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Original Contribution |

Total Knee Arthroplasty Volume, Utilization, and Outcomes Among Medicare Beneficiaries, 1991-2010 FREE

Peter Cram, MD, MBA; Xin Lu, MS; Stephen L. Kates, MD; Jasvinder A. Singh, MD, MPH; Yue Li, PhD; Brian R. Wolf, MD, MS
[+] Author Affiliations

Author Affiliations: Division of General Internal Medicine, Department of Internal Medicine (Dr Cram and Ms Lu) and Department of Orthopaedic Surgery (Dr Wolf), University of Iowa Carver College of Medicine, Iowa City; CADRE, Iowa City Veterans Administration Medical Center, Iowa City (Dr Cram); Departments of Orthopaedic Surgery (Dr Kates) and Community and Preventive Medicine (Dr Li), University of Rochester, Rochester, New York; and Department of Medicine, University of Alabama at Birmingham and Birmingham Veterans Affairs Medical Center (Dr Singh).


JAMA. 2012;308(12):1227-1236. doi:10.1001/2012.jama.11153.
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Published online

Context Total knee arthroplasty (TKA) is one of the most common and costly surgical procedures performed in the United States.

Objective To examine longitudinal trends in volume, utilization, and outcomes for primary and revision TKA between 1991 and 2010 in the US Medicare population.

Design, Setting, and Participants Observational cohort of 3 271 851 patients (aged ≥65 years) who underwent primary TKA and 318 563 who underwent revision TKA identified in Medicare Part A data files.

Main Outcome Measures We examined changes in primary and revision TKA volume, per capita utilization, hospital length of stay (LOS), readmission rates, and adverse outcomes.

Results Between 1991 and 2010 annual primary TKA volume increased 161.5% from 93 230 to 243 802 while per capita utilization increased 99.2% (from 31.2 procedures per 10 000 Medicare enrollees in 1991 to 62.1 procedures per 10 000 in 2010). Revision TKA volume increased 105.9% from 9650 to 19 871 while per capita utilization increased 59.4% (from 3.2 procedures per 10 000 Medicare enrollees in 1991 to 5.1 procedures per 10 000 in 2010). For primary TKA, LOS decreased from 7.9 days (95% CI, 7.8-7.9) in 1991-1994 to 3.5 days (95% CI, 3.5-3.5) in 2007-2010 (P < .001). For primary TKA, rates of adverse outcomes resulting in readmission remained stable between 1991-2010, but rates of all-cause 30-day readmission increased from 4.2% (95% CI, 4.1%-4.2%) to 5.0% (95% CI, 4.9%-5.0%) (P < .001). For revision TKA, the decrease in hospital LOS was accompanied by an increase in all-cause 30-day readmission from 6.1% (95% CI, 5.9%-6.4%) to 8.9% (95% CI, 8.7%-9.2%) (P < .001) and an increase in readmission for wound infection from 1.4% (95% CI, 1.3%-1.5%) to 3.0% (95% CI, 2.9%-3.1%) (P < .001).

Conclusions Increases in TKA volume have been driven by both increases in the number of Medicare enrollees and in per capita utilization. We also observed decreases in hospital LOS that were accompanied by increases in hospital readmission rates.

Figures in this Article

Total knee arthroplasty (TKA) is a common and safe procedure typically performed for relief of symptoms in patients with severe knee arthritis. Available data suggest that approximately 600 000 TKA procedures are performed annually in the United States at a cost of approximately $15 000 per procedure ($9 billion per year in aggregate).14 While TKA does not typically reduce mortality, the procedure results in marked improvements in health-related quality of life and functional status and is highly cost-effective.2,5 Total knee arthroplasty is now among the most common major surgical procedures performed in the United States.6

The increase in TKA can be viewed as an indication of the success of this procedure in safely reducing pain and improving functional status for an aging population.7,8 However, the increase in TKA can also be viewed as yet another source of strain on government, insurers, individuals, and businesses struggling with unremitting growth in health care spending.911 Despite the clinical and economic policy importance of TKA, there are few analyses evaluating recent trends over time in use of and outcomes associated with TKA.1,1214

Thus, the primary objective of our study was to evaluate longitudinal trends in primary and revision TKA volume, per capita utilization, and outcomes in the US Medicare population. The secondary objective was to examine patient and hospital factors associated with increased risk for hospital readmission given the growing likelihood of bundled payments for orthopedics in the near future.15,16

Data

We linked 2 sequential Medicare Provider Analysis and Review (MedPAR) Part A data files (the first covering the period from 1991-2005 and the second from 2006-2010), each containing a 100% sample of hospitalizations for fee-for-service beneficiaries. These data were used to identify all enrollees aged 65 years and older who underwent primary or revision TKA between 1991 and 2010. Patients were identified using International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM) procedure codes 81.54 for primary and codes 80.06, 81.55, 00.80, 00.81, 00.82, 00.83, and 00.84 for revision TKA.1720

The Part A files contain a range of data collected from discharge abstracts for all hospitalized fee-for-service Medicare enrollees including patient demographics; ICD-9-CM codes for primary and secondary diagnoses and procedures; admission source (eg, emergency department or transfer from outside hospital); admission and discharge dates; discharge disposition (eg, home, nursing home, inpatient rehabilitation, transfer to another acute care hospital, dead); death occurring up to 3 years after discharge; each patient's unique Medicare beneficiary number allowing for identification of patient readmissions; and each hospital's unique 6-digit identification number. Comorbid conditions present on the index admission were identified using algorithms described by Elixhauser et al,21,22 which consider 30 specific conditions and exclude comorbid conditions that may represent complications of care or that are related to the primary reason for hospitalization. This project was approved by the University of Iowa Institutional Review Board.

Our intention was to examine changes in volume, utilization, and outcomes of patients undergoing primary and revision TKA procedures. To generate appropriate analytical cohorts, we applied several inclusion and exclusion criteria (eFigure 1 and eFigure 2). First, we excluded Medicare HMO enrollees because the MedPAR data are incomplete for enrollees in such plans.

Second, we limited our cohort to the first primary (or revision) TKA performed on a given patient during any 30-day period using methods we have described previously.23 We also excluded bilateral or staged procedures that occurred within the 30-day window; this exclusion is necessary because Medicare data historically have not included sidedness for a specific procedure. Thus, for a patient who underwent 2 primary TKA procedures in close temporal proximity, it is impossible to know if this represented an initial primary procedure followed by an early complication requiring a second procedure or a planned bilateral (ie, staged) procedure.

Third, because primary TKA is most often an elective procedure whereas revision TKA can be either an elective or more urgent procedure, we applied separate exclusion criteria to the primary and revision TKA populations in accordance with prior studies as described below. For primary TKA (eFigure 1), we sequentially excluded patients admitted through the emergency department (n = 18 497) and patients admitted after transfer from another acute care hospital (n = 3295); these exclusion criteria were developed to select a population of primary elective TKA patients. The revision TKA population (eFigure 2) did not exclude these types of patients because revision TKA can be an emergent or unscheduled procedure and thus exclusion of these populations would not make sense.

