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.

Comparison of a 2.7-mm and 3.5-mm locking compression plate for ulnar fractures: a biomechanical evaluation.

Objectives: Implant prominence after ulnar fracture fixation may be mitigated by the use of lower profile plates. The biomechanical strength and stability of 2.7-mm and 3.5-mm locking compression plates for fixation were compared. Methods: Two fracture conditions, transverse (N = 10) and oblique (N = 10), were evaluated in an in vitro study. Half of the specimens for each condition were fixed with 2.7-mm plates and the other half with 3.5-mm plates, all fixed with conventional dynamic compression mechanisms. Specimens were loaded under ±2 Nm of cyclic axial torsion, then under 10 Nm of cyclic cantilever bending, and bending to failure. Interfragmentary motion and strain were analyzed to determine construct stability as a function of fracture pattern and plate size. Results: Interfragmentary motion was significantly larger in all constructs fixed with 2.7-mm plates, compared with 3.5-mm plates (P < 0.01). The 2.7-mm constructs with transverse fractures had the greatest motion, ranging between 5° and 10° under axial rotation and 5.0-6.0 mm under bending. Motions were the lowest for 3.5-mm constructs with oblique fractures, ranging between 3.2 and 4.2 mm under bending and 2°-3.5° for axial rotation. For oblique fractures, the bending moment at ultimate failure was 31.4 ± 3.6 Nm for the 2.7-mm constructs and 10.0 ± 1.9 Nm for 3.5-mm constructs (P < 0.01). Similarly, for transverse fractures, the bending moment was 17.9 ± 4.0 Nm for the 2.7-mm constructs and 9.7 ± 1.3 Nm for the 3.5-mm constructs (P < 0.01). Conclusions: Although 3.5-mm plates were more effective at reducing fracture motion, they were consistently associated with refracture at the distal-most screw hole under load to failure. By contrast, 2.7-mm plates plastically deformed despite excessive loads, potentially avoiding a subsequent fracture. Level of Evidence: Level V.

Higher Rates of Readmission After Acute Total Hip Arthroplasty Versus Open Reduction Internal Fixation for Elderly Acetabular Fractures, a National Study From 2010 to 2019.

OBJECTIVES: To evaluate the initial complications and short-term readmissions and reoperations after open reduction internal fixation (ORIF) versus acute total hip arthroplasty (THA) for elderly acetabular fractures. DESIGN: Retrospective database review. SETTING: All hospitalizations in the National Readmissions Database and National Inpatient Sample. PATIENTS/PARTICIPANTS: Patients 60 years of age or older with closed acetabular fractures managed surgically identified from the National Readmissions Database or National Inpatient Sample between 2010 and 2019. INTERVENTION: Acute THA with or without ORIF. MAIN OUTCOME MEASUREMENTS: 30-, 90-, and 180-day readmissions and reoperations and index hospitalization complications. RESULTS: An estimated 12,538 surgically managed acetabular fractures in elderly patients occurred nationally between 2010 and 2019, with 10,008 (79.8%) undergoing ORIF and 2529 (20.2%) undergoing THA. Length of stay was 1.7 days shorter ( P < 0.001) and probability of nonhome discharge was reduced (OR 0.68, P = 0.009) for THA patients than for ORIF patients. THA was associated with lower rates of pneumonia (4.6 vs. 9.1%, P < 0.001) and other respiratory complications (10.2 vs. 17.6%) when compared with ORIF. At 30 days, THA patients had higher rates of readmission (13.9 vs. 10.1%, P = 0.007), related readmission (5.4 vs. 1.2%, P < 0.001), readmission for dislocation (3.1 vs. 0.3%, P < 0.001), and reoperations (2.9 vs. 0.9%, P = 0.002). At 180 days, THA patients had higher rates of related readmission (10.1% vs. 3.9%, P < 0.001), readmission for dislocation (5.1% vs. 1.3%, P < 0.001), and readmission for SSI (3.4 vs. 0.8%, P = 0.005). CONCLUSIONS: Acute THA is associated with lower length of stay and certain index hospitalization complications, but higher rates of readmissions for related reasons and specifically for dislocation. LEVEL OF EVIDENCE: Therapeutic Level III. See Instructions for Authors for a complete description of levels of evidence.

