision risk did not change over time (P ≥ .844 at 1 year and .627 at 2 years). Dislocation was the most common reason for revision (MOP: 23.5%; COP: 24.8%). Overall adjusted revision risks were not different between MOP and COP up to 10 years of follow-up (P ≥ .181). CONCLUSIONS: Concerns with corrosion for metal heads do not appear to result in significantly elevated revision risk for MOP at up to 10 years. Corrosion does not appear as a primary reason for revision compared to other mechanisms.

What Are the Migration Patterns for U.S. Primary Total Joint Arthroplasty Patients?

BACKGROUND: Evaluation of total joint arthroplasty (TJA) patient-reported outcomes and survivorship requires that records of the index and potential revision arthroplasty procedure are reliably captured. Until the goal of the American Joint Replacement Registry (AJRR) of more-complete nationwide capture is reached, one must assume that patient migration from hospitals enrolled in the AJRR to nonAJRR hospitals occurs. Since such migration might result in loss to followup and erroneous conclusions on survivorship and other outcomes of interest, we sought to quantify the level of migration and identify factors that might be associated with migration in a specific AJRR population. QUESTIONS/PURPOSES: (1) What are the out-of-state and within-state migration patterns of U.S. Medicare TJA patients over time? (2) What patient demographic and institutional factors are associated with these patterns? METHODS: Hospital records of Medicare fee-for-service beneficiaries enrolled from January 1, 2004 to December 31, 2015, were queried to identify primary TJA procedures. Because of the nationwide nature of the Medicare program, low rates of loss to followup among Medicare beneficiaries, as well as long-established enrollment and claims processing procedures, this database is ideal for examining patient migration after TJA. We identified an initial cohort of 5.33 million TJA records from 2004 to 2016; after excluding patients younger than 65 years of age, those enrolled solely due to disability, those enrolled in a Medicare HMO, or residing outside the United States, the final analytical dataset consisted of 1.38 million THAs and 3.03 million TKAs. The rate of change in state or county of residence, based on Medicare annual enrollment data, was calculated as a function of patient demographic and institutional factors. A multivariate Cox model with competing risk adjustment was used to evaluate the association of patient demographic and institutional factors with risk of out-of-state or out-of-county (within-state) migration. RESULTS: One year after the primary arthroplasty, 0.61% (95% confidence interval [CI], 0.60-0.61; p < 0.001 for this and all comparisons in this Results section) of Medicare patients moved out of state and another 0.62% (95% CI, 0.60-0.63) moved to a different county within the same state. Five years after the primary arthroplasty, approximately 5.41% (95% CI, 5.39-5.44) of patients moved out of state and another 5.50% (95% CI, 5.46-5.54) Medicare patients moved to a different county within the same state. Among numerous factors of interest, women were more likely to migrate out of state compared with men (hazard ratios [HR], 1.06), whereas black patients were less likely (HR, 0.82). Patients in the Midwest were less likely to migrate compared with patients in the South (HR, 0.74). Patients aged 80 and older were more likely to migrate compared with 65- to 69-year-old patients (HR, 1.19). Patients with higher Charlson Comorbidity Index scores compared with 0 were more likely to migrate (index of 5+; HR, 1.19). CONCLUSIONS: Capturing detailed information on patients who migrate out of county or state, with associated changes in medical facility, requires a nationwide network of participating registry hospitals. At 5 years from primary arthroplasty, more than 10% of Medicare patients were found to migrate out of county or out of state, and the rate increases to 18% after 10 years. Since it must be assumed that younger patients might exhibit even higher migration levels, these findings may help inform public policy as a "best-case" estimate of loss to followup under the current AJRR capture area. Our study reinforces the need to continue aggressive hospital recruitment to the AJRR, while future research using an increasingly robust AJRR database may help establish the migration patterns of nonMedicare patients. LEVEL OF EVIDENCE: Level III, therapeutic study.

Does Timing of Primary Total Hip Arthroplasty Prior to or After Lumbar Spine Fusion Have an Effect on Dislocation and Revision Rates?

