Neural network models accurately predict discharge disposition after revision total knee arthroplasty?

Machine learning model outperforms the ACS Risk Calculator in predicting non-home discharge following primary total knee arthroplasty

PURPOSE

Despite the increase in outpatient total knee arthroplasty (TKA) procedures, many patients are still discharged to non-home locations following index surgery. The ability to accurately predict non-home discharge (NHD) following TKAs has the potential to promote a reduction in associated adverse events and excess healthcare costs. This study aimed to evaluate whether a machine learning (ML) model could outperform the American College of Surgeons (ACS) Risk Calculator in predicting NHD following TKA, using the same set of clinical variables. We hypothesised that the ML model would outperform the ACS Risk Calculator.

METHODS

Data from 365,240 patients who underwent a primary TKA between 2013 and 2020 were extracted from the ACS-National Surgical Quality Improvement Program database and used to develop an artificial neural network (ANN) to predict discharge disposition following primary TKA. The ANN and ACS calculator were assessed and compared using discrimination, calibration and decision curve analysis.

RESULTS

Age (>68 years), BMI (>35.5 kg/m2) and ASA Class (≥2) were found to be the most important variables in predicting NHD following TKA. When compared to the ACS calculator, the ANN model demonstrated a significantly superior ability to distinguish the area under the receiver operating characteristic curve (AUC) among NHD patients and provided probability predictions well aligned with the true outcomes (AUCANN = 0.69, AUCACS = 0.50, p = 0.002, slopeANN = 0.85, slopeACS = 4.46, interceptANN = 0.04, and interceptACS = 0.06).

CONCLUSION

Our findings support the hypothesis that machine learning models outperform the ACS Risk Calculator in predicting non-home discharge after TKA, even when constrained to the same clinical variables. Our findings underscore the potential benefits of integrating machine learning models into clinical practice for improving preoperative patient risk identification, optimisation, counselling and clinical decision-making.

LEVEL OF EVIDENCE

III.

Post-Acute Care Trends and Disparities After Joint Replacements in the United States, 1991-2018: A Systematic Review

OBJECTIVE

To review evidence on post-acute care (PAC) use and disparities related to race and ethnicity and rurality in the United States over the past 2 decades among individuals who underwent major joint replacement (MJR).

DESIGN

Systematic review.

SETTING AND PARTICIPANTS

We included studies that examined US PAC trends and racial and ethnic and/or urban vs rural differences among individuals who are aged ≥18 years with hospitalization after MJR.

METHODS

We searched large academic databases (PubMed, CINAHL, Embase, Web of Science, and Scopus) for peer-reviewed, English language articles from January 1, 2000, and January 26, 2022.

RESULTS

Seventeen studies were reviewed. Studies (n = 16) consistently demonstrated that discharges post-MJR to skilled nursing facilities (SNFs) or nursing homes (NHs) decreased over time, whereas evidence on discharges to inpatient rehab facilities (IRFs), home health care (HHC), and home without HHC services were mixed. Most studies (n = 12) found that racial and ethnic minority individuals, especially Black individuals, were more frequently discharged to PAC institutions than white individuals. Demographic factors (ie, age, sex, comorbidities) and marital status were not only independently associated with discharges to institutional PAC, but also among racial and ethnic minority individuals. Only one study found urban-rural differences in PAC use, indicating that urban-dwelling individuals were more often discharged to both SNF/NH and HHC than their rural counterparts.

CONCLUSIONS AND IMPLICATIONS

Despite declines in institutional PAC use post-MJR over time, racial and minority individuals continue to experience higher rates of institutional PAC discharges compared with white individuals. To address these disparities, policymakers should consider measures that target multimorbidity and the lack of social and structural support among socially vulnerable individuals. Policymakers should also consider initiatives that address the economic and structural barriers experienced in rural areas by expanding access to telehealth and through improved care coordination.