Development and External Validation of a Risk Calculator for Prediction of Major Complications and Readmission After Anterior Cervical Discectomy and Fusion.

STUDY DESIGN: A retrospective, case-control study. OBJECTIVE: We aim to build a risk calculator predicting major perioperative complications after anterior cervical fusion. In addition, we aim to externally validate this calculator with an institutional cohort of patients who underwent anterior cervical discectomy and fusion (ACDF). SUMMARY OF BACKGROUND DATA: The average age and proportion of patients with at least one comorbidity undergoing ACDF have increased in recent years. Given the increased morbidity and cost associated with perioperative complications and unplanned readmission, accurate risk stratification of patients undergoing ACDF is of great clinical utility. METHODS: This is a retrospective cohort study of adults who underwent anterior cervical fusion at any nonfederal California hospital between 2015 and 2017. The primary outcome was major perioperative complication or 30-day readmission. We built standard and ensemble machine learning models for risk prediction, assessing discrimination, and calibration. The best-performing model was validated on an external cohort comprised of consecutive adult patients who underwent ACDF at our institution between 2013 and 2020. RESULTS: A total of 23,184 patients were included in this study; there were 1886 cases of major complication or readmissions. The ensemble model was well calibrated and demonstrated an area under the receiver operating characteristic curve of 0.728. The variables most important for the ensemble model include male sex, medical comorbidities, history of complications, and teaching hospital status. The ensemble model was evaluated on the validation cohort (n=260) with an area under the receiver operating characteristic curve of 0.802. The ensemble algorithm was used to build a web-based risk calculator. CONCLUSION: We report derivation and external validation of an ensemble algorithm for prediction of major perioperative complications and 30-day readmission after anterior cervical fusion. This model has excellent discrimination and is well calibrated when tested on a contemporaneous external cohort of ACDF cases.

Increasing Rate of Shoulder Arthroplasty for Geriatric Proximal Humerus Fractures in the United States, 2010-2019.

Introduction: The two historically dominant surgical options for displaced geriatric proximal humerus (PHFx) fractures are open reduction internal fixation (ORIF) and hemiarthroplasty (HA). However, shoulder arthroplasty (SA), predominantly in the form of reverse total shoulder arthroplasty (RTSA), has emerged as an attractive treatment option. We aim to compare the utilization trends, complications, and costs associated with surgical management of geriatric proximal humerus fractures (PHFs) between 2010 and 2019. We hypothesized that 1) the proportion of patients undergoing SA would increase over time, 2) the short-term complication rate in patients undergoing SA would decline over time, and 3) hospital related costs would decline for SA patients over time. Patients and Methods: The National Inpatient Sample was queried from 2010 to 2019 to identify all PHFx in patients aged 65 or older that underwent ORIF, SA, or HA. Multivariable regression was used to evaluate differences between fixation methods regarding health care utilization metrics, hospital costs, and index hospital complications. The primary outcome of interest was the method of surgical management utilized in the treatment of geriatric PHFs, and secondary outcomes of interest included hospitalization cost, length of stay (LOS), discharge destination and index hospitalization complications. Results: A total of 105 886 geriatric patients that underwent surgical management of PHFx were identified. While the proportion undergoing ORIF decreased from 59% to 29%, the proportion undergoing SA increased from 9% to 67%. Hospital costs decreased over time for patients treated with SA and increased for those treated with ORIF. Compared to ORIF, SA was associated with higher cost, decreased length of stay, and lower mortality and complication rates. Conclusion: Over the last decade, SA has become the most common surgical treatment modality performed for geriatric PHFx. Index hospital complications are reduced in SA patients compared to ORIF patients, driven largely by a lower rate of blood transfusion. Although costs are decreasing and average length of stay is now lower in SA patients compared to ORIF patients, SA remains associated with higher hospital costs overall.

A Risk Calculator for the Prediction of C5 Nerve Root Palsy After Instrumented Cervical Fusion.

