Importance of CD8 Tex cell-associated gene signatures in the prognosis and immunology of osteosarcoma

As a highly aggressive bone malignancy, osteosarcoma poses a significant therapeutic challenge, especially in the setting of metastasis or recurrence. This study aimed to investigate the potential of CD8-Tex cell-associated genes as prognostic biomarkers to reveal the immunogenomic profile of osteosarcoma and guide therapeutic decisions. mRNA expression data and clinical details of osteosarcoma patients were obtained from the TCGA database (TARGET-OS dataset). The GSE21257 dataset (from the GEO database) was used as an external validation set to provide additional information on osteosarcoma specimens. 84 samples from the TARGET-OS dataset were used as the training set, and 53 samples from the GSE21257 dataset served as the external validation cohort. Univariate Cox regression analysis was utilized to identify CD8 Tex cell genes associated with prognosis. The LASSO algorithm was performed for 1000 iterations to select the best subset to form the CD8 Tex cell gene signature (TRS). Final genes were identified using the multivariate Cox regression model of the LASSO algorithm. Risk scores were calculated to categorize patients into high- and low-risk groups, and clinical differences were explored by Kaplan-Meier survival analysis to assess model performance. Prediction maps were constructed to estimate 1-, 3-, and 5 year survival rates for osteosarcoma patients, including risk scores for CD8 Texcell gene markers and clinicopathologic factors. The ssGSEA algorithm was used to assess the differences in immune function between TRS-defined high- and low-risk groups. TME and immune cell infiltration were further assessed using the ESTIMATE and CIBERSORT algorithms. To explore the relationship between immune checkpoint gene expression levels and the two risk-defined groups. A CD8 Tex cell-associated gene signature was extracted from the TISCH database and prognostic markers including two genes were developed. The high-risk group showed lower survival, and model performance was validated by ROC curves and C-index. Predictive plots were constructed to demonstrate survival estimates, combining CD8 Tex cell gene markers and clinical factors. This study provides valuable insights into the molecular and immune characteristics of osteosarcoma and offers potential avenues for advances in therapeutic approaches.

Unraveling the genetic basis of the causal association between inflammatory cytokines and osteonecrosis

Background

Previous studies have reported that the occurrence and development of osteonecrosis is closely associated with immune-inflammatory responses. Mendelian randomization was performed to further assess the causal correlation between 41 inflammatory cytokines and osteonecrosis.

Methods

Two-sample Mendelian randomization utilized genetic variants for osteonecrosis from a large genome-wide association study (GWAS) with 606 cases and 209,575 controls of European ancestry. Another analysis included drug-induced osteonecrosis with 101 cases and 218,691 controls of European ancestry. Inflammatory cytokines were sourced from a GWAS abstract involving 8,293 healthy participants. The causal relationship between exposure and outcome was primarily explored using an inverse variance weighting approach. Multiple sensitivity analyses, including MR-Egger, weighted median, simple model, weighted model, and MR-PRESSO, were concurrently applied to bolster the final results.

Results

The results showed that bFGF, IL-2 and IL2-RA were clinically causally associated with the risk of osteonecrosis (OR=1.942, 95% CI=1.13-3.35, p=0.017; OR=0.688, 95% CI=0.50-0.94, p=0.021; OR=1.386, 95% CI=1.04-1.85, p = 0.026). there was a causal relationship between SCF and drug-related osteonecrosis (OR=3.356, 95% CI=1.09-10.30, p=0.034).

Conclusion

This pioneering Mendelian randomization study is the first to explore the causal link between osteonecrosis and 41 inflammatory cytokines. It conclusively establishes a causal association between osteonecrosis and bFGF, IL-2, and IL-2RA. These findings offer valuable insights into osteonecrosis pathogenesis, paving the way for effective clinical management. The study suggests bFGF, IL-2, and IL-2RA as potential therapeutic targets for osteonecrosis treatment.

Exploring the ferroptosis-related gene lipocalin 2 as a potential biomarker for sepsis-induced acute respiratory distress syndrome based on machine learning

BACKGROUND

Sepsis is a major cause of mortality in patients, and ARDS is one of the most common outcomes. The pathophysiology of acute respiratory distress syndrome (ARDS) caused by sepsis is significantly impacted by genes related to ferroptosis.

METHODS

In this study, Weighted gene co-expression network analysis (WGCNA), protein-protein interaction (PPI) networks, functional enrichment analysis, and machine learning were employed to identify characterized genes and to construct receiver operating characteristic (ROC) curves. Additionally, DNA methylation levels were quantified and single-cell analysis was conducted. To validate the alterations in the expression of Lipocalin-2 (LCN2) and ferroptosis-related proteins in the in vitro model, Western blotting was carried out, and the changes in intracellular ROS and Fe2+ levels were detected.

RESULTS

A combination of eight machine learning algorithms, including RFE, LASSO, RandomForest, SVM-RFE, GBDT, Bagging, XGBoost, and Boruta, were used with a machine learning model to highlight the significance of LCN2 as a key gene in sepsis-induced ARDS. Analysis of immune cell infiltration showed a positive correlation between neutrophils and LCN2. In a cell model induced by LPS, it was found that Ferrostatin-1 (Fer-1), a ferroptosis inhibitor, was able to reverse the expression of LCN2. Knocking down LCN2 in BEAS-2B cells reversed the LPS-induced lipid peroxidation, Fe2+ levels, ACSL4, and GPX4 levels, indicating that LCN2, a ferroptosis-related gene (FRG), plays a crucial role in mediating ferroptosis.

CONCLUSION

Upon establishing an FRG model for individuals with sepsis-induced ARDS, we determined that LCN2 could be a dependable marker for predicting survival in these patients. This finding provides a basis for more accurate ARDS diagnosis and the exploration of innovative treatment options.

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-1β. 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.

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.

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.

Exploring the causal relationship between inflammatory cytokines and immunoinflammatory dermatoses: a Mendelian randomization study

Objectives

Previous studies have shown that the onset and progression of several immunoinflammatory dermatoses are closely related to specific immune-inflammatory responses. To further assess the causal relationship between 41 inflammatory cytokines and immunoinflammatory dermatoses, we used a Mendelian randomization method.

Methods

Mendelian two-sample randomization utilized inflammatory cytokines from a GWAS abstract containing 8,293 healthy participants as well as psoriasis (4,510 cases and 212,242 controls), atopic dermatitis (7,024 cases and 198,740 controls), and vitiligo (131 cases and 207,482 controls). The causal relationship between exposure and outcome was explored primarily using inverse variance weighting. In addition, multiple sensitivity analyses, including MR-Egger, weighted median, simple model, weighted model, and MR-PRESSO, were simultaneously applied to enhance the final results.

Results

The results showed that in clinical practice, IL-4 and IL-1RA were suggestive indicators of atopic dermatitis risk (OR = 0.878, 95% CI = 0.78-0.99, p = 0.036; OR = 0.902, 95% CI = 0.82-1.00, p = 0.045). SCGF-b was a suggestive indicator of psoriasis risk (OR = 1.095, 95% CI = 1.01-1.18, p = 0.023). IL-4 is a suggestive indicator of vitiligo risk (OR = 2.948, 95% CI = 1.28-6.79, p = 0.011).