BACKGROUND: Patients undergoing primary total hip arthroplasty (THA) following lumbar spine fusion have an increased incidence of dislocation compared to those without prior lumbar fusion. The purpose of this study is to determine if timing of THA prior to or after lumbar fusion would have an effect on dislocation and revision incidence in patients with both hip and lumbar spine pathology. METHODS: One hundred percent Medicare inpatient claims data from 2005 to 2015 were used to compare dislocation and revision risks in patients with primary THA with pre-existing lumbar spine fusion vs THA with subsequent lumbar spine fusion within 1, 2, and 5 years after the index THA. A total of 42,300 patients met inclusion criteria, 28,668 patients of which underwent THA with pre-existing lumbar spinal fusion (LSF) and 13,632 patients who had prior THA and subsequent LSF. Patients who had THA first followed by LSF were further stratified based on the interval between index THA and subsequent LSF (1, 2, and 5 years), making 4 total groups for comparison. Multivariate cox regression analysis was performed adjusting for age, socioeconomic status, race, census region, gender, Charlson score, pre-existing conditions, discharge status, length of stay, and hospital characteristics. RESULTS: Patients with prior LSF undergoing THA had a 106% increased risk of dislocation compared to those with LSF done 5 years after THA (P < .001). Risk of revision THA was greater in the pre-existing LSF group by 43%, 41%, and 49% at 1, 2, and 5 years post THA compared to the groups with THA done first with subsequent LSF. Dislocation was the most common etiology for revision THA in all groups, but significantly higher in the prior LSF group (26.6%). CONCLUSION: Results of this study demonstrate that sequence of surgical intervention for concomitant lumbar and hip pathology requiring LSF and THA respectively significantly impacts the fate of the THA performed. Patients with prior LSF undergoing THA are at significantly higher risk of dislocation and subsequent revision compared to those with THA first followed by delayed LSF. LEVEL OF EVIDENCE: 3.

Reanalysis of Diesel Engine Exhaust and Lung Cancer Mortality in the Diesel Exhaust in Miners Study Cohort Using Alternative Exposure Estimates and Radon Adjustment.

The Diesel Exhaust in Miners Study (DEMS) (United States, 1947-1997) reported positive associations between diesel engine exhaust exposure, estimated as respirable elemental carbon (REC), and lung cancer mortality. This reanalysis of the DEMS cohort used an alternative estimate of REC exposure incorporating historical data on diesel equipment, engine horsepower, ventilation rates, and declines in particulate matter emissions per horsepower. Associations with cumulative REC and average REC intensity using the alternative REC estimate and other exposure estimates were generally attenuated compared with original DEMS REC estimates. Most findings were statistically nonsignificant; control for radon exposure substantially weakened associations with the original and alternative REC estimates. No association with original or alternative REC estimates was detected among miners who worked exclusively underground. Positive associations were detected among limestone workers, whereas no association with REC or radon was found among workers in the other 7 mines. The differences in results based on alternative exposure estimates, control for radon, and stratification by worker location or mine type highlight areas of uncertainty in the DEMS data.

An Assessment of the Cox Proportional Hazards Regression Model for Epidemiologic Studies.

The basic assumptions of the Cox proportional hazards regression model are rarely questioned. This study addresses whether hazard ratio, i.e., relative risk (RR), estimates using the Cox model are biased when these assumptions are violated. We investigated also the dependence of RR estimates on temporal exposure characteristics, and how inadequate control for a strong, time-dependent confounder affects RRs for a modest, correlated risk factor. In a realistic cohort of 500,000 adults constructed using the National Cancer Institute Smoking History Generator, we used the Cox model with increasing control of smoking to examine the impact on RRs for smoking and a correlated covariate X. The smoking-associated RR was strongly modified by age. Pack-years of smoking did not sufficiently control for its effects; simultaneous control for effect modification by age and time-dependent cumulative exposure, exposure duration, and time since cessation improved model fit. Even then, residual confounding was evident in RR estimates for covariate X, for which spurious RRs ranged from 0.980 to 1.017 per unit increase. Use of the Cox model to control for a time-dependent strong risk factor yields unreliable RR estimates unless detailed, time-varying information is incorporated in analyses. Notwithstanding, residual confounding may bias estimated RRs for a modest risk factor.

Are Ceramic Bearings Becoming Cost-Effective for All Patients?