Statistical Analysis

We examined the demographic characteristics and prevalence of key comorbid conditions for patients who underwent TKA between 1991 and 2010; for simplicity, data are presented separately for each 4-year period (eg, 1991-1994, 1995-1998, etc). We examined changes in the mean number of comorbid conditions per patient during each 4-year period. We used analysis of variance for comparisons of continuous variables and the χ2 test for categorical variables and tested for differences in linear trends. All analyses were performed separately for primary and revision TKA patients.

We used graphical methods to plot the annual primary and revision TKA Medicare volume over time. We calculated per capita TKA utilization rates by dividing the number of procedures performed each year by the number of beneficiaries enrolled in the fee-for-service Medicare program and plotted these results graphically.

We compared linear trends in several important outcomes of interest for primary and revision TKA: hospital length of stay (LOS); discharge disposition; selected arthroplasty complications resulting in readmission within 30 days of discharge; and all-cause readmission rates within 30 days of discharge. Discharge disposition was categorized as home, skilled or intermediate care (which also incorporated outpatient rehabilitation), inpatient rehabilitation, and other. We examined changes in the rates of 6 separate adverse outcomes occurring during the index admission (mortality) or readmission within 30 days of discharge (mortality, pulmonary embolism, deep vein thrombosis, wound infection, postoperative sepsis, and myocardial infarction) that have been examined in prior studies of arthroplasty using administrative data.20,24,25

We also examined changes in rates of a composite outcome representing the occurrence of one or more of the individual adverse outcomes as well as all-cause readmission within 30 days of discharge. To evaluate the reasons for readmission among the primary and revision TKA cohorts, we applied the Agency for Healthcare Research and Quality (AHRQ) Clinical Classification Software (CCS).26 This software synthesizes more than 14 000 ICD-9-CM codes into 231 mutually exclusive clinically meaningful disease categories. For each 4-year study period, we examined the 5 most common categories associated with readmission and the proportion of all readmissions during each period that were associated with each category; this allowed us to examine how the causes of readmission have changed over time.

We used standard logistic regression to calculate risk-adjusted 30-day readmission rates and composite outcome [(observed/adjusted) × unadjusted 20-year rates] and used standard linear regression to calculate risk-adjusted hospital LOS [(observed −adjusted) + unadjusted 20-year LOS].27 These models adjusted for age (categorized as 65-69 years, 70-74, 75-79, and ≥80 years), sex, race (categorized as white, black, and other), and comorbidities to account for the changing demographics of the TKA populations over time.28 Race was included in these models to allow us to account for previously documented racial disparities in joint arthroplasty when calculating standardized utilization rates for our analysis.29,30 We used graphical methods to plot discharge disposition, hospital LOS, readmission rates, and composite outcome between 1991 and 2010. All analyses were conducted separately for the primary and revision TKA cohorts.

We conducted several supplemental analyses of interest. Focusing on the most recent 4 years of data (2007-2010), we examined the relationship between patient and hospital factors and hospital readmission; as in prior analyses, primary and revision TKA were examined separately. We used bivariate methods to compare differences in patient and hospital factors among patients who did and did not experience readmission within 30 days of discharge. We then examined both patient-level and hospital-level factors that may have affected the 30-day readmission rate by employing a series of 4 standard logistic regression models that progressively adjusted for an increasing array of factors. In all models, the dependent variable was a binary variable with the value of 1 if a given patient was readmitted and 0 if not.

Model 1 adjusted for patient demographics alone (ie, age, race, sex); model 2 added adjustment for the number of comorbidities; model 3 added adjustment for hospital teaching status (major, minor, and nonteaching) and hospital procedural volume (calculated separately for the primary and revision TKA cohort and categorized by hospital volume quartiles); and model 4 added additional adjustment for each patient's hospital LOS, modeled in its log-transformed state. In all 4 models, we also included calendar year (2007, 2008, 2009, and 2010) to account for underlying temporal trends. We conducted several sensitivity analyses. In particular, we repeated our analyses after adding back excluded populations (eg, primary TKA cases admitted through the emergency department). We also repeated our analyses looking at 90-day outcomes rather than 30-day outcomes.

All P values are 2-tailed, with P <.05 deemed statistically significant. All statistical analyses were performed using SAS version 9.2.

Our final study population included 3 271 851 elective primary TKAs and 318 563 revision TKAs between 1991 and 2010. The total number of fee-for-service Medicare enrollees increased from 29 892 351 in 1991 to 39 250 746 in 2010, whereas the number of primary TKA procedures increased from 93 230 in 1991 to 243 802 in 2010 (an increase of 161.5%) (Figure 1). The number of revision TKA procedures increased from 9650 in 1991 to 19 871 in 2010 (an increase of 105.9%) (Figure 1).

Place holder to copy figure label and caption
Figure 1. Primary and Revision Total Knee Athroplasty Medicare Volume Between 1991 and 2010
Graphic Jump Location

Y-axis shown in blue indicates range from 0 to 25 000 procedures per year.

During the same period, the per capita utilization of primary TKA increased by 99.2% (Figure 2) and the per capita utilization of revision TKA increased by 56.8% (Figure 2). For primary TKA, the mean (SD) age increased from 73.8 (5.8) years (95% CI, 73.8-73.8 years) in 1991-1994 to 74.2 (6.2) years (95% CI, 74.2-74.2 years) in 2007-2010, (P <.001). The prevalence of diabetes increased from 10.5% (95% CI, 10.4%-10.6%) to 21.7% (95% CI, 21.6%-21.7%) and the prevalence of obesity increased from 4.0% (95% CI, 3.9%-4.0%) to 11.5% (95% CI, 11.4%-11.6%; P < .001 for each). Trends were similar for revision TKA (Table 1). In particular, the mean (SD) age increased from 74.2 (5.9) years (95% CI, 74.1-74.3 years) in 1991-1994 to 74.8 (6.5) years (95% CI, 74.7-74.8 years) in 2007-2010 (P <.001). The prevalence of diabetes increased from 11.0% (95% CI, 10.7%-11.3%) to 24.2% (95% CI, 23.9%-24.5%) and the prevalence of obesity increased from 3.7% (95% CI, 3.5%-3.8%) to 10.1% (95% CI, 9.9%-10.3%) (P < .001 for each).

Place holder to copy figure label and caption
Figure 2. Primary and Revision Medicare Total Knee Athroplasty Utilization Between 1991 and 2010
Graphic Jump Location

Y-axis shown in blue indicates range from 0 to 7 procedures per year per 10 000 enrollees. The range of variability (1 SD) in primary total knee arthroplasty (TKA); procedures per 10 000 Medicare enrollees for 1991 was 31.0 to 31.4; for 2010, 5.0 to 5.1. For revision TKA, the range of variability (1 SD; procedures per 10 000 Medicare enrollees) for 1991 was 3.2 to 3.3; for 2010, 5.0 to 5.1.