Conclusion

Our findings suggest that circulating inflammatory cytokines may play a crucial role in the pathogenesis of chronic skin inflammation. IL-4 and IL-1RA may have inhibitory roles in the risk of developing atopic dermatitis, while SCGF-b may have a promoting role in the risk of developing psoriasis. Furthermore, IL-4 may contribute to the risk of developing vitiligo. These results provide insights into further understanding the mechanisms of chronic skin inflammation and offer new targets and strategies for the prevention and treatment of related diseases.

Genetic perspectives on the influence of circulating cytokines on acne: A Mendelian randomization study

Previous studies have reported that the occurrence and development of acne are closely associated with immune-inflammatory responses. Mendelian randomization was performed to further assess the causal correlation between 41 inflammatory cytokines and acne. Mendelian two-sample randomization utilized genetic variants for acne from a large open genome-wide association study (1299 cases and 211,139 controls of European ancestry) and inflammatory cytokines from a genome-wide association study abstract containing 8293 healthy participants. The causal relationship between exposure and outcome was explored primarily using an inverse variance weighting approach. In addition, multiple sensitivity analyses including MR-Egger, weighted median, simple model, weighted model, and MR-PRESSO were applied simultaneously to enhance the final results. The results suggest that il-10, MIP-1A, and SCGF-β are suggestive of the risk of acne in clinical practice (OR = 0.799, 95% CI = 0.641-0.995, P = .045; OR = 0.55, 95% CI = 0.388-0.787, P = .001; OR = 1. 152, 95% CI = 1.001-1.325, P = .048). Our study conclusively identified a causal relationship between il-10 and circulating levels of acne risk and a suggestive link between MIP-1A and SCGF-β and acne. Our study may provide greater insight into the pathogenesis of acne and develop effective management strategies for the clinic. We believe that IL-10, MIP-1A, and SCGF-β could be potential therapeutic targets for acne development.

No Differences in Exercise Performance, Perceptual Response, or Safety Were Observed Among 3 Blood Flow Restriction Devices

Purpose

To compare 3 separate blood flow restriction (BFR) systems in their capacity to reduce repetitions to failure, impact perceptual responses, and cause adverse events during a low-load free-flow exercise.

Methods

The study included healthy subjects aged 18 years or older who presented to an ambulatory-care sports medicine clinic. On day 1, participants' demographic characteristics and anthropomorphic measurements were recorded. Each participant performed dumbbell biceps curl repetitions to failure using 20% of his or her 1-repetition maximum weight with each arm. Participants were exposed to 3 different tourniquet systems for familiarization. On day 2, each participant's arm was randomized to a cuff system, and the participant performed 2 sets of biceps curl repetitions to failure with the cuff inflated. Repetitions to failure, rating of perceived effort (RPE), rating of perceived discomfort, and pulse oxygenation levels were recorded after each set. On day 3, participants completed a survey of their perceived delayed-onset muscle soreness.

Results

The final analysis was performed on 42 arms, with 14 limbs per system. The study population had a mean age of 28.7 ± 2.4 years and a mean body mass index of 24.9 ± 4.3. All 3 systems successfully reduced repetitions to failure compared with unrestricted low-load exercise from baseline to BFR set 1 and from baseline to BFR set 2. There were no significant between-group differences among BFR systems regarding the number of repetitions to failure performed at baseline versus BFR set 1 or BFR set 2. The Delfi Personalized Tourniquet System (PTS) cohort had the greatest reductions in repetitions to failure from BFR set 1 to BFR set 2 (P = .002) and reported the highest RPE after set 2 (P = .025).

Conclusions

The Delfi PTS, SmartCuffs Pro, and BStrong BFR systems were each safe and were able to significantly reduce repetitions to failure compared with a low-load free-flow condition when used in a BFR exercise protocol. The Delfi PTS system may produce a higher RPE with prolonged use in comparison to the other systems.

Level of Evidence

Level II, prospective cohort study.

Deep Learning Artificial Intelligence Tool for Automated Radiographic Determination of Posterior Tibial Slope in Patients With ACL Injury

Background

An increased posterior tibial slope (PTS) corresponds with an increased risk of graft failure after anterior cruciate ligament (ACL) reconstruction (ACLR). Validated methods of manual PTS measurements are subject to potential interobserver variability and can be inefficient on large datasets.

Purpose/Hypothesis

To develop a deep learning artificial intelligence technique for automated PTS measurement from standard lateral knee radiographs. It was hypothesized that this deep learning tool would be able to measure the PTS on a high volume of radiographs expeditiously and that these measurements would be similar to previously validated manual measurements.

Study Design

Cohort study (diagnosis); Level of evidence, 2.

Methods

A deep learning U-Net model was developed on a cohort of 300 postoperative short-leg lateral radiographs from patients who underwent ACLR to segment the tibial shaft, tibial joint surface, and tibial tuberosity. The model was trained via a random split after an 80 to 20 train-validation scheme. Masks for training images were manually segmented, and the model was trained for 400 epochs. An image processing pipeline was then deployed to annotate and measure the PTS using the predicted segmentation masks. Finally, the performance of this combined pipeline was compared with human measurements performed by 2 study personnel using a previously validated manual technique for measuring the PTS on short-leg lateral radiographs on an independent test set consisting of both pre- and postoperative images.

Results

The U-Net semantic segmentation model achieved a mean Dice similarity coefficient of 0.885 on the validation cohort. The mean difference between the human-made and computer-vision measurements was 1.92° (σ = 2.81° [P = .24]). Extreme disagreements between the human and machine measurements, as defined by ≥5° differences, occurred <5% of the time. The model was incorporated into a web-based digital application front-end for demonstration purposes, which can measure a single uploaded image in Portable Network Graphics format in a mean time of 5 seconds.

Conclusion

We developed an efficient and reliable deep learning computer vision algorithm to automate the PTS measurement on short-leg lateral knee radiographs. This tool, which demonstrated good agreement with human annotations, represents an effective clinical adjunct for measuring the PTS as part of the preoperative assessment of patients with ACL injuries.

Risk factors for short-term mortality in elderly hip fracture patients with complicated heart failure in the ICU: A MIMIC-IV database analysis using nomogram

BACKGROUND

Hip fracture is a prevalent and hazardous injury among the elderly population that often results in intensive care unit (ICU) admission due to various complications, despite advanced medical science. One common complication experienced in the ICU by elderly hip fracture patients is heart failure, which significantly impacts short-term survival rates. Currently, there is a deficit of adequate predictive models to forecast the short-term risk of death following heart failure for elderly hip fracture patients in the ICU. This study aims to identify independent risk factors for all-cause mortality within 30 days for elderly patients with hip fractures and heart failure while in the ICU in order to develop a predictive model.

METHOD

A total of 641 elderly patients with hip fractures combined with heart failure were recruited from the Medical Information Mart for Intensive Care IV dataset and randomized to the training and validation sets. The primary outcome was all-cause mortality within 30 days. The least absolute shrinkage and selection operator regression was used to reduce data dimensionality and select features. Multivariate logistic regression was used to build predictive models. Consistency index (C-index), receiver operating characteristic curve, and decision curve analysis (DCA) were used to measure the predictive performance of the nomogram.