BACKGROUND: The purpose of this study is to analyze whether the cost for ceramic-on-polyethylene (C-PE) and ceramic-on-ceramic (COC) bearings used in primary total hip arthroplasty (THA) was changing over time, and if the cost differential between ceramic bearings and metal-on-polyethylene (M-PE) bearings was approaching the previously published tipping point for cost-effectiveness of $325. METHODS: A total of 245,077 elderly Medicare patients (65+) who underwent primary THA between 2010 and 2015 were identified from the United States Medicare 100% national administrative hospital claims database. The inpatient hospital cost, calculated using cost-to-charge ratios, and hospital payment were analyzed. The differential cost of C-PE and COC bearings, compared to M-PE, were evaluated using parametric and nonparametric models. RESULTS: After adjustment for patient and clinical factors, and the year of surgery, the mean hospital cost and payments for primary THA with a C-PE or COC was within ±1% of the cost for primary THA with M-PE bearings (P < .001). From the nonparametric analysis, the median hospital cost was $318-$360 more for C-PE and COC than M-PE. The differential in median Medicare payment for THA with ceramic bearings compared to M-PE was <$100. Cost differentials were found to decrease significantly over time (P < .001). CONCLUSION: Patient and clinical factors had a far greater impact on the cost of inpatient THA surgery than bearing selection. Because we found that costs and cost differentials for ceramic bearings were decreasing over time, and approaching the tipping point, it is likely that the cost-effectiveness thresholds relative to M-PE are likewise changing over time and should be revisited in light of this study.

Are We Winning or Losing the Battle With Periprosthetic Joint Infection: Trends in Periprosthetic Joint Infection and Mortality Risk for the Medicare Population.

BACKGROUND: Periprosthetic joint infection (PJI) is a potentially deadly complication of total joint arthroplasty. This study was designed to address how the incidence of PJI and outcome of treatment, including mortality, are changing in the population over time. METHODS: Primary total hip arthroplasty (THA) and total knee arthroplasty (TKA) patients with PJI from the 100% Medicare inpatient data set (2005-2015) were identified. Cox proportional hazards regression models for risk of PJI after THA/TKA (accounting for competing risks) or risk of all-cause mortality after PJI were adjusted for patient and clinical factors, with year included as a covariate to test for time trends. RESULTS: The unadjusted 1-year and 5-year risk of PJI was 0.69% and 1.09% for THA and 0.74% and 1.38% for TKA, respectively. After adjustment, PJI risk did not change significantly by year for THA (P = .63) or TKA (P = .96). The unadjusted 1-year and 5-year overall survival after PJI diagnosis was 88.7% and 67.2% for THA and 91.7% and 71.7% for TKA, respectively. After adjustment, the risk of mortality after PJI decreased significantly by year for THA (hazard ratio = 0.97; P < .001) and TKA (hazard ratio = 0.97; P < .001). CONCLUSION: Despite recent clinical focus on preventing PJI, we are unable to detect substantial decline in the risk of PJI over time, although mortality after PJI has declined. Because PJI risk appears not to be changing over time, the incidence of PJI is anticipated to scale up proportionately with the demand for THA and TKA, which is projected to increase substantially in the coming decade.

Risk Adjustment Is Necessary in Medicare Bundled Payment Models for Total Hip and Knee Arthroplasty.

BACKGROUND: Concerns exist that high-risk patients in alternative payment models may face difficulties with access to care without proper risk adjustment. The purpose of this study is to identify the effect of medical and orthopedic specific risk factors on the cost of a 90-day episode of care following total hip (THA) and knee arthroplasty (TKA). METHODS: We queried the Medicare 5% Limited Data Set for all patients undergoing primary THA and TKA from 2010 to 2014. To evaluate the cost of an episode of care, we calculated all claims for 90 days following surgery. Multivariate analysis was performed to quantify the added episode-of-care costs for demographic variables, geography, medical comorbidities, and orthopedic specific risk factors. RESULTS: Of the 58,809 TKA patients, the median 90-day Medicare costs was $23,800 (interquartile range, $18,900-$32,300), while the median of the 27,293 THA patients was $24,000 (interquartile range, $18,500-$33,900). Independent risk factors (all P < .05) resulting in at least a 10% increase in episode-of-care costs following TKA included malnutrition, age over 85, male gender, pulmonary disorder, failed internal fixation, Northeast region, lower socioeconomic status, neurologic disorder, and rheumatoid arthritis. Independent risk factors (all P < .05) resulting in at least a 10% increase in episode-of-care costs following THA included malnutrition, male gender, age over 85, failed internal fixation, hip dysplasia, Northeast region, neurologic disorder, lower socioeconomic status, conversion THA, avascular necrosis, and depression. CONCLUSION: Certain comorbidities and orthopedic risk factors increase 90-day episode-of-care costs in the Medicare population. The current lack of proper risk stratification could be a powerful driver of decreased access to care for our most medically challenged members of society.

