Comparing Postoperative Readmission Rates Between Veterans Receiving Total Knee Arthroplasty in the Veterans Health Administration Versus Community Care.

BACKGROUND: There are growing concerns that Veterans' increased use of Veterans Health Administration (VA)-purchased care in the community may lead to lower quality of care. OBJECTIVE: We compared rates of hospital readmissions following elective total knee arthroplasties (TKAs) that were either performed in VA or purchased by VA through community care (CC) at both the national and facility levels. METHODS: Three-year cohort study using VA and CC administrative data from the VA's Corporate Data Warehouse (October 1, 2016-September 30, 2019). We obtained Medicare data to capture readmissions that were paid by Medicare. We used the Centers for Medicare and Medicaid Services (CMS) methods to identify unplanned, 30-day, all-cause readmissions. A secondary outcome, TKA-related readmissions, identified readmissions resulting from complications of the index surgery. We ran mixed-effects logistic regression models to compare the risk-adjusted odds of all-cause and TKA-related readmissions between TKAs performed in VA versus CC, adjusting for patients' sociodemographic and clinical characteristics. PRINCIPAL FINDINGS: Nationally, the odds of experiencing an all-cause or TKA-related readmission were significantly lower for TKAs performed in VA versus CC (eg, the odds of experiencing an all-cause readmission in VA were 35% of those in CC. At the facility level, most VA facilities performed similarly to their corresponding CC providers, although there were 3 VA facilities that performed worse than their corresponding CC providers. CONCLUSIONS: Given VA's history in providing high-quality surgical care to Veterans, it is important to closely monitor and track whether the shift to CC for surgical care will impact quality in both settings over time.

A Tool to Estimate Risk of 30-day Mortality and Complications After Hip Fracture Surgery: Accurate Enough for Some but Not All Purposes? A Study From the ACS-NSQIP Database.

BACKGROUND: Surgical repair of hip fracture carries substantial short-term risks of mortality and complications. The risk-reward calculus for most patients with hip fractures favors surgical repair. However, some patients have low prefracture functioning, frailty, and/or very high risk of postoperative mortality, making the choice between surgical and nonsurgical management more difficult. The importance of high-quality informed consent and shared decision-making for frail patients with hip fracture has recently been demonstrated. A tool to accurately estimate patient-specific risks of surgery could improve these processes. QUESTIONS/PURPOSES: With this study, we sought (1) to develop, validate, and estimate the overall accuracy (C-index) of risk prediction models for 30-day mortality and complications after hip fracture surgery; (2) to evaluate the accuracy (sensitivity, specificity, and false discovery rates) of risk prediction thresholds for identifying very high-risk patients; and (3) to implement the models in an accessible web calculator. METHODS: In this comparative study, preoperative demographics, comorbidities, and preoperatively known operative variables were extracted for all 82,168 patients aged 18 years and older undergoing surgery for hip fracture in the American College of Surgeons National Surgical Quality Improvement Program (ACS-NSQIP) between 2011 and 2017. Eighty-two percent (66,994 of 82,168 ) of patients were at least 70 years old, 21% (17,007 of 82,168 ) were at least 90 years old, 70% (57,260 of 82,168 ) were female, and 79% (65,301 of 82,168 ) were White. A total of 5% (4260 of 82,168) of patients died within 30 days of surgery, and 8% (6786 of 82,168) experienced a major complication. The ACS-NSQIP database was chosen for its clinically abstracted and reliable data from more than 600 hospitals on important surgical outcomes, as well as rich characterization of preoperative demographic and clinical predictors for demographically diverse patients. Using all the preoperative variables in the ACS-NSQIP dataset, least absolute shrinkage and selection operator (LASSO) logistic regression, a type of machine learning that selects variables to optimize accuracy and parsimony, was used to develop and validate models to predict two primary outcomes: 30-day postoperative mortality and any 30-day major complications. Major complications were defined by the occurrence of ACS-NSQIP complications including: on a ventilator longer than 48 hours, intraoperative or postoperative unplanned intubation, septic shock, deep incisional surgical site infection (SSI), organ/space SSI, wound disruption, sepsis, intraoperative or postoperative myocardial infarction, intraoperative or postoperative cardiac arrest requiring cardiopulmonary resuscitation, acute renal failure needing dialysis, pulmonary embolism, stroke/cerebral vascular accident, and return to the operating room. Secondary outcomes were six clusters of complications recently developed and increasingly used for the development of surgical risk models, namely: (1) pulmonary complications, (2) infectious complications, (3) cardiac events, (4) renal complications, (5) venous thromboembolic events, and (6) neurological events. Tenfold cross-validation was used to assess overall model accuracy with C-indexes, a measure of how well models discriminate patients who experience an outcome from those who do not. Using the models, the predicted risk of outcomes for each patient were used to estimate the accuracy (sensitivity, specificity, and false discovery rates) of a wide range of predicted risk thresholds. We then implemented the prediction models into a web-accessible risk calculator. RESULTS: The 30-day mortality and major complication models had good to fair discrimination (C-indexes of 0.76 and 0.64, respectively) and good calibration throughout the range of predicted risk. Thresholds of predicted risk to identify patients at very high risk of 30-day mortality had high specificity but also high false discovery rates. For example, a 30-day mortality predicted risk threshold of 15% resulted in 97% specificity, meaning 97% of patients who lived longer than 30 days were below that risk threshold. However, this threshold had a false discovery rate of 78%, meaning 78% of patients above that threshold survived longer than 30 days and might have benefitted from surgery. The tool is available here: https://s-spire-clintools.shinyapps.io/hip_deploy/ . CONCLUSION: The models of mortality and complications we developed may be accurate enough for some uses, especially personalizing informed consent and shared decision-making with patient-specific risk estimates. However, the high false discovery rate suggests the models should not be used to restrict access to surgery for high-risk patients. Deciding which measures of accuracy to prioritize and what is "accurate enough" depends on the clinical question and use of the predictions. Discrimination and calibration are commonly used measures of overall model accuracy but may be poorly suited to certain clinical questions and applications. Clinically, overall accuracy may not be as important as knowing how accurate and useful specific values of predicted risk are for specific purposes.Level of Evidence Level III, therapeutic study.