RESULT

Our results showed that these variables including MCH, MCV, INR, monocyte percentage, neutrophils percentage, creatinine, and combined sepsis were independent factors for death within 30 days in elderly patients with hip fracture combined with heart failure in the ICU. The C-index was 0.869 (95% CI 0.823-0.916) and 0.824 (95% CI 0.749-0.900) for the training and validation sets, respectively. The results of the area under the curve and decision curve analysis (DCA) confirmed that the nomogram performed well in predicting elderly patients with hip fractures combined with heart failure in the ICU.

CONCLUSION

We developed a new nomogram model for predicting 30-day all-cause mortality in elderly patients with hip fractures combined with heart failure in the ICU, which could be a valid and useful clinical tool for clinicians for targeted treatment and prognosis prediction.

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.

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.

Nano-calcipotriol as a potent anti-hepatic fibrosis agent

Calcipotriol (CAL) has been widely studied as a fibrosis inhibitor and used to treat plaque psoriasis via transdermal administration. The clinical application of CAL to treat liver fibrosis is bottlenecked by its unsatisfactory pharmacokinetics, biodistribution, and side effects, such as hypercalcemia in patients. The exploration of CAL as a safe and effective antifibrotic agent remains a major challenge. Therefore, we rationally designed and synthesized a self-assembled drug nanoparticle encapsulating CAL in its internal hydrophobic core for systematic injection (termed NPs/CAL) and further investigated the beneficial effect of the nanomaterial on liver fibrosis. C57BL/6 mice were used as the animal model, and human hepatic stellate cell line LX-2 was used as the cellular model of hepatic fibrogenesis. Immunofluorescence staining, flow cytometry, western blotting, immunohistochemical staining, and in vitro imaging were used for evaluating the efficacy of NPs/CAL treatment. We found NPs/CAL can be quickly internalized in vitro, thus potently deactivating LX-2 cells. In addition, NPs/CAL improved blood circulation and the accumulation of CAL in liver tissue. Importantly, NPs/CAL strongly contributed to the remission of liver fibrosis without inducing hypercalcemia. Overall, our work identifies a promising paradigm for the development of nanomaterial-based agents for liver fibrosis therapy.

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.

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.

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.

Translation and validation of a Chinese version of the body talk scale for women and men

Body talk has received increasing research attention in recent years, with accumulating evidence supporting the link between body talk and eating and body image disturbances. However, research on body talk in China is still relatively scarce and generally focused on fat talk, especially in women, and much remains unknown about muscle talk and positive body talk for both Chinese women and men. To promote a better understanding of body talk in the Chinese context, the present study adapted the Body Talk Scale (BTS) into Chinese Mandarin (i.e., C-BTS) and evaluated the factor structure and psychometric properties of the C-BTS in Chinese adult women and men. The English version of the BTS was translated into Chinese Mandarin with standard procedures. With 300 Chinese women (Mage = 29.48 years, SD = 7.26) and 300 men (Mage = 29.36 years, SD = 6.81), we examined the factor structure and gender invariance of the C-BTS, as well as internal consistency reliability, test-retest reliability, and construct validity, including convergent, concurrent, and incremental validity of the C-BTS. The results indicated that, consistent with the development study of the BTS, the C-BTS had three subscales (i.e., Negative Fat Talk, Negative Muscle Talk, and Positive Body Talk) and good reliability and validity. The findings demonstrate that the C-BTS can be a useful measure of body talk in both Chinese women and men.

Metabolite Neu5Ac triggers SLC3A2 degradation promoting vascular endothelial ferroptosis and aggravates atherosclerosis progression in ApoE-/-mice

Background: Atherosclerosis (AS) is still the major cause of cardiovascular disease (CVD) as well as stroke. Endothelial metabolic disorder has been found to be activated and then promote endothelial cells (ECs) injury, which is regarded to initiate AS progression. N-acetylneuraminic acid (Neu5Ac), a metabolite produced by hexosamine-sialic acid pathway branching from glucose metabolism, was presented as a notable biomarker of CVD and is positively correlated with ECs function. However, few studies explain whether Neu5Ac regulate AS progression by affecting EC function as well as its involved mechanisms are still unknown. Methods: Here, we mimicked an animal model in ApoE-/- mice which displaying similar plasma Neu5Ac levels with AS model to investigate its effect on AS progression. Results: We found that Neu5Ac exacerbated plaques area and increased lipids in plasma in absence of HFD feeding, and ECs inflammatory injury was supposed as the triggering factor upon Neu5Ac treatment with increasing expression of IL-1β, ICAM-1, and promoting ability of monocyte adhesion to ECs. Mechanistic studies showed that Neu5Ac facilitated SLC3A2 binding to ubiquitin and then triggered P62 mediated degradation, further leading to accumulation of lipid peroxidation in ECs. Fer-1 could inhibit ECs injury and reverse AS progression induced by Neu5Ac in ApoE-/- mice. Interestingly, mitochondrial dysfunction was also partly participated in ECs injury after Neu5Ac treatment and been reversed by Fer-1. Conclusions: Together, our study unveils a new mechanism by which evaluated metabolite Neu5Ac could promote SLC3A2 associated endothelial ferroptosis to activate ECs injury and AS plaque progression, thus providing a new insight into the role of Neu5Ac-ferroptosis pathway in AS. Also, our research revealed that pharmacological inhibition of ferroptosis may provide a novel therapeutic strategy for premature AS.

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.

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.

Artificial Intelligence Can Define and Predict the "Optimal Observed Outcome" After Anterior Shoulder Instability Surgery: An Analysis of 200 Patients With 11-Year Mean Follow-Up

Purpose

The purpose of this study was to use unsupervised machine learning clustering to define the "optimal observed outcome" after surgery for anterior shoulder instability (ASI) and to identify predictors for achieving it.

Methods

Medical records, images, and operative reports were reviewed for patients <40 years old undergoing surgery for ASI. Four unsupervised machine learning clustering algorithms partitioned subjects into "optimal observed outcome" or "suboptimal outcome" based on combinations of actually observed outcomes. Demographic, clinical, and treatment variables were compared between groups using descriptive statistics and Kaplan-Meier survival curves. Variables were assessed for prognostic value through multivariate stepwise logistic regression.

Results

Two hundred patients with a mean follow-up of 11 years were included. Of these, 146 (64%) obtained the "optimal observed outcome," characterized by decreased: postoperative pain (23% vs 52%; P < 0.001), recurrent instability (12% vs 41%; P < 0.001), revision surgery (10% vs 24%; P = 0.015), osteoarthritis (OA) (5% vs 19%; P = 0.005), and restricted motion (161° vs 168°; P = 0.001). Forty-one percent of patients had a "perfect outcome," defined as ideal performance across all outcomes. Time from initial instability to presentation (odds ratio [OR] = 0.96; 95% confidence interval [CI], 0.92-0.98; P = 0.006) and habitual/voluntary instability (OR = 0.17; 95% CI, 0.04-0.77; P = 0.020) were negative predictors of achieving the "optimal observed outcome." A predilection toward subluxations rather than dislocations before surgery (OR = 1.30; 95% CI, 1.02-1.65; P = 0.030) was a positive predictor. Type of surgery performed was not a significant predictor.