Table Graphic Jump LocationTable 1. Characteristics of Medicare Beneficiaries Receiving Primary and Revision Total Knee Arthroplasty (TKA) Between 1991 and 2010a

For primary TKA, the mean hospital LOS declined from 7.9 days (95% CI, 7.8-7.9) in 1991-1994 to 3.5 days (95% CI, 3.5-3.5) in 2007-2010 (Table 2), a relative decline of 55.7% (P < .001). The percentage of patients discharged home after primary TKA declined from 67.5% (95% CI, 67.3%-67.6%) in 1991-1994 to 39.9% (95% CI, 39.8%-40.0%) in 1999-2002 before increasing to 56.2% (95% CI, 56.1%-56.3%) in 2007-2010 (Table 2 and eFigure 3); alternatively the percentage of patients discharged to inpatient rehabilitation increased from 14.6% (95% CI, 14.5%-14.7%) in 1991-1994 to 29.4% (95% CI, 29.3%-29.5%) in 1999-2002 before declining to 11.4% (95% CI, 11.3%-11.4%) in 2007-20120 and discharge to outpatient rehabilitation facilities increased steadily throughout the study period. Unadjusted mortality within 30 days after discharge decreased from 0.5% (95% CI, 0.4%-0.5%) in 1991-1994 to 0.3% (95% CI, 0.3%-0.3%) in 2007-2010, a 40% relative reduction (P < .001). Unadjusted rates of most other adverse outcomes remained relatively stable over the study period as did the rate of the composite outcome (Table 2).

Table Graphic Jump LocationTable 2. Unadjusted Outcomes (LOS, Complication Rates, and 30-Day Readmission Rates) for Primary Total Knee Arthroplasty (TKA) Between 1991 and 2010a

In contrast, all-cause 30-day readmission rates increased from 4.2% (95% CI, 4.1%-4.2%) in 1991-1994 to 5.0% (95% CI, 4.9%-5.0%) in 2007-2010 (P < .001)(Table 2). In adjusted analyses, we found that although hospital LOS for primary TKA declined throughout the study period (eFigure 4), both 30-day all-cause readmission rates (eFigure 5) and rates of the composite outcome (eFigure 6) declined initially, but have been increasing in recent years. In an analysis of the diagnoses and conditions associated with readmission after primary TKA, we observed relatively little change over time (eTable 1) with surgical and cardiac complications being relatively common as well as gastrointestinal hemorrhage and infection, particularly in recent years.

For revision TKA, the mean hospital LOS declined from 8.9 days (95% CI, 8.8-8.9) in 1991-1994 to 5.0 days (95% CI, 5.0-5.0) in 2007-2010, a relative decline of 43.8% (P < .001; Table 3). Trends in discharge disposition after revision TKA (Table 3 and eFigure 3) demonstrated a similar pattern to that which was observed for primary TKA, a decline in discharges to home or inpatient rehabilitation and an increase in discharge to skilled care and outpatient rehabilitation. Mortality within 30 days of discharge increased modestly from 0.7% (95% CI, 0.6%-0.7%) in 1991-1994 to 0.9% (95% CI, 0.8%-0.9%) in 2007-2010 (a 28.6% relative increase) and readmission for wound infection, hemorrhage, sepsis, and myocardial infarction each increased by more than 100% (P < .001 for each). For revision TKA, the unadjusted rate of the composite outcome increased from 2.7% (95% CI, 2.6%-2.9%) in 1991-1994 to 5.3% (95% CI, 5.2%-5.5%) in 2007-2010 (P < .001). All-cause unadjusted readmission rates within 30 days of discharge increased from 6.1% (95% CI, 5.9%-6.4%) to 8.9% (95% CI, 8.7%-9.2%) during the study period (eFigure 5). The most common causes of readmission after revision TKA are displayed in eTable 2.

Table Graphic Jump LocationTable 3. Unadjusted Outcomes (LOS, Complication Rates, and 30-Day Readmission Rates) for Revision Total Knee Arthroplasty (TKA) Between 1991 and 2010a

In adjusted analyses, revision TKA demonstrated a steady decrease in hospital LOS (eFigure 4) accompanied by an initial decline in readmissions that has reversed in recent years (eFigure 5) and by an increase in both unadjusted and adjusted rates of the composite outcome (eFigure 6).

In bivariate comparison of patients who were and were not readmitted within 30 days after primary TKA (Table 4), we found that patients who were readmitted were older than those who were not readmitted (mean age, 75.6 vs 74.1 years, P < .001), less likely to be women, more likely to be black, and had a higher number of comorbid conditions (mean number of conditions 2.1 among nonreadmitted vs 2.5 among readmitted, P < .001). Findings were generally similar for the revision TKA cohort (Table 4). In our regression analyses focusing on primary TKA readmissions (Table 5), several patient-level factors were associated with increased odds of hospital readmission including older age, black race, male sex, greater number of comorbid conditions, and longer hospital LOS during the index admission. Hospital factors including minor teaching and nonteaching status (as compared with major teaching) as well as greater primary TKA hospital volume were associated with decreased patient readmission rates (Table 5). Results for revision TKA were generally similar (Table 5). Results of sensitivity analyses (available by request from the authors) were similar to the main results described above.

Table Graphic Jump LocationTable 4. Characteristics of Medicare Beneficiaries Receiving Primary and Revision Total Knee Arthroplasty (TKA) Who Did and Did Not Experience Readmission Within 30 Days of Discharge (2007-2010)a
Table Graphic Jump LocationTable 5. Factors Associated With Increased Odds of Readmission for Primary and Revision Total Knee Arthroplasty (TKA) (2007-2010)

In an analysis of Medicare administrative data from 1991-2010, we identified a number of interesting trends related to TKA. First, we found a marked increase in the volume of primary TKA procedures being performed, an increase that appeared to be driven not only by an increase in the number of Medicare enrollees but also a substantial increase in the per-capita utilization of TKA procedures. Second, we observed changes in patients' discharge disposition over time with a decline in the use of inpatient rehabilitation and an increase in the use of outpatient rehabilitation. Third, we found a significant decrease in hospital LOS that was accompanied by increasing readmission rates over the past decade.

Primary Medicare TKA volume increased approximately 162% from 93 230 in 1991 to 243 802 in 2010 and revision volume increased 106% from 9650 in 1991 to 19 871 in 2010. These figures suggest that growth in primary and revision TKA volume is being driven by both an increase in the number of Medicare enrollees and an increase in per capita arthroplasty utilization. Our findings extend those of a limited body of prior research that has demonstrated increasing volume and per capita utilization of knee arthroplasty.1,31,32 This growth is likely driven by a combination of factors including an expansion in the types of patients considered likely to benefit from TKA, an aging population, and an increasing prevalence of certain conditions that predispose patients to osteoarthritis, most notably obesity.33

It is important to note the apparent stabilization of joint arthroplasty utilization in recent years. Our findings extend the work of Bini and colleagues34 who found evidence of slowing growth in joint arthroplasty utilization within the Kaiser-Permanente health care system between 2000 and 2009. It is unclear whether this slowing of joint arthroplasty growth is the result of the protracted US economic downturn, saturation of patient demand for arthroplasty, changes in reimbursement, or changes in provider beliefs about the risks and benefits of arthroplasty.35,36

The growth in TKA should prompt consideration of whether too many (or too few) of these procedures are being performed both in aggregate and among key patient subgroups defined by race, sex, or age.30,37,38 Any effort to answer this question raises the issues of TKA indications and appropriateness. A number of clinical practice guidelines for TKA have been developed to guide clinicians and policy makers in evaluating appropriateness.3943 These guidelines typically suggest consideration of TKA for patients with severe functional limitation unresponsive to conservative management (ie, medications and physical therapy).

