Development of Diagnostic Quality Metrics for Prosthetic Joint Infection.

ABSTRACT: Although well-accepted clinical practice guidelines exist for the diagnosis of prosthetic joint infection (PJI), little is known about the quality of diagnosis for PJI. The identification of quality gaps in the diagnosis of PJI would facilitate the development of care structures and processes to shorten time to diagnosis and reduce the significant morbidity, mortality, and economic burden associated with this condition. Hence, we sought to develop valid clinical quality measures to improve the timeliness and accuracy of PJI diagnosis. We convened a nine-member multidisciplinary national panel of PJI experts including orthopedic surgeons, infectious disease specialists, an emergency medicine physician, and a patient previously treated for PJI to review, discuss, and rate the validity of proposed measures using a modification of the RAND-UCLA appropriateness method. In total, 57 permutations of six proposed measures were rated. Populations considered to be at high enough risk for PJI that certain care processes should always be performed were identified by the panel. Among the proposed quality measures, the panel rated five as valid. These novel clinical quality measures could provide insight into care gaps in the diagnosis of PJI.

Defining Patient-relevant Thresholds and Change Scores for the HOOS JR and KOOS JR Anchored on the Patient-acceptable Symptom State Question.

BACKGROUND: When evaluating the results of clinical research studies, readers need to know that patients perceive effect sizes, not p values. Knowing the minimum clinically important difference (MCID) and the patient-acceptable symptom state (PASS) threshold for patient-reported outcome measures helps us to ascertain whether our interventions result in improvements that are large enough for patients to care about, and whether our treatments alleviate patient symptoms sufficiently. Prior studies have developed the MCID and PASS threshold for the Hip Disability and Osteoarthritis Outcome Score for Joint Replacement (HOOS JR) and Knee Injury and Osteoarthritis Outcome Score for Joint Replacement (KOOS JR) anchored on satisfaction with surgery, but to our knowledge, neither the MCID nor the PASS thresholds for these instruments anchored on a single-item PASS question have been described. QUESTIONS/PURPOSES: (1) What are the MCID (defined here as the HOOS/KOOS JR change score associated with achieving PASS) and PASS threshold for the HOOS JR and KOOS JR anchored on patient responses to the single-item PASS instrument? (2) How do patient demographic factors such as age, gender, and BMI correlate with MCID and PASS thresholds using the single-item PASS instrument? METHODS: Between July 2020 and September 2021, a total of 10,970 patients underwent one primary unilateral THA or TKA and completed at least one of the three surveys (preoperative HOOS or KOOS JR, 1-year postoperative HOOS or KOOS JR, and 1-year postoperative single-item anchor) at one large, academic medical center. Of those, only patients with data for all three surveys were eligible, leaving 13% (1465 total; 783 THAs and 682 TKAs) for analysis. Despite this low percentage, the overall sample size was large, and there was little difference between completers and noncompleters in terms of demographics or baseline patient-reported outcome measure scores. Patients undergoing bilateral total joint arthroplasty or revision total joint arthroplasty and those without all three surveys at 1 year of follow-up were excluded. A receiver operating characteristic curve analysis, leveraging a 1-year, single-item PASS (that is, "Do you consider that your current state is satisfactory?" with possible answers of "yes" or "no") as the anchor was then used to establish the MCID and PASS thresholds among the 783 included patients who underwent primary unilateral THA and 682 patients who underwent primary unilateral TKA. We also explored the associations of age at the time of surgery (younger than 65 years or 65 years and older), gender (men or women), BMI (< 30 or ≥ 30 kg/m 2 ), and baseline Patient-Reported Outcome Measure Information System-10 physical and mental component scores (< 50 or ≥ 50) for each of the MCID and PASS thresholds through stratified analyses. RESULTS: For the HOOS JR, the MCID associated with the PASS was 23 (95% CI 18 to 31), with an area under the receiver operating characteristic curve of 0.75, and the PASS threshold was 81 (95% CI 77 to 85), with an area under the receiver operating characteristic curve of 0.81. For the KOOS JR, the MCID was 16 (95% CI 14 to 18), with an area under the receiver operating characteristic curve of 0.75, and the PASS threshold was 71 (95% CI 66 to 73) with an area under the receiver operating characteristic curve of 0.84. Stratified analyses indicated higher change scores and PASS threshold for younger men undergoing THA and higher PASS thresholds for older women undergoing TKA. CONCLUSION: Here, we demonstrated the utility of a single patient-centered anchor question, raising the question as to whether simply collecting a postoperative PASS is an easier way to measure success than collecting preoperative and postoperative patient-reported outcome measures and then calculating MCIDs and the substantial clinical benefit. LEVEL OF EVIDENCE: Level III, therapeutic study.

