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.

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.

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.

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.

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.

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.

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.

Can Machine Learning Algorithms Predict Which Patients Will Achieve Minimally Clinically Important Differences From Total Joint Arthroplasty?

BACKGROUND: Identifying patients at risk of not achieving meaningful gains in long-term postsurgical patient-reported outcome measures (PROMs) is important for improving patient monitoring and facilitating presurgical decision support. Machine learning may help automatically select and weigh many predictors to create models that maximize predictive power. However, these techniques are underused among studies of total joint arthroplasty (TJA) patients, particularly those exploring changes in postsurgical PROMs. QUESTION/PURPOSES: (1) To evaluate whether machine learning algorithms, applied to hospital registry data, could predict patients who would not achieve a minimally clinically important difference (MCID) in four PROMs 2 years after TJA; (2) to explore how predictive ability changes as more information is included in modeling; and (3) to identify which variables drive the predictive power of these models. METHODS: Data from a single, high-volume institution's TJA registry were used for this study. We identified 7239 hip and 6480 knee TJAs between 2007 and 2012, which, for at least one PROM, patients had completed both baseline and 2-year followup surveys (among 19,187 TJAs in our registry and 43,313 total TJAs). In all, 12,203 registry TJAs had valid SF-36 physical component scores (PCS) and mental component scores (MCS) at baseline and 2 years; 7085 and 6205 had valid Hip and Knee Disability and Osteoarthritis Outcome Scores for joint replacement (HOOS JR and KOOS JR scores), respectively. Supervised machine learning refers to a class of algorithms that links a mapping of inputs to an output based on many input-output examples. We trained three of the most popular such algorithms (logistic least absolute shrinkage and selection operator (LASSO), random forest, and linear support vector machine) to predict 2-year postsurgical MCIDs. We incrementally considered predictors available at four time points: (1) before the decision to have surgery, (2) before surgery, (3) before discharge, and (4) immediately after discharge. We evaluated the performance of each model using area under the receiver operating characteristic (AUROC) statistics on a validation sample composed of a random 20% subsample of TJAs excluded from modeling. We also considered abbreviated models that only used baseline PROMs and procedure as predictors (to isolate their predictive power). We further directly evaluated which variables were ranked by each model as most predictive of 2-year MCIDs. RESULTS: The three machine learning algorithms performed in the poor-to-good range for predicting 2-year MCIDs, with AUROCs ranging from 0.60 to 0.89. They performed virtually identically for a given PROM and time point. AUROCs for the logistic LASSO models for predicting SF-36 PCS 2-year MCIDs at the four time points were: 0.69, 0.78, 0.78, and 0.78, respectively; for SF-36 MCS 2-year MCIDs, AUROCs were: 0.63, 0.89, 0.89, and 0.88; for HOOS JR 2-year MCIDs: 0.67, 0.78, 0.77, and 0.77; for KOOS JR 2-year MCIDs: 0.61, 0.75, 0.75, and 0.75. Before-surgery models performed in the fair-to-good range and consistently ranked the associated baseline PROM as among the most important predictors. Abbreviated LASSO models performed worse than the full before-surgery models, though they retained much of the predictive power of the full before-surgery models. CONCLUSIONS: Machine learning has the potential to improve clinical decision-making and patient care by helping to prioritize resources for postsurgical monitoring and informing presurgical discussions of likely outcomes of TJA. Applied to presurgical registry data, such models can predict, with fair-to-good ability, 2-year postsurgical MCIDs. Although we report all parameters of our best-performing models, they cannot simply be applied off-the-shelf without proper testing. Our analyses indicate that machine learning holds much promise for predicting orthopaedic outcomes. LEVEL OF EVIDENCE: Level III, diagnostic study.

The impact of obesity on resource utilization among patients undergoing total joint arthroplasty.