Conclusion

After surgery for ASI, 64% of patients achieved the "optimal observed outcome" defined as minimal postoperative pain, no recurrent instability or OA, low revision surgery rates, and increased range of motion, of whom only 41% achieved a "perfect outcome." Positive predictors were shorter time to presentation and predilection toward preoperative subluxations over dislocations.

Level of Evidence

Retrospective cohort, level IV.

Selective degradation of antibiotic in a novel Cu7S4/peroxydisulfate system via heterogeneous Cu(III) formation: Performance, mechanism and toxicity evaluation

Efficient degradation of antibiotic by peroxydisulfate (PDS)-based advanced oxidation processes in complex water environment is challenging due to the interference of impurities and the low activation efficiency of PDS caused by its symmetric structure. Herein, a novel Cu₇S₄/PDS system was developed, which can selectively remove tetracycline hydrochloride (TC) without interference of inorganic ions (e.g., Cl^- and HCO₃^-) and natural organic matter (e.g., humic acid). The results of quenching and probe experiments demonstrated that surface high-valent copper species (Cu(III)), rather than radicals and ¹O₂, are main active species for TC degradation. Cu(III) can be generated via Cu(I)/O₂ and Cu(II)/Cu(I)/PDS systems and the S species on the surface of Cu₇S₄ promotes the cycle of Cu(II)/Cu(I) and Cu(III)/Cu(II), resulting in continuous generation of Cu(III). In addition, the degradation pathways of TC were proposed based on product analysis and DFT theory calculations. The acute toxicity, developmental toxicity and mutagenicity of treated TC were significantly reduced according to the results of toxicity estimation software tool. This study shows a promising Cu₇S₄/PDS system for the degradation and detoxication of antibiotic in complex water environment, while also providing a comprehensive understanding of PDS activation by Cu₇S₄ to generate active Cu(III) species.

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.

Predicting the Risk of Posttraumatic Osteoarthritis After Primary Anterior Cruciate Ligament Reconstruction: A Machine Learning Time-to-Event Analysis

BACKGROUND

There is a significant long-term risk of posttraumatic osteoarthritis (PTOA) after anterior cruciate ligament reconstruction (ACLR). Elucidating the risk factors and successfully identifying at-risk patients is challenging.

PURPOSE/HYPOTHESIS

The purpose of this study was to produce machine learning survival models that can identify (1) patients at risk of symptomatic PTOA and (2) patients who are at risk of undergoing total knee arthroplasty (TKA) after ACLR. It was hypothesized that these models would outperform traditional Kaplan-Meier estimators.

STUDY DESIGN

Case-control study; Level of evidence, 3.

METHODS

A geographic database was used to identify patients undergoing ACLR between 1990 and 2016 with a minimum 7.5-year follow-up. Models were used to analyze various factors to predict the rate and time to (1) symptomatic osteoarthritis and (2) TKA using random survival forest (RSF) algorithms. Performance was measured using out-of-bag (OOB) c-statistic, calibration, and Brier score. The predictive performances of the RSF models were compared with Kaplan-Meier estimators. Model interpretability was enhanced utilizing global variable importance and partial dependence curves.

RESULTS

A total of 974 patients with ACLR and a minimum follow-up of 7.5 years were included; among these, 215 (22.1%) developed symptomatic osteoarthritis, and 25 (2.6%) progressed to TKA. The RSF algorithms achieved acceptable good to excellent predictive performance for symptomatic arthritis (OOB c-statistic, 0.75; Brier score, 0.128) and progression to TKA (OOB c-statistic, 0.89; Brier score, 0.026), respectively. Significant predictors of symptomatic PTOA included increased pain scores, older age, increased body mass index, increased time to ACLR, total number of arthroscopic surgeries before the diagnosis of arthritis, positive pivot-shift test after reconstruction, concomitant chondral injury, secondary meniscal tear, early (<250 days) or delayed (>500 days) return to sports or activity, and use of allograft. Significant predictors for TKA included older age, increased pain scores, total number of arthroscopic surgeries, high-demand activity/occupation, hypermobility, higher body mass index, systemic inflammatory disease, increased time to surgery, early (<250 days) or delayed (>500 days) return to sports or activity, and midsubstance tears. The Brier score over time revealed that RSF models outperformed traditional Kaplan-Meier estimators.

CONCLUSION

Machine learning survival models were used to reliably identify patients at risk of developing symptomatic PTOA, and these models consistently outperformed traditional Kaplan-Meier estimators. Strong predictors for the development of PTOA after ACLR included increased pain scores at injury and postoperative visit, older age at injury, total number of arthroscopic procedures, positive postoperative pivot-shift test, and secondary meniscal tear.

Grey matter volume loss in Parkinson's disease psychosis and its relationship with serotonergic gene expression: A meta-analysis

BACKGROUND

Neuroanatomical alterations underlying psychosis in Parkinson's Disease (PDP) remain unclear. We carried out a meta-analysis of MRI studies investigating the neural correlates of PDP and examined its relation with dopaminergic and serotonergic receptor gene expression.

METHODS

PubMed, Web of Science and Embase were searched for MRI studies (k studies = 10) of PDP compared to PD patients without psychosis (PDnP). Seed-based d Mapping with Permutation of Subject Images and multiple linear regression analyses was used to examine the relationship between pooled estimates of grey matter volume (GMV) loss in PDP and D1/D2 and 5-HT1a/5-HT2a receptor gene expression estimates from Allen Human Brain Atlas.

RESULTS

We observed lower grey matter volume in parietal-temporo-occipital regions (PDP n = 211, PDnP, n = 298). GMV loss in PDP was associated with local expression of 5-HT1a (b = 0.109, p = 0.012) and 5-HT2a receptors (b= -0.106, p = 0.002) but not dopaminergic receptors.

CONCLUSION

Widespread GMV loss in the parieto-temporo-occipital regions may underlie PDP. Association between grey matter volume and local expression of serotonergic receptor genes may suggest a role for serotonergic receptors in PDP.

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.

Development and validation of a prognostic nomogram model for ICU patients with alcohol-associated cirrhosis

BACKGROUND

The prognosis of patients with alcohol-associated cirrhosis (ALC) admitted to the intensive care unit (ICU) is poor. We developed and validated a nomogram (NIALC) for ICU patients with ALC.

METHODS

Predictors of mortality were defined by a machine learning method in a cohort of 394 ICU patients with ALC from the Medical Information Mart for Intensive Care database. Then the nomogram (NIALC) was constructed and evaluated using the AUC. The MELD, MELD-sodium, Child-Pugh, and CLIF-SOFA scores were then compared with NIALC. Two datasets of 394 and 501 ICU patients with ALC were utilized for model validation.

RESULTS

In-hospital mortality was 41% and 21% in the training and external validation sets. Predictors included were blood urea nitrogen, total bilirubin, prothrombin time, serum creatinine, lactate, partial thromboplastin time, phosphate, mean arterial pressure, lymphocytes, fibrinogen, and albumin. The AUCs for the NIALC were 0.767 and 0.760 in the two validation cohorts, which were better than those of the MELD, MELD-sodium, Child-Pugh, and CLIF-SOFA.

CONCLUSION

We developed a nomogram for ICU patients with ALC, which demonstrated better discriminative ability than previous prognostic scores. This nomogram could be conveniently used to facilitate the individualized prediction of death in ICU patients with ALC.