BACKGROUND: C5 palsy is a common postoperative complication after cervical fusion and is associated with increased health care costs and diminished quality of life. Accurate prediction of C5 palsy may allow for appropriate preoperative counseling and risk stratification. We primarily aim to develop an algorithm for the prediction of C5 palsy after instrumented cervical fusion and identify novel features for risk prediction. Additionally, we aim to build a risk calculator to provide the risk of C5 palsy. METHODS: We identified adult patients who underwent instrumented cervical fusion at a tertiary care medical center between 2013 and 2020. The primary outcome was postoperative C5 palsy. We developed ensemble machine learning, standard machine learning, and logistic regression models predicting the risk of C5 palsy-assessing discrimination and calibration. Additionally, a web-based risk calculator was built with the best-performing model. RESULTS: A total of 1024 patients were included, with 52 cases of C5 palsy. The ensemble model was well-calibrated and demonstrated excellent discrimination with an area under the receiver-operating characteristic curve of 0.773. The following features were the most important for ensemble model performance: diabetes mellitus, bipolar disorder, C5 or C4 level, surgical approach, preoperative non-motor neurologic symptoms, degenerative disease, number of fused levels, and age. CONCLUSIONS: We report a risk calculator that generates patient-specific C5 palsy risk after instrumented cervical fusion. Individualized risk prediction for patients may facilitate improved preoperative patient counseling and risk stratification as well as potential intraoperative mitigating measures. This tool may also aid in addressing potentially modifiable risk factors such as diabetes and obesity.

Oncomodulin (OCM) uniquely regulates calcium signaling in neonatal cochlear outer hair cells.

In cochlear outer hair cells (OHCs), a network of Ca2+ channels, pumps and Ca2+-binding proteins (CaBPs) regulates the localization, spread, and magnitude of free Ca2+ ions. During early postnatal development, OHCs express three prominent mobile EF-hand CaBPs: oncomodulin (OCM), α-parvalbumin (APV) and sorcin. We have previously shown that deletion of Ocm (Ocm-/-) gives rise to progressive cochlear dysfunction in young adult mice. Here, we show that changes in Ca2+ signaling begin early in postnatal development of Ocm-/- mice. While mutant OHCs exhibit normal electrophysiological profiles compared to controls, their intracellular Ca2+ signaling is altered. The onset of OCM expression at postnatal day 3 (P3) causes a developmental change in KCl-induced Ca2+ transients in OHCs and leads to slower KCl-induced Ca2+ transients than those elicited in cells from Ocm-/- littermates. We compared OCM buffering kinetics with other CaBPs in animal models and cultured cells. In a double knockout of Ocm and Apv (Ocm-/-;Apv-/-), mutant OHCs show even faster Ca2+ kinetics, suggesting that APV may also contribute to early postnatal Ca2+ signaling. In transfected HEK293T cells, OCM slows Ca2+ kinetics more so than either APV or sorcin. We conclude that OCM controls the intracellular Ca2+ environment by lowering the amount of freely available [Ca2+]i in OHCs and transfected HEK293T cells. We propose that OCM plays an important role in shaping the development of early OHC Ca2+ signals through its inimitable Ca2+ buffering capacity.

Development of a Machine Learning Algorithm for Prediction of Complications and Unplanned Readmission Following Primary Anatomic Total Shoulder Replacements.

