Machine Learning Model Developed to Aid in Patient Selection for Outpatient Total Joint Arthroplasty

Complication rates are not higher after outpatient compared to inpatient fast-track total hip arthroplasty: a propensity-matched prospective comparative study

PURPOSE

Concerns remain with regards to safety of fast-track (FT) and especially outpatient procedures. The purpose of this study was to compare complication rates and clinical outcomes of propensity-matched patients who received FT total hip arthroplasty (THA) in outpatient versus inpatient settings. The hypothesis was that 90-day postoperative complication rates of outpatient FT THA would not be higher than after inpatient FT THA.

METHODS

This is a prospective study of consecutive patients who received FT THA at various rates of outpatient and inpatient surgery by 10 senior surgeons (10 centres). The decision between outpatient and inpatient surgery was made on a case-by-case basis depending on the surgeon and patient. All patients were followed until 90 days after surgery. Complications, readmissions and reoperations were collected, and their severity was assessed according to Clavien-Dindo. Patients completed Oxford Hip Score (OHS) at the latest follow-up.

RESULTS

Compared to inpatient FT THA, patients scheduled for outpatient FT THA had no significant differences in 90-day postoperative complication rates (10.7% vs. 12.9%, p = 0.129). There were no significant differences between the 2 groups in 90-day readmission rates and reoperation rates, in severity of postoperative complications, and in time of occurrence of postoperative complications.

CONCLUSIONS

There were no differences in rates of intraoperative complications, 90-day postoperative complications, readmissions, or reoperations between outpatient and inpatient FT THA. These findings may help hesitant surgeons to move towards outpatient THA pathways as there is no greater risk of early postoperative complications that could be more difficult to manage after discharge.

Outpatient vs. inpatient designation in total hip arthroplasty: can we predict who will require hospitalization?

INTRODUCTION

Following removal of total hip arthroplasty (THA) from the inpatient only (IPO) list by the Center for Medicare Services (CMS), arthroplasty surgeons face increased pressure to perform procedures on an outpatient (OP) basis. The purposes of the present study were to compare patients booked for THA as OP who required conversion to IP status postoperatively, to patients who were booked as, and remained OP, and to identify factors predictive of conversion from OP to IP status.

METHODS

We retrospectively reviewed all patients who underwent a primary THA at our institution between January 1, 2020 and April 26, 2022. All patients included were originally scheduled for OP surgery and were separated based on conversion to IP status postoperatively. Multiple regression analyses were used to determine the significance of all perioperative variables. Modeling via binary logistic regressions were used to determine factors predictive of status conversion.

RESULTS

Of 1,937 patients, 372 (19.2%) designated as OP preoperatively required conversion to IP status postoperatively. These patients had significantly higher facility discharge rates (P < 0.001) and 90-day readmission rates (P = 0.024). Patients aged 65 and older (P < 0.001), females (P < 0.001), patients with Black/African American race (P = 0.027), with a recovery room arrival time after 12 pm (P < 0.001), with a BMI > 30 kg/m2 (P = 0.001), and with a Charlson Comorbidity Index (CCI) ≥ 4 (P = 0.013) were Powered by Editorial Manager® and ProduXion Manager® from Aries Systems Corporation more likely to require conversion to IP designation. Marital status and time of procedure were also significant factors, as patients who were married (P < 0.001) and who were the first case of the day (P < 0.001) were less likely to be converted to IP.

CONCLUSION

Several factors were identified which could help determine appropriate hospital designation status at the time of surgical booking to ultimately avoid insurance claim denials. These included BMI, certain demographic factors, CCI ≥ 4, and patients 65 or older.

LEVEL III EVIDENCE

Retrospective Cohort Study.

Oral Postoperative Antibiotic Prophylaxis for Outpatient Total Hip and Knee Arthroplasty: Describing Current Practices

BACKGROUND

Despite an increase in outpatient total hip arthroplasty (THA) and total knee arthroplasty (TKA), large-scale data are lacking on current practice for antibiotic prophylaxis prescribing. We aimed to describe current oral antibiotic prophylaxis practices nationally for outpatient THA and TKA.