Survival of Hydroxyapatite-Coated Versus Non-hydroxyapatite-Coated Total Hip Arthroplasty Implants in a Veteran Population.

BACKGROUND: Hydroxyapatite (HA) coatings were introduced to improve uncemented implant osteointegration and to prevent loosening and osteolysis. However, data regarding these implants have been inconsistent. We studied the effect of HA coating of femoral stems and acetabular cups on component revision after primary total hip arthroplasty (THA) in the veteran population. METHODS: We identified patients who underwent uncemented primary THA at any Veterans Health Administration (VHA) hospital from 2000 to 2017 and who had implants that were available as either HA-coated or non-HA-coated models. The endpoint was removal of the component of interest for any reason. For each individual stem and shell, unadjusted and adjusted (for age and body mass index) Cox proportional hazards models were used to estimate hazard ratios for the risk of explantation between HA-coated and non-HA-coated implants of the same type. RESULTS: A total of 262 HA-coated cups, 4580 non-HA-coated cups, 4767 HA-coated stems, and 9397 non-HA-coated stems were available for analysis. The mean follow-up time was 9.01 years (43,627 total implant-years) for cups and 7.13 years (101,004 total implant-years) for stems. One of the two shells reviewed had significantly lower survivorship and an elevated hazard ratio for explantation with HA coating. Among the five implant pairs of stems, and the other shell, implant survival and hazard ratios for explantation were not affected by HA coating. CONCLUSION: HA coating did not improve THA implant survival in our veteran population. Although HA-coated versions of hip implants tend to be more costly than their noncoated counterparts, these results do not support their general use.

Prevalence of Hepatitis C Virus Infection in the Veteran Population Undergoing Total Joint Arthroplasty: An Update.

BACKGROUND: In 2012, we reported on the prevalence of hepatitis C virus (HCV) infection in Veterans Affairs (VA) patients undergoing total joint arthroplasty (TJA) at our center. In this patient population, 8.4% were antibody positive and 4.5% were viremic with HCV. In 2014, the first all-oral direct-acting antiviral treatment for hepatitis C became available. The Department of Veterans Affairs then underwent an aggressive program to eradicate hepatitis C from the veteran population. The purpose of this report is to provide updated information on the prevalence of HCV viremia among patients undergoing primary TJA at the same center. METHODS: A retrospective review was performed of all patients undergoing primary TJA at a single VA medical center in 2019. Anti-HCV antibody and HCV viremia prevalence were calculated. Comparisons were made to data from a previously reported cohort of patients who had undergone TJA at the same center from 2007 to 2009. RESULTS: Thirty-three (11.6%) of 285 patients screened preoperatively were positive for the hepatitis C antibody. Only one of the 33 anti-HCV-positive patients was viremic at the time of screening for an overall viremic prevalence of 0.4%. We found no statistically significant difference in the birth year, or anti-HCV antibody-positive rate from the prior cohort, but the prevalence of HCV viremia decreased significantly. CONCLUSION: Because direct-acting antiviral HCV treatment has become available, HCV viremia among VA patients undergoing TJA has been reduced from 4.5% to 0.4%. Surgeons are still advised to minimize the risk of sharps injury.

