Risk Stratification in Orthopaedics.

Proper predictive tools are essential to guide patient selection, optimization, category of surgical admission (inpatient, outpatient surgery), and discharge disposition, and predict the risk of readmissions and complications after orthopaedic procedures. Therefore, identification and optimization of patients' perioperative risk for surgery is essential, and understanding these basic concepts is crucial to maximizing patient care quality. It is important to define risk, stratify the existing preoperative attributes, and review key concepts of patient-specific risk calculation and documentation.

Treating Hepatitis C Before Total Knee Arthroplasty is Cost-Effective: A Markov Analysis.

BACKGROUND: Patients who have the hepatitis C virus (HCV) have increased mortality and complication rates following total knee arthroplasty (TKA). Recent advances in HCV therapy have enabled clinicians to eradicate the disease using direct-acting antivirals (DAAs); however, its cost-effectiveness before TKA remains to be demonstrated. The aim of this study was to perform a cost-effectiveness analysis comparing no therapy to DAAs before TKA. METHODS: A Markov model using input values from the published literature was performed to evaluate the cost-effectiveness of DAA treatment before TKA. Input values included event probabilities, mortality, cost, and health state quality-adjusted life-year (QALY) values for patients who have and do not have HCV. Patients who have HCV were modeled to have an increased rate of periprosthetic joint infection (PJI) infection (9.9 to 0.7%). The incremental cost-effectiveness ratio (ICER) of no therapy versus DAA was compared to a willingness-to-pay threshold of $100,000/QALY. Sensitivity analyses were performed to investigate the effects of uncertainty associated with input variables. RESULTS: Total knee arthroplasty in the setting of no therapy and DAA added 8.1 and 13.5 QALYs at a cost of $25,000 and $114,900. The ICER associated with DAA in comparison to no therapy was $16,800/QALY, below the willingness-to-pay threshold of $100,000/QALY. Sensitivity analyses demonstrated that the ICER was affected by patient age, inflation rate, DAA cost and effectiveness, HCV-associated mortality, and DAA-induced reduction in PJI rate. CONCLUSION: Direct-acting antiviral treatment before TKA reduces risk of PJI and is cost-effective. Strong consideration should be given to treating patients who have HCV before elective TKA. LEVEL OF EVIDENCE: Cost-effectiveness Analysis; Level III.

Who Are the Anatomic Outliers Undergoing Total Knee Arthroplasty? A Computed Tomography-Based Analysis of the Hip-Knee-Ankle Axis Across 1,352 Preoperative Computed Tomographies Using a Deep Learning and Computer Vision-Based Pipeline.

BACKGROUND: Dissatisfaction after total knee arthroplasty (TKA) ranges from 15 to 30%. While patient selection may be partially responsible, morphological and reconstructive challenges may be determinants. Preoperative computed tomography (CT) scans for TKA planning allow us to evaluate the hip-knee-ankle axis and establish a baseline phenotypic distribution across anatomic parameters. The purpose of this cross-sectional analysis was to establish the distributions of 27 parameters in a pre-TKA cohort and perform threshold analysis to identify anatomic outliers. METHODS: There were 1,352 pre-TKA CTs that were processed. A 2-step deep learning pipeline of classification and segmentation models identified landmark images and then generated contour representations. We used an open-source computer vision library to compute measurements for 27 anatomic metrics along the hip-knee axis. Normative distribution plots were established, and thresholds for the 15th percentile at both extremes were calculated. Metrics falling outside the central 70th percentile were considered outlier indices. A threshold analysis of outlier indices against the proportion of the cohort was performed. RESULTS: Significant variation exists in pre-TKA anatomy across 27 normally distributed metrics. Threshold analysis revealed a sigmoid function with a critical point at 9 outlier indices, representing 31.2% of subjects as anatomic outliers. Metrics with the greatest variation related to deformity (tibiofemoral angle, medial proximal tibial angle, lateral distal femoral angle), bony size (tibial width, anteroposterior femoral size, femoral head size, medial femoral condyle size), intraoperative landmarks (posterior tibial slope, transepicondylar and posterior condylar axes), and neglected rotational considerations (acetabular and femoral version, femoral torsion). CONCLUSIONS: In the largest non-industry database of pre-TKA CTs using a fully automated 3-stage deep learning and computer vision-based pipeline, marked anatomic variation exists. In the pursuit of understanding the dissatisfaction rate after TKA, acknowledging that 31% of patients represent anatomic outliers may help us better achieve anatomically personalized TKA, with or without adjunctive technology.