Quality Indicators for Osteoporosis in Adults: A Review of Performance Measures by the American College of Physicians.

Primary osteoporosis is characterized by decreasing bone mass and density and reduced bone strength that leads to a higher risk for fracture, especially hip and spine fractures. The prevalence of osteoporosis in the United States is estimated at 12.6% for adults older than 50 years. Although it is most frequently diagnosed in White and Asian females, it still affects males and females of all ethnicities. Osteoporosis is considered a major health issue, which has prompted the development and use of several performance measures to assess and improve the effectiveness of screening, diagnosis, and treatment. These performance measures are often used in accountability, public reporting, and/or payment programs. However, the reliability, validity, evidence, attribution, and meaningfulness of performance measures have been questioned. The purpose of this paper is to present a review of current performance measures on osteoporosis and inform physicians, payers, and policymakers in their selection of performance measures for this condition. The Performance Measurement Committee identified 6 osteoporosis performance measures relevant to internal medicine physicians, only 1 of which was found valid at all levels of attribution. This paper also proposes a performance measure concept to address a performance gap for the initial approach to therapy for patients with a new diagnosis of osteoporosis based on the current American College of Physicians guideline.

Population-Based Applications and Analytics Using Patient-Reported Outcome Measures.

The intersection of big data and artificial intelligence (AI) has resulted in advances in numerous areas, including machine learning, computer vision, and natural language processing. Although there are many potentially transformative applications of AI in health care, including precision medicine, this industry has been slow to adopt these technologies. At the same time, the operations of health care have historically been system-directed and physician-directed rather than patient-centered. The application of AI to patient-reported outcome measures (PROMs), which provide insight into patient-centered health outcomes, could steer research and healthcare delivery toward decisions that optimize outcomes important to patients. Historically, PROMs have only been collected within research registries. However, the increasing availability of PROMs within electronic health records has led to their inclusion in big data ecosystems, where they can inform or be informed by other data elements. The use of big data to analyze PROMs can help establish norms, evaluate data distribution, and determine proportions of patients achieving change or threshold standards. This information can be used for benchmarking, risk adjustment, predictive modeling, and ultimately improving the health of individuals and populations.

Defining a successful total knee arthroplasty: a systematic review of metrics of clinically important changes.

BACKGROUND: Despite the increasing use of patient-reported outcome measures (PROMs), the methodology used to evaluate clinically significant postoperative outcomes after total knee arthroplasty (TKA) is variable. The review aimed to survey studies with identified PROM-based metrics of clinical efficacy and the assessment procedures after TKA. METHODS: The MEDLINE database was queried from 2008-2020. Inclusion criteria were: full texts, English language, primary TKA with minimum one-year follow-up, use of metrics for assessing clinical outcomes with PROMs, and primary derivations of metrics. The following PROM-based metrics were identified: minimal clinically important difference (MCID), minimum detectable change (MDC), patient acceptable symptom state (PASS), and substantial clinical benefit (SCB). Study design, PROM value data, and methods of derivation for metrics were recorded. RESULTS: We identified 18 studies (including 46,173 patients) that met the inclusion criteria. Across these studies, 10 different PROMs were employed, and MCID was derived in 15 studies (83%). The MCID was calculated using anchor-based techniques in nine studies (50%) and distribution techniques in eight studies (44%). PASS values were presented in two studies (11%) and SCB in one study (6%) using an anchor-based method; MDC was derived in four studies (22%) using the distribution method. CONCLUSION: There is variability in the TKA literature with respect to the definition and derivation of measurements of clinically significant outcomes. Standardization of these values may have implications for optimal case selection and PROM-based quality measurement, ultimately improving patient satisfaction and outcomes.