Can Machine Learning Methods Produce Accurate and Easy-to-Use Preoperative Prediction Models of One-Year Improvements in Pain and Functioning After Knee Arthroplasty?

BACKGROUND: Approximately 15%-20% of total knee arthroplasty (TKA) patients do not experience clinically meaningful improvements. We sought to compare the accuracy and parsimony of several machine learning strategies for developing predictive models of failing to experience minimal clinically important differences in patient-reported outcome measures (PROMs) 1 year after TKA. METHODS: Patients (N = 587) in 3 large Veteran Health Administration facilities completed PROMs before and 1 year after TKA (92% follow-up). Preoperative PROMs and electronic health record data were used to develop and validate models to predict failing to experience at least a minimal clinically important difference in Knee Injury and Osteoarthritis Outcome Score (KOOS) Total, KOOS JR, and KOOS subscales (Pain, Symptoms, Activities of Daily Living, Quality of Life, and recreation). Several machine learning strategies were used for model development. Ten-fold cross-validation and bootstrapping were used to produce measures of overall accuracy (C-statistic, Brier Score). The sensitivity and specificity of various predicted probability cut-points were examined. RESULTS: The most accurate models produced were for the Activities of Daily Living, Pain, Symptoms, and Quality of Life subscales of the KOOS (C-statistics 0.76, 0.72, 0.72, and 0.71, respectively). Strategies varied substantially in terms of the numbers of inputs required to achieve similar accuracy, with none being superior for all outcomes. CONCLUSION: Models produced in this project provide estimates of patient-specific improvements in major outcomes 1 year after TKA. Integrating these models into clinical decision support, informed consent and shared decision making could improve patient selection, education, and satisfaction. LEVEL OF EVIDENCE: Level III, diagnostic study.

Perioperative Opioid Prescribing Patterns and Readmissions After Total Knee Arthroplasty in a National Cohort of Veterans Health Administration Patients.

OBJECTIVE: Among Veterans Health Administration (VHA) patients who undergo total knee arthroplasty (TKA) nationally, what are the underlying readmission rates and associations with perioperative opioid use, and are there associations with other factors such as preoperative health care utilization? METHODS: We retrospectively examined the records of 5,514 TKA patients (primary N = 4,955, 89.9%; revision N = 559, 10.1%) over one fiscal year (October 1, 2010-September 30, 2011) across VHA hospitals nationwide. Opioid use was classified into no opioids, tramadol only, short-acting only, or any long-acting. We measured readmission within 30 days and the number of days to readmission within 30 days. Extended Cox regression models were developed. RESULTS: The overall 30-day hospital readmission rate was 9.6% (N = 531; primary 9.5%, revision 11.1%). Both readmitted patients and the overall sample were similar on types of preoperative opioid use. Relative to patients without opioids, patients in the short-acting opioids only tier had the highest risk for 30-day hospital readmission (hazard ratio = 1.38, 95% confidence interval = 1.14-1.67). Preoperative opioid status was not associated with 30-day readmission. Other risk factors for 30-day readmission included older age (≥66 years), higher comorbidity and diagnosis-related group weights, greater preoperative health care utilization, an urban location, and use of preoperative anticonvulsants. CONCLUSIONS: Given the current opioid epidemic, the routine prescribing of short-acting opioids after surgery should be carefully considered to avoid increasing risks of 30-day hospital readmissions and other negative outcomes, particularly in the context of other predisposing factors.

Replacement of Fascia Iliaca Catheters with Continuous Erector Spinae Plane Blocks Within a Clinical Pathway Facilitates Early Ambulation After Total Hip Arthroplasty.

OBJECTIVE: The optimal continuous peripheral nerve block (CPNB) technique for total hip arthroplasty (THA) that maximizes both analgesia and mobility is unknown. Continuous erector spinae plane (ESP) blocks were implemented at our institution as a replacement for fascia iliaca (FI) catheters to improve our THA clinical pathway. We designed this study to test the hypothesis that this change will increase early postoperative ambulation for elective primary THA patients. METHODS: We identified all consecutive primary unilateral THA cases six months before and six months after the clinical pathway change to ESP catheters. All other aspects of the THA clinical pathway and multimodal analgesic regimen including perineural infusion protocol did not change. The primary outcome was total ambulation distance (meters) on postoperative day 1. Other outcomes included total ambulation on postoperative day 2, combined two-day ambulation distance, pain scores, opioid consumption, inpatient length of stay, and minor and major adverse events. RESULTS: Eighty-eight patients comprised the final sample (43 FI and 45 ESP). Postoperative day 1 total ambulation distance was greater for the ESP group compared with the FI group (median [10th-90th percentiles] = 24.4 [0.0-54.9] vs 9.1 [0.7-45.7] meters, respectively, P = 0.036), and two-day ambulation distance was greater for the ESP group compared with the FI group (median [10th-90th percentiles] = 68.6 [9.0-128.0] vs 46.6 [3.7-104.2] meters, respectively, P = 0.038). There were no differences in pain scores, opioid use, or other outcomes. CONCLUSIONS: Replacing FI catheters with continuous ESP blocks within a clinical pathway results in increased early ambulation by elective primary THA patients.

