Outcomes of Blood Flow Restriction Training After ACL Reconstruction in NCAA Division I Athletes.

Background: Blood flow restriction training (BFRT) is a safe and potentially effective adjunctive therapeutic modality for postoperative rehabilitation related to various knee pathologies. However, there is a paucity of literature surrounding BFRT in high-performance athletes after anterior cruciate ligament reconstruction (ACLR). Purpose: To (1) compare the overall time to return to sports (RTS) in a cohort of National Collegiate Athletic Association (NCAA) Division I athletes who underwent a standardized rehabilitation program either with or without BFRT after ACLR and (2) identify a postoperative time interval for which BFRT has the maximum therapeutic benefit. Study Design: Cohort study; Level of evidence, 3. Methods: A total of 55 student-athletes who underwent ACLR between 2000 and 2023 while participating in NCAA Division I sports at a single institution were included in this study. Athletes were allocated to 1 of 2 groups based on whether they participated in a standardized postoperative rehabilitation program augmented with BFRT (BFRT group; n = 22) or completed the standardized protocol alone (non-BFRT group [control]; n = 33). Our primary outcome measure was time to RTS. The secondary outcome measure was handheld dynamometry quadriceps strength testing at various postoperative time points, converted to a limb symmetry index (LSI). Quadriceps strength was not tested between the BFRT and non-BFRT groups because of the limited amount of data on the control group. Results: The mean age at the date of surgery was 18.59 ± 1.10 years for the BFRT group and 19.45 ± 1.30 years for the non-BFRT group (P = .011), and the mean RTS time was 409 ± 134 days from surgery for the BFRT group and 332 ± 100 days for the non-BFRT cohort (P = .047). For the BFRT group, the mean quadriceps strength LSI increased by 0.67% (95% CI, 0.53%-0.81%) for every week of rehabilitation, and there was a significantly positive rate of change in quadriceps strength in weeks 13-16 compared with weeks 9-12 (ΔLSI, 8.22%; P < .001). Conclusion: In elite NCAA Division I athletes, a statistically significant delay was observed in RTS with BFRT compared with standardized physical therapy alone after undergoing ACLR. There also appeared to be an early window during the rehabilitation period where BFRT had a beneficial impact on quadriceps strength.

Similar Medium-Term Revision Rates Following Acute Total Hip Arthroplasty Versus Open Reduction and Internal Fixation for Acetabular Fractures in the Elderly.

BACKGROUND: The management of elderly acetabular fractures is complex, with high rates of conversion total hip arthroplasty (THA) after open reduction and internal fixation (ORIF), but potentially higher rates of complications after acute THA. METHODS: The California Office of Statewide Health Planning and Development database was queried between 2010 and 2017 for all patients aged 60 years or older who sustained a closed, isolated acetabular fracture and underwent ORIF, THA, or a combination. Chi-square tests and Student t tests were used to identify demographic differences between groups. Multivariate regression was used to evaluate predictors of 30-day readmission and 90-day complications. Kaplan-Meier (KM) survival analysis and Cox proportional hazards model were used to estimate the revision surgery-free survival (revision-free survival [RFS]), with revision surgery defined as conversion THA, revision ORIF, or revision THA. RESULTS: A total of 2,184 surgically managed acetabular fractures in elderly patients were identified, with 1,637 (75.0%) undergoing ORIF and 547 (25.0%) undergoing THA with or without ORIF. Median follow-up was 295 days (interquartile range, 13 to 1720 days). 99.4% of revisions following ORIF were for conversion arthroplasty. Unadjusted KM analysis showed no difference in RFS between ORIF and THA (log-rank test P = 0.27). RFS for ORIF patients was 95.1%, 85.8%, 78.3%, and 71.4% at 6, 12, 24 and 60 months, respectively. RFS for THA patients was 91.6%, 88.9%, 87.2%, and 78.8% at 6, 12, 24 and 60 months, respectively. Roughly 50% of revisions occurred within the first year postoperatively (49% for ORIF, 52% for THA). In propensity score-matched analysis, there was no difference between RFS on KM analysis ( P = 0.22). CONCLUSIONS: No difference was observed in medium-term RFS between acute THA and ORIF for elderly acetabular fractures in California. Revision surgeries for either conversion or revision THA were relatively common in both groups, with roughly half of all revisions occurring within the first year postoperatively. LEVEL OF EVIDENCE: III.