The effect of Tabata-style functional high-intensity interval training on cardiometabolic health and physical activity in female university students

Introduction: The increasing prevalence of metabolic syndrome and physical inactivity enhances exposure to cardiometabolic risk factors in university students. High-intensity interval training (HIIT) improved cardiometabolic health in clinical adults but the evidence in the university setting is limited. Furthermore, few studies examined the effect of low-volume HIIT on habitual physical activity (PA). Therefore, the primary aim of this study was to evaluate the efficacy of 12-week Tabata-style functional HIIT for improving multiple cardiometabolic health outcomes and habitual PA. We also investigated whether changes in habitual PA over the intervention period had an impact on exercise-induced health outcomes. Methods: 122 female freshmen were randomized into the Tabata group (n = 60) and the control (n = 62). The Tabata training protocol involved 8 × 20 s maximal repeated functional exercises followed by 10 s rest with a frequency of 3 times per week for 12 weeks. Body composition, maximal oxygen uptake (VO_2max), blood pressure (BP), blood lipids, fasting glucose and insulin, C-reactive protein and PA were objectively measured using standardized methods. Dietary intake was measured using a valid food frequency questionnaire. All variables were measured pre- and post-intervention. Results: Mixed linear modelling results showed that there were large intervention effects on VO_2max (p < 0.001, d = 2.53, 95% CI: 2.03 to 3.00 for relative VO_2max; p < 0.001, d = 2.24, 95% CI: 1.76 to 2.68 for absolute VO_2max), resting heart rate (p < 0.001, d = -1.82, 95% CI: -2.23 to -1.37), systolic BP (p < 0.001, d = -1.24, 95% CI: -1.63 to -0.84), moderate-to-vigorous intensity physical activity (MVPA) (p < 0.001, d = 2.31, 95% CI: 1.83 to 2.77), total PA (p < 0.001, d = 1.98, 95% CI: 1.53 to 2.41); moderate effects on %BF (p < 0.001, d = -1.15, 95% CI: -1.53 to -0.75), FM (p < 0.001, d = -1.08, 95% CI: -1.46 to -0.69), high-density lipoprotein (HDL) (p < 0.001, d = 1.04, 95% CI: 0.65 to 1.42), total cholesterol (p = 0.001, d = -0.64, 95% CI: -1.00 to -0.26); small effects on BMI (p = 0.011, d = -0.48, 95% CI: -0.84 to 0.11), WC (p = 0.043, d = -0.37, 95% CI: -0.74 to -0.01), low-density lipoprotein (p = 0.003, d = -0.57, 95% CI: -0.93 to -0.19), HOMA-IR (p = 0.026, d = -0.42, 95% CI: -0.78 to -0.05) and fasting insulin (p = 0.035, d = -0.40, 95% CI: -0.76 to -0.03). Regression analysis showed that only the percentage change of HDL was associated with the change of MVPA (b = 0.326, p = 0.015) and TPA (b = 0.480, p = 0.001). Conclusion: From the findings of the study we can conclude that 12-week low-volume Tabata-style functional HIIT was highly effective for university female students to improve cardiorespiratory fitness, body fat, some cardiometabolic health outcomes and habitual PA.

Associations between dairy consumption, physical activity, and blood pressure in Chinese young women

Introduction

The prevalence of hypertension (HTN) has been increasing in young adults. A healthy dietary pattern and increasing physical activity (PA) are commonly recommended as lifestyle modifications needed to manage blood pressure (BP). However, little is known about the relationship between dairy intake, PA, and BP in Chinese young women. The aim of this study was to examine whether BP was associated with dairy intake, moderate-to-vigorous intensity physical activity (MVPA) and total physical activity (TPA) in a sample of Chinese young women.

Methods

A total of 122 women (20.4 ± 1.4) who had complete data sets from the Physical Fitness in Campus (PFIC) study were included in this cross-sectional analysis. Data related to dairy intake and PA was collected using a food frequency questionnaire and an accelerometer. BP was measured following standardized procedures. The association between BP with dairy intake and PA was examined using multivariable linear regression models.

Results

After controlling for potential covariables, we observed a significant and independent relationship only between systolic BP with dairy intake [standardized beta (b) = -0.275, p < 0.001], MVPA (b = -0.167, p = 0.027), and TPA (b = -0.233, p = 0.002). Furthermore, we found a decrease of 5.82 ± 2.94, 1.13 ± 1.01, and 1.10 ± 0.60 mm Hg in systolic BP for daily additional servings of dairy, 10 min of MVPA, and 100 counts per minute of TPA, respectively.

Conclusion

Our results suggested that the higher amount of dairy consumption or PA was associated with lower level of SBP in Chinese young women.

Early ACLR and Risk and Timing of Secondary Meniscal Injury Compared With Delayed ACLR or Nonoperative Treatment: A Time-to-Event Analysis Using Machine Learning

BACKGROUND

Surgical and nonoperative management of anterior cruciate ligament (ACL) injuries seek to mitigate the risk of knee instability and secondary meniscal injury. However, the associated risk and timing of secondary meniscal tears have not been completely elucidated.

PURPOSE

To compare risk and timing of secondary meniscal injury between patients receiving nonoperative management, delayed ACL reconstruction (ACLR), and early ACLR using a machine learning survival analysis.

STUDY DESIGN

Cohort study; Level of evidence, 3.

METHODS

A geographic database was used to identify and review records of patients with a diagnosis of ACL rupture between 1990 and 2016 with minimum 2-year follow-up. Patients undergoing ACLR were matched 1:1 with nonoperatively treated controls. Rate and time to secondary meniscal tear were compared using random survival forest algorithms; independent models were developed and internally validated for predicting injury-free duration in both cohorts. Performance was measured using out-of-bag c-statistic, calibration, and Brier score. Model interpretability was enhanced using global variable importance and partial dependence curves.

RESULTS

The study included 1369 patients who underwent ACLR and 294 patients who had nonoperative treatment. After matching, no significant differences in rates of secondary meniscal tear were found (P = .09); subgroup analysis revealed the shortest periods of meniscal survival in patients undergoing delayed ACLR. The random survival forest algorithm achieved excellent predictive performance for the ACLR cohort, with an out-of-bag c-statistic of 0.80 and a Brier score of 0.11. Significant variables for risk of meniscal tear for the ACLR cohort included time to return to sports or activity ≤350 days, time to surgery ≥50 days, age at injury ≤40 years, and high-impact or rotational landing sports, whereas those in the nonoperative cohort model included time to RTS ≤200 days, visual analog scale pain score >3 at consultation, hypermobility, and noncontact sports.

CONCLUSION

Delayed ACLR demonstrated the greatest long-term risk of meniscal injury compared with nonoperative treatment or early ACLR. Risk factors for decreased meniscal survival after ACLR included increased time to surgery, shorter time to return to sports or activity, older age at injury, and involvement in high-impact or rotational landing sports. Pending careful external validation, these models may be deployed in the clinical space to provide real-time insights and enhance decision making.

Implementation of Machine Learning to Predict Cost of Care Associated with Ambulatory Single-Level Lumbar Decompression

BACKGROUND

With the emergence of the concept of value-based care, efficient resource allocation has become an increasingly prominent factor in surgical decision-making. Validated machine learning (ML) models for cost prediction in outpatient spine surgery are limited. As such, we developed and internally validated a supervised ML algorithm to reliably identify cost drivers associated with ambulatory single-level lumbar decompression surgery.