Background: The demand and incidence of anatomic total shoulder arthroplasty (aTSA) procedures is projected to increase substantially over the next decade. There is a paucity of accurate risk prediction models which would be of great utility in minimizing morbidity and costs associated with major post-operative complications. Machine learning is a powerful predictive modeling tool and has become increasingly popular, especially in orthopedics. We aimed to build a ML model for prediction of major complications and readmission following primary aTSA. Methods: A large California administrative database was retrospectively reviewed for all adults undergoing primary aTSA between 2015 to 2017. The primary outcome was any major complication or readmission following aTSA. A wide scope of standard ML benchmarks, including Logistic regression (LR), XGBoost, Gradient boosting, AdaBoost and Random Forest were employed to determine their power to predict outcomes. Additionally, important patient features to the prediction models were indentified. Results: There were a total of 10,302 aTSAs with 598 (5.8%) having at least one major post-operative complication or readmission. XGBoost had the highest discriminative power (area under receiver operating curve AUROC of 0.689) of the 5 ML benchmarks with an area under precision recall curve AURPC of 0.207. History of implant complication, severe chronic kidney disease, teaching hospital status, coronary artery disease and male sex were the most important features for the performance of XGBoost. In addition, XGBoost identified teaching hospital status and male sex as markedly more important predictors of outcomes compared to LR models. Conclusion: We report a well calibrated XGBoost ML algorithm for predicting major complications and 30-day readmission following aTSA. History of prior implant complication was the most important patient feature for XGBoost performance, a novel patient feature that surgeons should consider when counseling patients.

Clinical and Radiographic Differentiation of Pediatric Patellar Sleeve Fractures and Other Inferior Pole Pathologies.

BACKGROUND: Inferior pole patellar sleeve fractures (PSFs) are rare injuries that occur in skeletally immature patients that may be missed or falsely diagnosed as an inferior pole fracture (IPF) or Sinding-Larsen-Johansson syndrome (SLJS). PURPOSE: The objective of this study was to evaluate and compare clinical and radiographic features of patients with PSF, IPF, and SLJS. STUDY DESIGN: Cross-sectional study; Level of evidence, 3. METHODS: Retrospective review of skeletally immature patients diagnosed with PSF, IPF, and SLJS between 2011 and 2019 at a single urban academic center was performed. Chart and radiographic review was completed. Between-group differences were assessed using analysis of variance, Kruskal-Wallis test, and Fisher exact test, as appropriate to variable distributions. RESULTS: In total, 125 patients (82% male) were included, with a mean (SD) age of 10.7 (2) years (16 PSF, 51 IPF, 58 SLJS). There were no significant differences in patient characteristics between the 3 groups. One hundred percent of the patients with PSF (P < .001) and IPF (P < .001) had acute trauma, compared with 24% of patients with SLJS. Fewer patients with PSF had an intact straight leg raise (38%) compared with those with IPF (94%; P < .001) and SLJS (98%; P < .001). Fewer patients with SLJS had knee swelling (41%) compared with those with PSF (93%; P < .001) and IPF (94%; P < .001). More patients with PSF had knee effusion (81%) compared with those with IPF (37%; P = 0.011) and SLJS (3%; P < .001). More patients with SLJS were able to bear weight (88%) compared with those with IPF (12%; P < .001) and PSF (0; P < .001). Radiographically, compared with those with IPF and SLJS, patients with PSFs had increased mean prepatellar swelling (6.1 [P < .001] and 6.5 [P < .001] vs 12.9 mm), intra-articular effusion (6.1 [P = .014] and 4.9 [P = .001] vs 9.2 mm), maximum fragment size (26 [P = .004] and 17.7 [P < .001] vs 45.3 mm), and maximum fragment displacement (1.24 [P = .002] and 1.45 [P = .003] vs 13.30 mm), respectively. Compared with those with SLJS (1.10), patients with PSF (1.92; P < .001) and IPF (1.22; P < .001) had patella alta with higher mean Insall-Salvati ratios >1.2. CONCLUSION: Differences in clinical features such as ability to straight leg raise, knee swelling, knee effusion, ability to bear weight, and radiographic features, such as prepatellar swelling, intra-articular effusion, fragment displacement/size/shape/location, and patellar height, can all be helpful in recognizing rare pediatric inferior patellar pathology, thus increasing efficiency in indicating for higher-level imaging to reach a prompt diagnosis and provide appropriate treatment.

Outcomes of Modular Knee Arthrodesis for Challenging Periprosthetic Joint Infections.