A predictive algorithm for perioperative complications and readmission after ankle arthrodesis.

PURPOSE: Ankle arthrodesis is a mainstay of surgical management for ankle arthritis. Accurately risk-stratifying patients who undergo ankle arthrodesis would be of great utility. There is a paucity of accurate prediction models that can be used to pre-operatively risk-stratify patients for ankle arthrodesis. We aim to develop a predictive model for major perioperative complication or readmission after ankle arthrodesis. METHODS: This is a retrospective cohort study of adult patients who underwent ankle arthrodesis at any non-federal California hospital between 2015 and 2017. The primary outcome is readmission within 30 days or major perioperative complication. We build logistic regression and ML models spanning different classes of modeling approaches, assessing discrimination and calibration. We also rank the contribution of the included variables to model performance for prediction of adverse outcomes. RESULTS: A total of 1084 patients met inclusion criteria for this study. There were 131 patients with major complication or readmission (12.1%). The XGBoost algorithm demonstrates the highest discrimination with an area under the receiver operating characteristic curve of 0.707 and is well-calibrated. The features most important for prediction of adverse outcomes for the XGBoost model include: diabetes, peripheral vascular disease, teaching hospital status, morbid obesity, history of musculoskeletal infection, history of hip fracture, renal failure, implant complication, history of major fracture. CONCLUSION: We report a well-calibrated algorithm for prediction of major perioperative complications and 30-day readmission after ankle arthrodesis. This tool may help accurately risk-stratify patients and decrease likelihood of major complications.

Machine learning-driven identification of novel patient factors for prediction of major complications after posterior cervical spinal fusion.

PURPOSE: Posterior cervical fusion is associated with increased rates of complications and readmission when compared to anterior fusion. Machine learning (ML) models for risk stratification of patients undergoing posterior cervical fusion remain limited. We aim to develop a novel ensemble ML algorithm for prediction of major perioperative complications and readmission after posterior cervical fusion and identify factors important to model performance. METHODS: This is a retrospective cohort study of adults who underwent posterior cervical fusion at non-federal California hospitals between 2015 and 2017. The primary outcome was readmission or major complication. We developed an ensemble model predicting complication risk using an automated ML framework. We compared performance with standard ML models and logistic regression (LR), ranking contribution of included variables to model performance. RESULTS: Of the included 6822 patients, 18.8% suffered a major complication or readmission. The ensemble model demonstrated slightly superior predictive performance compared to LR and standard ML models. The most important features to performance include sex, malignancy, pneumonia, stroke, and teaching hospital status. Seven of the ten most important features for the ensemble model were markedly less important for LR. CONCLUSION: We report an ensemble ML model for prediction of major complications and readmission after posterior cervical fusion with a modest risk prediction advantage compared to LR and benchmark ML models. Notably, the features most important to the ensemble are markedly different from those for LR, suggesting that advanced ML methods may identify novel prognostic factors for adverse outcomes after posterior cervical fusion.

Prediction of Major Complications and Readmission After Lumbar Spinal Fusion: A Machine Learning-Driven Approach.

BACKGROUND: Given the significant cost and morbidity of patients undergoing lumbar fusion, accurate preoperative risk-stratification would be of great utility. We aim to develop a machine learning model for prediction of major complications and readmission after lumbar fusion. We also aim to identify the factors most important to performance of each tested model. METHODS: We identified 38,788 adult patients who underwent lumbar fusion at any California hospital between 2015 and 2017. The primary outcome was major perioperative complication or readmission within 30 days. We build logistic regression and advanced machine learning models: XGBoost, AdaBoost, Gradient Boosting, and Random Forest. Discrimination and calibration were assessed using area under the receiver operating characteristic curve and Brier score, respectively. RESULTS: There were 4470 major complications (11.5%). The XGBoost algorithm demonstrates the highest discrimination of the machine learning models, outperforming regression. The variables most important to XGBoost performance include angina pectoris, metastatic cancer, teaching hospital status, history of concussion, comorbidity burden, and workers' compensation insurance. Teaching hospital status and concussion history were not found to be important for regression. CONCLUSIONS: We report a machine learning algorithm for prediction of major complications and readmission after lumbar fusion that outperforms logistic regression. Notably, the predictors most important for XGBoost differed from those for regression. The superior performance of XGBoost may be due to the ability of advanced machine learning methods to capture relationships between variables that regression is unable to detect. This tool may identify and address potentially modifiable risk factors, helping risk-stratify patients and decrease complication rates.