METHODS

A retrospective review of the New York State Ambulatory Surgical Database was performed to identify patients who underwent single-level lumbar decompression from 2014 to 2015. Patients with a length of stay of >0 were excluded. Using pre- and intraoperative parameters (features) derived from the New York State Ambulatory Surgical Database, an optimal supervised ML model was ultimately developed and internally validated after 5 candidate models were rigorously tested, trained, and compared for predictive performance related to total charges. The best performing model was then evaluated by testing its performance on identifying relationships between features of interest and cost prediction. Finally, the best performing algorithm was entered into an open-access web application.

RESULTS

A total of 8402 patients were included. The gradient-boosted ensemble model demonstrated the best performance assessed via internal validation. Major cost drivers included anesthesia type, operating room time, race, patient income and insurance status, community type, worker's compensation status, and comorbidity index.

CONCLUSIONS

The gradient-boosted ensemble model predicted total charges and associated cost drivers associated with ambulatory single-level lumbar decompression using a large, statewide database with excellent performance. External validation of this algorithm in future studies may guide practical application of this clinical tool.

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.

[Autophagy promotes recurrence of nasopharyngeal carcinoma via inducing the formation of dormant polyploid giant cancer cells]

Objective: To explore the effect of dormant polyploid giant cancer cells (PGCC) on nasopharyngeal carcinoma (NPC) recurrence and to clarify the role of inhibition of autophagy in inhibiting NPC-PGCC formation and preventing NPC recurrence. Methods: NPC cells-derived PGCC (NPC-PGCC) were induced by paclitaxel (PTX), and the morphology, polyploid characteristics and cell activity of PGCC were identified by light microscopy, immunofluorescence and Live/Dead cell double staining assays. RNA-seq was used to analyze the differentially expressed genes between NPC-PGCC and diploid nasopharyngeal carcinoma cells CNE2. Functional enrichment and pathway annotation analysis of differentially expressed genes were performed using Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG). The level of autophagy in NPC-PGCC cells was assessed by Western Blot and transmission electron microscopy analysis. The role of autophagy in the formation of NPC-PGCC and the effect of NPC-PGCC on the recurrence of nasopharyngeal carcinoma were studied using a highly clinically relevant mouse nasopharyngeal carcinoma recurrence model. Statistical analysis was performed using GraphPad Prism 6 and P-values<0.05 were considered statistically significant. Results: NPC-PGCC induced by paclitaxel had the characteristics of burst-like division after dormancy. GO enrichment and KEGG pathway analyses identified the significant biological processes and pathways mainly concentrated in autophagy and related pathways involving the differentially expressed genes between NPC-PGCC and diploid nasopharyngeal carcinoma cells CNE2. The autophagy level was significantly enhanced in NPC-PGCC cells. In a highly clinically relevant mouse nasopharyngeal carcinoma recurrence model, the number of PGCC in the primary tumor of the nude mice treated with cisplatin were higher than those of the other groups. In nude mice pretreated with autophagy inhibitor and then co-treatment with autophagy inhibitor and cisplatin, the number of PGCC in primary tumors was less and the recurrence rate was significantly lower than in other groups. Conclusions: The mechanism of dormant polyploid giant cancer cells formation is related to autophagy. Inhibition of autophagy can inhibit the formation of PGCC and thus prevent the recurrence of nasopharyngeal carcinoma.

The promising roles of macrophages in geriatric hip fracture

As aging becomes a global burden, the incidence of hip fracture (HF), which is the most common fracture in the elderly population and can be fatal, is rapidly increasing, and its extremely high fatality rate places significant medical and financial burdens on patients. Fractures trigger a complex set of immune responses, and recent studies have shown that with aging, the immune system shows decreased activity or malfunctions in a process known as immune senescence, leading to disease and death. These phenomena are the reasons why elderly individuals typically exhibit chronically low levels of inflammation and increased rates of infection and chronic disease. Macrophages, which are key players in the inflammatory response, are critical in initiating the inflammatory response, clearing pathogens, controlling the innate and adaptive immune responses and repairing damaged tissues. Tissue-resident macrophages (TRMs) are widely present in tissues and perform immune sentinel and homeostatic functions. TRMs are combinations of macrophages with different functions and phenotypes that can be directly influenced by neighboring cells and the microenvironment. They form a critical component of the first line of defense in all tissues of the body. Immune system disorders caused by aging could affect the biology of macrophages and thus the cascaded immune response after fracture in various ways. In this review, we outline recent studies and discuss the potential link between monocytes and macrophages and their potential roles in HF in elderly individuals.

Objectively determined physical activity and adiposity measures in adult women: A systematic review and meta-analysis

The prevalence of adiposity is increasing among adult women. Although emerging evidence suggest that all patterns of heightened physical activity (PA) are important to benefit adiposity, the relationship between objectively assessed intensities of PA and adiposity in women has not yet been assessed. Therefore, this systematic review and meta-analysis aims to qualitatively synthesize and quantitatively assess the evidence for any relationship between objectively measured PA and a wide range of adiposity indicators to guide PA prescription in adult women. Four databases (PubMed, Web of Science, Scopus, and the Cochrane library) were searched for eligible studies. 35 studies were included (25 observational and 10 interventional studies), with a total of 9,176 women from 20 countries included. The overall pooled correlation for random effects model (n = 1 intervention and n = 15 cross-sectional studies) revealed that the total volume of physical activity (TPA) was moderately associated with percentage body fat (%BF) (r = −0.59; 95% CI: −1.11, −0.24; p = 0.003). There was a weak but significant association between MVPA with body mass index (BMI), waist circumference (WC), and visceral adiposity. Daily steps were significantly associated with BMI, %BF, WC, and fat mass, with the strongest association with %BF (r = −0.41; 95% CI: −0.66, −0.19; p < 0.001). Walking programs resulting in increasing daily steps only had a significant effect on WC (SMD = −0.35; 95% CI: −0.65, −0.05; p = 0.02). Overall, objectively determined PA in terms of steps, TPA and MVPA were favorably associated with adiposity outcomes. The improvement in adiposity can be achieved by simply accumulating more PA than previously and adiposity is more likely to be benefited by PA performed at higher intensity. Nonetheless, these results should be interpreted with caution as there were a small number of studies included in the meta-analysis and the majority of studies included utilized cross-sectional designs.

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.

Machine Learning for Predicting Lower Extremity Muscle Strain in National Basketball Association Athletes

Background

In professional sports, injuries resulting in loss of playing time have serious implications for both the athlete and the organization. Efforts to quantify injury probability utilizing machine learning have been met with renewed interest, and the development of effective models has the potential to supplement the decision-making process of team physicians.

Purpose/Hypothesis

The purpose of this study was to (1) characterize the epidemiology of time-loss lower extremity muscle strains (LEMSs) in the National Basketball Association (NBA) from 1999 to 2019 and (2) determine the validity of a machine-learning model in predicting injury risk. It was hypothesized that time-loss LEMSs would be infrequent in this cohort and that a machine-learning model would outperform conventional methods in the prediction of injury risk.

Study Design

Case-control study; Level of evidence, 3.