Risk of Opioid-Related Adverse Events After Primary and Revision Total Knee Arthroplasty.

Postoperative analgesia after primary total knee arthroplasty (TKA) and revision knee arthroplasty (RKA) can be reliant on the use of opioids and may lead to opioid-related adverse events (ORAEs). This study evaluated the risk of ORAEs following TKA and RKA using the 5% Medicare claims (2010-2013) database. There were 41,702 TKA and 3817 RKA patients who met the inclusion criteria. At 90 days, respiratory complications were the most common complication (TKA: 6.12%; RKA: 8.01%), followed by postoperative nausea and vomiting (TKA: 2.86%; RKA: 3.95%), and urinary retention complications (TKA: 2.79%; RKA: 3.20%). For TKA, risk factors for respiratory complications included older age, lower socioeconomic status, more comorbidities, obesity, chronic obstructive pulmonary disease, white race, and patients in the Midwest and West (vs. South) (p 002). The average Medicare payment for treating complications within 90 days of a TKA was $6206 and $6222 following RKA. Awareness risks for ORAEs in select patients can assist in developing a multimodal postoperative pain control and patient education protocols. (Journal of Surgical Orthopaedic Advances 27(2):148-154, 2018).

Therapeutic radiation for lymphoma and risk of second primary malignant mesothelioma.

PURPOSE: This large, population-based U.S. study of lymphoma patients followed for up to four decades enables detailed analysis of second primary mesothelioma risk after radiotherapy. METHODS: U.S. Surveillance, Epidemiology, and End Results data were used to identify second primary mesothelioma among patients diagnosed with Hodgkin lymphoma (HL) or non-Hodgkin lymphoma (NHL) between 1973 and 2014. Standardized incidence ratios (SIRs) were calculated by radiotherapy. Multivariate adjusted associations were examined using competing risks survival analysis. RESULTS: Among 47,219 HL patients (19,538 irradiated) and 252,090 NHL patients (52,454 irradiated), second primary mesothelioma developed among 28 lymphoma patients who received radiotherapy and 59 who did not. Mesothelioma risk was increased among HL and NHL patients treated with radiotherapy [SIR = 1.78, 95% confidence interval (CI) 1.18-2.58], but not without radiotherapy. After multivariate adjustment, radiotherapy was associated with increased mesothelioma risk (relative risk = 1.64, 95% CI 1.05-2.57), especially in lymphoma patients diagnosed before 1995 and after a latency of at least 10 years, and apparently with younger age at diagnosis. CONCLUSIONS: The increase in second primary mesothelioma risk following radiotherapy for lymphoma is independent of several patient and disease characteristics, and is higher with earlier treatment era and longer latency.

Which Clinical and Patient Factors Influence the National Economic Burden of Hospital Readmissions After Total Joint Arthroplasty?

