N-acetylneuraminic acid modulates SQSTM1/p62 sialyation-mediated ubiquitination degradation contributing to vascular endothelium dysfunction in experimental atherosclerosis mice.

Sialic acid (SIA) has been reported to be a risk factor for atherosclerosis (AS) due to its high plasma levels in such patients. However, the effect of increasing SIA in circulation on endothelial function during AS progression remains unclear. In the present study, ApoE-/- mice and endothelial cells line (HUVEC cells) were applied to investigate the effect of SIA on AS progression and its potential molecular mechanism. In vivo, mice were injected intraperitoneally with Neu5Ac (main form of SIA) to keep high-level SIA in circulation. ORO, H&E, and Masson staining were applied to detect the plaque progression. In vitro, HUVECs were treated with Neu5Ac at different times, CCK-8, RT-PCR, western blot, and immunoprecipitation methods were used to analyze its effects on endothelial function and the potential involved mechanism. Results from the present study showed that high plasma levels of Neu5Ac in ApoE-/- mice could aggravate the plaque areas as well as increase necrotic core areas and collagen fiber contents. Remarkably, Neu5Ac levels in circulation displayed a positive correlation with AS plaque areas. Furthermore, results from HUVECs showed that Neu5Ac inhibited cells viability in a time/dose-dependent manner, by then induced the activation of inflammation makers such as ICAM-1 and IL-1ß. Mechanism study showed that the activation of excessive autophagy medicated by SQSTM1/p62 displayed an important role in endothelium inflammatory injury. Neu5Ac could modify SQSTM1/p62 as a sialylation protein, and then increase its level with ubiquitin binding, further inducing ubiquitination degradation and being involved in the excessive autophagy pathway. Inhibition of sialylation by P-3Fax-Neu5Ac, a sialyltransferase inhibitor, reduced the binding of SQSTM1/p62 to ubiquitin. Together, these findings indicated that Neu5Ac increased SQSTM1/p62-ubiquitin binding through sialylation modification, thereby inducing excessive autophagy and subsequent endothelial injury. Inhibition of SQSTM1/p62 sialylation might be a potential strategy for preventing such disease with high levels of Neu5Ac in circulation.

Monocyte alteration in elderly hip fracture healing: monocyte promising role in bone regeneration.

Individual aged with various change in cell and cellular microenvironments and the skeletal system undergoes physiological changes that affect the process of bone fracture healing. These changes are accompanied by alterations in regulating critical genes involved in this healing process. Unfortunately, the elderly are particularly susceptible to hip bone fractures, which pose a significant burden associated with higher morbidity and mortality rates. A notable change in older adults is the increased expression of activation, adhesion, and migration markers in circulating monocytes. However, there is a decrease in the expression of co-inhibitory molecules. Recently, research evidence has shown that the migration of specific monocyte subsets to the site of hip fracture plays a crucial role in bone resorption and remodeling, especially concerning age-related factors. In this review, we summarize the current knowledge about uniqueness characteristics of monocytes, and their potential regulation and moderation to enhance the healing process of hip fractures. This breakthrough could significantly contribute to the comprehension of aging process at a fundamental aging mechanism through this initiative would represent a crucial stride for diagnosing and treating age related hip fracture.

Root Repair Has Superior Radiological and Clinical Outcomes Than Partial Meniscectomy and Nonoperative Treatment in the Management of Meniscus Root Tears: A Systematic Review.

PURPOSE: To compile and analyze structural and clinical outcomes after meniscus root tear treatment as currently described in the literature. METHODS: A review was conducted to identify studies published since 2011 on efficacy of repair, meniscectomy, and nonoperative management in the treatment of meniscus root tears. Patient cohorts were grouped into treatment categories, with medial and lateral root tears analyzed separately; data were collected on patient demographics, structural outcomes including joint space width, degree of medial meniscal extrusion, progression to total knee arthroplasty, and patient-reported outcome measures. Risk of bias was assessed using the MINORS (methodological index for non-randomized studies) criteria. Heterogeneity was measured using the I-statistic, and outcomes were summarized using forest plots without pooled means. RESULTS: The 56 included studies comprised a total of 3,191 patients. Mean age among the included studies ranged from 24.6 to 65.6 years, whereas mean follow-up ranged from 12 to 125.9 months. Heterogeneity analysis identified significant differences between studies. Change in joint space width ranged from -2.4 to -0.6 mm (i.e., decreased space) after meniscectomy (n = 186) and -0.9 to -0.1 mm after root repair (n = 209); change in medial meniscal extrusion ranged from -0.6 to 6.5 mm after root repair (n = 521) and 0.2 to 4.2 mm after meniscectomy (n = 66); and event rate for total knee arthroplasty ranged from 0.00 to 0.22 after root repair (n = 205), 0.35 to 0.60 after meniscectomy (n = 53), and 0.27 to 0.35 after nonoperative treatment (n = 93). Root repair produced the greatest numerical increase in International Knee Documentation Committee and Lysholm scores of the 3 treatment arms. In addition, root repair improvements in Knee Injury and Osteoarthritis Outcome Score Pain (range: 22-32), Sports and Recreational Activities (range: 23-36), Quality of Life (range: 22-42), and Symptoms subscales (range: 10-19), in studies with low risk of bias. CONCLUSIONS: The literature reporting on the treatment of meniscus root tears is heterogenous and largely limited to Level III and IV studies. Current evidence suggests root repair may be the most effective treatment strategy in lessening joint space narrowing of the knee and producing improvements in patient-reported outcomes. LEVEL OF EVIDENCE: Level IV, systematic review of Level II-IV studies.