While Cobos et al44 estimated that as many as 25% of TKA procedures performed in Spain might be considered inappropriate, few such studies have been performed in the United States.12 Conducting studies investigating appropriateness has historically been difficult because of a lack of a national joint arthroplasty registry, although there have been encouraging developments recently to suggest that this may change.45,46 Thus, it is difficult to determine the extent to which the growth in TKA utilization represents growth in appropriate use of a highly effective procedure or overuse of a highly reimbursed procedure for which indications still depend on clinical judgment. It is likely that both factors are at play.

Our finding of significant changes in patient's discharge dispositions following TKA over the 20-year study period is important and hints at the complexities of restraining cost growth. The increase in the percentage of TKA patients discharged to inpatient rehabilitation and skilled care during the 1990s is consistent with prior reports.47 These reports typically relate the increased use of post–acute care to the implementation of the prospective payment system for acute care hospitals in 1983, which in turn created a powerful incentive for hospitals to reduce hospital LOS by rapidly discharging patients to post–acute care settings when patients were too ill to safely be discharged home.48,49 However, the rapid increase in Medicare post–acute care spending in the 1990s prompted passage of the Balanced Budget Act (BBA) of 1997 and implementation of a prospective payment system for outpatient skilled care in 1998 and inpatient rehabilitation in 2002.48,50 Our results are consistent with the anticipated effects of these policy changes, a reduction in the use of post–acute care and an increase in the percentage of patients being discharged home after TKA since 2004.

The finding of declining hospital LOS accompanied by increasing readmission rates mirrors results of a number of recent studies.51,52 The results of our study as well as other publications suggest that there are limitations to what extent LOS can be reduced and that cost savings from further LOS reductions are unlikely to materialize.51,52 In particular, there is an inherent tradeoff between shorter hospital LOS, greater need for post–acute care, and higher readmission rates.

A number of other findings merit mention. Our finding of increased comorbidity over time likely reflects a combination of factors including increasingly aggressive coding practices and increasing prevalence of certain comorbidities (eg, diabetes and obesity).53,54 The increasing rates of many surgical complications including myocardial infarction, infection, and hemorrhage particularly after revision TKA accompanied by a much smaller increase in mortality is interesting. It seems likely that many of these increases reflect more aggressive testing combined with detection bias resulting from newer more sensitive diagnostic tests (eg, troponin for myocardial infarction or D-dimer for deep vein thrombosis) rather than a true increase in surgical complications.55 However it is also possible that the incidence of certain complications such as myocardial infarction may be increasing, perhaps as a consequence of a greater burden of obesity and diabetes.

Arguably, the most concerning complication is the increase in readmissions for infection in the revision TKA cohort. While there are well recognized limitations in administrative data for identifying surgical site infections,56,57 our findings should not be discounted prematurely.58,59 There are several potential explanations for increasing infection rates in the revision TKA population. One possibility is that the increase in infections represents an increase in revision TKAs being performed specifically to treat infected prostheses. If this were the case, the increase in revision TKA procedures performed would constitute infections that were “present on admission.”60,61 Alternatively, it is possible that the increase in infections represents a real increase in postoperative surgical infections after revision TKA perhaps as a consequence of the increasingly resistant organisms colonizing hospitals. It is also possible that reduced hospital LOS may lead to reduced vigilance for early signs of superficial wound infection in the postoperative period resulting in higher rates of serious infectious complications. In either case, the increase in infection rates associated with revision TKA warrants close attention.

In addition, the increase in primary TKA utilization (99%) has been larger than the increase in revision TKA utilization (51%) over the past 20 years. It is possible that this reflects the durability of modern implants and improved surgical technique resulting in a reduced likelihood that patients undergoing primary TKA will require a revision procedure in the future.62 Alternatively, it is possible that the rapid increase in primary TKA over the past 20 years will eventually result in a substantial increase in demand for revision TKA procedures as prosthetic devices wear over time, a possibility that would have significant clinical and economic implications.35

Our study has a number of limitations. First, our study was limited to fee-for-service Medicare beneficiaries who constitute approximately 60% of the TKA population.14,31 Our findings should be extrapolated with caution to other populations including younger patients and Medicare managed care enrollees. Second, our study relied upon administrative data and thus we were unable to evaluate a number of important arthroplasty outcomes including functional status and patient satisfaction. Third, we lacked clinical detail and therefore were unable to determine the indications for TKA at the level of the individual patient. Likewise, we lacked chart review data for identification of TKA outcomes and complications. Fourth, we focused our analysis on TKA adverse outcomes resulting in hospital readmission within 30 days of discharge. We were unable to identify complications that did not result in an inpatient admission and our 30-day follow up interval would not capture certain late complications (eg, infection) that may become apparent over a longer time period.25

In summary, over the past 20 years, increases in TKA volume have been driven by both an increase in the number of Medicare enrollees and increase in per capita utilization. We also observed decreased hospital LOS that was accompanied by increased hospital readmission rates and rising rates of infections complications.

Corresponding Author: Peter Cram, MD, MBA, Division of General Internal Medicine, University of Iowa Carver College of Medicine, 200 Hawkins Dr, 6GH SE, Iowa City, IA 52242 (peter-cram@uiowa.edu).

Author Contributions: Dr Cram and Ms Lu had full access to all of the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis.

Study concept and design: Cram, Lu, Wolf.

Acquisition of data: Cram, Lu.

Analysis and interpretation of data: Cram, Lu, Kates, Singh, Li, Wolf.

Drafting of the manuscript: Cram, Lu, Kates.

Critical revision of the manuscript for important intellectual content: Cram, Singh, Li, Wolf.

Statistical analysis: Lu.

Obtained funding: Cram.

Administrative, technical, or material support: Cram, Lu, Singh, Li, Wolf.

Study supervision: Cram, Kates, Wolf.

Conflict of Interest Disclosures: All authors have completed and submitted the ICMJE Form for Disclosure of Potential Conflicts of Interest. Dr Cram reported that he is supported by a K24 award from NIAMS (AR062133) and by the Department of Veterans Affairs and that he has received consulting fees from The Consumers Union (publisher of Consumer Reports magazine) and Vanguard Health Inc for work advice on quality improvement initiatives. Dr Kates reported that he receives institutional research funding from AHRQ, Synthes USA, the American Geriatrics Society, the John Hartford Foundation, and the AO Research Foundation. Dr Singh reported that he receives institutional research funding from AHRQ, US Food and Drug Administration, NIA, Takeda Pharmaceuticals, and Savient Pharmaceuticals; is a consultant for Takeda, Novartis, Savient, URL, and Ardea; has received travel grants from Allergan, Wyeth, Amgen, and Takeda; and has received speaker honoraria from Abbott. No other authors reported disclosures.

Funding/Support: This work is funded in-part by grants R01 HL085347 from NHLBI and R01 AG033035 from NIA.

Role of the Sponsor: The sponsors had no role in the design and conduct of the study; in the collection, analysis, and interpretation of data; in the preparation of the manuscript; or in the review or approval of the manuscript.

Disclaimer: The views expressed in this article are those of the authors and do not necessarily represent the views of the Department of Veterans Affairs.

Online-Only Material:An Author Video Interview is available here.