Machine Learning on Medicare Claims Poorly Predicts the Individual Risk of 30-Day Unplanned Readmission After Total Joint Arthroplasty, Yet Uncovers Interesting Population-level Associations With Annual Procedure Volumes.

BACKGROUND: Unplanned hospital readmissions after total joint arthroplasty (TJA) represent potentially serious adverse events and remain a critical measure of hospital quality. Predicting the risk of readmission after TJA may provide patients and clinicians with valuable information for preoperative decision-making. QUESTIONS/PURPOSES: (1) Can nonlinear machine-learning models integrating preoperatively available patient, surgeon, hospital, and county-level information predict 30-day unplanned hospital readmissions in a large cohort of nationwide Medicare beneficiaries undergoing TJA? (2) Which predictors are the most important in predicting 30-day unplanned hospital readmissions? (3) What specific information regarding population-level associations can we obtain from interpreting partial dependency plots (plots describing, given our modeling choice, the potentially nonlinear shape of associations between predictors and readmissions) of the most important predictors of 30-day readmission? METHODS: National Medicare claims data (chosen because this database represents a large proportion of patients undergoing TJA annually) were analyzed for patients undergoing inpatient TJA between October 2016 and September 2018. A total of 679,041 TJAs (239,391 THAs [61.3% women, 91.9% White, 52.6% between 70 and 79 years old] and 439,650 TKAs [63.3% women, 90% White, 55.2% between 70 and 79 years old]) were included. Model features included demographics, county-level social determinants of health, prior-year (365-day) hospital and surgeon TJA procedure volumes, and clinical classification software-refined diagnosis and procedure categories summarizing each patient's Medicare claims 365 days before TJA. Machine-learning models, namely generalized additive models with pairwise interactions (prediction models consisting of both univariate predictions and pairwise interaction terms that allow for nonlinear effects), were trained and evaluated for predictive performance using area under the receiver operating characteristic (AUROC; 1.0 = perfect discrimination, 0.5 = no better than random chance) and precision-recall curves (AUPRC; equivalent to the average positive predictive value, which does not give credit for guessing "no readmission" when this is true most of the time, interpretable relative to the base rate of readmissions) on two holdout samples. All admissions (except the last 2 months' worth) were collected and split randomly 80%/20%. The training cohort was formed with the random 80% sample, which was downsampled (so it included all readmissions and a random, equal number of nonreadmissions). The random 20% sample served as the first test cohort ("random holdout"). The last 2 months of admissions (originally held aside) served as the second test cohort ("2-month holdout"). Finally, feature importances (the degree to which each variable contributed to the predictions) and partial dependency plots were investigated to answer the second and third research questions. RESULTS: For the random holdout sample, model performance values in terms of AUROC and AUPRC were 0.65 and 0.087, respectively, for THA and 0.66 and 0.077, respectively, for TKA. For the 2-month holdout sample, these numbers were 0.66 and 0.087 and 0.65 and 0.075. Thus, our nonlinear models incorporating a wide variety of preoperative features from Medicare claims data could not well-predict the individual likelihood of readmissions (that is, the models performed poorly and are not appropriate for clinical use). The most predictive features (in terms of mean absolute scores) and their partial dependency graphs still confer information about population-level associations with increased risk of readmission, namely with older patient age, low prior 365-day surgeon and hospital TJA procedure volumes, being a man, patient history of cardiac diagnoses and lack of oncologic diagnoses, and higher county-level rates of hospitalizations for ambulatory-care sensitive conditions. Further inspection of partial dependency plots revealed nonlinear population-level associations specifically for surgeon and hospital procedure volumes. The readmission risk for THA and TKA decreased as surgeons performed more procedures in the prior 365 days, up to approximately 75 TJAs (odds ratio [OR] = 1.2 for TKA and 1.3 for THA), but no further risk reduction was observed for higher annual surgeon procedure volumes. For THA, the readmission risk decreased as hospitals performed more procedures, up to approximately 600 TJAs (OR = 1.2), but no further risk reduction was observed for higher annual hospital procedure volumes. CONCLUSION: A large dataset of Medicare claims and machine learning were inadequate to provide a clinically useful individual prediction model for 30-day unplanned readmissions after TKA or THA, suggesting that other factors that are not routinely collected in claims databases are needed for predicting readmissions. Nonlinear population-level associations between low surgeon and hospital procedure volumes and increased readmission risk were identified, including specific volume thresholds above which the readmission risk no longer decreases, which may still be indirectly clinically useful in guiding policy as well as patient decision-making when selecting a hospital or surgeon for treatment. LEVEL OF EVIDENCE: Level III, therapeutic study.