Examining an integrated sociocultural and objectification model of thinness- and muscularity-oriented disordered eating in Chinese older men and women.

OBJECTIVE: We tested an integrated model of three prominent theories of disordered eating (tripartite influence theory, objectification theory, and social comparison theory) in a sample of older Chinese men and women. METHOD: Chinese older men (n = 270) and women (n = 160) completed questionnaires assessing the tripartite influence, objectification, and social comparison theories and thinness- and muscularity-oriented disordered eating. Two structural equation models were tested in Chinese older men and women. RESULTS: The integrated model showed good model fit and described meaningful variance in thinness- and muscularity-oriented disordered eating in Chinese older men and women. Higher appearance pressures were uniquely related to higher muscularity-oriented disordered eating in men. Across both gender groups, higher thinness internalization was uniquely related to higher thinness- and muscularity-oriented disordered eating, and in women only, higher muscularity internalization was uniquely related to lower thinness-oriented disordered eating. In men, higher upward and downward body image comparisons were uniquely related to higher and lower, respectively, muscularity-oriented disordered eating. In women, higher upward body image comparisons were only uniquely related to higher muscularity-oriented disordered eating while higher downward body image comparisons were uniquely related to both outcomes. Higher body shame was uniquely related to higher thinness-oriented disordered eating across both groups and in men alone, higher body shame was also uniquely related to higher muscularity-oriented disordered eating. DISCUSSION: Findings, which tested the integration of tripartite influence, objectification, and social comparison theories, inform the prevention and treatment of disordered eating in Chinese older populations. PUBLIC SIGNIFICANCE: The present study is the first to describe theories of disordered eating (tripartite influence, objectification, and social comparison) in Chinese older adults. Findings suggested good model fit and the integrated models described meaningful variance in thinness- and muscularity-oriented disordered eating in Chinese older women and men. Findings extend existing theories of disordered eating and, pending further study, may inform theory-driven prevention and treatment approaches in Chinese older adults.

Decoding the immune landscape following hip fracture in elderly patients: unveiling temporal dynamics through single-cell RNA sequencing.

BACKGROUND: Hip fractures in the elderly have significant consequences, stemming from the initial trauma and subsequent surgeries. Hidden blood loss and stress due to concealed injury sites could impact the whole osteoimmune microenvironment. This study employs scRNA-seq technique to map immune profiles in elderly hip fracture patients from post-trauma to the recovery period, investigating the dynamic changes of immune inflammation regulation subgroups. METHODS: We collected peripheral blood samples from four elderly hip fracture patients (two males and two females, all > 75 years of age) at three different time points (24 h post-trauma, 24 h post-operation, and day 7 post-operation) and applied scRNA-seq technique to analyze the cellular heterogeneity and identify differentially expressed genes in peripheral blood individual immune cells from elderly hip fracture patients. RESULTS: In this study, we analyzed the composition and gene expression profiles of peripheral blood mononuclear cells (PBMCs) from elderly hip fracture patients by scRNA-seq and further identified new CD14 monocyte subpopulations based on marker genes and transcriptional profiles. Distinct gene expression changes were observed in various cell subpopulations at different time points. C-Mono2 monocyte mitochondria-related genes were up-regulated and interferon-related and chemokine-related genes were down-regulated within 24 h post-operation. Further analysis of gene expression profiles at day 7 post-operation showed that C-Mono2 monocytes showed downregulation of inflammation-related genes and osteoblast differentiation-related genes. However, the expression of these genes in cytotoxic T cells, Treg cells, and B cell subsets exhibited a contrasting trend. GZMK+CD8+ cytotoxic T cells showed downregulation of chemokine-related genes, and Treg cells showed upregulation of genes related to the JAK/STAT signaling pathway. Furthermore, we examined interactions among diverse immune cell subsets, pinpointing specific ligand-receptor pairs. These findings imply cross-talk and communication between various cell types in the post-traumatic immune response. CONCLUSIONS: Our study elucidates the notable alterations in immune cell subpopulations during different stages of hip fracture in elderly patients, both in terms of proportions and differential gene expressions. These changes provide significant clinical implications for tissue repair, infection prevention, and fracture healing in clinic.