PURPOSE: The presence of obesity poses a challenge for clinical and administrative staff in the peri-operative setting. Evidence indicates that obesity may increase peri-operative complications. However, data on resource utilization in patients undergoing total knee and hip arthroplasty remain rare. Using national data, we sought to determine whether increasing levels of patient obesity is associated with greater resource utilization. We hypothesized that patient care in individuals with a body mass index (BMI) greater than 40 is associated with longer operative and anaesthetic times, longer hospital stays, and greater readmission rates. METHODS: We utilized national data from the National Surgical Quality Improvement Project and identified patients who underwent primary total knee arthroplasty (TKA) and total hip arthroplasty (THA). Patients were divided into three groups according to their BMI (18.5 BMI < 30, 40 BMI < 45, and 45 BMI). The groups were compared regarding associated operating room utilization, length of stay, and readmission rates. RESULTS: Our study showed that TKA and THA patients with higher BMI required significantly longer operation-related times and had higher total length of hospital stay. Higher BMI patients also carried higher odds of readmissions within 30 days in both TKA and THA groups. CONCLUSION: We conclude that BMI status needs to be considered for both medical and economic reasons by health care institutions and payers, in order to make prudent decisions in a world where health care expenses are rising rapidly alongside the increasing obesity epidemic, and resources are becoming increasingly scarce.

What Are the Minimal and Substantial Improvements in the HOOS and KOOS and JR Versions After Total Joint Replacement?

BACKGROUND: Patient-reported outcome measures (PROMs) are a gold standard for measuring therapeutic outcomes in research. Extending their use to inform clinical care decisions, determine the appropriateness of therapeutic choices, and assess healthcare quality is attractive but will require our professional community to establish valid estimates of minimal and substantial clinical improvements. QUESTIONS/PURPOSES: The purposes of this study were (1) to assess the validity of estimates for the minimal clinically important difference (MCID) calculated using distribution- and anchor-based methods by determining whether they exceed the minimal detectable change (MDC) for the Hip Disability and Osteoarthritis Outcome Score (HOOS) and the Knee Injury and Osteoarthritis Outcome Score (KOOS) domains, the HOOS, joint replacement (JR) and the KOOS, JR among patients who underwent THA or TKA; (2) to determine substantial clinical benefit thresholds for the HOOS and KOOS domains, the HOOS, JR, and the KOOS, JR among patients who underwent THA or TKA; and (3) to assess the proportions of patients who underwent THA or TKA who achieved an MCID for the HOOS and KOOS domains, HOOS, JR, and KOOS, JR based on distribution-based and anchor-based methods as well as the percentages of patients who achieved substantial clinical benefit using the anchor-based method. METHODS: Medicare patients enrolled in our institutional joint replacement registry who subsequently underwent THA (n = 2323) or TKA (n = 2630) between 2007 and 2012 completed HOOS or KOOS preoperatively and 2 years postoperatively. Short-form joint replacement (JR) versions of each PROM were derived from the full PROMs. Of all eligible patients, 78% (3161 of 4080) of THAs and 74% of TKAs (3815 of 5156) consented to join the registry and completed a baseline survey, 88% (2796 of 3161) of THAs and 85% (3230 of 3815) of TKAs were eligible for followup survey administration, and 83% of THAs (2323 of 2796) and 81% (2630 of 3230) of TKAs returned 2-year surveys. For each HOOS domain, KOOS domain, HOOS, JR, and KOOS, JR, we calculated the calibration variation of the instrument (MDC) with confidence intervals (CIs) reflecting 80% (MDC80), 90% (MDC90), and 95% (MDC95) certainty; we calculated the smallest difference joint health patients might detect (MCID) using distribution- and anchor-based approaches and the difference that can be considered a large improvement in joint health (substantial clinical benefit) using an anchor-based approach. RESULTS: Patients undergoing THA were 57% female with a mean (± SD) age of 73 ± 6 years, whereas patients undergoing TKA were 63% female with a mean age of 74 ± 6 years. Depending on the CI chosen for the MDC, values ranged from 7 to 16 for the HOOS and KOOS domains and the JRs. The MCIDs ranged from 6 to 9 for the distribution-based approach and 7 to 36 for the anchor-based approach. All HOOS and KOOS domains and all JR scores are scores from 0 (worst joint health) to 100 (best joint health). The MCIDs calculated using the distribution-based approach were not valid, because they were lower than the MDC for all HOOS/KOOS domains and both JRs at every confidence level. The anchor-based receiver operating characteristic approach, on the other hand, resulted in MCIDs exceeding MDC80 for seven of eight HOOS/KOOS domains and MDC95 for both JR scores. For all domains and JR versions, substantial clinical benefits ranged from 15 to 36, exceeding MDC95 in all domains and JR scores. Across HOOS and KOOS domains as well as the JR, the proportion of patients undergoing THA who achieved an MCID ranged from 77% to 95% with the distribution-based method and from 67% to 96% using the anchor-based method. The proportion achieving substantial clinical benefit ranged from 67% to 85%. CONCLUSIONS: The MDC and MCID differ greatly based on assumptions and methods used. The MCID anchor-based approach had superior construct and face validity compared with the MCID distribution-based approach, which never exceeded even small MDCs. Achieving consensus about standard definitions of meaningful improvement will be necessary to maximize utility of these PROMs to inform clinical care or performance measurement. LEVEL OF EVIDENCE: Level III, diagnostic study.