Predicting 30-day unplanned hospital readmission after revision total knee arthroplasty: machine learning model analysis of a national patient cohort

Revision total knee arthroplasty (TKA) is associated with a higher risk of readmission than primary TKA. Identifying individual patients predisposed to readmission can facilitate proactive optimization and increase care efficiency. This study developed machine learning (ML) models to predict unplanned readmission following revision TKA using a national-scale patient dataset. A total of 17,443 revision TKA cases (2013-2020) were acquired from the ACS NSQIP database. Four ML models (artificial neural networks, random forest, histogram-based gradient boosting, and k-nearest neighbor) were developed on relevant patient variables to predict readmission following revision TKA. The length of stay, operation time, body mass index (BMI), and laboratory test results were the strongest predictors of readmission. Histogram-based gradient boosting was the best performer in distinguishing readmission (AUC: 0.95) and estimating the readmission probability for individual patients (calibration slope: 1.13; calibration intercept: -0.00; Brier score: 0.064). All models produced higher net benefit than the default strategies of treating all or no patients, supporting the clinical utility of the models. ML demonstrated excellent performance for the prediction of readmission following revision TKA. Optimization of important predictors highlighted by our model may decrease preventable hospital readmission following surgery, thereby leading to reduced financial burden and improved patient satisfaction.

Enabling Personalized Medicine in Orthopaedic Surgery Through Artificial Intelligence: A Critical Analysis Review

» The application of artificial intelligence (AI) in the field of orthopaedic surgery holds potential for revolutionizing health care delivery across 3 crucial domains: (I) personalized prediction of clinical outcomes and adverse events, which may optimize patient selection, surgical planning, and enhance patient safety and outcomes; (II) diagnostic automated and semiautomated imaging analyses, which may reduce time burden and facilitate precise and timely diagnoses; and (III) forecasting of resource utilization, which may reduce health care costs and increase value for patients and institutions.» Computer vision is one of the most highly studied areas of AI within orthopaedics, with applications pertaining to fracture classification, identification of the manufacturer and model of prosthetic implants, and surveillance of prosthesis loosening and failure.» Prognostic applications of AI within orthopaedics include identifying patients who will likely benefit from a specified treatment, predicting prosthetic implant size, postoperative length of stay, discharge disposition, and surgical complications. Not only may these applications be beneficial to patients but also to institutions and payors because they may inform potential cost expenditure, improve overall hospital efficiency, and help anticipate resource utilization.» AI infrastructure development requires institutional financial commitment and a team of clinicians and data scientists with expertise in AI that can complement skill sets and knowledge. Once a team is established and a goal is determined, teams (1) obtain, curate, and label data; (2) establish a reference standard; (3) develop an AI model; (4) evaluate the performance of the AI model; (5) externally validate the model, and (6) reinforce, improve, and evaluate the model's performance until clinical implementation is possible.» Understanding the implications of AI in orthopaedics may eventually lead to wide-ranging improvements in patient care. However, AI, while holding tremendous promise, is not without methodological and ethical limitations that are essential to address. First, it is important to ensure external validity of programs before their use in a clinical setting. Investigators should maintain high quality data records and registry surveillance, exercise caution when evaluating others' reported AI applications, and increase transparency of the methodological conduct of current models to improve external validity and avoid propagating bias. By addressing these challenges and responsibly embracing the potential of AI, the medical field may eventually be able to harness its power to improve patient care and outcomes.

Scoping Review: Anesthesiologist Involvement in Alternative Payment Models, Value Measurement, and Nonclinical Capabilities for Success in the United States of America

The US healthcare sector is undergoing significant payment reforms, leading to the emergence of Alternative Payment Models (APMs) aimed at improving clinical outcomes and patient experiences while reducing costs. This scoping review provides an overview of the involvement of anesthesiologists in APMs as found in published literature. It specifically aims to categorize and understand the breadth and depth of their participation, revolving around 3 main axes or "Aims": (1) shaping APMs through design and implementation, (2) gauging the value and quality of care provided by anesthesiologists within these models, and (3) enhancing nonclinical abilities of anesthesiologists for promoting more value in care. To map out the existing literature, a comprehensive search of relevant electronic databases was conducted, yielding a total of 2173 articles, of which 24 met the inclusion criteria, comprising 21 prospective or retrospective cohort studies, 2 surveys, and 1 case-control cohort study. Eleven publications (45%) discussed value-based, bundled, or episode-based payments, whereas the rest discussed non-payment-based models, such as Enhanced Recovery After Surgery (7 articles, 29%), Perioperative Surgical Home (4 articles, 17%), or other models (3 articles, 13%).The review identified key themes related to each aim. The most prominent themes for aim 1 included protocol standardization (16 articles, 67%), design and implementation leadership (8 articles, 33%), multidisciplinary collaboration (7 articles, 29%), and role expansion (5 articles, 21%). For aim 2, the common themes were Process-Based & Patient-Centric Metrics (1 article, 4%), Shared Accountability (3 articles, 13%), and Time-Driven Activity-Based Costing (TDABC) (3 articles, 13%). Furthermore, we identified a wide range of quality metrics, spanning 8 domains that were used in these studies to evaluate anesthesiologists' performance. For aim 3, the main extracted themes included Education on Healthcare Transformation and Policies (3 articles, 13%), Exploring Collaborative Leadership Skills (5 articles, 21%), and Embracing Advanced Analytics and Data Transparency (4 articles, 17%).Findings revealed the pivotal role of anesthesiologists in the design, implementation, and refinement of these emerging delivery and payment models. Our results highlight that while payment models are shifting toward value, patient-centered metrics have yet to be widely accepted for use in measuring quality and affecting payment for anesthesiologists. Gaps remain in understanding how anesthesiologists assess their direct impact and strategies for enhancing the sustainability of anesthesia practices. This review underscores the need for future research contributing to the successful adaptation of clinical practices in this new era of healthcare delivery.