BACKGROUND: The Affordable Care Act of 2010 advanced the economic model of bundled payments for total joint arthroplasty (TJA), in which hospitals will be financially responsible for readmissions, typically at 90 days after surgery. However, little is known about the financial burden of readmissions and what patient, clinical, and hospital factors drive readmission costs. QUESTIONS/PURPOSES: (1) What is the incidence, payer mix, and demographics of THA and TKA readmissions in the United States? (2) What patient, clinical, and hospital factors are associated with the cost of 30- and 90-day readmissions after primary THA and TKA? (3) Are there any differences in the economic burden of THA and TKA readmissions between payers? (4) What types of THA and TKA readmissions are most costly to the US hospital system? METHODS: The recently developed Nationwide Readmissions Database from the Healthcare Cost and Utilization Project (2006 hospitals from 21 states) was used to identify 719,394 primary TJAs and 62,493 90-day readmissions in the first 9 months of 2013 based on International Classification of Diseases, 9th Revision, Clinical Modification codes. We classified the reasons for readmissions as either procedure- or medical-related. Cost-to-charge ratios supplied with the Nationwide Readmissions Database were used to compute the individual per-patient cost of 90-day readmissions as a continuous variable in separate general linear models for THA and TKA. Payer, patient, clinical, and hospital factors were treated as covariates. We estimated the national burden of readmissions by payer and by the reason for readmission. RESULTS: The national rates of 30- and 90-day readmissions after THA were 4% (95% confidence interval [CI], 4.2%-4.5%) and 8% (95% CI, 7.5%-8.1%), respectively. The national rates of 30- and 90-day readmissions after primary TKA were 4% (95% CI, 3.8%-4.0%) and 7% (95% CI, 6.8%-7.2%), respectively. The five most important variables responsible for the cost of 90-day THA readmissions (in rank order, based on the Type III F-statistic, p < 0.001) were length of stay (LOS), all patient-refined diagnosis-related group (APR DRG) severity, type of readmission (that is, medical- versus procedure-related), hospital ownership, and age. Likewise, the five most important variables responsible for the cost of 90-day TKA readmissions were LOS, APR DRG severity, gender, hospital procedure volume, and hospital ownership. After adjusting for covariates, mean 90-day readmission costs reimbursed by private insurance were, on average, USD 1324 and USD 1372 greater than Medicare (p < 0.001) for THA and TKA, respectively. In the 90 days after TJA, two-thirds of the total annual readmission costs were covered by Medicare. In 90 days after THA, more readmissions were still associated with procedure-related complications, including infections, dislocations, and periprosthetic fractures, which in aggregate account for 59% (95% CI, 59.1%-59.6%) of the total readmission costs to the US healthcare system. For TKA, 49% of the total readmission cost (95% CI, 48.8%-49.6%) in 90 days for the United States was associated with procedure issues, most notably including infections. CONCLUSIONS: Hospital readmissions up to 90 days after TJA represent a massive economic burden on the US healthcare system. Approximately half of the total annual economic burden for readmissions in the United States is medical and unrelated to the joint replacement procedure and half is related to procedural complications. CLINICAL RELEVANCE: This national study underscores LOS during readmission as a primary cost driver, suggesting that hospitals and doctors further optimize, to the extent possible, the clinical pathways for the hospitalization of readmitted patients. Because patients readmitted as a result of infection, dislocation, and periprosthetic fractures are the most costly types of readmissions, efforts to reduce the LOS for these types of readmissions will have the greatest impact on their economic burden. Additional clinical research is needed to determine the extent to which, if any, the LOS during readmissions can be reduced without sacrificing quality or access of care.

Has Health Care Reform Legislation Reduced the Economic Burden of Hospital Readmissions Following Primary Total Joint Arthroplasty?

BACKGROUND: The purpose of this study was to determine whether the cost of readmissions after primary total hip and knee arthroplasty (THA and TKA) has decreased since the introduction of health care reform legislation and what patient, clinical, and hospital factors drive such costs. METHODS: The 100% Medicare inpatient dataset was used to identify 1,654,602 primary THA and TKA procedures between 2010 and 2014. The per-patient cost of readmissions was evaluated in general linear models in which the year of surgery and patient, clinical, and hospital factors were treated as covariates in separate models for THA and TKA. RESULTS: The year-to-year risk of 90-day readmission was reduced by 2% and 4% (P < .001) for THA and TKA, respectively. By contrast, the cost of readmissions did not change significantly over time. The 5 most important variables associated with the cost of 90-day THA readmissions (in rank order) were the nature of the readmission (ie, due to medical or procedure-related reasons), the length of stay, hospital's teaching status, discharge disposition, and hospital's overall total joint arthroplasty volume. The top 5 factors associated with the cost of 90-day TKA readmissions were (in rank order) the length of stay, hospital's teaching status, discharge disposition, patient's gender, and age. CONCLUSION: Although readmission rates declined slightly, the results of this study do not support the hypothesis that readmission costs have decreased since the introduction of health care reform legislation. Instead, we found that clinical and hospital factors were among the most important cost drivers.

Surgical Risks and Costs of Care are Greater in Patients Who Are Super Obese and Undergoing THA.