BACKGROUND: Modular knee arthrodesis (MKA) is a salvage treatment option for patients with challenging periprosthetic joint infections (PJI). The purpose of this study was to investigate the outcomes of patients who underwent MKA for PJI with a single technique and determine if specific factors are associated with MKA failure. METHODS: This was a retrospective review of 81 patients who underwent MKA at a single institution. Knee Society Scores were recorded before MKA and at the final follow-up (mean 52 months). Poisson regression was used to calculate rate ratios for MKA failure secondary to infection. RESULTS: The mean patient age was 67 years; most patients were McPherson B hosts (56.8%) and had type 3 extremities (53.1%), and all had a type III infection (chronic, >4 wks). Forty-six percent of patients had a prior explantation (59.5% failed 2-stage, 40.5% failed spacer). Staphylococcus epidermidis and Staphylococcus aureus were the most common organisms, 22.2% and 18.5%, respectively. Thirty percent of patients had at least one reoperation, excluding reimplantation (14.8% irrigation and debridement/wound closure, 9.9% MKA exchange, and 7.4% amputation). Of 82.7% of MKA patients with no evidence of infection, 82.1% (56 patients) underwent reimplantation endoprosthetic reconstruction, and 67.3% of these remained infection-free at the final follow-up. DISCUSSION: MKA is a salvage option for challenging PJI cases that may serve as definitive surgical management or as a bridge to endoprosthetic reconstruction for patients who have failed prior infection control procedures.

A Critical Analysis of Lateral Versus Central Endpoint in Distal Tibia Nailing: Does It Affect Alignment?

OBJECTIVES: To evaluate the effect of a traditional "center-center" end point for distal tibia nailing in comparison with a lateral-of-center end point on fracture malalignment in a cadaver model. METHODS: Nine matched pairs of human cadaveric lower-extremity specimens were used to model the effect of nail end point on fracture alignment in extra-articular distal tibia fractures. After simulation of the fracture through a standardized osteotomy, 1 member of each pair was fixed with an intramedullary nail using a "center-center" end point, whereas a lateral-of-center end point was used for the other member of the pair. Specimens were stripped of soft tissue, and digital calipers were used to measure fracture translation and gap medially, laterally, anteriorly, and posteriorly. Coronal plane angulation at each fracture was measured on the final mortise image. RESULTS: The average coronal angulation was 7.0 degrees of valgus (with a SD of 4.1) in central-end point specimens versus 0.2 degrees of valgus (SD = 1.5) in lateral-end point specimens ( P < 0.001). Lateral-end point specimens also demonstrated significantly less fracture gap medially (mean 0.2 vs. 3.1 mm for central-end point specimens, P < 0.001), anteriorly (mean 0.1 vs. 1.3 mm, P = 0.003), and posteriorly (mean 0.3 vs. 2.2 mm, P = 0.003). Lateral-end point specimens also showed less lateral translation (mean 0.6 vs. 1.6 mm, P = 0.006). CONCLUSIONS: Lateral-of-center nail end points may help surgeons restore native alignment in extra-articular distal tibia fractures and avoid valgus malalignment.

Disparities in ACL Reconstruction: the Influence of Gender and Race on Incidence, Treatment, and Outcomes.

PURPOSE OF REVIEW: Anterior cruciate ligament (ACL) rupture is a common injury that has important clinical and economic implications. We aimed to review the literature to identify gender, racial and ethnic disparities in incidence, treatment, and outcomes of ACL injury. RECENT FINDINGS: Females are at increased risk for ACL injury compared to males. Intrinsic differences such as increased quadriceps angle and increased posterior tibial slope may be contributing factors. Despite lower rates of injury, males undergo ACL reconstruction (ACLR) more frequently. There is conflicting evidence regarding gender differences in graft failure and ACL revision rates, but males demonstrate higher return to sport (RTS) rates. Females report worse functional outcome scores and have worse biomechanical metrics following ACLR. Direct evidence of racial and ethnic disparities is limited, but present. White athletes have greater risk of ACL injury compared to Black athletes. Non-White and Spanish-speaking patients are less likely to undergo ACLR after ACL tear. Black and Hispanic youth have greater surgical delay to ACLR, increased risk for loss to clinical follow-up, and less physical therapy sessions, thereby leading to greater deficits in knee extensor strength during rehabilitation. Hispanic and Black patients also have greater risk for hospital admission after ACLR, though this disparity is improving. Females have higher rates of ACL injury with inconclusive evidence on anatomic predisposition and ACL failure rate differences between genders. Recent literature has suggested inferior RTS and functional outcomes following ACLR in females. Though there is limited and mixed data on incidence and outcome differences between races and ethnic groups, recent studies suggest there may be disparities in those who undergo ACLR and time to treatment.