Postoperative Activity and Knee Function of Patients after Total Knee Arthroplasty: A Sensor-Based Monitoring Study

Inertial measurement units (IMUs) are increasingly being used to assess knee function. The aim of the study was to record patients' activity levels and to detect new parameters for knee function in the early postoperative phase after TKA. Twenty patients (n = 20) were prospectively enrolled. Two sensors were attached to the affected leg. The data were recorded from the first day after TKA until discharge. Algorithms were developed for detecting steps, range of motion, horizontal, sitting and standing postures, as well as physical therapy. The mean number of steps increased from day 1 to discharge from 117.4 (SD ± 110.5) to 858.7 (SD ± 320.1), respectively. Patients' percentage of immobilization during daytime (6 a.m. to 8 p.m.) was 91.2% on day one and still 69.9% on the last day. Patients received daily continuous passive motion therapy (CPM) for a mean of 36.4 min (SD ± 8.2). The mean angular velocity at day 1 was 12.2 degrees per second (SD ± 4.4) and increased to 28.7 (SD ± 16.4) at discharge. This study shows that IMUs monitor patients' activity postoperatively well, and a wide range of interindividual motion patterns was observed. These sensors may allow the adjustment of physical exercise programs according to the patient's individual needs.

A role for artificial intelligence applications inside and outside of the operating theatre: a review of contemporary use associated with total knee arthroplasty

BACKGROUND

Artificial intelligence (AI) has become involved in many aspects of everyday life, from voice-activated virtual assistants built into smartphones to global online search engines. Similarly, many areas of modern medicine have found ways to incorporate such technologies into mainstream practice. Despite the enthusiasm, robust evidence to support the utility of AI in contemporary total knee arthroplasty (TKA) remains limited. The purpose of this review was to provide an up-to-date summary of the use of AI in TKA and to explore its current and future value.

METHODS

Initially, a structured systematic review of the literature was carried out, following PRISMA search principles, with the aim of summarising the understanding of the field and identifying clinical and knowledge gaps.

RESULTS

A limited body of published work exists in this area. Much of the available literature is of poor methodological quality and many published studies could be best described as "demonstration of concepts" rather than "proof of concepts". There exists almost no independent validation of reported findings away from designer/host sites, and the extrapolation of key results to general orthopaedic sites is limited.

CONCLUSION

While AI has certainly shown value in a small number of specific TKA-associated applications, the majority to date have focused on risk, cost and outcome prediction, rather than surgical care, per se. Extensive future work is needed to demonstrate external validity and reliability in non-designer settings. Well-performed studies are warranted to ensure that the scientific evidence base supporting the use of AI in knee arthroplasty matches the global hype.