A Multidisciplinary Patient-Specific Opioid Prescribing and Tapering Protocol Is Associated with a Decrease in Total Opioid Dose Prescribed for Six Weeks After Total Hip Arthroplasty.

OBJECTIVE: This retrospective cohort study tested the hypothesis that implementing a multidisciplinary patient-specific discharge protocol for prescribing and tapering opioids after total hip arthroplasty (THA) will decrease the morphine milligram equivalent (MME) dose of opioids prescribed. METHODS: With institutional review board approval, we analyzed a Perioperative Surgical Home database and prescription data for all primary THA patients three months before (PRE) and three months after (POST) implementation of this new discharge opioid protocol based on patients' prior 24-hour inpatient opioid consumption. The primary outcome was total opioid dosage in MME prescribed and opioid refills for six weeks after surgery. Secondary outcomes included the number of tablets and MME prescribed at discharge, in-hospital opioid consumption, length of stay, and postoperative complications. RESULTS: Forty-nine cases (25 PRE and 24 POST) were included. Total median (10th-90th percentiles) MME for six weeks postoperatively was 900 (57-2082) MME PRE vs 295 (69-741) MME POST (mean difference = 721, 95% confidence interval [CI] = 127-1316, P = 0.007, Mann-Whitney U test). Refill rates did not differ. The median (10th-90th percentiles) initial discharge prescription in MME was 675 (57-1035) PRE vs 180 (18-534) POST (mean difference = 387, 95% CI = 156-618, P = 0.003, Mann-Whitney U test) MME. There were no differences in other outcomes. CONCLUSIONS: Implementation of a patient-specific prescribing and tapering protocol decreases the mean six-week dosage of opioid prescribed by 63% after THA without increasing the refill rate.

Editorial Commentary: Augmenting Suture Anchor Fixation: Why Has It Not Caught on?

Biomechanical studies with reliable clinical applicability are challenging to carry out. The results can be heavily dependent on the materials being tested (condition and ages of specimens), environmental conditions (temperature, moisture), magnitude and direction of loading, loading characteristics (static, dynamic), loading cycles and frequency, and how one measures and defines failure. The interested reader gains more confidence in the results and recommendations of a biomechanics study if the methodology reasonably models real-world scenarios and multiple studies from different labs all come to the same general conclusion.

Social Determinants of Health and Patient-Reported Outcomes Following Total Hip and Knee Arthroplasty in Veterans.

BACKGROUND: Social determinants of health (SDOH) are the conditions in which people are born, grow, live, work, and age. They are associated with disparities in outcomes following total joint arthroplasty (TJA). These disparities occur even in equal-access healthcare systems such as the Veterans Health Administration (VHA). Our goal was to determine whether SDOH affect patient-reported outcome measures (PROMs) following TJA in VHA patients. METHODS: Patients scheduled to undergo total hip or knee arthroplasty at VHA Hospitals in Minneapolis, MN, Palo Alto, CA, and San Francisco, CA, prospectively completed PROMs before and 1 year after surgery. PROMs included the Hip disability and Osteoarthritis Outcome Score, the Knee injury and Osteoarthritis Outcome Score, and their Joint Replacement subscores. SDOH included race, ethnicity, marital status, education, and employment status. The level of poverty in each patient's neighborhood was determined. Medical comorbidities were recorded. Univariate and multivariate analyses were performed to determine whether SDOH were significantly associated with PROM improvement after surgery. RESULTS: On multivariate analysis, black race was significantly negatively correlated with knee PROM improvement and Hispanic ethnicity was significantly negatively correlated with hip PROM improvement compared to whites. Higher baseline PROM scores and lower age were significantly associated with lower PROM improvement. Significant associations were also found based on education, gender, comorbidities, and neighborhood poverty. CONCLUSION: Minority VHA patients have lower improvement in PROM scores after TJA than white patients. Further research is required to identify the reasons for these disparities and to design interventions to reduce them.

Can Machine Learning Methods Produce Accurate and Easy-to-use Prediction Models of 30-day Complications and Mortality After Knee or Hip Arthroplasty?