Assessing Ability for ChatGPT to Answer Total Knee Arthroplasty-Related Questions.

BACKGROUND: Artificial intelligence in the field of orthopaedics has been a topic of increasing interest and opportunity in recent years. Its applications are widespread both for physicians and patients, including use in clinical decision-making, in the operating room, and in research. In this study, we aimed to assess the quality of ChatGPT answers when asked questions related to total knee arthroplasty. METHODS: ChatGPT prompts were created by turning 15 of the American Academy of Orthopaedic Surgeons Clinical Practice Guidelines into questions. An online survey was created, which included screenshots of each prompt and answers to the 15 questions. Surgeons were asked to grade ChatGPT answers from 1 to 5 based on their characteristics: (1) relevance, (2) accuracy, (3) clarity, (4) completeness, (5) evidence-based, and (6) consistency. There were 11 Adult Joint Reconstruction fellowship-trained surgeons who completed the survey. Questions were subclassified based on the subject of the prompt: (1) risk factors, (2) implant/intraoperative, and (3) pain/functional outcomes. The average and standard deviation for all answers, as well as for each subgroup, were calculated. Inter-rater reliability (IRR) was also calculated. RESULTS: All answer characteristics were graded as being above average (ie, a score > 3). Relevance demonstrated the highest scores (4.43 ± 0.77) by surgeons surveyed, and consistency demonstrated the lowest scores (3.54 ± 1.10). ChatGPT prompts in the Risk Factors group demonstrated the best responses, while those in the Pain/Functional Outcome group demonstrated the lowest. The overall IRR was found to be 0.33 (poor reliability), with the highest IRR for relevance (0.43) and the lowest for evidence-based (0.28). CONCLUSIONS: ChatGPT can answer questions regarding well-established clinical guidelines in total knee arthroplasty with above-average accuracy but demonstrates variable reliability. This investigation is the first step in understanding large language model artificial intelligence like ChatGPT and how well they perform in the field of arthroplasty.

Patient-Specific Safe Zones for Acetabular Component Positioning in Total Hip Arthroplasty: Mathematically Accounting for Spinopelvic Biomechanics.

BACKGROUND: Despite a growing understanding of spinopelvic biomechanics in total hip arthroplasty (THA), there is no validated approach for executing patient-specific acetabular component positioning. The purpose of this study was to (1) validate quantitative, patient-specific acetabular "safe zone" component positioning from spinopelvic parameters and (2) characterize differences between quantitative patient-specific acetabular targets and qualitative hip-spine classification targets. METHODS: From 2,457 consecutive primary THA patients, 22 (0.88%) underwent revision for instability. Spinopelvic parameters were measured prior to index THA. Acetabular position was measured following index and revision arthroplasty. Using a mathematical proof, we developed an open-source tool translating a surgeon-selected, preoperative standing acetabular target to a patient-specific safe zone intraoperative acetabular target. Difference between the patient-specific safe zone and the actual component position was compared before and after revision. Hip-spine classification targets were compared to patient-specific safe zone targets. RESULTS: Of the 22 who underwent revision, none dislocated at follow-up (4.6 [range, 1 to 6.9]). Patient-specific safe zone targets differed from prerevision acetabular component position by 9.1 ± 4.2° inclination/13.3 ± 6.7° version; after revision, the mean difference was 3.2 ± 3.0° inclination/5.3 ± 2.7° version. Differences between patient-specific safe zones and the median and extremes of recommended hip-spine classification targets were 2.2 ± 1.9° inclination/5.6 ± 3.7° version and 3.0 ± 2.3° inclination/7.9 ± 3.5° version, respectively. CONCLUSION: A mathematically derived, patient-specific approach accommodating spinopelvic biomechanics for acetabular component positioning was validated by approximating revised, now-stable hips within 5° version and 3° inclination. These patient-specific safe zones augment the hip-spine classification with prescriptive quantitative targets for nuanced preoperative planning.