Performance Measures for Physicians Providing Clinical Care Using Telemedicine: A Position Paper From the American College of Physicians.

There has been an exponential growth in the use of telemedicine services to provide clinical care, accelerated by the COVID-19 pandemic. Clinical care delivered via telemedicine has become a major and accepted method of health care delivery for many patients. There is an urgent need to understand quality of care in the telemedicine environment. This American College of Physicians position paper presents 6 recommendations to ensure the appropriate use of performance measures to evaluate quality of clinical care provided in the telemedicine environment.

Metrics of Clinically Important Changes in Total Hip Arthroplasty: A Systematic Review.

BACKGROUND: Although patient-reported outcome measures (PROMs) have become a regularly used metric, there is little consensus on the methodology used to determine clinically relevant postoperative outcomes. We systematically reviewed the literature for studies that have identified metrics of clinical efficacy after total hip arthroplasty (THA) including minimal clinically important difference (MCID), patient acceptable symptom state (PASS), minimal detectable change (MDC), and substantial clinical benefit (SCB). METHODS: A systematic review examining quantitative metrics for assessing clinical improvement with PROMs following THA was conducted according to Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines using the MEDLINE database from 2008 to 2020. Inclusion criteria included full texts, English language, primary THA with minimum 1-year follow-up, use of metrics for assessing clinical outcomes with PROMs, and primary derivations of those metrics. Sixteen studies (24,487 THA patients) met inclusion criteria and 11 different PROMs were reported. RESULTS: MCIDs were calculated using distribution methods in 7 studies (44%), anchor methods in 2 studies (13%), and both methods in 2 studies (13%). MDC was calculated in 2 studies, PASS was reported in 1 study using anchor-based method, and SCB was calculated in 1 study using anchor-based method. CONCLUSION: There is a lack of consistency in the literature regarding the use and interpretation of PROMs to assess patient satisfaction. MCID was the most frequently reported measure, while MDC, SCB, and PASS were used relatively infrequently. Method of derivation varied based on the PROM used; distribution method was more frequently used for MCID.

Eliciting Activity Goals With a Self-Administered Survey Among Patients With Hip or Knee Osteoarthritis.

Background: Success of treatment for hip or knee osteoarthritis (OA) should be evaluated relative to patients' personal activity goals. Questions/Purposes: We sought to ascertain important principles for collecting such goals and developed a survey informed by those principles to facilitate better shared decision-making. Methods: From a series of 100 patient interviews inquiring about specific activity goals, we identified 6 principles for goal collection that are important to patients and physicians and could practically facilitate better shared decision-making (phase 1). Incorporating these principles, we designed a self-administered survey to measure specific pretreatment activity goals, piloting in 1 surgeon's office (phase 2). During office visits, the feasibility of achieving stated goals was discussed between the surgeon and the patient, and goal modifications were recorded. Results: The phase 2 survey was administered to 252 patients, among whom 130 were women (51.6%); 215 (85.3%), white; mean age, 58.5 years; mean body mass index, 30.2 kg/m2; and 92.9% had 1 or more goals, totaling 106 unique goals. Patient demographics were associated with having goals for walking, running, exercising, golfing, tennis, and stairs. Hip and knee patients could last perform their goal on average 21.7 and 38.6 months prior (P = .002). Patient and surgeon agreed to modify goals 19% of the time, more often among younger patients (P = .001) and for running (64% modified, P < .0001) and skiing (42%, P = .0026), but less often for walking (14%, P = .0430) and golf (0%, P = .0204). Conclusions: Patients' activity goals can be captured by a self-administered survey, collected before an office visit, and used to facilitate shared decision-making.