BACKGROUND: Existing universal and procedure-specific surgical risk prediction models of death and major complications after elective total joint arthroplasty (TJA) have limitations including poor transparency, poor to modest accuracy, and insufficient validation to establish performance across diverse settings. Thus, the need remains for accurate and validated prediction models for use in preoperative management, informed consent, shared decision-making, and risk adjustment for reimbursement. QUESTIONS/PURPOSES: The purpose of this study was to use machine learning methods and large national databases to develop and validate (both internally and externally) parsimonious risk-prediction models for mortality and complications after TJA. METHODS: Preoperative demographic and clinical variables from all 107,792 nonemergent primary THAs and TKAs in the 2013 to 2014 American College of Surgeons-National Surgical Quality Improvement Program (ACS-NSQIP) were evaluated as predictors of 30-day death and major complications. The NSQIP database was chosen for its high-quality data on important outcomes and rich characterization of preoperative demographic and clinical predictors for demographically and geographically diverse patients. Least absolute shrinkage and selection operator (LASSO) regression, a type of machine learning that optimizes accuracy and parsimony, was used for model development. Tenfold validation was used to produce C-statistics, a measure of how well models discriminate patients who experience an outcome from those who do not. External validation, which evaluates the generalizability of the models to new data sources and patient groups, was accomplished using data from the Veterans Affairs Surgical Quality Improvement Program (VASQIP). Models previously developed from VASQIP data were also externally validated using NSQIP data to examine the generalizability of their performance with a different group of patients outside the VASQIP context. RESULTS: The models, developed using LASSO regression with diverse clinical (for example, American Society of Anesthesiologists classification, comorbidities) and demographic (for example, age, gender) inputs, had good accuracy in terms of discriminating the likelihood a patient would experience, within 30 days of arthroplasty, a renal complication (C-statistic, 0.78; 95% confidence interval [CI], 0.76-0.80), death (0.73; 95% CI, 0.70-0.76), or a cardiac complication (0.73; 95% CI, 0.71-0.75) from one who would not. By contrast, the models demonstrated poor accuracy for venous thromboembolism (C-statistic, 0.61; 95% CI, 0.60-0.62) and any complication (C-statistic, 0.64; 95% CI, 0.63-0.65). External validation of the NSQIP- derived models using VASQIP data found them to be robust in terms of predictions about mortality and cardiac complications, but not for predicting renal complications. Models previously developed with VASQIP data had poor accuracy when externally validated with NSQIP data, suggesting they should not be used outside the context of the Veterans Health Administration. CONCLUSIONS: Moderately accurate predictive models of 30-day mortality and cardiac complications after elective primary TJA were developed as well as internally and externally validated. To our knowledge, these are the most accurate and rigorously validated TJA-specific prediction models currently available (http://med.stanford.edu/s-spire/Resources/clinical-tools-.html). Methods to improve these models, including the addition of nonstandard inputs such as natural language processing of preoperative clinical progress notes or radiographs, should be pursued as should the development and validation of models to predict longer term improvements in pain and function. LEVEL OF EVIDENCE: Level III, diagnostic study.

Are Case Volume and Facility Complexity Level Associated With Postoperative Complications After Hip Fracture Surgery in the Veterans Affairs Healthcare System?