METHODS

This nationwide retrospective cohort study included primary outpatient THA or TKA procedures in patients aged 18 to 64 years from 2018 to 2021 using a national claims database. Oral antibiotic prescriptions filled perioperatively (defined as 5 days before to 3 days after surgery) were extracted; these were categorized and assumed to represent postoperative prophylaxis. Multivariable logistic regression measured associations between patient and surgery characteristics and perioperative oral antibiotic prophylaxis. Adjusted odds ratios (ORs) and 95% confidence intervals (CIs) are reported.

RESULTS

Oral antibiotic prescriptions were filled in 16.5% of 73,015 outpatient THA and TKA (18.4% of 24,857 THAs, 15.5% of 48,158 TKAs) procedures. Prescriptions were most often for cephalosporins (74.3%), with cephalexin (52.8%), and cefadroxil (19.1%) being the most common. Non-cephalosporin antibiotics prescribed were mainly clindamycin (6.8%), sulfamethoxazole-trimethoprim (6.7%), and doxycycline (6.2%). The odds of receiving oral antibiotic prophylaxis were higher for THA compared to TKA (OR 1.13, 95% CI 1.09 to 1.18, P < .001) and in the presence of obesity, diabetes, and autoimmune conditions (OR 1.08 to 1.13, P < .001 to .01). Ambulatory surgery center procedures also had significantly increased odds of prophylaxis compared to hospital-based outpatient surgeries (OR 2.62, 95% CI 2.51 to 2.73, P < .001). Additionally, regional and time-based variations were noted.

CONCLUSIONS

Perioperative oral antibiotic prophylaxis prescriptions were filled in only 16.5% of outpatient THA and TKA cases, with variation in the type of antibiotic prescribed. The receipt of any prophylaxis and specific medications was associated with demographic, clinical, and procedure-related characteristics. Follow-up research will evaluate associations with infection risk reduction.

Outpatient Total Hip Arthroplasty Volume up Nearly 8-Fold After Regulatory Changes With Expanding Demographics and Unchanging Outcomes: A 10-Year Analysis

BACKGROUND

With the removal of total hip arthroplasty (THA) from the inpatient-only (IPO) lists, the orthopedic landscape across the United States has changed rapidly. Thus, this study aimed to: 1) characterize the change in THA volume for outpatient and inpatient surgeries; 2) elucidate demographical differences before and after removal from the IPO list; and 3) analyze 30-day complications, readmissions, and reoperations.

METHODS

The National Surgical Quality Improvement Program database was queried for primary THAs between January 2010 and December 2021. The primary outcome was the annual volume of outpatient and inpatient THAs. Secondary outcomes involved 30-day complications, readmissions, and reoperations. The variables between cohorts were analyzed using goodness-of-fit Chi-square tests with summary statistics.

RESULTS

Of the 332,423 THAs between 2010 and 2021, 88% were inpatient THAs (n = 292,974) and 12% were outpatient THAs (n = 39,449). From 2019 to 2021, the volume of inpatient THA decreased by 55% (42,779 to 19,075), while outpatient THA increased by 751% (2,518 to 21,424). Patients who had a THA after 2019 were older (P < .001), more commonly women (P < .001), white (P < .001), and more likely American Society of Anesthesiologists Class III (P < .001). The outpatient cohort had fewer 30-day complications, readmissions, and reoperations. The length of stay for both cohorts decreased until 2019, before increasing in 2020 and 2021 for inpatient THAs, while home discharge and operative time increased for both.

CONCLUSIONS

The volume of outpatient THA increased almost eightfold after its removal from the IPO lists in 2020. Despite expanding eligibility with older patients and more comorbidities, 30-day complications, readmissions, and reoperations remain low. These findings support the safe transition to outpatient THA with appropriate patient selection and optimization.