A Total Knee Arthroplasty Preoperative Optimization Program Managed by an Advanced Practice Provider (Physician Assistant) Decreases Complications and Cost: A Pilot Study.

BACKGROUND: Studies have shown that optimizing modifiable risk factors leads to improved outcomes, with decreased lengths of stay (LOS), readmissions, complications, and hospital costs. Our goal was to demonstrate that use of an advanced practice provider, physician assistant (PA), within an orthopaedic practice would support these outcomes. METHODS: A preoperative optimization program managed by a PA was instituted at an academic medical center. From November 2019 to December 2022, a pilot group of fifteen (15) consecutive primary total knee arthroplasty (TKA) patients who were successfully optimized with the PA-managed program prior to TKA were matched 2:1 to a cohort of thirty (30) TKA patients who did not undergo optimization. Demographics and the modified readmission risk assessment tool score were used to match patients. Variables evaluated included LOS, emergency department visits, and hospital readmissions within 30 and 90 days after surgery, complications, and hospital costs of care. RESULTS: Optimized patients had less complications (P = .004) and significantly shorter (P < .001) mean LOS (1.27 days vs 2.97 days) compared to nonoptimized patients. The difference of hospital cost between cohorts for the primary admission was significant (P = .049). When readmission costs were included, the average hospital cost for the nonoptimized group was significantly higher than the optimized group (P = .018). CONCLUSIONS: Preoperative optimization led by a PA demonstrated significant reductions in LOS and the costs of care between optimized and non-optimized patients, along with decreased complications.

Is Tomorrow's Health Care Today: A Look at Current Payment Models for the Future?

The promise of controlling spending and improving the quality of care incentivizes health care providers to prioritize value through alternative payment models. Findings regarding improved value and cost savings of the Comprehensive Care for Joint Replacement (CJR) redesign are consistent throughout selected metropolitan hospitals. Before refinement can take place, reporting on baseline financial status is a necessity to ensure the starting point of hospitals before CJR takes effect. Evidence-based protocols, outcomes-based measures to evaluate results, and cooperation across specialties to deliver high quality care will be necessary to insure improved care throughout the episode. This commentary reviews the CJR program and provides recommendations for the near future in order to best serve the needs of patients as we move forward in the bundled payments direction.

Treating Hepatitis C Prior to Total Hip Arthroplasty is Cost-effective: A Markov Analysis.

BACKGROUND: Patients infected with the hepatitis C virus (HCV) have high complication rates following total hip arthroplasty (THA). Advances in HCV therapy now enable clinicians to eradicate the disease; however, its cost-effectiveness from an orthopaedic perspective remains to be demonstrated. We sought to conduct a cost-effectiveness analysis comparing no therapy to direct-acting antiviral (DAA) therapy prior to THA among HCV-positive patients. METHODS: A Markov model was utilized to evaluate the cost-effectiveness of treating HCV with DAA prior to THA. The model was powered with event probabilities, mortality, cost, and quality-adjusted life year (QALY) values for patients with and without HCV that were obtained from the published literature. This included treatment costs, successes of HCV eradication, incidences of superficial or periprosthetic joint infection (PJI), probabilities of utilizing various PJI treatment modalities, PJI treatment success/failures, and mortality rates. The incremental cost-effectiveness ratio was compared to a willingness-to-pay threshold of $50,000/QALY. RESULTS: Our Markov model indicates that in comparison to no therapy, DAA prior to THA is cost-effective for HCV-positive patients. THA in the setting of no therapy and DAA added 8.06 and 14.39 QALYs at a mean cost of $28,800 and $115,800. The incremental cost-effectiveness ratio associated with HCV DAA in comparison to no therapy was $13,800/QALY, below the willingness-to-pay threshold of $50,000/QALY. CONCLUSION: Hepatitis C treatment with DAA prior to THA is cost-effective at all current drug list prices. Given these findings, strong consideration should be given to treating patients for HCV prior to elective THA. LEVEL OF EVIDENCE: Cost-effectiveness Analysis; Level III.