Editorial Commentary: Machine Learning and Artificial Intelligence Are Tools Requiring Physician and Patient Input When Screening Patients at Risk for Extended, Postoperative Opioid Use.

As the implementation of artificial intelligence in orthopedic surgery research flourishes, so grows the need for responsible use. Related research requires clear reporting of algorithmic error rates. Recent studies show that preoperative opioid use, male sex, and greater body mass index are risk factors for extended, postoperative opioid use, but may result in high false positive rates. Thus, to be applied clinically when screening patients, these tools require physician and patient input, and nuanced interpretation, as the utility of these screening tools diminish without providers interpreting and acting on the information. Machine learning and artificial intelligence should be viewed as tools that can facilitate these human conversations among patients, orthopedic surgeons, and health care providers.

Platelet-Rich Plasma and Marrow Venting May Serve as Cost-Effective Augmentation Techniques for Isolated Meniscal Repair: A Decision-Analytical Markov Model-Based Analysis.

PURPOSE: To evaluate the cost-effectiveness of 3 isolated meniscal repair (IMR) treatment strategies: platelet-rich plasma (PRP)-augmented IMR, IMR with a marrow venting procedure (MVP), and IMR without biological augmentation. METHODS: A Markov model was developed to evaluate the baseline case: a young adult patient meeting the indications for IMR. Health utility values, failure rates, and transition probabilities were derived from the published literature. Costs were determined based on the typical patient undergoing IMR at an outpatient surgery center. Outcome measures included costs, quality-adjusted life-years (QALYs), and the incremental cost-effectiveness ratio (ICER). RESULTS: Total costs of IMR with an MVP were $8,250; PRP-augmented IMR, $12,031; and IMR without PRP or an MVP, $13,326. PRP-augmented IMR resulted in an additional 2.16 QALYs, whereas IMR with an MVP produced slightly fewer QALYs, at 2.13. Non-augmented repair produced a modeled gain of 2.02 QALYs. The ICER comparing PRP-augmented IMR versus MVP-augmented IMR was $161,742/QALY, which fell well above the $50,000 willingness-to-pay threshold. CONCLUSIONS: IMR with biological augmentation (MVP or PRP) resulted in a higher number of QALYs and lower costs than non-augmented IMR, suggesting that biological augmentation is cost-effective. Total costs of IMR with an MVP were significantly lower than those of PRP-augmented IMR, whereas the number of additional QALYs produced by PRP-augmented IMR was only slightly higher than that produced by IMR with an MVP. As a result, neither treatment dominated over the other. However, because the ICER of PRP-augmented IMR fell well above the $50,000 willingness-to-pay threshold, IMR with an MVP was determined to be the overall cost-effective treatment strategy in the setting of young adult patients with isolated meniscal tears. LEVEL OF EVIDENCE: Level III, economic and decision analysis.

Machine learning can accurately predict risk factors for all-cause reoperation after ACLR: creating a clinical tool to improve patient counseling and outcomes.

PURPOSE: Identifying predictive factors for all-cause reoperation after anterior cruciate ligament reconstruction could inform clinical decision making and improve risk mitigation. The primary purposes of this study are to (1) determine the incidence of all-cause reoperation after anterior cruciate ligament reconstruction, (2) identify predictors of reoperation after anterior cruciate ligament reconstruction using machine learning methodology, and (3) compare the predictive capacity of the machine learning methods to that of traditional logistic regression. METHODS: A longitudinal geographical database was utilized to identify patients with a diagnosis of new anterior cruciate ligament injury. Eight machine learning models were appraised on their ability to predict all-cause reoperation after anterior cruciate ligament reconstruction. Model performance was evaluated via area under the receiver operating characteristics curve. To explore modeling interpretability and radiomic feature influence on the predictions, we utilized a game-theory-based method through SHapley Additive exPlanations. RESULTS: A total of 1400 patients underwent anterior cruciate ligament reconstruction with a mean postoperative follow-up of 9 years. Two-hundred and eighteen (16%) patients experienced a reoperation after anterior cruciate ligament reconstruction, of which 6% of these were revision ACL reconstruction. SHapley Additive exPlanations plots identified the following risk factors as predictive for all-cause reoperation: diagnosis of systemic inflammatory disease, distal tear location, concomitant medial collateral ligament repair, higher visual analog scale pain score prior to surgery, hamstring autograft, tibial fixation via radial expansion device, younger age at initial injury, and concomitant meniscal repair. Pertinent negatives, when compared to previous studies, included sex and timing of surgery. XGBoost was the best-performing model (area under the receiver operating characteristics curve of 0.77) and outperformed logistic regression in this regard. CONCLUSIONS: All-cause reoperation after anterior cruciate ligament reconstruction occurred at a rate of 16%. Machine learning models outperformed traditional statistics and identified diagnosis of systemic inflammatory disease, distal tear location, concomitant medial collateral ligament repair, higher visual analog scale pain score prior to surgery, hamstring autograft, tibial fixation via radial expansion device, younger age at initial injury, and concomitant meniscal repair as predictive risk factors for reoperation. Pertinent negatives, when compared to previous studies, included sex and timing of surgery. These models will allow surgeons to tabulate individualized risk for future reoperation for patients undergoing anterior cruciate ligament reconstruction. LEVEL OF EVIDENCE: III.