Presenteeism and absenteeism before and after single-level lumbar spine surgery.

BACKGROUND CONTEXT: Health can impact work performance through absenteeism, time spent away from work, and presenteeism, inhibited at-work performance. Low back pain is common and costly, both in terms of direct medical expenditures and indirect reduced work performance. PURPOSE: Surgery for lumbar spinal pathology is an important part of treatment for patients who do not respond to nonsurgical management. While the indirect costs of return to work and absenteeism among employed patients undergoing lumbar spine surgery have been studied, little work has been done to quantify presenteeism before and after lumbar spine surgery. STUDY DESIGN/SETTING: Prospective cohort study at a single high-volume urban musculoskeletal specialty hospital. PATIENT SAMPLE: Patients undergoing single-level lumbar spinal fusion and/or decompression surgery. OUTCOME MEASURES: Presenteeism and absenteeism were measured using the World Health Organization's Health and Work Performance Questionnaire before surgery, as well as 6 weeks, 6 months, and 12 months after surgery. METHODS: Average presenteeism and absenteeism were evaluated at pre-surgical baseline and each follow-up timepoint. Monthly average time lost to presenteeism and absenteeism were calculated before surgery and 12 months after surgery. Study data were collected and managed using REDCap electronic data capture tools with support from Clinical and Translational Science Center grant, UL1TR002384. One author discloses royalties, private investments, consulting fees, speaking/teaching arrangements, travel, board of directorship, and scientific advisory board membership totaling >$300,000. RESULTS: We enrolled 134 employed surgical patients, among whom 115 (86%) responded at 6 weeks, 105 (78%) responded at 6 months, and 115 (86%) responded at 12 months. Preoperatively, mean age was 56.4 years (median 57.5), and 41.0% were women; 68 (50.7%) had only decompressions, while 66 (49.3%) had fusions. Among respondents at each time point, 98%, 92%, and 92% were still employed, among whom 76%, 96%, and 96% had resumed working, respectively (median 29 days). Average at-work performance among working patients (who responded at each pair of timepoints) moved from 75.4 to 78.7 between baseline and 6 weeks, 71.8 to 85.9 between baseline and 6 months, and 73.0 to 88.1 between baseline and 12 months. Gains were concentrated among the 52.0% of patients whose at-work performance was declining (and low) leading up to surgery. Average absenteeism was relatively unmoved between baseline and each follow-up. Before surgery, the monthly average time lost to presenteeism and absenteeism was 19.8% and 18.9%, respectively; 12 months after surgery, these numbers were 9.7% and 16.0%; changes represent a mitigated loss of 13.0 percentage points of average monthly value. CONCLUSIONS: Presenteeism and absenteeism contributed roughly evenly to preoperative average monthly lost time. Although average changes in absenteeism and 6-week at-work performance were small, average changes in at-work performance at 6 and 12 months were significant. Cost-benefit analyses of lumbar spine surgery should therefore consider improved presenteeism, which appears to offset some of the direct and indirect costs of surgical treatment.

Patient and Surgeon Risk-Taking Regarding Total Joint Arthroplasty.

