Orthopedic Surgeon and Care Team Perceptions and Use of Patient-Reported Outcomes in Total Joint Replacement Patients.

BACKGROUND: The recent shift toward value-based health care and bundled payments in orthopedic surgery has increased the use patient-reported outcomes (PROs) in standard clinical care. Such assessments of patient function and satisfaction are particularly important among total joint arthroplasty (TJA) patients to monitor postoperative health. PURPOSE: The purpose of this study was to assess orthopedic care team perceptions of current and future PRO usage and compare current rates and modes of PRO collection between three urban, academic health care systems. METHODS: A literature search was conducted on current PRO uses and barriers to their adoption to generate a 26-question survey. The survey was disseminated to orthopedic surgeons and care team members at three academic health care institutions (institutions A, B, and C). Responses were analyzed for qualitative and quantitative insights. RESULTS: Among institutions A, B, and C, PRO collection generally declined from baseline (60%, 90%, 89%) to 6 weeks (67%, 82%, 71%) and 3 months postoperatively (44%, 36%, 47%). However, there were large variations in reported PRO collection intervals among institutions. Respondents reported assessing patient baseline functional status as the most useful current application of PROs and cited the prediction of patient benefit from TJA as the most useful future application for PROs. Though respondents were largely optimistic about PRO utility in clinical care, a small minority remained skeptical. CONCLUSIONS: Perceptions of PRO utilization and collection intervals varied considerably among respondents. For PROs to be an accurate and useful clinical tool, standardization and thorough understanding of PRO collection among orthopedic care team members is essential.

Metric Selection, Metric Targets, and Risk Adjustment Should be Considered in the Design of Gainsharing Models for Bundled Payment Programs in Total Joint Arthroplasty.

BACKGROUND: Under bundled payment models, gainsharing presents an important mechanism to ensure engagement and reward innovation. We hypothesized that metric selection, metric targets, and risk adjustment would impact surgeons' performance in gainsharing models. METHODS: Patients undergoing total joint arthroplasty at an urban health system from 2017 to September 2018 were included. Gainsharing metrics included the following: length of stay, % discharge-to-home, 90-day readmission rate, % of patients with episode spend under target price, and % of patients with patient-reported outcomes (PROs) collected. Four scenarios were created to evaluate how metric selection/adjustment impacted surgeons' performance designation: scenario 1 used "aspirational targets" (>60th percentile), scenario 2 used "acceptable targets" (>50th percentile), scenario 3 risk-adjusted surgeon performance prior to comparing aspirational targets, and scenario 4 included a PRO collection metric. Number of metrics achieved determined performance tier, with higher tiers getting a greater share of the gainsharing pool. RESULTS: In total, 2776 patients treated by 12 surgeons met inclusion criteria (mean length of stay 3.0 days, readmission rate 4.0%, discharge-to-home 74%, episode spend under target price 85%, PRO collection 56%). Lowering of metric targets (scenario 1 vs. 2) resulted in a 75% increase in the number of high performers and 98% of the gainsharing pool being eligible for distribution. Risk adjustment (scenario 3) caused 50% of providers to move to higher performance tiers and potential payments to increase by 28%. Adding the PRO metric did not change performance. CONCLUSION: Quality metric/target selection and risk adjustment profoundly impact surgeons' performance in gainsharing contracts. This impacts how successful these contracts can be in driving innovation and dis-incentivizing the "cherry picking" of patients. LEVEL OF EVIDENCE: Level III.

The Cost of Poor Mental Health in Total Joint Arthroplasty.

BACKGROUND: The purpose of this analysis was to evaluate (1) the impact of depression on resource utilization and financial outcomes in bundled total joint arthroplasty (TJA) and (2) whether similar effects are seen using baseline patient-reported outcome scores. METHODS: All elective bundled TJA cases from 2017 to 2018 at an academic system in the New York City area were included. We analyzed variables associated with cost differences seen between patients with and without depression, and between patients with low (<40th percentile) and high baseline (>60th percentile) Veterans RAND 12-Item Health Survey mental component scores (MCSs). We also analyzed whether depression or low MCS could predict worse financial outcomes. RESULTS: Our population included 825 patients, 418 with patient-reported outcome scores data. Depression was associated with higher rates of skilled nursing facility (SNF) discharge (42.7% vs 36.5%, P = .04), SNF payments ($16,200 vs $12,100, P = .0002), and average total episode costs ($31,000 vs $27,000, P = .04). Depression predicted bundle cost to be greater than target price (OR 1.82, 95% CI: 1.04-.16; P = .04) and SNF payment greater than 75th percentile (OR: 1.91; 95% CI: 1.00-3.65; P < .05). Similar effects were not seen using MCS. CONCLUSION: This is the first study to determine that depression predicts bundle cost greater than target price and SNF payment greater than 75th percentile. Our results emphasize the importance of accurate preoperative assessment of mental health in optimization of care, focusing on attenuating the increased SNF payments associated with depression. As similar effects were not seen using MCS, future studies should analyze the use of validated screening tools for depression, such as the PHQ-9, for more accurate assessments of patient mental health in TJA.