Risk factors for secondary meniscus tears can be accurately predicted through machine learning, creating a resource for patient education and intervention.

PURPOSE: This study sought to develop and internally validate a machine learning model to identify risk factors and quantify overall risk of secondary meniscus injury in a longitudinal cohort after primary ACL reconstruction (ACLR). METHODS: Patients with new ACL injury between 1990 and 2016 with minimum 2-year follow-up were identified. Records were extensively reviewed to extract demographic, treatment, and diagnosis of new meniscus injury following ACLR. Four candidate machine learning algorithms were evaluated to predict secondary meniscus tears. Performance was assessed through discrimination using area under the receiver operating characteristics curve (AUROC), calibration, and decision curve analysis; interpretability was enhanced utilizing global variable importance plots and partial dependence curves. RESULTS: A total of 1187 patients underwent ACLR; 139 (11.7%) experienced a secondary meniscus tear at a mean time of 65 months post-op. The best performing model for predicting secondary meniscus tear was the random forest (AUROC = 0.790, 95% CI: 0.785-0.795; calibration intercept = 0.006, 95% CI: 0.005-0.007, calibration slope = 0.961 95% CI: 0.956-0.965, Brier's score = 0.10 95% CI: 0.09-0.12), and all four machine learning algorithms outperformed traditional logistic regression. The following risk factors were identified: shorter time to return to sport (RTS), lower VAS at injury, increased time from injury to surgery, older age at injury, and proximal ACL tear. CONCLUSION: Machine learning models outperformed traditional prediction models and identified multiple risk factors for secondary meniscus tears after ACLR. Following careful external validation, these models can be deployed to provide real-time quantifiable risk for counseling and timely intervention to help guide patient expectations and possibly improve clinical outcomes. LEVEL OF EVIDENCE: III.

Incidence and 30- and 90-day readmission rates after primary shoulder arthroplasty in the United States: an analysis using the National Readmissions Database.

BACKGROUND: The field of shoulder arthroplasty has experienced a substantial increase in the number of procedures performed annually and a shift toward more common implantation of reverse shoulder arthroplasties (RSAs). Same-day discharge is perceived as beneficial for most patients as well as our health care system, and the number of shoulder procedures performed as same-day surgery has increased substantially. However, the potential benefits of same-day discharge after shoulder arthroplasty may be negatively influenced by unexpected readmissions. As such, an in-depth analysis of readmission rates after primary shoulder arthroplasty is particularly timely. METHODS: The National Readmissions Database was queried for primary shoulder arthroplasty procedures performed in the United States between 2016 and 2018. National incidences were calculated, and indications, patient demographic characteristics, comorbidities, facility characteristics, and rates and causes of 30- and 90-day readmissions were determined for all procedures and compared between anatomic total shoulder arthroplasty (TSA), anatomic hemiarthroplasty (HA), and RSA. RESULTS: During the study period, 336,672 primary shoulder arthroplasties were performed (37% TSAs, 57% RSAs, and 6% HAs). In 2018, national incidences per 100,000 inhabitants were 22.64 for RSA, 12.70 for TSA, and 1.50 for HA. The utilization of these procedures between 2016 and 2018 increased for RSA, decreased for HA, and remained constant for TSA, but these changes did not reach the level of statistical significance. The average all-cause 30-day readmission rates were 3.63%, 1.92%, and 3.81% for RSA, TSA, and HA, respectively, and the average all-cause 90-day readmission rates were 7.76%, 4.37%, and 9.18%, respectively. For both RSA and HA, the most common surgical diagnosis for 30-day and 90-day readmissions was dislocation (0.45% and 0.99%, respectively, for RSA and 0.21% and 0.67%, respectively, for HA). For TSA, the most common surgical diagnosis for 30-day readmission was infection (0.11%); however, this was surpassed by dislocation (0.28%) at 90 days. CONCLUSION: RSA surpassed TSA as the most frequently performed shoulder arthroplasty procedure in the United States between 2016 and 2018. During this period, the 90-day readmission rate was not negligible, with dislocation and infection as the leading orthopedic causes of readmission.