Methods

Performance data and rates of the 4 major muscle strain injury types (hamstring, quadriceps, calf, and groin) were compiled from the 1999 to 2019 NBA seasons. Injuries included all publicly reported injuries that resulted in lost playing time. Models to predict the occurrence of a LEMS were generated using random forest, extreme gradient boosting (XGBoost), neural network, support vector machines, elastic net penalized logistic regression, and generalized logistic regression. Performance was compared utilizing discrimination, calibration, decision curve analysis, and the Brier score.

Results

A total of 736 LEMSs resulting in lost playing time occurred among 2103 athletes. Important variables for predicting LEMS included previous number of lower extremity injuries; age; recent history of injuries to the ankle, hamstring, or groin; and recent history of concussion as well as 3-point attempt rate and free throw attempt rate. The XGBoost machine achieved the best performance based on discrimination assessed via internal validation (area under the receiver operating characteristic curve, 0.840), calibration, and decision curve analysis.

Conclusion

Machine learning algorithms such as XGBoost outperformed logistic regression in the prediction of a LEMS that will result in lost time. Several variables increased the risk of LEMS, including a history of various lower extremity injuries, recent concussion, and total number of previous injuries.

Modeling the Dynamic Exclusive Pedestrian Phase Based on Transportation Equity and Cost Analysis

The exclusive pedestrian phase (EPP) has proven to be an effective method of eliminating pedestrian–vehicle conflicts at signalized intersections. The existing EPP setting conditions take traffic efficiency and safety as optimization goals, which may contribute to unfair interactions between vehicles and pedestrians. This study develops a multiobjective optimization framework to determine the EPP setting criteria, with consideration for the tradeoff between transportation equity and cost. In transportation equity modeling and considering environmental conditions, the transportation equity index is proposed to quantify pedestrian–vehicle equity differences. In cost modeling, traffic safety and efficiency factors are converted into monetary values, and the pedestrian–vehicle interaction is introduced. To validate the proposed optimization framework, a video-based data collection is conducted on wet and dry environment conditions at the selected intersection. The parameters in the proposed model are calibrated based on the results of the video analysis. This study compares the performance of the multiobjective evolutionary algorithm based on decomposition (MOEA) and the nondominated sorting genetic algorithm II (NSGA-II) methods in building the sets of nondominated solutions. The optimization results show that the decrease in transportation equity will lead to an increase in cost. The obtained Pareto front approximations correspond to diverse signal timing patterns and achieve a balance between optimizing either objective to different extents. The sensitivity analysis reveals the application domains for the EPP and the traditional two-way control phase (TWC) under different vehicular/pedestrian demand, yielding rate, and environment conditions. The EPP control is more suitable at intersections with high pedestrian volumes and low yielding rates, especially in wet conditions. The results provide operational guidelines for decision-makers for properly selecting the pedestrian phase pattern at signalized intersections.

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.

[Clinical epidemiological survey of primary liver cancer in Yunnan province from 2005 to 2014]

Objective: To investigate the clinical characteristics and changing trends of primary liver cancer in Yunnan province from 2005 to 2014, in order to provide theoretical basis for the prevention and treatment of liver cancer in this region. Methods: A retrospective survey was used to select inpatient cases of liver cancer who were initially diagnosed and treated in our hospital from 2005 to 2014 with simple random sampling. Patients socio-demographic and clinicopathological characteristics were extracted by a unified and standardized questionnaire, and the data were statistically analyzed. Results: A total of 1000 cases with liver cancer were included, aged (53.2±11.2) years, with a male-to-female ratio of 5.99/1.00. There was no significant change in the gender and age composition ratio of patients in the past 10 years. The proportion of patients with lower education level (primary or junior high school) were increased from 21.8% to 23.4%, and the proportion of patients with relatively higher education level were decreased from 58% to 38.2% (P<0.001). Smokers and non-smokers patients were decreased and increased from 58.8% to 44.4%, and 41.2% to 55.6% (P<0.001). The proportion of drinker patients were decreased from 46.4% to 35.2%. The proportion of patients with advanced liver cancer (stage C and D) were increased, while the proportion of patients with stage A and B showed a downward trend (P<0.001). The proportion of HBsAg-positive patients showed an upward trend, that is, rising from 69% in 2005 to 82% in 2014 (P=0.043). The proportion of HBeAg-positive patients showed a steady trend (P=0.008). The use rate of ultrasound examination in patients with liver cancer were decreased from 91.0% to 58.0% (P=0.001), while the use rate of computed tomography (CT), MRI, and PET/CT examinations were increased from 81.0% to 84.0% (P=0.05), 0 to 22% (P<0.001), and 0 to 3% (P=0.026) between 2005 to 2014. The proportion of surgical patients were increased (P=0.005), but the proportion of interventional patients did not change significantly (P=0.590). Surgery and interventional therapy were the most common treatment methods, and the proportion of patients treated with surgery over the past 10 years showed an upward trend (P=0.005), while the proportion of interventional therapy remained at a high level with no significant change (P=0.590). Conclusion: In Yunnan province, the incidence of liver cancer increases with age, and the proportion of male with liver cancer is almost six times that of women. Moreover, the low positive rate of alpha-fetoprotein levels and advanced clinical stage in this region are presently the main challenges against the liver cancer prevention and treatment. The application scope of CT, magnetic resonance imaging, PET-CT and other examination methods has gradually expanded, but the treatment methods are still mainly surgery and interventional therapy.

Effects of Low-Volume High-Intensity Interval Exercise on 24 h Movement Behaviors in Inactive Female University Students

The purpose of this study was to examine if low-volume, high-intensity interval exercise (HIIE) was associated with changes in 24-h movement behaviors. A quasi-experimental study design was used. We collected accelerometry data from 21 eligible participants who consistently wore an ActiGraph for a period of two-weeks. Differences in behaviors were analyzed using a paired t-test and repeated measures analysis of variance. Regression analysis was used to explore relationships with factors that impacted changes. The results indicated a compensatory increase in sedentary time (ST) (4.4 ± 6.0%, p < 0.01) and a decrease in light-intensity physical activity (LPA) (−7.3 ± 16.7%, p < 0.05). Meanwhile, moderate-intensity physical activity (MPA), vigorous-intensity physical activity (VPA), and total physical activity (TPA) increased following exercise (p < 0.001). Sleep duration and prolonged sedentary time were reduced (p < 0.05). Exercise intensity and aerobic capacity were associated with changes in ST. The results from the study indicate that participating in a low-volume HIIE encouraged participants who were previously inactive to become more active. The observations of increases in ST may have displaced a prolonged sitting time. The decrease in sleeping time observed may be reflecting an increased sleep quality in connection with increased higher-intensity PA.

A Machine Learning Algorithm Outperforms Traditional Multiple Regression to Predict Risk of Unplanned Overnight Stay Following Outpatient Medial Patellofemoral Ligament Reconstruction

Purpose

To determine whether conventional logistic regression or machine learning algorithms were more precise in identifying the risk factors for unplanned overnight admission after medial patellofemoral ligament (MPFL) reconstruction.

Methods

A retrospective review of the prospectively collected National Surgical Quality Improvement Program database was performed to identify patients who underwent outpatient MPFL reconstruction from 2006–2018. Patients admitted overnight were identified as those with length of stay of 1 or more days. Models were generated using random forest, extreme gradient boosting, adaptive boosting, or elastic net penalized logistic regression, and an additional model was produced as a weighted ensemble of the 4 final algorithms. The predictive capacity of these models was compared to that of logistic regression.