BACKGROUND: Hospital-related factors associated with mortality and morbidity after hip fracture surgery are not completely understood. The Veterans Health Administration (VHA) is the largest single-payer, networked healthcare system in the country serving a relatively homogenous patient population with facilities that vary in size and resource availability. These characteristics provide some degree of financial and patient-level controls to explore the association, if any, between surgical volume and facility resource availability and hospital performance regarding postoperative complications after hip fracture surgery. QUESTIONS/PURPOSES: (1) Do VHA facilities with the highest complexity level designation (Level 1a) have a disproportionate number of better-than-expected performance outliers for major postoperative complications compared with lower-complexity level facilities? (2) Do VHA facilities with higher hip fracture surgical volume have a disproportionate number of better-than-expected performance outliers for major postoperative complications compared with lower-volume facilities? METHODS: We explored the Veterans Affairs Surgical Quality Improvement Project (VASQIP) database from October 2001 to September 2012 for records of hip fracture surgery performed. Data reliability of the VASQIP database has been previously validated. We excluded nine of the 98 VHA facilities for contributing fewer than 30 records. The remaining 89 VHA facilities provided 23,029 records. The VHA designates a complexity level to each facility based on multiple criteria. We labeled facilities with a complexity Level 1a (38 facilities)-the highest achievable VHA designated complexity level-as high complexity; we labeled all other complexity level designations as low complexity (51 facilities). Facility volume was divided into tertiles: high (> 277 hip fracture procedures during the sampling frame), medium (204 to 277 procedures), and low (< 204 procedures). The patient population treated by low-complexity facilities was older, had a higher prevalence of severe chronic obstructive pulmonary disease (26% versus 22%, p < 0.001), and had a higher percentage of patients having surgery within 2 days of hospital admission (83% versus 76%, p < 0.001). High-complexity facilities treated more patients with recent congestive heart failure exacerbation (4% versus 3%, p < 0.001). We defined major postoperative complications as having at least one of the following: death within 30 days of surgery, cardiac arrest requiring cardiopulmonary resuscitation, new q-wave myocardial infarction, deep vein thrombosis and/or pulmonary embolism, ventilator dependence for at least 48 hours after surgery, reintubation for respiratory or cardiac failure, acute renal failure requiring renal replacement therapy, progressive renal insufficiency with a rise in serum creatinine of at least 2 mg/dL from preoperative value, pneumonia, or surgical site infection. We used the observed-to-expected ratio (O/E ratio)-a risk-adjusted metric to classify facility performance-for major postoperative complications to assess the performance of VHA facilities. Outlier facilities with 95% confidence intervals (95% CI) for O/E ratio completely less than 1.0 were labeled "exceed expectation;" those that were completely greater than 1.0 were labeled "below expectation." We compared differences in the distribution of outlier facilities between high and low-complexity facilities, and between high-, medium-, and low-volume facilities using Fisher's exact test. RESULTS: We observed no association between facility complexity level and the distribution of outlier facilities (high-complexity: 5% exceeded expectation, 5% below expectation; low-complexity: 8% exceeded expectation, 2% below expectation; p = 0.742). Compared with high-complexity facilities, the adjusted odds ratio for major postoperative complications for low-complexity facilities was 0.85 (95% CI, 0.67-1.09; p = 0.108).We observed no association between facility volume and the distribution of outlier facilities: 3% exceeded expectation and 3% below expectation for high-volume; 10% exceeded expectation and 3% below expectation for medium-volume; and 7% exceeded expectation and 3% below expectation for low-volume; p = 0.890). The adjusted odds ratios for major postoperative complications were 0.87 (95% CI, 0.73-1.05) for low- versus high-volume facilities and 0.89 (95% CI, 0.79-1.02] for medium- versus high-volume facilities (p = 0.155). CONCLUSIONS: These results do not support restricting facilities from treating hip fracture patients based on historical surgical volume or facility resource availability. Identification of consistent performance outliers may help health care organizations with multiple facilities determine allocation of services and identify characteristics and processes that determine outlier status in the interest of continued quality improvement. LEVEL OF EVIDENCE: Level III, therapeutic study.

Mobile Compression Reduces Bleeding-related Readmissions and Wound Complications After THA and TKA.

BACKGROUND: The use of chemoprophylaxis to prevent thromboembolic disease after primary THA and TKA can be associated with postoperative bleeding complications. Mechanical prophylaxis has been studied as an alternative to chemoprophylaxis with greater safety in patients undergoing THA, but no data have been published comparing the safety of chemoprophylaxis versus mechanical methods for patients undergoing TKA. The risk of readmission resulting from bleeding and venous thromboembolism (VTE) has also not been determined for patients undergoing THA or TKA when treated with low-molecular-weight heparin (LMWH) alone compared with mechanical prophylaxis plus aspirin (ASA). QUESTION/PURPOSES: We sought to answer four questions: For the THA and TKA cohorts, respectively, (1) was the incidence of readmission resulting from VTE and bleeding complications higher with LMWH than mobile compression plus ASA; and (2) was the incidence of wound bleeding complications higher with LMWH than mechanical compression plus ASA? For the TKA cohort specifically, (3) was the frequency of systemic bleeding events and complications related to chemical prophylaxis higher with LMWH compared with mechanical compression plus ASA? (4) Was there a difference in symptomatic VTEs between LMWH and mechanical compression plus ASA? METHODS: Between November 2008 and April 2011, 632 patients underwent primary THA and TKA. Seventy-two patients (11%) were identified before surgery as being at high risk for VTE (31 patients) or bleeding (41 patients) and were excluded from the study. Five hundred sixty patients (89%) were considered to be at standard risk for VTE and bleeding and comprise the study cohort. Between November 2008 and November 2009, 252 patients (76 THAs, 176 TKAs) underwent THA and TKA and were treated with LMWH (5 mg dalteparin given subcutaneously daily for 14 days) and in-hospital nonmobile mechanical compression. Between November 2009 and April 2011, a total of 308 patients undergoing THA and TKA (108 THAs, 200 TKAs) were treated using a mobile compression device plus oral aspirin once daily for 2 weeks after surgery. All complications and readmissions that occurred within 6 weeks of surgery were noted. There were no differences between the VTE treatment groups with regard to age, sex, or body mass index. RESULTS: For the THA cohort, there was no difference in the frequency of readmission for a bleeding complication (wound or systemic) between the two groups (2.6% for LMWH versus 0.9% for mobile compression; p = 0.57; odds ratio [OR], 2.9). Patients undergoing TKA treated with LMWH had higher readmission rates within 6 weeks of surgery because of a bleeding complication, a wound infection, or the development of a VTE (6.8% for LMWH versus 1.5% for mobile compression; p = 0.015; OR, 4.8). For the THA cohort, there was higher wound bleeding complication frequency with LMWH (9.2% for LMWH versus 0.9% for mechanical compression; p = 0.009; OR, 10.9). Patients undergoing TKA treated with LMWH had a higher frequency of wound bleeding complications or infection (3.9% for LMWH versus 0.5% for mobile compression; p = 0.028; OR, 8.2). Patients undergoing TKA treated with LMWH had higher rates of systemic bleeding or a complication secondary to LMWH administration (2.8% for LMWH versus 0% for mobile compression; p = 0.022; OR, 12.8). No difference was noted in the rate of symptomatic VTEs between either group (for THA: 2.6% for the LMWH group versus 1.9% for the mechanical compression group; p = 1; for TKA: 1.1% versus 0%, respectively; p = 0.22). CONCLUSIONS: Based on these results, we advocate for routine use of mobile mechanical compression devices in the prevention of VTEs and complications associated with more potent chemical anticoagulants. However, more focused randomized clinical trials are needed to validate these findings. LEVEL OF EVIDENCE: Level III, therapeutic study.