Development of a Novel, Potentially Universal Machine Learning Algorithm for Prediction of Complications After Total Hip Arthroplasty.

BACKGROUND: As the prevalence of hip osteoarthritis increases, the number of total hip arthroplasty (THA) procedures performed is also projected to increase. Accurately risk-stratifying patients who undergo THA would be of great utility, given the significant cost and morbidity associated with developing perioperative complications. We aim to develop a novel machine learning (ML)-based ensemble algorithm for the prediction of major complications after THA, as well as compare its performance against standard benchmark ML methods. METHODS: This is a retrospective cohort study of 89,986 adults who underwent primary THA at any California-licensed hospital between 2015 and 2017. The primary outcome was major complications (eg infection, venous thromboembolism, cardiac complication, pulmonary complication). We developed a model predicting complication risk using AutoPrognosis, an automated ML framework that configures the optimally performing ensemble of ML-based prognostic models. We compared our model with logistic regression and standard benchmark ML models, assessing discrimination and calibration. RESULTS: There were 545 patients who had major complications (0.61%). Our novel algorithm was well-calibrated and improved risk prediction compared to logistic regression, as well as outperformed the other four standard benchmark ML algorithms. The variables most important for AutoPrognosis (eg malnutrition, dementia, cancer) differ from those that are most important for logistic regression (eg chronic atherosclerosis, renal failure, chronic obstructive pulmonary disease). CONCLUSION: We report a novel ensemble ML algorithm for the prediction of major complications after THA. It demonstrates superior risk prediction compared to logistic regression and other standard ML benchmark algorithms. By providing accurate prognostic information, this algorithm may facilitate more informed preoperative shared decision-making.

Adipose-derived Human Perivascular Stem Cells May Improve Achilles Tendon Healing in Rats.

BACKGROUND: Achilles tendon rupture is a common injury and the best treatment option remains uncertain between surgical and nonoperative methods. Biologic approaches using multipotent stem cells such as perivascular stem cells pose a possible treatment option, although there is currently a paucity of evidence regarding their clinical therapeutic use. QUESTIONS/PURPOSES: The purpose of this study was to determine whether injected perivascular stem cells (PSCs) would (1) improve histologic signs of tendon healing (such as percent area of collagen); and (2) improve biomechanical properties (peak load or stiffness) in a rat model of Achilles tendon transection. METHODS: Two subtypes of PSCs were derived from human adipose tissue: pericytes (CD146CD34CD45CD31) and adventitial cells (CD146CD34CD45CD31). Thirty-two athymic rats underwent right Achilles transection and were randomized to receive injection with saline (eight tendons), hydrogel (four tendons), pericytes in hydrogel (four tendons), or adventitial cells in hydrogel (eight tendons) 3 days postoperatively with the left serving as an uninjured control. Additionally, a subset of pericytes was labeled with CM-diI to track cell viability and localization. At 3 weeks, the rats were euthanized, and investigators blinded to treatment group allocation evaluated tendon healing by peak load and stiffness using biomechanical testing and percent area of collagen using histologic analysis with picrosirius red staining. RESULTS: Histologic analysis showed a higher mean percent area collagen for pericytes (30%) and adventitial cells (28%) than hydrogel (21%) or saline (26%). However, a nonparametric statistical analysis yielded no statistical difference. Mechanical testing demonstrated that the pericyte group had a higher peak load than the saline group (41 ± 7 N versus 26 ± 9 N; mean difference 15 N; 95% confidence interval [CI], 4-27 N; p = 0.003) and a higher peak load than the hydrogel group (41 ± 7 N versus 25 ± 3 N; mean difference 16; 95% CI, 8-24 N; p = 0.001). The pericyte group demonstrated higher stiffness than the hydrogel group (36 ± 12 N/mm versus 17 ± 6 N/mm; mean difference 19 N/mm; 95% CI, 5-34 N/mm; p = 0.005). CONCLUSIONS: Our results suggest that injection of PSCs improves mechanical but not the histologic properties of early Achilles tendon healing. CLINICAL RELEVANCE: This is a preliminary study that provides more insight into the use of adipose-derived PSCs as a percutaneous therapy in the setting of Achilles tendon rupture. Further experiments to characterize the function of these cells may serve as a pathway to development of minimally invasive intervention aimed at improving nonoperative management while avoiding the complications associated with surgical treatment down the line.