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.

Cost-effectiveness of fixation versus arthroplasty for geriatric distal femur fractures.

INTRODUCTION: Geriatric distal femur fractures are challenging to treat. The high mortality rate associated with a loss of mobility in this population has led some authors to compare distal femur fractures to femoral neck fractures with respect to the importance of rapidly regaining mobility in the geriatric population. Acute distal femur replacement has been advocated by some as a preferred treatment over internal fixation because arthroplasty may facilitate a more rapid return to a patient's baseline mobility level. The purpose of this study was to systematically review the literature on the costs and outcomes of fixation and arthroplasty in the geriatric distal femur fracture population and to employ decision modeling techniques to generate evidence-based treatment recommendations. METHODS: A systematic literature review of clinical studies published since 2000 was conducted to synthesize the available data on outcomes, reoperation rates, and mortality rates after fixation or arthroplasty for distal femur fractures in patients with an average age greater than 70 years. A Markov decision analysis model was created. Costs, health state utilities, reoperation rates, and mortality rates were derived from the systematic literature review and publicly available data. The model was analyzed via probabilistic statistical analysis as well as sensitivity analyses with a willingness-to-pay threshold set at $100,000 per QALY and a 5-year time horizon. RESULTS: From a US societal perspective, fixation was associated with a greater quality of life benefit (2.44 QALYs vs. 2.34 QALYs) and lower cost ($25,556 vs. $65,536) compared with distal femur replacement for geriatric distal femur fractures. Probabilistic analysis demonstrated that 82 in 100 model outcomes favored fixation over arthroplasty and 18 in 100 model outcomes favored distal femur replacement. Sensitivity analyses demonstrated that this result was robust to small deviations in the cost and functional outcome variables in the model. CONCLUSION: Compared to distal femur replacement, ORIF is likely to be a more cost-effective treatment for distal femur fractures in the geriatric patient population, though this recommendation is tempered by the relatively low quality of evidence available on the comparative functional outcomes of these treatments.

A Novel, Potentially Universal Machine Learning Algorithm to Predict Complications in Total Knee Arthroplasty.

BACKGROUND: There remains a lack of accurate and validated outcome-prediction models in total knee arthroplasty (TKA). While machine learning (ML) is a powerful predictive tool, determining the proper algorithm to apply across diverse data sets is challenging. AutoPrognosis (AP) is a novel method that uses automated ML framework to incorporate the best performing stages of prognostic modeling into a single well-calibrated algorithm. We aimed to compare various ML methods to AP in predictive performance of complications after TKA. METHODS: Thirty-eight preoperative patient demographics and clinical features from all primary TKAs performed at California-licensed hospitals between 2015 and 2017 were evaluated as predictors of major complications after TKA. Traditional logistic regression (LR), various other ML methods (XGBoost, Gradient Boosting, AdaBoost, and Random Forest), and AP were used for model building to determine discriminative power (area under receiver operating curve), calibration (Brier score), and feature importance. RESULTS: Between 2015 and 2017, there were a total of 156,750 TKAs with 1109 (0.7%) total major complications. AP had the highest discriminative performance with area under receiver operating curve 0.679 compared with LR, XGBoost, Gradient Boosting, AdaBoost, and Random Forest (0.617, 0.601, 0.662, 0.657, and 0.545, respectively). AP (Brier score 0.007) had similar calibration as the other ML methods (0.006, 0.006, 0.022, 0.007, and 0.008, respectively). The variables that are most important for AP differ from those that are most important for LR. CONCLUSION: Compared to conventional ML algorithms, AP has superior discriminative ability with similar calibration and suggests nonlinear relationships between variables in outcomes of TKA.

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.