Recent Outcomes and Challenges of Artificial Intelligence, Machine Learning, and Deep Learning in Neurosurgery

Neurosurgeons receive extensive technical training, which equips them with the knowledge and skills to specialise in various fields and manage the massive amounts of information and decision-making required throughout the various stages of neurosurgery, including preoperative, intraoperative, and postoperative care and recovery. Over the past few years, artificial intelligence (AI) has become more useful in neurosurgery. AI has the potential to improve patient outcomes by augmenting the capabilities of neurosurgeons and ultimately improving diagnostic and prognostic outcomes as well as decision-making during surgical procedures. By incorporating AI into both interventional and non-interventional therapies, neurosurgeons may provide the best care for their patients. AI, machine learning (ML), and deep learning (DL) have made significant progress in the field of neurosurgery. These cutting-edge methods have enhanced patient outcomes, reduced complications, and improved surgical planning.

Can ChatGPT Answer Patient Questions Regarding Total Knee Arthroplasty?

The internet has introduced many resources frequently accessed by patients prior to orthopaedic visits. Recently, Chat Generative Pre-Trained Transformer, an artificial intelligence-based chat application, has become publicly and freely available. The interface uses deep learning technology to mimic human interaction and provide convincing answers to questions posed by users. With its rapidly expanding usership, it is reasonable to assume that patients will soon use this technology for preoperative education. Therefore, we sought to determine the accuracy of answers to frequently asked questions (FAQs) pertaining to total knee arthroplasty (TKA).Ten FAQs were posed to the chatbot during a single online interaction with no follow-up questions or repetition. All 10 FAQs were analyzed for accuracy using an evidence-based approach. Answers were then rated as "excellent response not requiring clarification," "satisfactory requiring minimal clarification," satisfactory requiring moderate clarification," or "unsatisfactory requiring substantial clarification."Of the 10 answers given by the chatbot, none received an "unsatisfactory" rating with the majority either requiring minimal (5) or moderate (4) clarification. While many answers required nuanced clarification, overall, answers tended to be unbiased and evidence-based, even when presented with controversial subjects.The chatbot does an excellent job of providing basic, evidence-based answers to patient FAQs prior to TKA. These data were presented in a manner that will be easily comprehendible by most patients and may serve as a useful clinical adjunct in the future.

Evaluation of Preoperative Variables that Improve the Predictive Accuracy of the Risk Assessment and Prediction Tool in Primary Total Hip Arthroplasty

INTRODUCTION

Discharge disposition after total joint arthroplasty may be predictable. Previous literature has attempted to improve upon models such as the Risk Assessment and Prediction Tool (RAPT) in an effort to optimize postoperative planning. The purpose of this study was to determine whether preoperative laboratory values and other previously unstudied demographic factors could improve the predictive accuracy of the RAPT.

METHODS

All patients included had RAPT scores in addition to the following preoperative laboratory values: red blood cell count, albumin, and vitamin D. All values were recorded within 90 days of surgery. Demographic variables including marital status, American Society of Anesthesiologists (ASA) scores, body mass index, Charlson Comorbidity Index, and depression were also evaluated. Binary logistic regression was used to determine the significance of each factor in association with discharge disposition.

RESULTS

Univariate logistic regression found significant associations between discharge disposition and all original RAPT factors as well as nonmarried patients (P < 0.001), ASA class 3 to 4 (P < 0.001), body mass index >30 kg/m2 (P = 0.065), red blood cell count <4 million/mm3 (P < 0.001), albumin <3.5 g/dL (P < 0.001), Charlson Comorbidity Index (P < 0.001), and a history of depression (P < 0.001). All notable univariate models were used to create a multivariate model with an overall predictive accuracy of 90.1%.

CONCLUSIONS

The addition of preoperative laboratory values and additional demographic data to the RAPT may improve its PA. Orthopaedic surgeons could benefit from incorporating these values as part of their discharge planning in THA. Machine learning may be able to identify other factors to make the model even more predictive.

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.

A Novel Patient Selection Tool Is Highly Efficacious at Identifying Candidates for Outpatient Surgery When Applied to a Nonselected Cohort of Patients in a Community Hospital

BACKGROUND

There is a paucity of validated selection tools to assess which patients can safely and predictably undergo same-day or 23-hour discharge in a community hospital. The purpose of this study was to assess the ability of our patient selection too to identify patients who are candidates for outpatient total joint arthroplasty (TJA) in a community hospital.