American Joint Replacement Registry Risk Calculator Does Not Predict 90-day Mortality in Veterans Undergoing Total Joint Replacement.

BACKGROUND: The American Joint Replacement Registry (AJRR) Total Joint Risk Calculator uses demographic and clinical parameters to provide risk estimates for 90-day mortality and 2-year periprosthetic joint infection (PJI). The tool is intended to help surgeons counsel their Medicare-eligible patients about their risk of death and PJI after total joint arthroplasty (TJA). However, for a predictive risk model to be useful, it must be accurate when applied to new patients; this has yet to be established for this calculator. QUESTIONS/PURPOSES: To produce accuracy metrics (ie, discrimination, calibration) for the AJRR mortality calculator using data from Medicare-eligible patients undergoing TJA in the Veterans Health Administration (VHA), the largest integrated healthcare system in the United States, where more than 10,000 TJAs are performed annually. METHODS: We used the AJRR calculator to predict risk of death within 90 days of surgery among 31,214 VHA patients older than 64 years of age who underwent primary TJA; data was drawn from the Veterans Affairs Surgical Quality Improvement Project (VASQIP) and VA Corporate Data Warehouse (CDW). We then used VHA mortality data to evaluate the extent to which the AJRR calculator estimates distinguished individuals who died compared with those who did not (C-statistic), and graphically depicted the relationship between estimated risk and observed mortality (calibration). As a secondary evaluation of the calculator, a sample of 39,300 patients younger than 65 years old was assigned to the youngest age group available to the user (65-69 years) as might be done in real-world practice. RESULTS: C-statistics for 90-day mortality for the older samples were 0.62 (95% CI, 0.60-0.64) and for the younger samples they were 0.46 (95% CI, 0.43-0.49), suggesting poor discrimination. Calibration analysis revealed poor correspondence between deciles of predicted risk and observed mortality rates. Poor discrimination and calibration mean that patients who died will frequently have a lower estimated risk of death than surviving patients. CONCLUSIONS: For Medicare-eligible patients receiving TJA in the VA, the AJRR risk calculator had a poor performance in the prediction of 90-day mortality. There are several possible reasons for the model's poor performance. Veterans Health Administration patients, 97% of whom were men, represent only a subset of the broader Medicare population. However, applying the calculator to a subset of the target population should not affect its accuracy. Other reasons for poor performance include a lack of an underlying statistical model in the calculator's implementation and simply the challenge of predicting rare events. External validation in a more representative sample of Medicare patients should be conducted to before assuming this tool is accurate for its intended use. LEVEL OF EVIDENCE: Level I, diagnostic study.

Antiseptics Commonly Used in Total Joint Arthroplasty Interact and May Form Toxic Products.