Automated Measurement of Lumbar Lordosis on Radiographs Using Machine Learning and Computer Vision.

STUDY DESIGN: Cross sectional database study. OBJECTIVE: To develop a fully automated artificial intelligence and computer vision pipeline for assisted evaluation of lumbar lordosis. METHODS: Lateral lumbar radiographs were used to develop a segmentation neural network (n = 629). After synthetic augmentation, 70% of these radiographs were used for network training, while the remaining 30% were used for hyperparameter optimization. A computer vision algorithm was deployed on the segmented radiographs to calculate lumbar lordosis angles. A test set of radiographs was used to evaluate the validity of the entire pipeline (n = 151). RESULTS: The U-Net segmentation achieved a test dataset dice score of 0.821, an area under the receiver operating curve of 0.914, and an accuracy of 0.862. The computer vision algorithm identified the L1 and S1 vertebrae on 84.1% of the test set with an average speed of 0.14 seconds/radiograph. From the 151 test set radiographs, 50 were randomly chosen for surgeon measurement. When compared with those measurements, our algorithm achieved a mean absolute error of 8.055° and a median absolute error of 6.965° (not statistically significant, P > .05). CONCLUSION: This study is the first to use artificial intelligence and computer vision in a combined pipeline to rapidly measure a sagittal spinopelvic parameter without prior manual surgeon input. The pipeline measures angles with no statistically significant differences from manual measurements by surgeons. This pipeline offers clinical utility in an assistive capacity, and future work should focus on improving segmentation network performance.

Biomaterials in Spinal Implants: A Review.

The aim to find the perfect biomaterial for spinal implant has been the focus of spinal research since the 1800s. Spinal surgery and the devices used therein have undergone a constant evolution in order to meet the needs of surgeons who have continued to further understand the biomechanical principles of spinal stability and have improved as new technologies and materials are available for production use. The perfect biomaterial would be one that is biologically inert/compatible, has a Young's modulus similar to that of the bone where it is implanted, high tensile strength, stiffness, fatigue strength, and low artifacts on imaging. Today, the materials that have been most commonly used include stainless steel, titanium, cobalt chrome, nitinol (a nickel titanium alloy), tantalum, and polyetheretherketone in rods, screws, cages, and plates. Current advancements such as 3-dimensional printing, the ProDisc-L and ProDisc-C, the ApiFix, and the Mobi-C which all aim to improve range of motion, reduce pain, and improve patient satisfaction. Spine surgeons should remain vigilant regarding the current literature and technological advancements in spinal materials and procedures. The progression of spinal implant materials for cages, rods, screws, and plates with advantages and disadvantages for each material will be discussed.

Relationship Between Baseline Patient-reported Outcomes and Demographic, Psychosocial, and Clinical Characteristics: A Retrospective Study.

INTRODUCTION: Alternative payment models in total lower extremity joint replacement (TJR) increasingly emphasize patient-reported outcomes (PROs) to link the latter to value-based payments. It is unclear to what extent demographic, psychosocial, and clinical characteristics are related to PROs measured preoperatively with the commonly used Hip/Knee Osteoarthritis Outcome Scores (HOOS/KOOS) and the Veterans RAND 12-Item Health Survey (VR-12) questionnaires. We aim to identify (1) the preoperative relationship between HOOS/KOOS and VR-12 scores and several demographic, psychosocial, and clinical patient characteristics and (2) the best modifiable factors for optimization, which may result in improved baseline PROs before TJR. METHODS: All TJR cases performed in 2017 at the two highest-volume hospitals within an urban academic health system were queried. Preoperative HOOS/KOOS and VR-12 surveys were administered through an e-collection platform. VR-12 physical and mental component scores (PCS, MCS) were generated. Patient information was extracted from the electronic health record. Bivariate and multivariate regression analyses were performed. Odds ratios (ORs) and 95% confidence intervals were reported. RESULTS: In univariate analysis, patients with HOOS/KOOS, VR-12 PCS, and MCS in the ≤25th percentile group were more likely to have an ASA score of ≥3 compared with those with higher scores. In multivariate analysis, increased and decreased odds of low HOOS/KOOS were associated with a one-unit increase in Charlson Comorbidity Index (OR, 1.16) and VR-12 MCS (OR, 0.97), respectively. Increased odds of low baseline VR-12 PCS and MCS were associated with ASA class ≥3 (OR, 1.65 and 1.40). Decreased odds of a low MCS were associated with an increase in HOOS/KOOS (OR, 0.98) (P ≤ 0.05 for all). CONCLUSION: Of the factors that are associated with low baseline PRO scores, preoperatively addressing mismanaged comorbidities, mental health, and physical function were identified as the best modifiable factors for optimization, which may result in improved baseline PROs before TJR.

Defining and Optimizing Value in Total Joint Arthroplasty From the Patient, Payer, and Provider Perspectives.