BACKGROUND: Decisions regarding care for osteoarthritis involve physicians helping patients understand likely benefits and harms of treatment. Little work has directly compared patient and surgeon risk-taking attitudes, which may help inform strategies for shared decision-making and improve patient satisfaction. METHODS: We surveyed patients contemplating total joint arthroplasty visiting a high-volume specialty hospital regarding general questions about risk-taking, as well as willingness to undergo surgery under hypothetical likelihoods of moderate improvement and complications. We compared responses from surgeons answering similar questions about willingness to recommend surgery. RESULTS: Altogether 82% (162/197) of patients responded, as did 65% (30/46) of joint replacement surgeons. Mean age among patients was 66.4 years; 58% were female. Surgeons averaged 399 surgeries in 2019. Responses were similar between groups for general, health, career, financial, and sports/leisure risk-taking (P > .20); surgeons were marginally more risk-taking in driving (P = .05). For willingness to have or recommend surgery, as the chance of benefit decreased, or the chance of harm increased, the percentage willing to have or recommend surgery decreased. Between a 70% and 95% chance of moderate improvement (for a 2% complication risk), as well as between a 90% and 95% chance of moderate improvement (for 4% and 6% complication risks), the percentage willing to have or recommend surgery was indistinguishable between patients and surgeons. However, for lower likelihoods of improvement, a higher percentage of patients were willing to undergo surgery than surgeons recommended. Patients were also more often indifferent between complication risks. CONCLUSION: Although patients and surgeons were often willing to have or recommend joint replacement surgery at similar rates, they diverged for lower-benefit higher-harm scenarios.

Defining the Patient Acceptable Symptom State for the HOOS JR and KOOS JR After Primary Total Joint Arthroplasty.

BACKGROUND: It is essential to quantify an acceptable outcome after total joint arthroplasty (TJA) in order to understand quality of care. The purpose of this study was to define patient acceptable symptom state (PASS) thresholds for the Knee injury and Osteoarthritis Outcome Score, Joint Replacement (KOOS JR) and the Hip disability and Osteoarthritis Outcome Score, Joint Replacement (HOOS JR) after TJA. METHODS: A receiver operating characteristic (ROC) curve analysis, leveraging 2-year satisfaction of "moderate improvement" or better as the anchor, was used to establish PASS thresholds among 5,216 patients who underwent primary total hip arthroplasty and 4,036 who underwent primary total knee arthroplasty from 2007 to 2012 with use of an institutional registry. Changes in PASS thresholds were explored by stratifying and recalculating these thresholds by age at the time of surgery (<70 or ≥70 years of age), sex (men or women), body mass index (BMI; <30 or ≥30 kg/m2), and baseline Short Form-36 (SF-36) physical and mental component scores (<50 or ≥50). RESULTS: The HOOS JR PASS threshold was 76.7 (area under the ROC curve [AUC] = 0.91), which was achieved by 4,334 patients (83.1%). The KOOS JR PASS threshold was 63.7 (AUC = 0.89), which was achieved by 3,461 patients (85.8%). Covariate stratification demonstrated that PASS thresholds were higher in men compared with women, and in those with higher preoperative SF-36 physical and mental scores (≥50) compared with lower SF-36 scores (<50). Results differed between instruments for BMI and age: higher BMI was associated with a lower PASS threshold for the HOOS JR but a higher PASS threshold for the KOOS JR. The HOOS JR PASS threshold was higher in patients who were <70 years of age compared with those who were ≥70 years of age, but was equivalent for the KOOS JR. CONCLUSIONS: The PASS thresholds for the HOOS JR and KOOS JR at 2 years after TJA were 76.7 and 63.7, respectively. The PASS thresholds were associated with certain preoperative covariates, suggesting that an acceptable symptom state after TJA is influenced by patient-specific factors. LEVEL OF EVIDENCE: Prognostic Level IV. See Instructions for Authors for a complete description of levels of evidence.

Peripheral nerve block anesthesia/analgesia for patients undergoing primary hip and knee arthroplasty: recommendations from the International Consensus on Anesthesia-Related Outcomes after Surgery (ICAROS) group based on a systematic review and meta-analysis of current literature.