BACKGROUND: Multiple antiseptics have been described for use in total joint arthroplasty infection, and the use of multiple antiseptic solutions during a single operation has been described. Our clinical experience is that chlorhexidine (CHX) and Dakin's solution (NaOCl) interact and form a precipitate. The purpose of this study is to determine whether this reaction could be replicated in a laboratory setting, and to determine if other commonly used antiseptics also visibly react when mixed. METHODS: Four percent chlorhexidine gluconate (CHX), 0.5% sodium hypochlorite (NaOCl), 3% hydrogen peroxide (H2O2), and 10% povidone-iodine (BTD) solutions were obtained and all possible combinations were mixed. Any visible reactions were noted and recorded, and a literature search was performed to characterize the reaction and products. RESULTS: CHX and NaOCl, CHX and H2O2, and CHX and BTD reacted instantly, forming a precipitate. NaOCl and H2O2 reacted to produce a gas. NaOCl and BTD reacted and produced a color change. The literature review revealed that at least 2 of the reactions tested (CHX + NaOCl and NaOCl + H2O2) could result in byproducts toxic to humans. CONCLUSION: Surgeons must be aware of these interactions when using antiseptic solutions during procedures. Caution should be used combining or mixing antiseptics, and we recommend against concomitant introduction in a surgical wound.

Prediction Models for 30-Day Mortality and Complications After Total Knee and Hip Arthroplasties for Veteran Health Administration Patients With Osteoarthritis.

BACKGROUND: Statistical models to preoperatively predict patients' risk of death and major complications after total joint arthroplasty (TJA) could improve the quality of preoperative management and informed consent. Although risk models for TJA exist, they have limitations including poor transparency and/or unknown or poor performance. Thus, it is currently impossible to know how well currently available models predict short-term complications after TJA, or if newly developed models are more accurate. We sought to develop and conduct cross-validation of predictive risk models, and report details and performance metrics as benchmarks. METHODS: Over 90 preoperative variables were used as candidate predictors of death and major complications within 30 days for Veterans Health Administration patients with osteoarthritis who underwent TJA. Data were split into 3 samples-for selection of model tuning parameters, model development, and cross-validation. C-indexes (discrimination) and calibration plots were produced. RESULTS: A total of 70,569 patients diagnosed with osteoarthritis who received primary TJA were included. C-statistics and bootstrapped confidence intervals for the cross-validation of the boosted regression models were highest for cardiac complications (0.75; 0.71-0.79) and 30-day mortality (0.73; 0.66-0.79) and lowest for deep vein thrombosis (0.59; 0.55-0.64) and return to the operating room (0.60; 0.57-0.63). CONCLUSIONS: Moderately accurate predictive models of 30-day mortality and cardiac complications after TJA in Veterans Health Administration patients were developed and internally cross-validated. By reporting model coefficients and performance metrics, other model developers can test these models on new samples and have a procedure and indication-specific benchmark to surpass.

CCL2/CCR2, but not CCL5/CCR5, mediates monocyte recruitment, inflammation and cartilage destruction in osteoarthritis.

OBJECTIVES: While various monocyte chemokine systems are increased in expression in osteoarthritis (OA), the hierarchy of chemokines and chemokine receptors in mediating monocyte/macrophage recruitment to the OA joint remains poorly defined. Here, we investigated the relative contributions of the CCL2/CCR2 versus CCL5/CCR5 chemokine axes in OA pathogenesis. METHODS: Ccl2-, Ccr2-, Ccl5- and Ccr5-deficient and control mice were subjected to destabilisation of medial meniscus surgery to induce OA. The pharmacological utility of blocking CCL2/CCR2 signalling in mouse OA was investigated using bindarit, a CCL2 synthesis inhibitor, and RS-504393, a CCR2 antagonist. Levels of monocyte chemoattractants in synovial tissues and fluids from patients with joint injuries without OA and those with established OA were investigated using a combination of microarray analyses, multiplexed cytokine assays and immunostains. RESULTS: Mice lacking CCL2 or CCR2, but not CCL5 or CCR5, were protected against OA with a concomitant reduction in local monocyte/macrophage numbers in their joints. In synovial fluids from patients with OA, levels of CCR2 ligands (CCL2, CCL7 and CCL8) but not CCR5 ligands (CCL3, CCL4 and CCL5) were elevated. We found that CCR2+ cells are abundant in human OA synovium and that CCR2+ macrophages line, invade and are associated with the erosion of OA cartilage. Further, blockade of CCL2/CCR2 signalling markedly attenuated macrophage accumulation, synovitis and cartilage damage in mouse OA. CONCLUSIONS: Our findings demonstrate that monocytes recruited via CCL2/CCR2, rather than by CCL5/CCR5, propagate inflammation and tissue damage in OA. Selective targeting of the CCL2/CCR2 system represents a promising therapeutic approach for OA.