Results

Of the 1307 patients identified, 221 (16.9%) required at least one overnight stay after MPFL reconstruction. Multivariate logistic regression found the following variables to be predictors of inpatient admission: age (odds ratio [OR] = 1.03 [95% confidence interval {CI} 1.02-1.04]; P <.001), spinal anesthesia (OR = 3.42 [95% CI 1.98-6.08]; P < .001), American Society of Anesthesiologists (ASA) class 3/4 (OR = 1.96 [95% CI 1.25-3.06]; P < .001), history of chronic obstructive pulmonary disease (COPD) (OR = 6.44 [95% CI 1.58-26.17]; P = .02), and body mass index (BMI) (OR = 1.03 [95% CI 1.01-1.05]; P < .001). The ensemble model achieved the best performance based on discrimination assessed via internal validation (area under the curve = 0.722). The variables determined most important by the ensemble model were increasing BMI, increasing age, ASA class, anesthesia, smoking, hypertension, lateral release, and history of COPD.

Conclusions

An internally validated machine learning algorithm outperformed logistic regression modeling in predicting the need for unplanned overnight hospitalization after MPFL reconstruction. In this model, the most significant risk factors for admission were age, BMI, ASA class, smoking status, hypertension, lateral release, and history of COPD. This tool can be deployed to augment provider assessment to identify high-risk candidates and appropriately set postoperative expectations for patients.

Clinical Relevance

Identifying and mitigating patient risk factors to prevent adverse surgical outcomes and hospitalizations is one of our primary goals. There may be a key role for machine learning algorithms to help successfully and efficiently risk stratify patients to decrease costs, appropriately set postoperative expectations, and increase the quality of delivered care.

Current Reviews in Musculoskeletal Medicine: Current Controversies for Treatment of Meniscus Root Tears

PURPOSE OF REVIEW

The role of the meniscus in preserving the biomechanical function of the knee joint has been clearly defined. The hypothesis that meniscus root integrity is a prerequisite for meniscus function is supported by the development of progressive knee osteoarthritis (OA) following meniscus root tears (MRTs) treated either non-operatively or with meniscectomy. Consequently, there has been a resurgence of interest in the diagnosis and treatment of MRTs. This review examines the contemporary literature surrounding the natural history, clinical presentation, evaluation, preferred surgical repair technique and outcomes.

RECENT FINDINGS

Surgeons must have a high index of suspicion in order to diagnose a MRT because of the nonspecific clinical presentation and difficult visualization on imaging. Compared with medial MRTs that commonly occur in middle age/older patients, lateral meniscus root injuries tend to occur in younger males with lower BMIs, less cartilage degeneration, and with concomitant ligament injury. Subchondral insufficiency fractures of the knee have been found to be associated with both MRTs and following arthroscopic procedures. Meniscus root repair has demonstrated good outcomes, and acute injuries with intact cartilage should be repaired. Cartilage degeneration, BMI, and malalignment are important considerations when choosing surgical candidates. Meniscus centralization has emerged as a viable adjunct strategy aimed at correcting meniscus extrusion. Meniscus root repair results in a decreased rate of OA and arthroplasty and is economically advantageous when compared with nonoperative treatment and partial meniscectomy. The transtibial pull-through technique with the addition of centralization for the medial meniscus is associated with encouraging early results.

Corticosteroid Injections After Rotator Cuff Repair Improve Function, Reduce Pain, and Are Safe: A Systematic Review

Purpose

To review the literature on postoperative corticosteroid injections (CSIs) following primary rotator cuff repair (RCR) to evaluate efficacy and adverse effects.

Methods

A systematic review of the MEDLINE, EMBASE, and Cochrane databases were performed to identify all studies published within the last 15 years, which reported on outcomes of postoperative CSIs following RCR. Studies including patients who received only preoperative CSIs and revision RCRs were excluded. Included studies were evaluated for study methodology, patient demographics, outcome measures, physical examination parameters, results of imaging studies, and adverse effects or clinical complications.

Results

Seven studies comprising 5,528 patients satisfied inclusion criteria. Among included patients, 54.8% were female and mean age range from 52.3 ± 13.0 to 62.7 ± 6.6 years. Only 1 included investigation was a Level I study. Overall, 4 of 5 studies reported significant improvements in pain and outcome scores (Constant score, American Shoulder and Elbow Surgeons score) compared with controls. Across all studies, the majority of these effects were statistically significant at 3 months postoperatively but not beyond this time point. Five of the 6 included investigations reported no increased rate of retears after postoperative CSIs. One study did find an increase in retear in patients receiving postoperative CSIs but was unable to determine whether these retears were present before the patient received the CSI. Another investigation reported an increased rate of infection only if the CSI was administered in the first postoperative month.

Conclusions

Postoperative CSIs may improve pain and function for up to 3 months following primary RCR but not at later follow-up time points. CSIs should be administered only after the first postoperative month to minimize the potential risk for adverse events.

Level of Evidence

Systematic review of level I-IV studies.

How Long Does It Take to Achieve Clinically Significant Outcomes After Isolated Biceps Tenodesis?

Background:

Clinically significant outcomes (CSOs) connect patient-reported outcome measures data to patient-perceived benefit. Although investigators have established threshold values for various CSOs, the timeline to achieve these outcomes after isolated biceps tenodesis (BT) has yet to be defined.

Purpose:

To define the time-dependent nature of minimal clinically important difference (MCID), substantial clinical benefit (SCB), and Patient Acceptable Symptom State (PASS) achievement after isolated BT.

Study Design:

Case series; Level of evidence, 4.

Methods:

The American Shoulder and Elbow Surgeons score (ASES), the Single Assessment Numeric Evaluation, and the Constant-Murley score (CMS) were administered preoperatively and at 6 and 12 months postoperatively to patients undergoing isolated BT between 2014 and 2018 at our institution. Cumulative probabilities for achieving MCID, SCB, and PASS were calculated using Kaplan-Meier survival analysis. Weibull parametric regression evaluated the hazard ratios (HRs) of achieving earlier MCID, SCB, and PASS.

Results:

Overall cohort (N = 190) achievement rates ranged between 77.8% and 83.2% for MCID, between 42.2% and 80.2% for SCB, and between 59.7% and 62.9% for PASS. Median achievement time was 5.3 to 6.1 months for MCID, 5.9 to 6.4 months for SCB, and 6.07 to 6.1 months for PASS. Multivariate Weibull parametric regression identified older age, male sex, higher body mass index, preoperative thyroid disease, smoking history, and higher preoperative CMS as predictors of delayed CSO achievement (HR, 1.01-6.41), whereas normal tendon on arthroscopy, defined as absence of tenosynovitis or tendon tear on arthroscopy, predicted earlier CSO achievement (HR, 0.19-0.46). Location of tenodesis and worker compensation status did not significantly predict the time to achieve CSOs on multivariate analysis.

Conclusion:

After isolated BT, patients can expect to attain CSO by 13 months postoperatively, with most patients achieving this between 5 and 8 months. Patients tend to take longer to achieve PASS than MCID and SCB.

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.