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Smith-Bindman R, Miglioretti DL, Larson EB. Rising use of diagnostic medical imaging in a large integrated health system.  Health Aff (Millwood). 2008;27(6):1491-1502
PubMed   |  Link to Article
Jain NB, Higgins LD, Ozumba D,  et al.  Trends in epidemiology of knee arthroplasty in the United States, 1990-2000.  Arthritis Rheum. 2005;52(12):3928-3933
PubMed   |  Link to Article
Khatod M, Inacio M, Paxton EW,  et al.  Knee replacement: epidemiology, outcomes, and trends in Southern California: 17,080 replacements from 1995 through 2004.  Acta Orthop. 2008;79(6):812-819
PubMed   |  Link to Article
Memtsoudis SG, Della Valle AG, Besculides MC, Gaber L, Laskin R. Trends in demographics, comorbidity profiles, in-hospital complications and mortality associated with primary knee arthroplasty.  J Arthroplasty. 2009;24(4):518-527
PubMed   |  Link to Article
Cutler DM, Ghosh K. The potential for cost savings through bundled episode payments.  N Engl J Med. 2012;366(12):1075-1077
PubMed   |  Link to Article
Miller DC, Gust C, Dimick JB, Birkmeyer N, Skinner J, Birkmeyer JD. Large variations in Medicare payments for surgery highlight savings potential from bundled payment programs.  Health Aff (Millwood). 2011;30(11):2107-2115
PubMed   |  Link to Article
Mitchell JB, Bubolz T, Paul JE,  et al.  Using Medicare claims for outcomes research.  Med Care. 1994;32(7):(suppl)  JS38-JS51
PubMed
Katz JN, Barrett J, Mahomed NN, Baron JA, Wright RJ, Losina E. Association between hospital and surgeon procedure volume and the outcomes of total knee replacement.  J Bone Joint Surg Am. 2004;86-A(9):1909-1916
PubMed
Losina E, Barrett J, Mahomed NN, Baron JA, Katz JN. Early failures of total hip replacement: effect of surgeon volume.  Arthritis Rheum. 2004;50(4):1338-1343
PubMed   |  Link to Article
Katz JN, Losina E, Barrett J,  et al.  Association between hospital and surgeon procedure volume and outcomes of total hip replacement in the United States Medicare population.  J Bone Joint Surg Am. 2001;83-A(11):1622-1629
PubMed
Elixhauser A, Steiner C, Harris DR, Coffey RM. Comorbidity measures for use with administrative data.  Med Care. 1998;36(1):8-27
PubMed   |  Link to Article
Quan H, Sundararajan V, Halfon P,  et al.  Coding algorithms for defining comorbidities in ICD-9-CM and ICD-10 administrative data.  Med Care. 2005;43(11):1130-1139
PubMed   |  Link to Article
Cram P, Lu X, Callaghan JJ, Vaughan-Sarrazin MS, Cai X, Li Y. Long-term trends in hip arthroplasty use and volume.  J Arthroplasty. 2012;27(2):278-285,e2PMID 21752578
PubMed   |  Link to Article
Hagen TP, Vaughan-Sarrazin MS, Cram P. Relation between hospital orthopaedic specialisation and outcomes in patients aged 65 and older: retrospective analysis of US Medicare data.  BMJ. 2010;340:c165Link to Article
Link to Article
Cram P, Ibrahim SA, Lu X, Wolf BR. Impact of alternative coding schemes on incidence rates of key complications after total hip arthroplasty: a risk-adjusted analysis of a national data set.  Geriatric Orthop Surg & Rehab. 2012;3(1):17-26Link to Article
Clinical Classification Software (CCS) for ICD9-CM Codes. http://www.hcup-us.ahrq.gov/toolssoftware/ccs/CCSUsersGuide.pdf. Accessed May 16, 2012
Shahian DM, Torchiana DF, Shemin RJ, Rawn JD, Normand SL. Massachusetts cardiac surgery report card: implications of statistical methodology.  Ann Thorac Surg. 2005;80(6):2106-2113
PubMed
Rothman KJ, Greenland S, Lash TL. Modern Epidemiology. Philadelphia, PA: Lippincott, Williams & Wilkins; 2008
Hausmann LR, Mor M, Hanusa BH,  et al.  The effect of patient race on total joint replacement recommendations and utilization in the orthopedic setting.  J Gen Intern Med. 2010;25(9):982-988
PubMed
Jones A, Kwoh CK, Kelley ME, Ibrahim SA. Racial disparity in knee arthroplasty utilization in the Veterans Health Administration.  Arthritis Rheum. 2005;53(6):979-981
PubMed
Kim S. Changes in surgical loads and economic burden of hip and knee replacements in the US: 1997-2004.  Arthritis Rheum. 2008;59(4):481-488
PubMed
Freid VM, Bernstein AB. Health care utilization among adults aged 55-64 years: how has it changed over the past 10 years?  NCHS Data Brief. 2010;(32):1-8
PubMed
Losina E, Thornhill TS, Rome BN, Wright J, Katz JN. The dramatic increase in total knee replacement utilization rates in the United States cannot be fully explained by growth in population size and the obesity epidemic.  J Bone Joint Surg Am. 2012;94(3):201-207
PubMed
Bini SA, Sidney S, Sorel M. Slowing demand for total joint arthroplasty in a population of 3.2 million.  J Arthroplasty. 2011;26(6):(suppl)  124-128
PubMed
Kurtz S, Ong K, Lau E, Mowat F, Halpern M. Projections of primary and revision hip and knee arthroplasty in the United States from 2005 to 2030.  J Bone Joint Surg Am. 2007;89(4):780-785
PubMed
Iorio R, Davis CM III, Healy WL, Fehring TK, O’Connor MI, York S. Impact of the economic downturn on adult reconstruction surgery: a survey of the American Association of Hip and Knee Surgeons.  J Arthroplasty. 2010;25(7):1005-1014
PubMed
Borrero S, Kwoh CK, Sartorius J, Ibrahim SA. Brief report: gender and total knee/hip arthroplasty utilization rate in the VA system.  J Gen Intern Med. 2006;21:(suppl 3)  S54-S57
PubMed
Ibrahim SA, Siminoff LA, Burant CJ, Kwoh CK. Understanding ethnic differences in the utilization of joint replacement for osteoarthritis: the role of patient-level factors.  Med Care. 2002;40(1):(suppl)  I44-I51
PubMed
Gossec L, Paternotte S, Maillefert JF,  et al; OARSI-OMERACT Task Force.   “total articular replacement as outcome measure in OA.” The role of pain and functional impairment in the decision to recommend total joint replacement in hip and knee osteoarthritis: an international cross-sectional study of 1909 patients. Report of the OARSI-OMERACT Task Force on total joint replacement.  Osteoarthritis Cartilage. 2011;19(2):147-154
PubMed   |  Link to Article
Naylor CD, Williams JI. Primary hip and knee replacement surgery: Ontario criteria for case selection and surgical priority.  Qual Health Care. 1996;5(1):20-30
PubMed   |  Link to Article
Escobar A, Quintana JM, Aróstegui I,  et al.  Development of explicit criteria for total knee replacement.  Int J Technol Assess Health Care. 2003;19(1):57-70
PubMed   |  Link to Article
Dieppe P, Lim K, Lohmander S. Who should have knee joint replacement surgery for osteoarthritis?  Int J Rheum Dis. 2011;14(2):175-180
PubMed   |  Link to Article
Escobar A, Quintana JM, Bilbao A,  et al.  Development of explicit criteria for prioritization of hip and knee replacement.  J Eval Clin Pract. 2007;13(3):429-434
PubMed   |  Link to Article
Cobos R, Latorre A, Aizpuru F,  et al.  Variability of indication criteria in knee and hip replacement: an observational study.  BMC Musculoskelet Disord. 2010;11:249
PubMed   |  Link to Article
Lonner JH. National joint replacement registry.  Am J Orthop (Belle Mead NJ). 2009;38(10):497-498
PubMed
Paxton EW, Ake CF, Inacio MC, Khatod M, Marinac-Dabic D, Sedrakyan A. Evaluation of total hip arthroplasty devices using a total joint replacement registry.  Pharmacoepidemiol Drug Saf. 2012;21:(suppl 2)  53-59
PubMed   |  Link to Article
Medicare Payment Advisory Commission (MedPAC).  Report to the Congress: Variation and Innovation in Medicare June 2003. http://www.medpac.gov/documents/June03_Entire_Report.pdf. Accessed August 31, 2012
McCall N, Korb J, Petersons A, Moore S. Reforming Medicare payment: early effects of the 1997 Balanced Budget Act on postacute care.  Milbank Q. 2003;81(2):277-303
PubMed   |  Link to Article
Chan L, Koepsell TD, Deyo RA,  et al.  The effect of Medicare's payment system for rehabilitation hospitals on length of stay, charges, and total payments.  N Engl J Med. 1997;337(14):978-985
PubMed   |  Link to Article
Buntin MB, Colla CH, Escarce JJ. Effects of payment changes on trends in post-acute care.  Health Serv Res. 2009;44(4):1188-1210
PubMed   |  Link to Article
Bueno H, Ross JS, Wang Y,  et al.  Trends in length of stay and short-term outcomes among Medicare patients hospitalized for heart failure, 1993-2006.  JAMA. 2010;303(21):2141-2147
PubMed   |  Link to Article
Cram P, Lu X, Kaboli PJ,  et al.  Clinical characteristics and outcomes of Medicare patients undergoing total hip arthroplasty, 1991-2008.  JAMA. 2011;305(15):1560-1567
PubMed   |  Link to Article
Silverman E, Skinner J. Medicare upcoding and hospital ownership.  J Health Econ. 2004;23(2):369-389
PubMed   |  Link to Article
Vaughan-Sarrazin MS, Lu X, Cram P. The impact of paradoxical comorbidities on risk-adjusted mortality of Medicare beneficiaries with cardiovascular disease.  Medicare Medicaid Res Rev. 2011;1(3):
PubMed  |  Link to Article
Welch HG, Schwartz L, Woloshin S. Overdiagnosed: Making People Sick in the Pursuit of Health.  Boston, MA: Beacon Press; 2011
Stevenson KB, Khan Y, Dickman J,  et al.  Administrative coding data, compared with CDC/NHSN criteria, are poor indicators of health care-associated infections.  Am J Infect Control. 2008;36(3):155-164
PubMed   |  Link to Article
Schweizer ML, Eber MR, Laxminarayan R,  et al.  Validity of ICD-9-CM coding for identifying incident methicillin-resistant Staphylococcus aureus (MRSA) infections: is MRSA infection coded as a chronic disease?  Infect Control Hosp Epidemiol. 2011;32(2):148-154
PubMed   |  Link to Article
Calderwood MS, Ma A, Khan YM,  et al; CDC Prevention Epicenters Program.  Use of Medicare diagnosis and procedure codes to improve detection of surgical site infections following hip arthroplasty, knee arthroplasty, and vascular surgery.  Infect Control Hosp Epidemiol. 2012;33(1):40-49
PubMed   |  Link to Article
Thomas C, Cadwallader HL, Riley TV. Surgical-site infections after orthopaedic surgery: statewide surveillance using linked administrative databases.  J Hosp Infect. 2004;57(1):25-30
PubMed   |  Link to Article
Glance LG, Osler TM, Mukamel DB, Dick AW. Impact of the present-on-admission indicator on hospital quality measurement: experience with the Agency for Healthcare Research and Quality (AHRQ) Inpatient Quality Indicators.  Med Care. 2008;46(2):112-119
PubMed   |  Link to Article
Glance LG, Dick AW, Osler TM, Mukamel DB. Does date stamping ICD-9-CM codes increase the value of clinical information in administrative data?  Health Serv Res. 2006;41(1):231-251
PubMed   |  Link to Article
Carr AJ, Robertsson O, Graves S,  et al.  Knee replacement.  Lancet. 2012;379(9823):1331-1340
PubMed   |  Link to Article