METHODS

A retrospective review of 223 consecutive (unselected) primary TJAs was performed. The patient selection tool was retrospectively applied to this cohort to determine eligibility for outpatient arthroplasty. Utilizing length of stay and discharge disposition, we identified the proportion of patients discharged home within 23 hours.

RESULTS

We found that 179 (80.1%) patients met eligibility criteria for short-stay TJA. Of the 223 patients in this study, 215 (96.4%) patients were discharged home; 17 (7.9%) were on the day of surgery, and 190 (88.3%) within 23 hours. Of the 179 eligible patients for short-stay discharge, 155 (86.6%) patients were discharged home within 23 hours. Overall, the sensitivity of the patient selection tool was 79%, the specificity was 92%, the positive predictive value was 87% and the negative predictive value was 96%.

CONCLUSION

In this study, we found that more than 80% of patients undergoing TJA in a community hospital are eligible for short-stay arthroplasty with this selection tool. We found that this selection tool is safe and effective at predicting short-stay discharge. Further studies are needed to better ascertain the direct effects of these specific demographic traits on their effects on short-stay protocols.

Assessing ChatGPT Responses to Common Patient Questions Regarding Total Hip Arthroplasty

BACKGROUND

The contemporary patient has access to numerous resources on common orthopaedic procedures before ever presenting for a clinical evaluation. Recently, artificial intelligence (AI)-driven chatbots have become mainstream, allowing patients to engage with interfaces that supply convincing, human-like responses to prompts. ChatGPT (OpenAI), a recently developed AI-based chat technology, is one such application that has garnered rapid growth in popularity. Given the likelihood that patients may soon call on this technology for preoperative education, we sought to determine whether ChatGPT could appropriately answer frequently asked questions regarding total hip arthroplasty (THA).

METHODS

Ten frequently asked questions regarding total hip arthroplasty were posed to the chatbot during a conversation thread, with no follow-up questions or repetition. Each response was analyzed for accuracy with use of an evidence-based approach. Responses were rated as "excellent response not requiring clarification," "satisfactory requiring minimal clarification," "satisfactory requiring moderate clarification," or "unsatisfactory requiring substantial clarification."

RESULTS

Of the responses given by the chatbot, only 1 received an "unsatisfactory" rating; 2 did not require any correction, and the majority required either minimal (4 of 10) or moderate (3 of 10) clarification. Although several responses required nuanced clarification, the chatbot's responses were generally unbiased and evidence-based, even for controversial topics.

CONCLUSIONS

The chatbot effectively provided evidence-based responses to questions commonly asked by patients prior to THA. The chatbot presented information in a way that most patients would be able to understand. This resource may serve as a valuable clinical tool for patient education and understanding prior to orthopaedic consultation in the future.

Outpatient Total Joint Arthroplasty: Are We Closing the Racial Disparities Gap?

INTRODUCTION

As ne arly half of all total joint arthroplasty (TJA) procedures are projected to be performed in the outpatient setting by 2026, the impact of this trend on health disparities remains to be explored. This study investigated the racial/ethnic differences in the proportion of TJA performed as outpatient as well as the impact of outpatient surgery on 30-day complication and readmission rates.

METHODS

The ACS National Surgical Quality Improvement Program was retrospectively reviewed for all patients who underwent primary, elective total hip and knee arthroplasty (THA, TKA) between 2011 and 2018. The proportion of TJA performed as an outpatient, 30-day complications, and 30-day readmission among African American, Hispanic, Asian, Native American/Alaskan, and Hawaiian/Pacific Islander patients were each compared to White patients (control group). Analyses were performed for each racial/ethnic group separately. A general linear model (GLM) was used to calculate the odds ratios for receiving TJA in an outpatient vs. inpatient setting while adjusting for age, gender, body mass index (BMI), functional status, and comorbidities.