Understanding risk for early dislocation resulting in reoperation within 90 days of reverse total shoulder arthroplasty: extreme rare event detection through cost-sensitive machine learning.

BACKGROUND: Reliable prediction of postoperative dislocation after reverse total shoulder arthroplasty (RSA) would inform patient counseling as well as surgical and postoperative decision making. Understanding interactions between multiple risk factors is important to identify those patients most at risk of this rare but costly complication. To better understand these interactions, a game theory-based approach was undertaken to develop machine learning models capable of predicting dislocation-related 90-day readmission following RSA. MATERIAL & METHODS: A retrospective review of the Nationwide Readmissions Database was performed to identify patients who underwent RSA between 2016 and 2018 with a subsequent readmission for prosthetic dislocation. Of the 74,697 index procedures included in the data set, 740 (1%) experienced a dislocation resulting in hospital readmission within 90 days. Five machine learning algorithms were evaluated for their ability to predict dislocation leading to hospital readmission within 90 days of RSA. Shapley additive explanation (SHAP) values were calculated for the top-performing models to quantify the importance of features and understand variable interaction effects, with hierarchical clustering used to identify cohorts of patients with similar risk factor combinations. RESULTS: Of the 5 models evaluated, the extreme gradient boosting algorithm was the most reliable in predicting dislocation (C statistic = 0.71, F2 score = 0.07, recall = 0.84, Brier score = 0.21). SHAP value analysis revealed multifactorial explanations for dislocation risk, with presence of a preoperative humerus fracture; disposition involving discharge or transfer to a skilled nursing facility, intermediate care facility, or other nonroutine facility; and Medicaid as the expected primary payer resulting in strong, positive, and unidirectional effects on increasing dislocation risk. In contrast, factors such as comorbidity burden, index procedure complexity and duration, age, sex, and presence or absence of preoperative glenohumeral osteoarthritis displayed bidirectional influences on risk, indicating potential protective effects for these variables and opportunities for risk mitigation. Hierarchical clustering using SHAP values identified patients with similar risk factor combinations. CONCLUSION: Machine learning can reliably predict patients at risk for postoperative dislocation resulting in hospital readmission within 90 days of RSA. Although individual risk for dislocation varies significantly based on unique combinations of patient characteristics, SHAP analysis revealed a particularly at-risk cohort consisting of young, male patients with high comorbidity burdens who are indicated for RSA after a humerus fracture. These patients may require additional modifications in postoperative activity, physical therapy, and counseling on risk-reducing measures to prevent early dislocation after RSA.

Health and Socioeconomic Risk Factors for Unplanned Hospitalization Following Ambulatory Unicompartmental Knee Arthroplasty: Development of a Patient Selection Tool Using Machine Learning.

BACKGROUND: Identifying ambulatory surgical candidates at risk for adverse surgical outcomes can optimize outcomes. The purpose of this study was to develop and internally validate a machine learning (ML) algorithm to predict contributors to unexpected hospitalizations after ambulatory unicompartmental knee arthroplasty (UKA). METHODS: A total of 2,521 patients undergoing UKA from 2006 to 2018 were retrospectively evaluated. Patients admitted overnight postoperatively were identified as those who had a length of stay ≥ 1 day were analyzed with four individual ML models (ie, random forest, extreme gradient boosting, adaptive boosting, and elastic net penalized logistic regression). An additional model was produced as a weighted ensemble of the four individual algorithms. Area under the receiver operating characteristics (AUROC) compared predictive capacity of these models to conventional logistic regression techniques. RESULTS: Of the 2,521 patients identified, 103 (4.1%) required at least one overnight stay following ambulatory UKA. The ML ensemble model achieved the best performance based on discrimination assessed via internal validation (AUROC = 87.3), outperforming individual models and conventional logistic regression (AUROC = 81.9-85.7). The variables determined most important by the ensemble model were cumulative time in the operating room, utilization of general anesthesia, increasing age, and patient residency in more urban areas. The model was integrated into a web-based open-access application. CONCLUSION: The ensemble gradient-boosted ML algorithm demonstrated the highest performance in identifying factors contributing to unexpected hospitalizations in patients receiving UKA. This tool allows physicians and healthcare systems to identify patients at a higher risk of needing inpatient care after UKA.

Identifying Modifiable Cost Drivers of Outpatient Unicompartmental Knee Arthroplasty With Machine Learning.