Figures

Place holder to copy figure label and caption
Figure 1. Primary and Revision Total Knee Athroplasty Medicare Volume Between 1991 and 2010
Graphic Jump Location

Y-axis shown in blue indicates range from 0 to 25 000 procedures per year.

Place holder to copy figure label and caption
Figure 2. Primary and Revision Medicare Total Knee Athroplasty Utilization Between 1991 and 2010
Graphic Jump Location

Y-axis shown in blue indicates range from 0 to 7 procedures per year per 10 000 enrollees. The range of variability (1 SD) in primary total knee arthroplasty (TKA); procedures per 10 000 Medicare enrollees for 1991 was 31.0 to 31.4; for 2010, 5.0 to 5.1. For revision TKA, the range of variability (1 SD; procedures per 10 000 Medicare enrollees) for 1991 was 3.2 to 3.3; for 2010, 5.0 to 5.1.

Tables

Table Graphic Jump LocationTable 1. Characteristics of Medicare Beneficiaries Receiving Primary and Revision Total Knee Arthroplasty (TKA) Between 1991 and 2010a
Table Graphic Jump LocationTable 2. Unadjusted Outcomes (LOS, Complication Rates, and 30-Day Readmission Rates) for Primary Total Knee Arthroplasty (TKA) Between 1991 and 2010a
Table Graphic Jump LocationTable 3. Unadjusted Outcomes (LOS, Complication Rates, and 30-Day Readmission Rates) for Revision Total Knee Arthroplasty (TKA) Between 1991 and 2010a
Table Graphic Jump LocationTable 4. Characteristics of Medicare Beneficiaries Receiving Primary and Revision Total Knee Arthroplasty (TKA) Who Did and Did Not Experience Readmission Within 30 Days of Discharge (2007-2010)a
Table Graphic Jump LocationTable 5. Factors Associated With Increased Odds of Readmission for Primary and Revision Total Knee Arthroplasty (TKA) (2007-2010)