BACKGROUND: The purpose of this study is to define value in bundled total joint arthroplasty (TJA) from the differing perspectives of the patient, payer/employer, and hospital/provider. METHODS: Demographic, psychosocial, clinical, financial, and patient-reported outcomes (PROs) data from 2017 to 2018 elective TJA cases at a multihospital academic health system were queried. Value was defined as improvement in PROs (preoperatively to 1 year postoperatively) for patients, improvement in PROs per $1000 of bundle cost for payers, and the normalized sum of improvement in PROs and hospital bundle margin for providers. Bivariate analysis was used to compare high value vs low value (>50th percentile vs <50th percentile). Multivariate analysis was performed to identify predictors. RESULTS: A total of 280 patients had PRO data, of which 71 had Medicare claims data. Diabetes (odds ratio [OR], 0.45; P = .02) predicted low value for patients; female gender (OR, 0.25), hypertension (OR, 0.17), pulmonary disease (OR, 0.12), and skilled nursing facility discharge (OR, 0.17) for payers (P ≤ .03 for all); and pulmonary disease (OR, 0.16) and skilled nursing facility discharge (OR, 0.19) for providers (P ≤ .04 for all). CONCLUSION: This is the first article to define value in TJA under a bundle payment model from multiple perspectives, providing a foundation for future studies analyzing value-based TJA.

Preoperative Patient-Reported Outcomes and Clinical Characteristics as Predictors of 90-Day Cost/Utilization and Complications.

BACKGROUND: With the advent of mandatory bundle payments for total joint arthroplasty (TJA), assessing patients' risk for increased 90-day complications and resource utilization is crucial. This study assesses the degree to which preoperative patient-reported outcomes predict 90-day complications, episode costs, and utilization in TJA patients. METHODS: All TJA cases in 2017 at 2 high-volume hospitals were queried. Preoperative HOOS/KOOS JR (Hip Injury and Osteoarthritis Outcome Score/Knee Injury and Osteoarthritis Outcome Score) and Veterans RAND 12-item health survey (VR-12) were administered to patients preoperatively via e-collection platform. For patients enrolled in the Medicare bundle, cost data were extracted from claims. Bivariate and multivariate regression analyses were performed. RESULTS: In total, 2108 patients underwent TJA in 2017; 1182 (56%) were missing patient-reported outcome data and were excluded. The final study population included 926 patients, 199 (21%) of which had available cost data. Patients with high bundle costs tended to be older, suffer from vascular disease and anemia, and have higher Charlson scores (P < .05 for all). These patients also had lower baseline VR-12 Physical Component Summary Score (PCS; 24 vs 30, P ≤ .001) and higher rates of extended length of stay, skilled nursing facility discharge, 90-day complications, and 90-day readmission (P ≤ .04 for all). In multivariate analysis, higher baseline VR-12 PCS was protective against extended length of stay, skilled nursing facility discharge, >75th percentile bundle cost, and 90-day bundle cost exceeding target bundle price (P < .01 for all). Baseline VR-12 Mental Component Summary Score and HOOS/KOOS JR were not predictive of complications or bundle cost. CONCLUSION: Low baseline VR-12 PCS is predictive of high 90-day bundle costs. Baseline HOOS/KOOS JR scores were not predictive of utilization or cost. Neither VR-12 nor HOOS/KOOS JR was predictive of 90-day readmission or complications.

Predictors of Discharge Disposition Following Laminectomy for Intradural Extramedullary Spinal Tumors.

OBJECTIVE: To identify independent risk factors for non-home discharge in patients undergoing laminectomy for intradural extramedullary spinal tumors. METHODS: We performed a retrospective cohort analysis of data from the American College of Surgeons National Surgical Quality Improvement Program from 2011 to 2014. Adult patients who underwent laminectomy for the excision of intradural extramedullary spinal tumors were included and divided into 2 groups based on home or non-home discharge disposition. We compared baseline patient characteristics, comorbidities, and operative factors between the 2 groups, and then performed multivariate regression analyses to identify independent risk factors for non-home discharge. RESULTS: A total of 1232 patients were included, of whom 248 (20.1%) were discharged to a non-home facility. Univariate analysis demonstrated that patients discharged to a non-home facility were more frequently aged ≥65 years and American Society of Anesthesiologists classification ≥3 with obesity, diabetes, dyspnea, functional dependence, cardiac comorbidity, renal comorbidity, and anemia. Operative factors correlated with non-home discharge were operative time of ≥4 hours and tumor location in the cervical or thoracic spine. Multivariate regression analysis identified age ≥65 years (odds ratio [OR] 2.73; confidence interval [CI] 1.80-4.13; P < 0.001), American Society of Anesthesiologists classification ≥3 (OR 2.36; CI 1.53-3.65; P < 0.001), dependent functional status (OR 4.30; CI 1.95-9.48; P < 0.001), hospital-acquired conditions (OR 2.32; CI 1.15-4.68; P = 0.019), and prolonged length of stay (OR 4.05; CI 2.72-6.03; P < 0.001) as predictors of non-home discharge. CONCLUSIONS: Early identification of patients at risk for non-home discharge is important in order to implement comprehensive discharge planning protocols that reduce inpatient length of stay, as well as associated complications and costs.