RESULTS

In total, 170,722 THAs and 285,920 TKAs were analyzed. Compared to White patients, non-White patients had higher likelihood of THA or TKA performed as an outpatient (OR 1.31 and 1.24 respectively for African American patients, OR 1.65 and 1.76 respectively for Hispanic patients, and OR 1.66 and 1.59 respectively for Asian patients, p < 0.001). Outpatient surgery did not lead to increased complications in any of the study groups compared to inpatient surgery (p > 0.05). However, readmission rates were significantly higher for outpatient TKA in all the study groups compared to inpatient TKA (OR range 2.47-10.15, p < 0.001). Complication and readmission rates were similar between inpatient and outpatient THA for all the study groups.

CONCLUSION

While this study demonstrated higher proportion of TJA performed as an outpatient among most non-White racial/ethnic groups, this observation should be tempered with the increased readmission rates observed in outpatient TKA, which could further the disparities gap in health outcomes.

The use of machine learning for predicting candidates for outpatient spine surgery: a review

While spine surgery has historically been performed in the inpatient setting, in recent years there has been growing interest in performing certain cervical and lumbar spine procedures on an outpatient basis. While conducting these procedures in the outpatient setting may be preferable for both the surgeon and the patient, appropriate patient selection is crucial. The employment of machine learning techniques for data analysis and outcome prediction has grown in recent years within spine surgery literature. Machine learning is a form of statistics often applied to large datasets that creates predictive models, with minimal to no human intervention, that can be applied to previously unseen data. Machine learning techniques may outperform traditional logistic regression with regards to predictive accuracy when analyzing complex datasets. Researchers have applied machine learning to develop algorithms to aid in patient selection for spinal surgery and to predict postoperative outcomes. Furthermore, there has been increasing interest in using machine learning to assist in the selection of patients who may be appropriate candidates for outpatient cervical and lumbar spine surgery. The goal of this review is to discuss the current literature utilizing machine learning to predict appropriate patients for cervical and lumbar spine surgery, candidates for outpatient spine surgery, and outcomes following these procedures.

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.

Development and benchmarking of machine learning models to classify patients suitable for outpatient lower extremity joint arthroplasty

STUDY OBJECTIVE

Performing hip or knee arthroplasty as an outpatient surgery has been shown to be operationally and financially beneficial for selected patients. By applying machine learning models to predict patients suitable for outpatient arthroplasty, health care systems can better utilize resources efficiently. The goal of this study was to develop predictive models for identifying patients likely to be discharged same-day following hip or knee arthroplasty.

DESIGN

Model performance was assessed with 10-fold stratified cross-validation, evaluated over baseline determined by the proportion of eligible outpatient arthroplasty over sample size. The models used for classification were logistic regression, support vector classifier, balanced random forest, balanced bagging XGBoost classifier, and balanced bagging LightGBM classifier.

SETTING

The patient records were sampled from arthroplasty procedures at a single institution from October 2013 to November 2021.

PATIENTS

The electronic intake records of 7322 knee and hip arthroplasty patients were sampled for the dataset. After data processing, 5523 records were kept for model training and validation.

INTERVENTIONS

None.

MEASUREMENTS

The primary measures for the models were the F1-score, area under the receiver operating characteristic curve (ROCAUC), and area under the precision-recall curve. To measure feature importance, the SHapley Additive exPlanations value (SHAP) were reported from the model with the highest F1-score.

RESULTS

The best performing classifier (balanced random forest classifier) achieved an F1-score of 0.347: an improvement of 0.174 over baseline and 0.031 over logistic regression. The ROCAUC for this model was 0.734. Using SHAP, the top determinant features of the model included patient sex, surgical approach, surgery type, and body mass index.

CONCLUSIONS

Machine learning models may utilize electronic health records to screen arthroplasty procedures for outpatient eligibility. Tree-based models demonstrated superior performance in this study.