BACKGROUND: Evidence-based international expert consensus regarding the impact of peripheral nerve block (PNB) use in total hip/knee arthroplasty surgery. METHODS: A systematic review and meta-analysis: randomized controlled and observational studies investigating the impact of PNB utilization on major complications, including mortality, cardiac, pulmonary, gastrointestinal, renal, thromboembolic, neurologic, infectious, and bleeding complications.Medline, PubMed, Embase, and Cochrane Library including Cochrane Database of Systematic Reviews, Cochrane Central Register of Controlled Trials, NHS Economic Evaluation Database, were queried from 1946 to August 4, 2020.The Grading of Recommendations Assessment, Development, and Evaluation approach was used to assess evidence quality and for the development of recommendations. RESULTS: Analysis of 122 studies revealed that PNB use (compared with no use) was associated with lower ORs for (OR with 95% CIs) for numerous complications (total hip and knee arthroplasties (THA/TKA), respectively): cognitive dysfunction (OR 0.30, 95% CI 0.17 to 0.53/OR 0.52, 95% CI 0.34 to 0.80), respiratory failure (OR 0.36, 95% CI 0.17 to 0.74/OR 0.37, 95% CI 0.18 to 0.75), cardiac complications (OR 0.84, 95% CI 0.76 to 0.93/OR 0.83, 95% CI 0.79 to 0.86), surgical site infections (OR 0.55 95% CI 0.47 to 0.64/OR 0.86 95% CI 0.80 to 0.91), thromboembolism (OR 0.74, 95% CI 0.58 to 0.96/OR 0.90, 95% CI 0.84 to 0.96) and blood transfusion (OR 0.84, 95% CI 0.83 to 0.86/OR 0.91, 95% CI 0.90 to 0.92). CONCLUSIONS: Based on the current body of evidence, the consensus group recommends PNB use in THA/TKA for improved outcomes. RECOMMENDATION: PNB use is recommended for patients undergoing THA and TKA except when contraindications preclude their use. Furthermore, the alignment of provider skills and practice location resources needs to be ensured. Evidence level: moderate; recommendation: strong.

Presenteeism and Absenteeism Before and After Total Hip and Knee Arthroplasty.

BACKGROUND: Absenteeism is costly, yet evidence suggests that presenteeism-illness-related reduced productivity at work-is costlier. We quantified employed patients' presenteeism and absenteeism before and after total joint arthroplasty (TJA). METHODS: We measured presenteeism (0-100 scale, 100 full performance) and absenteeism using the World Health Organization's Health and Work Performance Questionnaire before and after TJA among a convenience sample of employed patients. We captured detailed information about employment and job characteristics and evaluated how and among whom presenteeism and absenteeism improved. RESULTS: In total, 636 primary, unilateral TJA patients responded to an enrollment email, confirmed employment, and completed a preoperative survey (mean age: 62.1 years, 55.3% women). Full at-work performance was reported by 19.7%. Among 520 (81.8%) who responded to a 1-year follow-up, 473 (91.0%) were still employed, and 461 (88.7%) had resumed working. Among patients reporting at baseline and 1 year, average at-work performance improved from 80.7 to 89.4. A Wilcoxon signed-rank test indicated that postoperative performance was significantly higher than preoperative performance (P < .0001). The percentage of patients who reported full at-work performance increased from 20.9% to 36.8% (delta = 15.9%, 95% confidence interval = [10.0%, 21.9%], P < .0001). Presenteeism gains were concentrated among patients who reported declining work performance leading up to surgery. Average changes in absences were relatively small. Combined, the average monthly value lost by employers to presenteeism declined from 15.3% to 8.3% and to absenteeism from 16.9% to 15.5% (ie, mitigated loss of 8.4% of monthly value). CONCLUSION: Among employed patients before TJA, presenteeism and absenteeism were similarly costly. After, employed patients reported increased performance, concentrated among those with declining performance leading up to surgery.

Anaesthetic care of patients undergoing primary hip and knee arthroplasty: consensus recommendations from the International Consensus on Anaesthesia-Related Outcomes after Surgery group (ICAROS) based on a systematic review and meta-analysis.