References

Kurtz S, Mowat F, Ong K, Chan N, Lau E, Halpern M. Prevalence of primary and revision total hip and knee arthroplasty in the United States from 1990 through 2002.  J Bone Joint Surg Am. 2005;87(7):1487-1497
PubMed   |  Link to Article
Losina E, Walensky RP, Kessler CL,  et al.  Cost-effectiveness of total knee arthroplasty in the United States: patient risk and hospital volume.  Arch Intern Med. 2009;169(12):1113-1121
PubMed   |  Link to Article
Healy WL, Rana AJ, Iorio R. Hospital economics of primary total knee arthroplasty at a teaching hospital.  Clin Orthop Relat Res. 2010;(Aug):6
PubMed
Centers for Disease Control National Center for Health Statistics. FastStats: inpatient surgery. http://www.cdc.gov/nchs/fastats/insurg.htm Accessed August 31, 2012
Krummenauer F, Wolf C, Günther KP, Kirschner S. Clinical benefit and cost effectiveness of total knee arthroplasty in the older patient.  Eur J Med Res. 2009;14(2):76-84
PubMed   |  Link to Article
Finks JF, Osborne NH, Birkmeyer JD. Trends in hospital volume and operative mortality for high-risk surgery.  N Engl J Med. 2011;364(22):2128-2137
PubMed   |  Link to Article
Cram P, Vaughan-Sarrazin MS, Wolf B, Katz JN, Rosenthal GE. A comparison of total hip and knee replacement in specialty and general hospitals.  J Bone Joint Surg Am. 2007;89(8):1675-1684
PubMed   |  Link to Article
Manley M, Ong K, Lau E, Kurtz SM. Total knee arthroplasty survivorship in the United States Medicare population: effect of hospital and surgeon procedure volume.  J Arthroplasty. 2009;24(7):1061-1067
PubMed   |  Link to Article
Chernew M, Goldman D, Axeen S. How much savings can we wring from Medicare?  N Engl J Med. 2011;365(14):e29
PubMed   |  Link to Article
Baicker K, Chernew ME. The economics of financing Medicare.  N Engl J Med. 2011;365(4):e7
PubMed   |  Link to Article
Smith-Bindman R, Miglioretti DL, Larson EB. Rising use of diagnostic medical imaging in a large integrated health system.  Health Aff (Millwood). 2008;27(6):1491-1502
PubMed   |  Link to Article
Jain NB, Higgins LD, Ozumba D,  et al.  Trends in epidemiology of knee arthroplasty in the United States, 1990-2000.  Arthritis Rheum. 2005;52(12):3928-3933
PubMed   |  Link to Article
Khatod M, Inacio M, Paxton EW,  et al.  Knee replacement: epidemiology, outcomes, and trends in Southern California: 17,080 replacements from 1995 through 2004.  Acta Orthop. 2008;79(6):812-819
PubMed   |  Link to Article
Memtsoudis SG, Della Valle AG, Besculides MC, Gaber L, Laskin R. Trends in demographics, comorbidity profiles, in-hospital complications and mortality associated with primary knee arthroplasty.  J Arthroplasty. 2009;24(4):518-527
PubMed   |  Link to Article
Cutler DM, Ghosh K. The potential for cost savings through bundled episode payments.  N Engl J Med. 2012;366(12):1075-1077
PubMed   |  Link to Article
Miller DC, Gust C, Dimick JB, Birkmeyer N, Skinner J, Birkmeyer JD. Large variations in Medicare payments for surgery highlight savings potential from bundled payment programs.  Health Aff (Millwood). 2011;30(11):2107-2115
PubMed   |  Link to Article
Mitchell JB, Bubolz T, Paul JE,  et al.  Using Medicare claims for outcomes research.  Med Care. 1994;32(7):(suppl)  JS38-JS51
PubMed
Katz JN, Barrett J, Mahomed NN, Baron JA, Wright RJ, Losina E. Association between hospital and surgeon procedure volume and the outcomes of total knee replacement.  J Bone Joint Surg Am. 2004;86-A(9):1909-1916
PubMed
Losina E, Barrett J, Mahomed NN, Baron JA, Katz JN. Early failures of total hip replacement: effect of surgeon volume.  Arthritis Rheum. 2004;50(4):1338-1343
PubMed   |  Link to Article
Katz JN, Losina E, Barrett J,  et al.  Association between hospital and surgeon procedure volume and outcomes of total hip replacement in the United States Medicare population.  J Bone Joint Surg Am. 2001;83-A(11):1622-1629
PubMed
Elixhauser A, Steiner C, Harris DR, Coffey RM. Comorbidity measures for use with administrative data.  Med Care. 1998;36(1):8-27
PubMed   |  Link to Article
Quan H, Sundararajan V, Halfon P,  et al.  Coding algorithms for defining comorbidities in ICD-9-CM and ICD-10 administrative data.  Med Care. 2005;43(11):1130-1139
PubMed   |  Link to Article
Cram P, Lu X, Callaghan JJ, Vaughan-Sarrazin MS, Cai X, Li Y. Long-term trends in hip arthroplasty use and volume.  J Arthroplasty. 2012;27(2):278-285,e2PMID 21752578
PubMed   |  Link to Article
Hagen TP, Vaughan-Sarrazin MS, Cram P. Relation between hospital orthopaedic specialisation and outcomes in patients aged 65 and older: retrospective analysis of US Medicare data.  BMJ. 2010;340:c165Link to Article
Link to Article
Cram P, Ibrahim SA, Lu X, Wolf BR. Impact of alternative coding schemes on incidence rates of key complications after total hip arthroplasty: a risk-adjusted analysis of a national data set.  Geriatric Orthop Surg & Rehab. 2012;3(1):17-26Link to Article
Clinical Classification Software (CCS) for ICD9-CM Codes. http://www.hcup-us.ahrq.gov/toolssoftware/ccs/CCSUsersGuide.pdf. Accessed May 16, 2012
Shahian DM, Torchiana DF, Shemin RJ, Rawn JD, Normand SL. Massachusetts cardiac surgery report card: implications of statistical methodology.  Ann Thorac Surg. 2005;80(6):2106-2113
PubMed
Rothman KJ, Greenland S, Lash TL. Modern Epidemiology. Philadelphia, PA: Lippincott, Williams & Wilkins; 2008
Hausmann LR, Mor M, Hanusa BH,  et al.  The effect of patient race on total joint replacement recommendations and utilization in the orthopedic setting.  J Gen Intern Med. 2010;25(9):982-988
PubMed
Jones A, Kwoh CK, Kelley ME, Ibrahim SA. Racial disparity in knee arthroplasty utilization in the Veterans Health Administration.  Arthritis Rheum. 2005;53(6):979-981
PubMed
Kim S. Changes in surgical loads and economic burden of hip and knee replacements in the US: 1997-2004.  Arthritis Rheum. 2008;59(4):481-488
PubMed
Freid VM, Bernstein AB. Health care utilization among adults aged 55-64 years: how has it changed over the past 10 years?  NCHS Data Brief. 2010;(32):1-8
PubMed
Losina E, Thornhill TS, Rome BN, Wright J, Katz JN. The dramatic increase in total knee replacement utilization rates in the United States cannot be fully explained by growth in population size and the obesity epidemic.  J Bone Joint Surg Am. 2012;94(3):201-207
PubMed
Bini SA, Sidney S, Sorel M. Slowing demand for total joint arthroplasty in a population of 3.2 million.  J Arthroplasty. 2011;26(6):(suppl)  124-128
PubMed
Kurtz S, Ong K, Lau E, Mowat F, Halpern M. Projections of primary and revision hip and knee arthroplasty in the United States from 2005 to 2030.  J Bone Joint Surg Am. 2007;89(4):780-785
PubMed