BACKGROUND: Patients with morbid obesity, defined as a BMI greater than 40 kg/m2, and super obesity, defined as a BMI greater than 50 kg/m2, increasingly present for total hip replacement. There is disagreement in the literature whether these individuals have greater surgical risks and costs for the episode of care, and the magnitude of those risks and costs. There also is no established threshold for obesity as defined by BMI in identifying increased complications, risks, and costs of care. Until recently, analysis of higher BMI data was limited to small cohorts from hospital-based data banks, based on BMI or height and weight only, often as part of a multivariate analysis. On October 1, 2010 the Centers for Medicare & Medicaid Services added a fifth digit to the BMI data, V85.xx, in the Medicare data bank, which allowed data mining of cases of patients with higher BMI. To our knowledge, our study is the first large retrospective Medicare data mining study, which allows us to examine BMI levels greater than 40 and 50 kg/m2 to delineate risks, complications, and costs for these patients. QUESTIONS/PURPOSES: We sought to quantify (1) the surgical risk, and (2) the costs associated with complications after THA in patients who were morbidly obesity (BMI ≥ 40 kg/m2) or super obese (BMI ≥ 50 kg/m2). METHODS: This is a retrospective study of patients, using Medicare hospital claims data, who underwent THA. The ICD-9 Clinical Modification (CM) diagnosis code V85.4x was used to identify patients with morbid obesity and with super obesity from October 1, 2010 through December 31, 2014. Patients without any BMI-related diagnosis codes were used as the control group. Twelve complications occurring during the 90 days after THA were analyzed using multivariate Cox models adjusting for patient demographic, comorbidities, and institutional factors. In addition, hospital charges and payments were compared from primary surgery through the subsequent 90 days. RESULTS: Patients with morbid obesity had increased postoperative complications including prosthetic joint infection (hazard ratio [HR], 3.71; 95% CI, 3.2-4.31; p < 0.001), revision (HR, 1.91; 95% CI, 1.69-2.16; p < 0.001), and wound dehiscence (HR, 3.91; 95% CI, 3.14-4.86; p < 0.001). In addition, patients with morbid obesity had increased risk of deep vein thrombosis (HR, 1.43; 95% CI, 1.14-1.79; p < 0.002), pulmonary embolism (HR, 1.57; 95% CI, 1.25-1.99; p < 0.001), implant failure (HR, 1.48; 95% CI, 1.3-1.68; p < 0.001), acute renal failure (HR, 1.68; 95% CI, 1.56-1.80; p < 0.001), and all-cause readmission (HR, 1.48; 95% CI, 1.40-1.56; p < 0.001). However, death (HR, 0.94 95% CI, 0.73-1.19 p < 0.592), acute myocardial infarction (HR, 0.94; 95% CI, 0.74-1.2 p < 0.631), and dislocation (HR 1.07; 95% CI, 0.85-1.34; p < 0.585) were not different between patients in the control and morbidly obese groups. Super obese patients had an increased risk of infection (HR, 6.48; 95% CI, 4.54-9.25; p < 0.001), wound dehiscence (HR, 9.81; 95% CI, 6.31-15.24; p < 0.001), and readmission (HR, 2.16; 95% CI, 1.84-2.54; p < 0.001) compared with patients with normal BMI. Controlling for patient and institutional factors, each THA had mean total hospital charges of USD 88,419 among patients who were super obese compared with USD 73,827 for the control group, a difference of USD 14,591. Medicare payment for the patients who were super obese also was higher, but only by USD 3631. CONCLUSIONS: Patients who are super obese are at increased risk for serious complications compared with patients with morbid obesity, whose risks are elevated relative to patients whose BMI is less than 40 kg/m2. Costs of care for patients who were super obese, likewise, were increased. We present BMI outcomes to allow an objective basis for patient counseling, risk stratification, maintaining access to orthopaedic surgical care, and maintaining hospital operating margins. LEVEL OF EVIDENCE: Level III, therapeutic study.

Outcomes of Ceramic Bearings After Revision Total Hip Arthroplasty in the Medicare Population.

BACKGROUND: The purpose of this study was to analyze the utilization and outcomes of ceramic bearings used in revision total hip arthroplasty (R-THA) in the Medicare population. METHODS: A total of 31,809 patients aged >65 years at the time of revision surgery who underwent R-THA between 2005 and 2013 were identified from the United States Medicare 100% national administrative claims database. Outcomes of interest included relative risk of readmission (90 days) or infection, dislocation, rerevision, or mortality at any time point after revision. Propensity scores were developed to adjust for selection bias in the choice of bearing type at revision surgery. RESULTS: The utilization of ceramic-on-polyethylene (C-PE) and ceramic-on-ceramic (COC) bearings in R-THA increased from 5.3% to 26.6% and from 1.8% to 2.5% in between 2005 and 2013, respectively. For R-THA patients treated with C-PE bearings, there was reduced risk of 90-day readmission (hazard ratio, HR: 0.90, P = .007). We also observed a trend for reduced risk of infection with C-PE (HR: 0.88) that did not reach statistical significance (P = .14). For R-THA patients treated with COC bearings, there was reduced risk of dislocation (HR: 0.76, P = .04). There was no significant difference in risk of rerevision or mortality for either the C-PE or COC bearing cohorts when compared with the metal-on-polyethylene bearing cohort. CONCLUSION: Medicare patients treated in a revision scenario with ceramic bearings exhibit similar risk of rerevision, infection, or mortality as those treated with metal-on-polyethylene bearings. Conversely, we found an association between the use of specific ceramic bearings in R-THA and reduced risk of readmission (C-PE) and dislocation (COC).