A mapping review on preoperative prognostic factors and outcome measures of revision total knee arthroplasty

To map literature on prognostic factors related to outcomes of revision total knee arthroplasty (rTKA), to identify extensively studied factors and to guide future research into what domains need further exploration. We performed a systematic literature search in MEDLINE, Embase, and Web of Science. The search string included multiple synonyms of the following keywords: "revision TKA", "outcome" and "prognostic factor". We searched for studies assessing the association between at least one prognostic factor and at least one outcome measure after rTKA surgery. Data on sample size, study design, prognostic factors, outcomes, and the direction of the association was extracted and included in an evidence map. After screening of 5,660 articles, we included 166 studies reporting prognostic factors for outcomes after rTKA, with a median sample size of 319 patients (30 to 303,867). Overall, 50% of the studies reported prospectively collected data, and 61% of the studies were performed in a single centre. In some studies, multiple associations were reported; 180 different prognostic factors were reported in these studies. The three most frequently studied prognostic factors were reason for revision (213 times), sex (125 times), and BMI (117 times). Studies focusing on functional scores and patient-reported outcome measures as prognostic factor for the outcome after surgery were limited (n = 42). The studies reported 154 different outcomes. The most commonly reported outcomes after rTKA were: re-revision (155 times), readmission (88 times), and reinfection (85 times). Only five studies included costs as outcome. Outcomes and prognostic factors that are routinely registered as part of clinical practice (e.g. BMI, sex, complications) or in (inter)national registries are studied frequently. Studies on prognostic factors, such as functional and sociodemographic status, and outcomes as healthcare costs, cognitive and mental function, and psychosocial impact are scarce, while they have been shown to be important for patients with osteoarthritis.

Concerns surrounding application of artificial intelligence in hip and knee arthroplasty

Literature surrounding artificial intelligence (AI)-related applications for hip and knee arthroplasty has proliferated. However, meaningful advances that fundamentally transform the practice and delivery of joint arthroplasty are yet to be realized, despite the broad range of applications as we continue to search for meaningful and appropriate use of AI. AI literature in hip and knee arthroplasty between 2018 and 2021 regarding image-based analyses, value-based care, remote patient monitoring, and augmented reality was reviewed. Concerns surrounding meaningful use and appropriate methodological approaches of AI in joint arthroplasty research are summarized. Of the 233 AI-related orthopaedics articles published, 178 (76%) constituted original research, while the rest consisted of editorials or reviews. A total of 52% of original AI-related research concerns hip and knee arthroplasty (n = 92), and a narrative review is described. Three studies were externally validated. Pitfalls surrounding present-day research include conflating vernacular (“AI/machine learning”), repackaging limited registry data, prematurely releasing internally validated prediction models, appraising model architecture instead of inputted data, withholding code, and evaluating studies using antiquated regression-based guidelines. While AI has been applied to a variety of hip and knee arthroplasty applications with limited clinical impact, the future remains promising if the question is meaningful, the methodology is rigorous and transparent, the data are rich, and the model is externally validated. Simple checkpoints for meaningful AI adoption include ensuring applications focus on: administrative support over clinical evaluation and management; necessity of the advanced model; and the novelty of the question being answered.

Cite this article: Bone Joint J 2022;104-B(12):1292–1303.

Artificial intelligence algorithms accurately predict prolonged length of stay following revision total knee arthroplasty

PURPOSE

Although the average length of hospital stay following revision total knee arthroplasty (TKA) has decreased over recent years due to improved perioperative and intraoperative techniques and planning, prolonged length of stay (LOS) continues to be a substantial driver of hospital costs. The purpose of this study was to develop and validate artificial intelligence algorithms for the prediction of prolonged length of stay for patients following revision TKA.

METHODS

A total of 2512 consecutive patients who underwent revision TKA were evaluated. Those patients with a length of stay greater than 75th percentile for all length of stays were defined as patients with prolonged LOS. Three artificial intelligence algorithms were developed to predict prolonged LOS following revision TKA and these models were assessed by discrimination, calibration and decision curve analysis.

RESULTS

The strongest predictors for prolonged length of stay following revision TKA were age (> 75 years; p < 0.001), Charlson Comorbidity Index (> 6; p < 0.001) and body mass index (> 35 kg/m²; p < 0.001). The three artificial intelligence algorithms all achieved excellent performance across discrimination (AUC > 0.84) and decision curve analysis (p < 0.01).

CONCLUSION

The study findings demonstrate excellent performance on discrimination, calibration and decision curve analysis for all three candidate algorithms. This highlights the potential of these artificial intelligence algorithms to assist in the preoperative identification of patients with an increased risk of prolonged LOS following revision TKA, which may aid in strategic discharge planning.

LEVEL OF EVIDENCE

IV.