Artificial Intelligence for Automated Implant Identification in Total Hip Arthroplasty: A Multicenter External Validation Study Exceeding Two Million Plain Radiographs.

BACKGROUND: The surgical management of complications after total hip arthroplasty (THA) necessitates accurate identification of the femoral implant manufacturer and model. Automated image processing using deep learning has been previously developed and internally validated; however, external validation is necessary prior to responsible application of artificial intelligence (AI)-based technologies. METHODS: We trained, validated, and externally tested a deep learning system to classify femoral-sided THA implants as one of the 8 models from 2 manufacturers derived from 2,954 original, deidentified, retrospectively collected anteroposterior plain radiographs across 3 academic referral centers and 13 surgeons. From these radiographs, 2,117 were used for training, 249 for validation, and 588 for external testing. Augmentation was applied to the training set (n = 2,117,000) to increase model robustness. Performance was evaluated by area under the receiver operating characteristic curve, sensitivity, specificity, and accuracy. Implant identification processing speed was calculated. RESULTS: The training and testing sets were drawn from statistically different populations of implants (P < .001). After 1,000 training epochs by the deep learning system, the system discriminated 8 implant models with a mean area under the receiver operating characteristic curve of 0.991, accuracy of 97.9%, sensitivity of 88.6%, and specificity of 98.9% in the external testing dataset of 588 anteroposterior radiographs. The software classified implants at a mean speed of 0.02 seconds per image. CONCLUSION: An AI-based software demonstrated excellent internal and external validation. Although continued surveillance is necessary with implant library expansion, this software represents responsible and meaningful clinical application of AI with immediate potential to globally scale and assist in preoperative planning prior to revision THA.

The Changing Economic Value and Leverage of Arthroplasty Surgeons.

Moving THA off of the Inpatient Only (IPO) List for Center of Medicaid and Medicare Services (CMS) beneficiaries and the COVID-19 pandemic has caused a shift in delivery away from inpatient services and a decrease in demand. Medicare payments dramatically declined from 2019 to 2020. LOS decreases and shift to outpatient designations were accelerated by IPO list changes and COVID-19 issues. The percentage of SDD cases also increased. Other metrics favorable to decreased spending by CMS were increased discharge to home and decreased volume. These changes have a profound impact on surgeon-hospital relationships and surgeon compensation.

Practice Management Strategies Among Current Members of the American Association of Hip and Knee Surgeons.

BACKGROUND: A survey was conducted at the 2021 Annual Meeting of the American Association of Hip and Knee Surgeons (AAHKS) to evaluate current practice management strategies among AAHKS members. METHODS: An application was used by AAHKS members to answer both multiple-choice and yes or no questions. Specific questions were asked regarding the impact of COVID-19 pandemic on practice patterns. RESULTS: There was a dramatic acceleration in same day total joint arthroplasty with 85% of AAHKS members performing same day total joint arthroplasty. More AAHKS members remain in private practice (46%) than other practice types, whereas fee for service (34%) and relative value units (26%) are the major form of compensation. At the present time, 93% of practices are experiencing staffing shortages, and these shortages are having an impact on surgical volume. CONCLUSION: This survey elucidates the current practice patterns of AAHKS members. The pandemic has had a significant impact on some aspects of practice activity. Future surveys need to monitor changes in practice patterns over time.