Risk and Cost of 90-Day Complications in Morbidly and Superobese Patients After Total Knee Arthroplasty.

BACKGROUND: This study investigated the risk and cost of postoperative complications associated with morbid and super obesity after total knee arthroplasty (TKA). METHODS: A retrospective cohort study was conducted of patients who underwent TKA using Medicare hospital claims data. The International Classification of Diseases, Ninth Revision, Clinical Modification diagnosis code V85.4x was used to identify morbidly obese patients (body mass index [BMI] ≥40 kg/m(2)) and superobese patients (BMI ≥50 kg/m(2)) in 2011-2013. Patients without any BMI-related diagnosis codes were used as controls. Twelve complications occurred in the 90-day period after TKA were analyzed using multivariate Cox models, adjusting for patient demographic, morbidity, and institutional factors. In addition, hospital charges and payments were compared from primary surgery through subsequent 90 days. RESULTS: Morbidly obese patients showed a significantly elevated risk in most complications examined, with a 2-fold or higher risk in dislocation and wound dehiscence. In addition, death, periprosthetic joint infection, acute renal failure, and knee revision had significant hazard ratios between 1.5 and 2.0. However, risk of deep vein thrombosis and acute myocardial infarction did not increase for the morbidly obese patients. Superobese patients had significant increase in risk of infection, wound dehiscence, acute renal failures, revisions, death, and readmission compared with patients with BMI 40-49 kg/m(2). Significant dose-response trend was found between the level of BMI and risk for death, dislocation, implant failure, infection, readmission, revision, wound dehiscence, and acute renal failure. Controlling for patient and institutional factors, each TKA had an average total hospital charges of $75,884 among superobese patients, compared to $65,118 for the control group, a difference of $10,767. Medicare payment for the superobese patients was also higher, but only by $2703. CONCLUSION: Morbidly obese patients pose a significantly higher risk profile than normal-weight patients in a broad range of complications after TKA. Superobese patients add another layer of risk compared with less obese patients and are considerably more expensive to treat by health care systems. Technical difficulties and the high demand on resources present a severe challenge for providing treatment for such patients.

Which Hospital and Clinical Factors Drive 30- and 90-Day Readmission After TKA?

BACKGROUND: The purpose of this study was to analyze the hospital, clinical, and patient factors associated with inpatient readmission after total knee arthroplasty (TKA) in the Medicare population and to understand the primary reasons for readmission. METHODS: The Medicare 100% national hospital claims database was used to identify 952,593 older patients (65+) with a primary TKA in 3848 hospitals between 2010 and 2013. A multilevel logistic regression analysis with a clustered data structure was used to investigate the risk of all-cause 30- and 90-day readmission, incorporating hospital, clinical, and patient factors. RESULTS: At 30 days, readmission ranged from 0% to 22% (median, 4.9%), whereas at 90 days, readmission ranged from 0% to 32% (median, 8.6%). Geographic census region, hospital procedure volume, rural hospital location, and nonprofit ownership were the only significant hospital factors among those we studied. Evaluation of clinical factors showed use of a perioperative transfusion was associated with 13% greater risk; patients discharged to home had 25% lower risk; and surgeon volume and length of stay were also significant. These effect sizes were at least comparable to patient factors, such as age, gender, comorbidities, and socioeconomic status. The top 5 most frequently reported primary reasons for 30- or 90-day readmission in TKA were surgery and medical related: wound infection, deep infection, atrial fibrillation, cellulitis and abscess of leg, or pulmonary embolism. CONCLUSION: The results of this study support further optimization of anti-infection measures, both intraoperative and postoperative, to reduce the broad variation in hospital readmissions.