Discharge to Inpatient Facilities After Total Hip Arthroplasty Is Associated With Increased Postdischarge Morbidity.

BACKGROUND: Discharge disposition accounts for significant variability in costs after elective total hip arthroplasty (THA). Therefore, institutions must evaluate the short-term clinical outcomes associated with postdischarge care options. The present study intends to characterize the associations between short-term morbidity after primary THA and discharge destination. METHODS: Primary elective unilateral THA cases performed for osteoarthritis were identified in the American College of Surgeons National Surgical Quality Improvement Program registry from 2011 to 2014. Propensity scores were used to adjust for selection bias in discharge destination, based on demographics, obesity class, preoperative functional status, modified Charlson comorbidity index, American Society of Anesthesiologists (ASA) class, and the presence of predischarge complications. Propensity-adjusted multivariate logistic regressions were used to examine associations between discharge destination and postdischarge complications, controlling for selection bias based on observable patient characteristics. RESULTS: Among 54,837 THA cases included in the study, 40,576 (74%) were discharged home, and 14,261 (26%) were discharged to inpatient facilities. In multivariate propensity-adjusted analyses, patients discharged to continued inpatient care after THA were more likely to have septic complications (odds ratio, 2.34; 95% confidence interval, 1.58-3.45), urinary complications (1.51; 1.21-1.90), readmission (1.44; 1.29-1.59), wound complications (1.31; 1.09-1.57), and respiratory complications (1.93; 1.21-3.07). CONCLUSION: Discharge to continued inpatient care following THA is associated with increased odds of postdischarge morbidity and unplanned readmission, after propensity score adjustment for predischarge characteristics. Additional research is needed on the impact of devoting resources toward facilitating discharge to home after THA.

Continued Inpatient Care After Primary Total Knee Arthroplasty Increases 30-Day Post-Discharge Complications: A Propensity Score-Adjusted Analysis.

BACKGROUND: Discharge destination, either home or skilled care facility, after total knee arthroplasty (TKA) may be associated with significant variation in postacute care outcomes. The purpose of this study was to characterize the 30-day postdischarge outcomes after primary TKA relative to discharge destination. METHODS: All primary unilateral TKAs performed for osteoarthritis from 2011-2014 were identified in the National Surgical Quality Improvement Program database. Propensity scores based on predischarge characteristics were used to adjust for selection bias in discharge destination. Propensity-adjusted multivariable logistic regressions were used to examine associations between discharge destination and postdischarge complications. RESULTS: Among 101,256 primary TKAs identified, 70,628 were discharged home and 30,628 to skilled care facilities. Patients discharged to facilities were more frequently were female, older, higher body mass index class, higher Charlson comorbidity index and American Society of Anesthesiologists scores, had predischarge complications, received general anesthesia, and classified as nonindependent preoperatively. Propensity adjustment accounted for this selection bias. Patients discharged to skilled care facilities after TKA had higher odds of any major complication (odds ratio = 1.25; 95% confidence interval, 1.13-1.37) and readmission (odds ratio = 1.81; 95% confidence interval, 1.50-2.18). Skilled care was associated with increased odds for respiratory, septic, thromboembolic, and urinary complications. Associations with death, cardiac, and wound complications were not significant. CONCLUSION: After controlling for predischarge characteristics, discharge to skilled care facilities vs home after primary TKA is associated with higher odds of numerous complications and unplanned readmission. These results support coordination of care pathways to facilitate home discharge after hospitalization for TKA whenever possible.