The National Trend in Arthroplasty Surgery Location and the Economic Impact on Surgeons, Hospitals and ASCs.

BACKGROUND: We sought to understand the magnitude of the shift in care settings (hospital inpatient, hospital outpatient, or ambulatory surgery center) for primary total joint arthroplasty (TJA) and its economic impact on surgeons and hospitals. METHODS: We measured the shift in care settings for primary TJAs using national 100% sample Medicare fee-for-service (FFS) claims data from January 2017 through March 2021. We also measured the percent of case being discharged the same day over time. We calculated the national average hospital payment rate by setting and the weighted average hospital payment rates based on the mix of inpatient and outpatient cases over time. We compared average facility and physician payment rate changes over time across common types of surgeries. RESULTS: By the first quarter of 2021, 29% of Medicare FFS primary TJAs were performed hospital inpatient (down from 100% in 2017), 64% were performed hospital outpatient, and about 7% in an ambulatory surgery center. The percent of hospital-based primary TJAs that were discharged the same day increased from less than 2% in the first quarter of 2018 to over 18% in the first quarter of 2021. Medicare increased its payment rates for both inpatient and outpatient TJAs, which offset the impact of TJAs shifting from being performed inpatient to outpatient. The average Medicare payment rates for TJAs declined by more than they did for most other major procedures. CONCLUSION: There was a significant shift in care setting from hospital inpatient to hospital outpatient for Medicare primary TJAs. This shift led to lower average TJA payment rates to hospitals; however, the impact was attenuated due to the increasing Medicare reimbursement rates in each setting, particularly for outpatient cases.

The COVID-19 Pandemic and Centers of Medicaid and Medicare Services Regulatory Reform Impact on Total Hip Arthroplasty Metrics.

BACKGROUND: Shifts in demand, capacity, and site of service have impacted total hip arthroplasty (THA) volumes and revenues over the 2019-2021 time period. Moving THA off the inpatient-only (IPO) list and the COVID-19 pandemic has caused a shift in delivery away from inpatient services and a decrease in demand. METHODS: Medicare claims data were surveyed for the latest period available (April 1, 2020 to September 2020) and compared with a similar period in 2019 prior to THA removal from the IPO list and before the COVID-19 pandemic. Length of stay (LOS), admission status, site of service, discharge status, cost to CMS (Centers of Medicaid and Medicare Services), and racial disparities were analyzed. RESULTS: From 2019 to 2020, changes in primary THA metrics occurred (overall change in total joint arthroplasty [THA plus total knee arthroplasty metrics]): CMS THA volume decreased from 78,691 to 65,360, -16% (-22%); THA performed as an outpatient increased from 0% to 51% (141%); THA performed as same-day discharge increased from 3% to 12%, 325% (221%); overall LOS decreased from 1.91 to 1.46, -23% (-11%); inpatient LOS increased from 1.92 to 2.05, 7% (16%); outpatient LOS increased from 0.92 to 0.93, 1% (-12%); discharge home increased from 82% to 91%, 12.8% (11%); and CMS spending decreased from $1,033 million to $751 million, -27% (-27%). CONCLUSION: Medicare payments, LOS, discharge to facilities, and volume declined from 2019 to 2020 and were accelerated by IPO list changes and COVID-19 issues. Same-day discharge and hospital outpatient department cases also increased. THA metrics were not affected by race.

Worsening Arthroplasty Utilization With Widening Racial Variance During the COVID-19 Pandemic.