BACKGROUND: Implementing tools that identify cost-saving opportunities for ambulatory orthopaedic surgeries can improve access to value-based care. We developed and internally validated a machine learning (ML) algorithm to predict cost drivers of total charges after ambulatory unicompartmental knee arthroplasty (UKA). METHODS: We queried the New York State Ambulatory Surgery and Services database to identify patients who underwent ambulatory, defined as <24 hours of care before discharge, elective UKA between 2014 and 2016. A total of 1,311 patients were included. The median costs after ambulatory UKA were $14,710. Patient demographics and intraoperative parameters were entered into 4 candidate ML algorithms. The most predictive model was selected following internal validation of candidate models, with conventional linear regression as a benchmark. Global variable importance and partial dependence curves were constructed to determine the impact of each input parameter on total charges. RESULTS: The gradient-boosted ensemble model outperformed all candidate algorithms and conventional linear regression. The major differential cost drivers of UKA identified (in decreasing order of magnitude) were increased operating room time, length of stay, use of regional and adjunctive periarticular analgesia, utilization of computer-assisted navigation, and routinely sending resected tissue to pathology. CONCLUSION: We developed and internally validated a supervised ML algorithm that identified operating room time, length of stay, use of computer-assisted navigation, regional primary anesthesia, adjunct periarticular analgesia, and routine surgical pathology as essential cost drivers of UKA. Following external validation, this tool may enable surgeons and health insurance providers optimize the delivery of value-based care to patients receiving outpatient UKA. LEVEL OF EVIDENCE: III.

Defining Clinically Meaningful Subgroups for Risk Stratification in Patients Undergoing Revision Total Hip Arthroplasty: A Combined Unsupervised and Supervised Machine Learning Approach.

BACKGROUND: Studies developing predictive models from large datasets to risk-stratify patients under going revision total hip arthroplasties (rTHAs) are limited. We used machine learning (ML) to stratify patients undergoing rTHA into risk-based subgroups. METHODS: We retrospectively identified 7,425 patients who underwent rTHA from a national database. An unsupervised random forest algorithm was used to partition patients into high-risk and low-risk strata based on similarities in rates of mortality, reoperation, and 25 other postoperative complications. A risk calculator was produced using a supervised ML algorithm to identify high-risk patients based on preoperative parameters. RESULTS: There were 3,135 and 4,290 patients identified in the high-risk and low-risk subgroups, respectively. Each group significantly differed by rate of 30-day mortalities, unplanned reoperations/readmissions, routine discharges, and hospital lengths of stay (P < .05). An Extreme Gradient Boosting algorithm identified preoperative platelets < 200, hematocrit > 35 or < 20, increasing age, albumin < 3, international normalized ratio > 2, body mass index > 35, American Society of Anesthesia class ≥ 3, blood urea nitrogen > 50 or < 30, creatinine > 1.5, diagnosis of hypertension or coagulopathy, and revision for periprosthetic fracture and infection as predictors of high risk. CONCLUSION: Clinically meaningful risk strata in patients undergoing rTHA were identified using an ML clustering approach. Preoperative labs, demographics, and surgical indications have the greatest impact on differentiating high versus low risk. LEVEL OF EVIDENCE: III.

The role and regulation of apoptosis signal-regulated kinase 1 in liver disease.

ASK1, also known as MAP3K5, plays a vital role in the MAPK pathway. The MAPK pathway has a variety of biological functions and plays an important role in regulating cell proliferation, differentiation, and apoptosis. Studies have shown that ASK1 is involved in apoptosis, inflammation, oxidative stress, and other processes and plays an essential role in various liver diseases. Therefore, ASK1 can be a therapeutic target for treating liver disease. Here, we initially summarized the effect of ASK1 on liver disease and described the differential regulation of ASK1, including phosphorylation, ubiquitination and methylation, by which the effects of ASK1 on some liver diseases can be inhibited. Although much has been discovered about the phosphorylation of ASK1, the effects of other post-transcriptional modifications on the activity of ASK1 require further exploration. We hope that by summarizing the existing regulatory mechanism we can shed new light on the research and provide new ideas for finding ASK1-targeting drugs.

Physical Therapy Combined With Subacromial Cortisone Injection Is a First-Line Treatment Whereas Acromioplasty With Physical Therapy Is Best if Nonoperative Interventions Fail for the Management of Subacromial Impingement: A Systematic Review and Network Meta-Analysis.