Iorio R, Davis CM III, Healy WL, Fehring TK, O’Connor MI, York S. Impact of the economic downturn on adult reconstruction surgery: a survey of the American Association of Hip and Knee Surgeons.  J Arthroplasty. 2010;25(7):1005-1014
PubMed
Borrero S, Kwoh CK, Sartorius J, Ibrahim SA. Brief report: gender and total knee/hip arthroplasty utilization rate in the VA system.  J Gen Intern Med. 2006;21:(suppl 3)  S54-S57
PubMed
Ibrahim SA, Siminoff LA, Burant CJ, Kwoh CK. Understanding ethnic differences in the utilization of joint replacement for osteoarthritis: the role of patient-level factors.  Med Care. 2002;40(1):(suppl)  I44-I51
PubMed
Gossec L, Paternotte S, Maillefert JF,  et al; OARSI-OMERACT Task Force.   “total articular replacement as outcome measure in OA.” The role of pain and functional impairment in the decision to recommend total joint replacement in hip and knee osteoarthritis: an international cross-sectional study of 1909 patients. Report of the OARSI-OMERACT Task Force on total joint replacement.  Osteoarthritis Cartilage. 2011;19(2):147-154
PubMed   |  Link to Article
Naylor CD, Williams JI. Primary hip and knee replacement surgery: Ontario criteria for case selection and surgical priority.  Qual Health Care. 1996;5(1):20-30
PubMed   |  Link to Article
Escobar A, Quintana JM, Aróstegui I,  et al.  Development of explicit criteria for total knee replacement.  Int J Technol Assess Health Care. 2003;19(1):57-70
PubMed   |  Link to Article
Dieppe P, Lim K, Lohmander S. Who should have knee joint replacement surgery for osteoarthritis?  Int J Rheum Dis. 2011;14(2):175-180
PubMed   |  Link to Article
Escobar A, Quintana JM, Bilbao A,  et al.  Development of explicit criteria for prioritization of hip and knee replacement.  J Eval Clin Pract. 2007;13(3):429-434
PubMed   |  Link to Article
Cobos R, Latorre A, Aizpuru F,  et al.  Variability of indication criteria in knee and hip replacement: an observational study.  BMC Musculoskelet Disord. 2010;11:249
PubMed   |  Link to Article
Lonner JH. National joint replacement registry.  Am J Orthop (Belle Mead NJ). 2009;38(10):497-498
PubMed
Paxton EW, Ake CF, Inacio MC, Khatod M, Marinac-Dabic D, Sedrakyan A. Evaluation of total hip arthroplasty devices using a total joint replacement registry.  Pharmacoepidemiol Drug Saf. 2012;21:(suppl 2)  53-59
PubMed   |  Link to Article
Medicare Payment Advisory Commission (MedPAC).  Report to the Congress: Variation and Innovation in Medicare June 2003. http://www.medpac.gov/documents/June03_Entire_Report.pdf. Accessed August 31, 2012
McCall N, Korb J, Petersons A, Moore S. Reforming Medicare payment: early effects of the 1997 Balanced Budget Act on postacute care.  Milbank Q. 2003;81(2):277-303
PubMed   |  Link to Article
Chan L, Koepsell TD, Deyo RA,  et al.  The effect of Medicare's payment system for rehabilitation hospitals on length of stay, charges, and total payments.  N Engl J Med. 1997;337(14):978-985
PubMed   |  Link to Article
Buntin MB, Colla CH, Escarce JJ. Effects of payment changes on trends in post-acute care.  Health Serv Res. 2009;44(4):1188-1210
PubMed   |  Link to Article
Bueno H, Ross JS, Wang Y,  et al.  Trends in length of stay and short-term outcomes among Medicare patients hospitalized for heart failure, 1993-2006.  JAMA. 2010;303(21):2141-2147
PubMed   |  Link to Article
Cram P, Lu X, Kaboli PJ,  et al.  Clinical characteristics and outcomes of Medicare patients undergoing total hip arthroplasty, 1991-2008.  JAMA. 2011;305(15):1560-1567
PubMed   |  Link to Article
Silverman E, Skinner J. Medicare upcoding and hospital ownership.  J Health Econ. 2004;23(2):369-389
PubMed   |  Link to Article
Vaughan-Sarrazin MS, Lu X, Cram P. The impact of paradoxical comorbidities on risk-adjusted mortality of Medicare beneficiaries with cardiovascular disease.  Medicare Medicaid Res Rev. 2011;1(3):
PubMed  |  Link to Article
Welch HG, Schwartz L, Woloshin S. Overdiagnosed: Making People Sick in the Pursuit of Health.  Boston, MA: Beacon Press; 2011
Stevenson KB, Khan Y, Dickman J,  et al.  Administrative coding data, compared with CDC/NHSN criteria, are poor indicators of health care-associated infections.  Am J Infect Control. 2008;36(3):155-164
PubMed   |  Link to Article
Schweizer ML, Eber MR, Laxminarayan R,  et al.  Validity of ICD-9-CM coding for identifying incident methicillin-resistant Staphylococcus aureus (MRSA) infections: is MRSA infection coded as a chronic disease?  Infect Control Hosp Epidemiol. 2011;32(2):148-154
PubMed   |  Link to Article
Calderwood MS, Ma A, Khan YM,  et al; CDC Prevention Epicenters Program.  Use of Medicare diagnosis and procedure codes to improve detection of surgical site infections following hip arthroplasty, knee arthroplasty, and vascular surgery.  Infect Control Hosp Epidemiol. 2012;33(1):40-49
PubMed   |  Link to Article
Thomas C, Cadwallader HL, Riley TV. Surgical-site infections after orthopaedic surgery: statewide surveillance using linked administrative databases.  J Hosp Infect. 2004;57(1):25-30
PubMed   |  Link to Article
Glance LG, Osler TM, Mukamel DB, Dick AW. Impact of the present-on-admission indicator on hospital quality measurement: experience with the Agency for Healthcare Research and Quality (AHRQ) Inpatient Quality Indicators.  Med Care. 2008;46(2):112-119
PubMed   |  Link to Article
Glance LG, Dick AW, Osler TM, Mukamel DB. Does date stamping ICD-9-CM codes increase the value of clinical information in administrative data?  Health Serv Res. 2006;41(1):231-251
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Carr AJ, Robertsson O, Graves S,  et al.  Knee replacement.  Lancet. 2012;379(9823):1331-1340
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Cram P, Lu X, Kates SL, et al. Total knee arthroplasty volume, utilization, and outcomes among Medicare beneficiaries, 1991-2010. JAMA. doi:10.1001/2012.jama.11153

eFigure 1. Flow Diagram for Primary TKA Cohort Selection

eFigure 2. Flow Diagram for Revision TKA Cohort Selection

eTable 1. Five Most Common Diagnoses (Clinical Classification Categories) Associated with Readmission after Primary TKA between 1991 and 2010

eTable 2. Five Most Common Diagnoses (Clinical Classification Categories) Associated with Readmission after Revision TKA between 1991 and 2010

eFigure 3. Primary (panel 3a) and Revision (panel 3b) TKA Discharge Disposition between 1991 and 2010

eFigure 4. Unadjusted and Adjusted Primary and Revision TKA Hospital LOS between 1991 and 2010

eFigure 5. Unadjusted and Adjusted Primary and Revision TKA 30-day Readmission Rates between 1991 and 2010

eFigure 6. Unadjusted and Adjusted Primary and Revision TKA 30-day Composite Complication Rates between 1991 and 2010

eTable 3. Hospital Quartile Volumes for Model Development

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