BACKGROUND: Elective arthroplasty surgery in the United States came to a near-complete halt in the spring of 2019 as a response to the COVID-19 pandemic. Racial disparity has been a long-term concern in healthcare with increased focus during the pandemic. The purpose of this study is to evaluate the effects of COVID-19 and race on arthroplasty utilization trends during the pandemic. METHODS: We used 2019 and 2020 Center for Medicare and Medicaid Service fee-for-service claims data to compare arthroplasty volumes prior to and during the COVID-19 pandemic. We compared overall arthroplasty utilization rates between 2019 and 2020 and then sought to determine the effect of race and COVID-19, both independently and combined. RESULTS: There was a decrease in primary total knee arthroplasty (-28%), primary total hip arthroplasty (-14%), primary total hip arthroplasty for fracture (-2%), and revision arthroplasty (-14%) utilization between 2019 and 2020. The highest decrease in overall arthroplasty utilization was in the Hispanic population (34% decrease vs 19% decrease in the White population). We found that a non-White patient was 39.9% (P < .001) less likely to receive a total joint arthroplasty prior to COVID-19. The COVID-19 pandemic further exacerbated the pre-existing racial differences in arthroplasty utilization by decreasing the probability of receiving a total joint arthroplasty for non-White patient by another 12.9% (P < .001). CONCLUSION: We found an overall decreased utilization rate of arthroplasty during the COVID-19 pandemic with further decrease noted in all non-White populations. This raises significant concern for worsening racial disparity in arthroplasty caused by the ongoing pandemic.

Hospital Costs for Unsuccessful Two-Stage Revisions for Periprosthetic Joint Infection.

BACKGROUND: Although 2-stage exchange arthroplasty is the preferred surgical treatment for periprosthetic joint infection (PJI) in the United States, little is known about the risk of complications between stages, mortality, and the economic burden of unsuccessful 2-stage procedures. METHODS: The 2015-2019 Medicare 100% inpatient sample was used to identify 2-stage PJI revisions in total hip and knee arthroplasty patients using procedural codes. We used the Fine and Gray sub-distribution adaptation of the conventional Kaplan-Meier method to estimate the probability of completing the second stage of the 2-stage PJI infection treatment, accounting for death as a competing risk. Hospital costs were estimated from the hospital charges using "cost-to-charge" ratios from Centers for Medicare and Medicaid Services. RESULTS: A total of 5094 total hip arthroplasty and 13,062 total knee arthroplasty patients had an index revision for PJI during the study period. In the first 12 months following the first-stage explantation, the likelihood of completing a second-stage PJI revision was 43.1% (95% confidence interval [CI] 41.7-44.5) for hips and 47.9% (95% CI 47.0-48.8) for knees. Following explantation, 1-year patient survival rates for hip and knee patients were 87.4% (95% CI 85.8-88.9) and 91.4% (95% CI 90.6-92.2), respectively. The median additional cost for hospitalizations between stages was $23,582 and $20,965 per patient for hips and knees, respectively. Hospital volume, Northeast or Midwest region, and younger age were associated with reduced PJI costs (P < .05). CONCLUSION: Although viewed as the most preferred, the 2-stage revision strategy for PJI had less than a 50% chance of successful completion within the first year, and was associated with high mortality rates and substantial costs for treatment failure.

The 2021 Centers for Medicare and Medicaid Services Fee Schedule's Impact on Adult Reconstruction Surgeon Productivity and Reimbursement.

BACKGROUND: On December 20, 2020, the Centers for Medicare and Medicaid Services (CMS) finalized its proposed rule: CMS-1734-P. This 2021 Final Rule significantly changed Medicare total joint arthroplasty (TJA) reimbursement. The precise impact on surgeon productivity and reimbursement is unknown. In the present study, we sought to model the potential impact of these changes for multiple unique practice configurations. METHODS: A mathematical model was applied to CMS data to determine the impact of CMS-1734-F on multiple, theoretical TJA practice configurations. Variables tested were the annual percentage of revision vs primary arthroplasty cases performed and the annual percentage of operative vs office-based productivity. The model defined baseline annual surgeon productivity as the 2018 Medical Group Management Association hip and knee arthroplasty surgeon median productivity of 10,568 work relative value units (wRVUs). RESULTS: All modeled simulations demonstrated a year-to-year increase in wRVUs independent of practice configuration. However, simulations that demonstrated less than a 3.35% increase in wRVUs from year-to-year saw a decrease in reimbursement. Those simulations with higher wRVU increases tended to have a higher percentage of revision vs primary arthroplasty cases and/or had annual productivity that was derived to a greater extent from office encounters than surgical cases. CONCLUSION: The impact of CMS-1734-F will vary based on 3 factors: (1) the relative contribution of a surgeon's operative TJA practice compared with their office-based practice to their annual wRVUs; (2) the relative percentage of revision TJAs vs the percentage of primary TJAs performed; and (3) the relative percentage of primary TJA compared to non-arthroplasty surgeries as a component of overall operative practice. The decreased reimbursement will be disproportionately felt by arthroplasty surgeons who perform relatively fewer revision TJA procedures and whose office-based productivity makes up a smaller overall percentage of their annual workload.