BACKGROUND: Evidence-based international expert consensus regarding anaesthetic practice in hip/knee arthroplasty surgery is needed for improved healthcare outcomes. METHODS: The International Consensus on Anaesthesia-Related Outcomes after Surgery group (ICAROS) systematic review, including randomised controlled and observational studies comparing neuraxial to general anaesthesia regarding major complications, including mortality, cardiac, pulmonary, gastrointestinal, renal, genitourinary, thromboembolic, neurological, infectious, and bleeding complications. Medline, PubMed, Embase, and Cochrane Library including Cochrane Database of Systematic Reviews, Cochrane Central Register of Controlled Trials, NHS Economic Evaluation Database, from 1946 to May 17, 2018 were queried. Meta-analysis and Grading of Recommendations Assessment, Development and Evaluation approach was utilised to assess evidence quality and to develop recommendations. RESULTS: The analysis of 94 studies revealed that neuraxial anaesthesia was associated with lower odds or no difference in virtually all reported complications, except for urinary retention. Excerpt of complications for neuraxial vs general anaesthesia in hip/knee arthroplasty, respectively: mortality odds ratio (OR): 0.67, 95% confidence interval (CI): 0.57-0.80/OR: 0.83, 95% CI: 0.60-1.15; pulmonary OR: 0.65, 95% CI: 0.52-0.80/OR: 0.69, 95% CI: 0.58-0.81; acute renal failure OR: 0.69, 95% CI: 0.59-0.81/OR: 0.73, 95% CI: 0.65-0.82; deep venous thrombosis OR: 0.52, 95% CI: 0.42-0.65/OR: 0.77, 95% CI: 0.64-0.93; infections OR: 0.73, 95% CI: 0.67-0.79/OR: 0.80, 95% CI: 0.76-0.85; and blood transfusion OR: 0.85, 95% CI: 0.82-0.89/OR: 0.84, 95% CI: 0.82-0.87. CONCLUSIONS: Recommendation: primary neuraxial anaesthesia is preferred for knee arthroplasty, given several positive postoperative outcome benefits; evidence level: low, weak recommendation. RECOMMENDATION: neuraxial anaesthesia is recommended for hip arthroplasty given associated outcome benefits; evidence level: moderate-low, strong recommendation. Based on current evidence, the consensus group recommends neuraxial over general anaesthesia for hip/knee arthroplasty. TRIAL REGISTRY NUMBER: PROSPERO CRD42018099935.

When Stars Do Not Align: Overall Hospital Quality Star Ratings and the Volume-Outcome Association.

BACKGROUND: Volume-outcome relationships are well established for coronary artery bypass grafting and total joint arthroplasty surgery. Although the U.S. Centers for Medicare & Medicaid Services (CMS) Overall Hospital Quality Star Ratings program includes outcome quality measures for these procedures, these outcome quality measures are not counted toward the star ratings for low-volume hospitals. We sought to assess whether excluding low-volume hospitals from surgical quality measures with known volume-outcome relationships affects the star ratings. METHODS: We identified quality measures used in CMS's star ratings that are related to surgical procedures with a known volume-outcome relationship and tested for the presence of the volume-outcome association for each of these measures. We then imputed missing values for low-volume hospitals for each measure and otherwise identically repeated the CMS calculations in order to assess the percentages of hospitals with the same, better, or worse ratings. RESULTS: Among the measures used to calculate star ratings, we identified 4 quality measures (2 related to coronary artery bypass grafting and 2 related to total joint arthroplasty) with known volume-outcome relationships that were excluded from the calculations of the star ratings for low-volume hospitals. We confirmed a volume-outcome association in the CMS data for all 4 measures. When total joint arthroplasty complications were imputed for low-volume hospitals and then included in the calculation of the star ratings, over one-third of hospitals received a different rating; both low-volume and other hospitals were more often hurt than helped. Imputing the other 3 quality measures among low-volume hospitals left the ratings unchanged. CONCLUSIONS: The CMS star ratings do not fully represent the risks of undergoing procedures at low-volume hospitals, potentially misrepresent quality across facilities, and hence are of uncertain utility to consumers.