Neer Award 2018: Platelet-derived growth factor receptor α co-expression typifies a subset of platelet-derived growth factor receptor ß-positive progenitor cells that contribute to fatty degeneration and fibrosis of the murine rotator cuff.

BACKGROUND AND HYPOTHESIS: After massive tears, rotator cuff muscle often undergoes atrophy, fibrosis, and fatty degeneration. These changes can lead to high surgical failure rates and poor patient outcomes. The identity of the progenitor cells involved in these processes has not been fully elucidated. Platelet-derived growth factor receptor ß (PDGFRß) and platelet-derived growth factor receptor α (PDGFRα) have previously been recognized as markers of cells involved in muscle fibroadipogenesis. We hypothesized that PDGFRα expression identifies a fibroadipogenic subset of PDGFRß+ progenitor cells that contribute to fibroadipogenesis of the rotator cuff. METHODS: We created massive rotator cuff tears in a transgenic strain of mice that allows PDGFRß+ cells to be tracked via green fluorescent protein (GFP) fluorescence. We then harvested rotator cuff muscle tissues at multiple time points postoperatively and analyzed them for the presence and localization of GFP+ PDGFRß+ PDGFRα+ cells. We cultured, induced, and treated these cells with the molecular inhibitor CWHM-12 to assess fibrosis inhibition. RESULTS: GFP+ PDGFRß+ PDGFRα+ cells were present in rotator cuff muscle tissue and, after massive tears, localized to fibrotic and adipogenic tissues. The frequency of PDGFRß+ PDGFRα+ cells increased at 5 days after massive cuff tears and decreased to basal levels within 2 weeks. PDGFRß+ PDGFRα+ cells were highly adipogenic and significantly more fibrogenic than PDGFRß+ PDGFRα- cells in vitro and localized to adipogenic and fibrotic tissues in vivo. Treatment with CWHM-12 significantly decreased fibrogenesis from PDGFRß+ PDGFRα+ cells. CONCLUSION: PDGFRß+ PDGFRα+ cells directly contribute to fibrosis and fatty degeneration after massive rotator cuff tears in the mouse model. In addition, CWHM-12 treatment inhibits fibrogenesis from PDGFRß+ PDGFRα+ cells in vitro. Clinically, perioperative PDGFRß+ PDGFRα+ cell inhibition may limit rotator cuff tissue degeneration and, ultimately, improve surgical outcomes for massive rotator cuff tears.

Evaluation of the Trends, Concomitant Procedures, and Complications With Open and Arthroscopic Rotator Cuff Repairs in the Medicare Population.

BACKGROUND: Medicare insures the largest population of patients at risk for rotator cuff tears in the United States. PURPOSE: To evaluate the trends in incidence, concomitant procedures, and complications with open and arthroscopic rotator cuff repairs in Medicare patients. STUDY DESIGN: Cohort study; Level of evidence, 3. METHODS: All Medicare patients who had undergone open or arthroscopic rotator cuff repair from 2005 through 2011 were identified with a claims database. Annual incidence, concomitant procedures, and postoperative complications were compared between these 2 groups. RESULTS: In total, 372,109 rotator cuff repairs were analyzed. The incidence of open repairs decreased (from 6.0 to 4.3 per 10,000 patients, P < .001) while the incidence of arthroscopic repairs increased (from 4.5 to 7.8 per 10,000 patients, P < .001) during the study period. Patients in the arthroscopic group were more likely to have undergone concomitant subacromial decompression than those in the open group (87% vs 35%, P < .001), and the annual incidence of concomitant biceps tenodesis increased for both groups (from 3.8% to 11% for open and 2.2% to 16% for arthroscopic, P < .001). While postoperative complications were infrequent, patients in the open group were more likely to be diagnosed with infection within 6 months (0.86% vs 0.37%, P < .001) but no more likely to undergo operative debridement (0.43% vs 0.26%, P = .08). Additionally, patients in the open group were more likely to undergo intervention for shoulder stiffness within 1 year (1.4% vs 1.1%, P = .01). CONCLUSION: In the Medicare population, arthroscopic rotator cuff repairs have increased in incidence and now represent the majority of rotator cuff repair surgery. Among concomitant procedures, subacromial decompression was most commonly performed despite evidence suggesting a lack of efficacy. Infections and stiffness were rare complications that were slightly but significantly more frequent in open rotator cuff repairs.