Topical Vancomycin Powder and Dilute Povidone-Iodine Lavage Reduce the Rate of Early Periprosthetic Joint Infection After Primary Total Knee Arthroplasty.

BACKGROUND: Vancomycin powder and dilute povidone-iodine lavage (VIP) was introduced to reduce the incidence of periprosthetic joint infection (PJI) in high-risk total knee arthroplasty (TKA) patients. We hypothesize that VIP can reduce the incidence of early PJI in all primary TKA patients, regardless of preoperative risk. METHODS: An infection database of primary TKAs performed before a VIP protocol was implemented (January 2012-December 2013), during a time when only high-risk TKAs received VIP (January 2014-December 2015), and when all TKAs received VIP (January 2016-September 2019) at an urban, university-affiliated, not-for-profit orthopedic hospital was retrospectively reviewed to identify patients with PJI. Criteria used for diagnosis of PJI were the National Healthcare Safety Network and Musculoskeletal Infection Society guidelines. RESULTS: VIP reduced early primary TKA PJI incidence in both the high-risk and all-risk cohorts compared with the pre-VIP cohort by 44.6% and 56.4%, respectively (1.01% vs 0.56% vs 0.44%, P = .0088). In addition, after introducing VIP to all-risk TKA patients, compared with high-risk TKA patients, the relative risk of PJI dropped an additional 21.4%, but this finding did not reach statistical significance (0.56% vs 0.44%, P = .4212). There were no demographic differences between the 3 VIP PJI cohorts. CONCLUSION: VIP is associated with a reduced early PJI incidence after primary TKA, regardless of preoperative risk. With the literature supporting its safety and cost-effectiveness, VIP is a value-based intervention, but given the nature of this historical cohort study, a multicenter randomized controlled trial is underway to definitively confirm its efficacy.

Improving Arthroplasty Efficiency and Quality Through Concentrating Service Volume by Complexity: Surviving the Medicare Policy Changes.

We have an academic medical center (AMC), an associated community-based hospital (CBH) and several ambulatory care centers which are being prepared to provide same day discharge (SDD) total joint arthroplasty (TJA) and unicompartmental knee arthroplasty (UKA). The near-capacity AMC cared for medically and technically complicated TJA patients. The CBH wanted to increase volume, improve margins, and become a center of excellence with an efficient hospital outpatient department and SDD TJA experience. METHODS: We transitioned primary, uncomplicated TJA, UKA, and minimally invasive TJA to the CBH. Revision surgeries, patients with extensive comorbidities, and complex primaries were performed at the AMC. Protocols were developed to facilitate SDD UKA and total hip arthroplasty (THA) as well as rapid recovery protocols for total knee arthroplasty (TKA) at both hospitals. A protocol-based system was put in place to make both hospitals ready for the removal of TKA from the Inpatient-Only list to avoid Quality Improvement Organization and possible resultant Recovery Audit Contractor audits if referred after implementation. RESULTS: The CBH volume increased 36.7% (+239). AMC volume slightly decreased (-0.46%, -5) resulting in an increase in margin contribution for the system. CBH quality metrics (surgical site infections, length of stay, readmissions, and mortality) were improved. Surgeon satisfaction improved as their volume, efficiency, quality metrics, and finances were enhanced. Although CBH per case revenue was 80.3% and 74.4% of the AMC for THA and TKA, net margins were 3.6% and 18.8% higher for THA and TKA, respectively. Increased efficiency, lower hospital cost, and higher volume at the CBH allowed for an increase in revenue despite lower reimbursement per case. CONCLUSION: This strategy will help hospital systems improve net margins while improving patient care despite lower net revenue per TJA episode. These strategies will become increasingly important going forward with the transition of higher numbers of TJA patients to outpatient which will be subjected to further decreases in net revenue per patient.