Quality Varies Across Health Insurance Marketplace Pricing Regions.

BACKGROUND: Although consumers purchasing health plans in the new Health Insurance Marketplace will be provided information on the cost and quality of participating health plans, it is unclear whether the state-wide plan quality averages that will be reported will accurately represent quality at the pricing region level where care will be received. OBJECTIVES: To evaluate whether currently reported state-wide health plan quality scores accurately represent quality within pricing regions established for the Health Insurance Marketplace. RESEARCH DESIGN: Observational, historical cohort study using health plan administrative and pharmacy data. SUBJECTS: A total of 5.2 million members enrolled in the preferred provider organization health plans of 1 large commercial California insurer in 2012. MEASURES: State-wide and pricing region performance on each of the 17 Healthcare Effectiveness Data and Information Set (HEDIS) measures. RESULTS: Across the 17 measures assessed in each of the 19 pricing regions, scores were statistically different (P<0.05) than the overall plan rate for 176 (54%). Variations in scores across regions were observed for each measure ranging from 6.4-percentage points for engagement in treatment for people with dependence of alcohol or other drugs to 47.2-percentage points for appropriate testing for pharyngitis among children. CONCLUSIONS: Quality scores in California vary greatly across geographic regions. Statewide averages may misrepresent the quality of care that consumers are likely to receive within a geographic area making difficult assessments about the value of the health care.

Test ordering for preventive health care among family medicine residents.

OBJECTIVE: To determine which screening tests family medicine residents order as part of preventive health care. DESIGN: A cross-sectional survey. SETTING: Alberta and Ontario. PARTICIPANTS: First- and second-year family medicine residents at the University of Alberta in Edmonton, the University of Calgary in Alberta, and McMaster University in Hamilton, Ont, during the 2011 to 2012 academic year. MAIN OUTCOME MEASURES: Demographic information, Likert scale ratings assessing ordering attitudes, and selections from a list of 38 possible tests that could be ordered for preventive health care for sample 38-year-old and 55-year-old female and male patients. Descriptive and comparative statistics were calculated. RESULTS: A total of 318 of 482 residents (66%) completed the survey. Recommended or appropriate tests were ordered by 82% (for cervical cytology) to 95% (for fasting glucose measurement) of residents. Across the different sample patients, residents ordered an average of 3.3 to 5.7 inappropriate tests per patient, with 58% to 92% ordering at least 1 inappropriate test per patient. The estimated average excess costs varied from $38.39 for the 38-year-old man to $106.46 for the 55-year-old woman. More regular use of a periodic health examination screening template did not improve ordering (P = .88). CONCLUSION: In general, residents ordered appropriate preventive health tests reasonably well but also ordered an average of 3.3 to 5.7 inappropriate tests for each patient. Training programs need to provide better education for trainees around inappropriate screening and work hard to establish good ordering behaviour in preparation for entering practice.

Protein-mediated dethreading of a biotin-functionalised pseudorotaxane.

In this article, we describe the synthesis of new biotin-functionalised naphthalene derivatives 3 and 4 and their complexation behaviour with avidin and neutravidin using a range of analytical techniques. We have shown using 2-(4'-hydroxyazobenzene)benzoic acid displacement and ITC experiments, that compounds 3 and 4 have the propensity to form reasonably high-affinity bioconjugates with avidin and neutravidin. We have also demonstrated using (1)H NMR, UV-vis and fluorescence spectroscopy that the naphthalene moiety of 3 and 4 facilitates the formation of pseudorotaxane-like structures with 1 in water. We have then investigated the ability of avidin and neutravidin to modulate the complexation between 1 and 3 or 4. UV-vis and fluorescence spectroscopy has shown that in both cases the addition of the protein disrupts complexation between the naphthalene moieties of 3 and 4 with 1.