PURPOSE: To construct an algorithm to optimize clinical outcomes in subacromial impingement based on current, high-level evidence. METHODS: A systematic review of all clinical trials on subacromial impingement published from 1999 to 2020 was performed. Demographic, clinical, range of motion (ROM), and patient-reported outcome measure (PROM) data were collected. Interventions were compared via arm-based Bayesian network meta-analysis in a random-effects model and treatments ranked via surface under the cumulative ranking curves with respect to 3 domains: pain, PROMs, and ROM. RESULTS: A total of 35 studies comprising 3,643 shoulders (42% female, age 50 ± 5 years) were included. Arthroscopic decompression with acromioplasty ranked much greater than arthroscopic decompression alone for pain relief and PROM improvement, but the difference in absolute PROMs was not statistically significant. Corticosteroid injection (CSI) alone demonstrated inferior outcomes across all 3 domains (pain, PROMs, and ROM) with low cumulative rankings. Physical therapy (PT) with CSI demonstrated moderate-to-excellent clinical improvement across all 3 domains whereas PT alone demonstrated excellent ROM and low-moderate outcomes in pain and PROM domains. PT with nonsteroidal anti-inflammatory drugs or alternative therapies ranked highly for PROM outcomes and moderate for pain and ROM domains. Finally, platelet-rich plasma injections demonstrated moderate outcomes for pain, forward flexion, and abduction with very low-ranking outcomes for PROMs and external rotation. CONCLUSIONS: Arthroscopic decompression with acromioplasty and PT demonstrated superior outcomes whereas CSI demonstrated poor outcomes in all 3 domains (pain, PROMs, and ROM). For patients with significant symptoms, the authors recommend PT with CSI as a first-line treatment, followed by acromioplasty and PT if conservative treatment fails. For patients with symptoms limited to 1 to 2 domains, the authors recommend a shared decision-making approach focusing on treatment rankings within domains pertinent to individual patient symptomatology. LEVEL OF EVIDENCE: I, systematic review and network meta-analysis of Level I studies.

Duration of Care and Operative Time Are the Primary Drivers of Total Charges After Ambulatory Hip Arthroscopy: A Machine Learning Analysis.

PURPOSE: To develop a machine learning algorithm to predict total charges after ambulatory hip arthroscopy and create a risk-adjusted payment model based on patient comorbidities. METHODS: A retrospective review of the New York State Ambulatory Surgery and Services database was performed to identify patients who underwent elective hip arthroscopy between 2015 and 2016. Features included in initial models consisted of patient characteristics, medical comorbidities, and procedure-specific variables. Models were generated to predict total charges using 5 algorithms. Model performance was assessed by the root-mean-square error, root-mean-square logarithmic error, and coefficient of determination. Global variable importance and partial dependence curves were constructed to show the impact of each input feature on total charges. For performance benchmarking, the best candidate model was compared with a multivariate linear regression using the same input features. RESULTS: A total of 5,121 patients were included. The median cost after hip arthroscopy was $19,720 (interquartile range, $12,399-$26,439). The gradient-boosted ensemble model showed the best performance (root-mean-square error, $3,800 [95% confidence interval, $3,700-$3,900]; logarithmic root-mean-square error, 0.249 [95% confidence interval, 0.24-0.26]; R2 = 0.73). Major cost drivers included total hours in facility less than 12 or more than 15, longer procedure time, performance of a labral repair, age younger than 30 years, Elixhauser Comorbidity Index (ECI) of 1 or greater, African American race, residence in extreme urban and rural areas, and higher household and neighborhood income. CONCLUSIONS: The gradient-boosted ensemble model effectively predicted total charges after hip arthroscopy. Few modifiable variables were identified other than anesthesia type; nonmodifiable drivers of total charges included duration of care less than 12 hours or more than 15 hours, operating room time more than 100 minutes, age younger than 30 years, performance of a labral repair, and ECI greater than 0. Stratification of patients based on the ECI highlighted the increased financial risk borne by physicians via flat reimbursement schedules given variable degrees of comorbidities. LEVEL OF EVIDENCE: Level III, retrospective cohort study.

Machine-learning model successfully predicts patients at risk for prolonged postoperative opioid use following elective knee arthroscopy.

PURPOSE: Recovery following elective knee arthroscopy can be compromised by prolonged postoperative opioid utilization, yet an effective and validated risk calculator for this outcome remains elusive. The purpose of this study is to develop and validate a machine-learning algorithm that can reliably and effectively predict prolonged opioid consumption in patients following elective knee arthroscopy. METHODS: A retrospective review of an institutional outcome database was performed at a tertiary academic medical centre to identify adult patients who underwent knee arthroscopy between 2016 and 2018. Extended postoperative opioid consumption was defined as opioid consumption at least 150 days following surgery. Five machine-learning algorithms were assessed for the ability to predict this outcome. Performances of the algorithms were assessed through discrimination, calibration, and decision curve analysis. RESULTS: Overall, of the 381 patients included, 60 (20.3%) demonstrated sustained postoperative opioid consumption. The factors determined for prediction of prolonged postoperative opioid prescriptions were reduced preoperative scores on the following patient-reported outcomes: the IKDC, KOOS ADL, VR12 MCS, KOOS pain, and KOOS Sport and Activities. The ensemble model achieved the best performance based on discrimination (AUC = 0.74), calibration, and decision curve analysis. This model was integrated into a web-based open-access application able to provide both predictions and explanations. CONCLUSION: Following appropriate external validation, the algorithm developed presently could augment timely identification of patients who are at risk of extended opioid use. Reduced scores on preoperative patient-reported outcomes, symptom duration and perioperative oral morphine equivalents were identified as novel predictors of prolonged postoperative opioid use. The predictive model can be easily deployed in the clinical setting to identify at risk patients thus allowing providers to optimize modifiable risk factors and appropriately counsel patients preoperatively. LEVEL OF EVIDENCE: III.