Improving Resource Utilization for Arthroplasty Care by Leveraging Machine Learning and Optimization: A Systematic Review

Background

There is a growing demand for total joint arthroplasty (TJA) surgery. The applications of machine learning (ML), mathematical optimization, and computer simulation have the potential to improve efficiency of TJA care delivery through outcome prediction and surgical scheduling optimization, easing the burden on health-care systems. The purpose of this study was to evaluate strategies using advances in analytics and computational modeling that may improve planning and the overall efficiency of TJA care.

Methods

A systematic review including MEDLINE, Embase, and IEEE Xplore databases was completed from inception to October 3, 2022, for identification of studies generating ML models for TJA length of stay, duration of surgery, and hospital readmission prediction. A scoping review of optimization strategies in elective surgical scheduling was also conducted.

Results

Twenty studies were included for evaluating ML predictions and 17 in the scoping review of scheduling optimization. Among studies generating linear or logistic control models alongside ML models, only 1 found a control model to outperform its ML counterpart. Furthermore, neural networks performed superior to or at the same level as conventional ML models in all but 1 study. Implementation of mathematical and simulation strategies improved the optimization efficiency when compared to traditional scheduling methods at the operational level.

Conclusions

High-performing predictive ML-based models have been developed for TJA, as have mathematical strategies for elective surgical scheduling optimization. By leveraging artificial intelligence for outcome prediction and surgical optimization, there exist greater opportunities for improved resource utilization and cost-savings in TJA than when using traditional modeling and scheduling methods.

Major National Shifts to Outpatient Total Knee Arthroplasties in the United States: A 10-Year Trends Analysis of Procedure Volumes, Complications, and Healthcare Utilizations (2010 to 2020)

BACKGROUND

The removal of total knee arthroplasty (TKA) from inpatient-only lists accelerated changes in orthopaedic surgical practices across the United States. This study aimed to (1) quantify the annual volume of inpatient/outpatient primary TKAs; (2) compare patient characteristics before/after the year 2018; and (3) compare annual trends in 30-day readmissions, 30-day complications, and healthcare utilization parameters for inpatient/outpatient TKAs.

METHODS

The National Surgical Quality Improvement Program was reviewed (January 2010 to December 2020) for patients who underwent primary TKA (n = 470,456). The primary outcome was annual volumes of inpatient/outpatient TKA. Secondary outcomes included 30-day readmissions, 30-day reoperations, and 30-day major/minor complications. Demographic characteristics and healthcare utilization parameters (hospital lengths of stay and discharge dispositions) were compared between cohorts via Chi-square goodness-of-fit tests.

RESULTS

Overall, 89% had inpatient TKA (n = 416,972) and 11% had outpatient TKA (n = 53,854). Between 2017 and 2020, annual volumes of outpatient TKA increased by 1,925 (1,019 to 20,633), while inpatient TKA decreased by 53% (61,874 to 29,280). Patients who had outpatient TKA after 2018 were older (P < .001), predominantly males (P < .001), more commonly White (P < .001), and had a greater proportion of American Society of Anesthesiologists class III (P < .001). The inpatient cohort had higher rates of 30-day readmissions, reoperations, and complications. Average length of stay and nonhome discharges decreased for both cohorts.

CONCLUSION

Outpatient TKA increased 20-fold at NSQIP hospitals. The changes in comorbidity profiles and the increase in volumes of outpatient TKA were not associated with a rise in cumulative 30-day readmissions and complications. Further research and policy endeavors should focus on identifying patients who still require or benefit from inpatient TKA.

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.

Machine Learning Algorithm to Predict Worsening of Flexion Range of Motion After Total Knee Arthroplasty

Background

Methods

Results

Conclusions

Same-Day Outpatient Lower-Extremity Joint Replacement: A Critical Analysis Review

➢ The economics of transitioning total joint arthroplasty (TJA) to standalone ambulatory surgery centers (ASCs) should not be capitalized on at the expense of patient safety in the absence of established superior patient outcomes. ➢ Proper patient selection is essential to maximizing safety and avoiding complications resulting in readmission. ➢ Ambulatory TJA programs should focus on reducing complications frequently associated with delays in discharge. ➢ The transition from hospital-based TJA to ASC-based TJA has substantial financial implications for the hospital, payer, patient, and surgeon.