Artificial Intelligence to Identify Arthroplasty Implants From Radiographs of the Knee.

BACKGROUND: Revisions and reoperations for patients who have undergone total knee arthroplasty (TKA), unicompartmental knee arthroplasty (UKA), and distal femoral replacement (DFR) necessitates accurate identification of implant manufacturer and model. Failure risks delays in care, increased morbidity, and further financial burden. Deep learning permits automated image processing to mitigate the challenges behind expeditious, cost-effective preoperative planning. Our aim was to investigate whether a deep-learning algorithm could accurately identify the manufacturer and model of arthroplasty implants about the knee from plain radiographs. METHODS: We trained, validated, and externally tested a deep-learning algorithm to classify knee arthroplasty implants from one of 9 different implant models from retrospectively collected anterior-posterior (AP) plain radiographs from four sites in one quaternary referral health system. The performance was evaluated by calculating the area under the receiver-operating characteristic curve (AUC), sensitivity, specificity, and accuracy when compared with a reference standard of implant model from operative reports. RESULTS: The training and validation data sets were comprised of 682 radiographs across 424 patients and included a wide range of TKAs from the four leading implant manufacturers. After 1000 training epochs by the deep-learning algorithm, the model discriminated nine implant models with an AUC of 0.99, accuracy 99%, sensitivity of 95%, and specificity of 99% in the external-testing data set of 74 radiographs. CONCLUSIONS: A deep learning algorithm using plain radiographs differentiated between 9 unique knee arthroplasty implants from four manufacturers with near-perfect accuracy. The iterative capability of the algorithm allows for scalable expansion of implant discriminations and represents an opportunity in delivering cost-effective care for revision arthroplasty.

Artificial Intelligence to Identify Arthroplasty Implants From Radiographs of the Hip.

BACKGROUND: The surgical management of complications surrounding patients who have undergone hip arthroplasty necessitates accurate identification of the femoral implant manufacturer and model. Failure to do so risks delays in care, increased morbidity, and further economic burden. Because few arthroplasty experts can confidently classify implants using plain radiographs, automated image processing using deep learning for implant identification may offer an opportunity to improve the value of care rendered. METHODS: We trained, validated, and externally tested a deep-learning system to classify total hip arthroplasty and hip resurfacing arthroplasty femoral implants as one of 18 different manufacturer models from 1972 retrospectively collected anterior-posterior (AP) plain radiographs from 4 sites in one quaternary referral health system. From these radiographs, 1559 were used for training, 207 for validation, and 206 for external testing. Performance was evaluated by calculating the area under the receiver-operating characteristic curve, sensitivity, specificity, and accuracy, as compared with a reference standard of implant model from operative reports with implant serial numbers. RESULTS: The training and validation data sets from 1715 patients and 1766 AP radiographs included 18 different femoral components across four leading implant manufacturers and 10 fellowship-trained arthroplasty surgeons. After 1000 training epochs by the deep-learning system, the system discriminated 18 implant models with an area under the receiver-operating characteristic curve of 0.999, accuracy of 99.6%, sensitivity of 94.3%, and specificity of 99.8% in the external-testing data set of 206 AP radiographs. CONCLUSIONS: A deep-learning system using AP plain radiographs accurately differentiated among 18 hip arthroplasty models from four industry leading manufacturers.