Incidence and risk factors for symptomatic venous thromboembolism following anterior cruciate ligament reconstruction.

PURPOSE: To determine the incidence of symptomatic venous thromboembolism (VTE) following anterior cruciate ligament (ACL) reconstruction using a large national database and to identify corresponding independent risk factors. METHODS: The Humana administrative claims database was reviewed for patients undergoing ACL reconstruction from 2007 to 2017. Patient demographics, medical comorbidities, as well as concurrent procedures were recorded. Postoperative incidence of VTE was measured by identifying symptomatic deep vein thrombosis (DVT) and pulmonary embolism (PE) at 30 days, 90 days, and 1 year postoperatively. Univariate analysis and binary logistic regression were performed to determine independent risk factors for VTE following surgery. RESULTS: A total of 11,977 patients were included in the study. The incidence of VTE was 1.01% (n = 120) and 1.22% (n = 146) at 30 and 90 days, respectively. Analysis of VTE events within the first postoperative year revealed that 69.6% and 84.3% of VTEs occurred within 30 and 90 days of surgery, respectively. Logistic regression identified age ≥ 45 (odds ratio [OR] = 1.88; 95% confidence interval [CI] 1.32-2.68; p < 0.001), inpatient surgery (OR = 2.07; 95% CI 1.01-4.24; p = 0.045), COPD (OR = 1.51; 95% CI 1.02-2.24; p = 0.041), and tobacco use (OR = 1.75; 95% CI 1.17-2.62; p = 0.007), as well as concurrent PCL reconstruction (OR = 3.85; 95% CI 1.71-8.67; p = 0.001), meniscal transplant (OR = 17.68; 95% CI 3.63-85.97; p < 0.001) or osteochondral allograft (OR = 15.73; 95% CI 1.79-138.43; p = 0.013) as independent risk factors for VTE after ACL reconstruction. CONCLUSIONS: The incidence of symptomatic postoperative VTE is low following ACL reconstruction, with the majority of cases occurring within 90 days of surgery. Risk factors include age ≥ 45, inpatient surgery, COPD, tobacco use and concurrent PCL reconstruction, meniscal transplant or osteochondral allograft. LEVEL OF EVIDENCE: III.

Identifying modifiable and nonmodifiable cost drivers of ambulatory rotator cuff repair: a machine learning analysis.

INTRODUCTION: Implementing novel tools that identify contributors to the cost of orthopedic procedures can help hospitals maximize efficiency, minimize waste, improve surgical decision-making, and practice value-based care. The purpose of this study was to develop and internally validate a machine learning algorithm to identify key drivers of total charges after ambulatory arthroscopic rotator cuff repair and compare its performance with a state-of-the-art statistical learning model. METHODS: A retrospective review of the New York State Ambulatory Surgery and Services Database was performed to identify patients who underwent elective outpatient rotator cuff repair (RCR) from 2015 to 2016. Initial models were constructed using patient characteristics (age, gender, insurance status, patient income, Elixhauser Comorbidity Index) as well as intraoperative variables (concomitant procedures and services, operative time). These were subsequently entered into 5 separate machine learning algorithms and a generalized additive model using natural splines. Global variable importance and partial dependence curves were constructed to identify the greatest contributors to cost. RESULTS: A total of 33,976 patients undergoing ambulatory RCR were included. Median total charges after ambulatory RCR were $16,017 (interquartile range: $11,009-$22,510). The ensemble model outperformed the generalized additive model and demonstrated the best performance on internal validation (root mean squared error: $7112, 95% confidence interval: 7036-7188; logarithmic root mean squared error: 0.354, 95% confidence interval: 0.336-0.373, R2: 0.53), and identified major drivers of total charges after RCR as increasing operating room time, patient income level, number of anchors used, use of local infiltration anesthesia/peripheral nerve blocks, non-White race/ethnicity, and concurrent distal clavicle excision. The model was integrated into a web-based open-access application capable of providing individual predictions and explanations on a case-by-case basis. CONCLUSION: This study developed an ensemble supervised machine learning algorithm that outperformed a sophisticated statistical learning model in predicting total charges after ambulatory RCR. Important contributors to total charges included operating room time, duration of care, number of anchors used, type of anesthesia, concomitant distal clavicle excision, community characteristics, and patient demographic factors. Generation of a patient-specific payment schedule based on the Agency for Healthcare Research and Quality risk of mortality highlighted the financial risk assumed by physicians in flat episodic reimbursement schedules given variable patient comorbidities and the importance of an accurate prediction algorithm to appropriately reward high-value care at low costs.