Krych, Aaron J

» Hamstring injuries are common among active and athletic individuals, especially those involved in high-speed running, distance running, or sports requiring sudden directional changes. Acute hamstring strains often occur as an eccentric strain during running or a stretch-type injury caused by simultaneous hip flexion and knee extension. Proximal hamstring tendinopathy is an overuse injury of the hamstring tendon as a result of chronic cicatrization of the musculotendinous unit. » Repeated stress to the hamstring tendon leads to increased cellularity of tendon fibers, disruption of collagen, and subsequent microinjury of the tissue that attaches the tendon to bone. » Management of hamstring injuries generally begins with nonoperative modalities consisting of eccentric rehabilitative exercise programs. Although various other treatment modalities are available, the comparative efficacy of these supportive measures is not well differentiated at this time. » In this article, we review the current literature with regard to the nonoperative treatment of hamstring injuries, specifically focusing on acute hamstring muscle strains and proximal hamstring tendinopathy in order to provide supplementary insight on the effectiveness of current modalities.

Purpose To compare the clinical efficacy in the resolution of patellar instability, patient‐reported outcomes (PROs), and complication and reoperation rates between patients who underwent grooveplasty (proximal trochleoplasty) and patients who underwent trochleoplasty as part of a combined patellofemoral stabilization procedure. Methods A retrospective chart review was performed to identify a cohort of patients who underwent grooveplasty and a cohort who underwent trochleoplasty at the time of patellar stabilization. Complications, reoperations, and PRO scores (Tegner, Kujala, and International Knee Documentation Committee scores) were collected at final follow‐up. The Kruskal‐Wallis test and Fisher exact test were performed when appropriate, and P < .05 was considered significant. Results Overall, 17 grooveplasty patients (18 knees) and 15 trochleoplasty patients (15 knees) were included. Seventy‐nine percent of patients were female, and the average follow‐up period was 3.9 years. The mean age at first dislocation was 11.8 years overall; most patients (65%) had more than 10 lifetime instability events and 76% of patients underwent prior knee‐stabilizing procedures. Trochlear dysplasia (Dejour classification) was similar between cohorts. Patients who underwent grooveplasty had a higher activity level ( P = .007) and a higher degree of patellar facet chondromalacia ( P = .008) at baseline. At final follow‐up, no patients had recurrent symptomatic instability after grooveplasty compared with 5 patients in the trochleoplasty cohort ( P = .013). There were no differences in postoperative International Knee Documentation Committee scores ( P = .870), Kujala scores ( P = .059), or Tegner scores ( P = .052). Additionally, there were no differences in complication rates (17% in grooveplasty cohort vs 13% in trochleoplasty cohort, P > .999) or reoperation rates (22% vs 13%, P = .665). Conclusions Proximal trochlear reshaping and removal of the supratrochlear spur (grooveplasty) in patients with severe trochlear dysplasia may offer an alternative strategy to complete trochleoplasty for the treatment of trochlear dysplasia in complex cases of patellofemoral instability. Grooveplasty patients showed less recurrent instability and similar PROs and reoperation rates compared with trochleoplasty patients. Level of Evidence Level III, retrospective comparative study.

Background and Objectives: The number of patients who undergo multiple operations on a knee is increasing. The objective of this study was to develop a deep learning algorithm that could detect 17 different surgical implants on plain knee radiographs. Materials and Methods: An internal dataset consisted of 5206 plain knee antero-posterior X-rays from a single, tertiary institute for model development. An external set contained 238 X-rays from another tertiary institute. A total of 17 different types of implants including total knee arthroplasty, unicompartmental knee arthroplasty, plate, and screw were labeled. The internal dataset was approximately split into a train set, a validation set, and an internal test set at a ratio of 7:1:2. You Only look Once (YOLO) was selected as the detection network. Model performances with the validation set, internal test set, and external test set were compared. Results: Total accuracy, total sensitivity, total specificity value of the validation set, internal test set, and external test set were (0.978, 0.768, 0.999), (0.953, 0.810, 0.990), and (0.956, 0.493, 0.975), respectively. Means ± standard deviations (SDs) of diagonal components of confusion matrix for these three subsets were 0.858 ± 0.242, 0.852 ± 0.182, and 0.576 ± 0.312, respectively. True positive rate of total knee arthroplasty, the most dominant class of the dataset, was higher than 0.99 with internal subsets and 0.96 with an external test set. Conclusion: Implant identification on plain knee radiographs could be automated using a deep learning technique. The detection algorithm dealt with overlapping cases while maintaining high accuracy on total knee arthroplasty. This could be applied in future research that analyzes X-ray images with deep learning, which would help prompt decision-making in clinics.

Background: There is a dearth of knowledge on anterior shoulder instability in older patients. Purpose/Hypothesis: The purposes of this study were to describe the incidence and epidemiology, injury characteristics, and treatment and outcomes in patients ≥50 years old with first-time anterior shoulder instability. We also describe the historical trends in diagnosis and treatment. It was hypothesized that the rates of obtaining a magnetic resonance imaging (MRI) scan and surgical intervention have increased over the past 20 years. Study Design: Descriptive epidemiology study. Methods: An established geographic database was used to identify 179 patients older than 50 years who experienced new onset anterior shoulder instability between 1994 and 2016. Medical records were reviewed to obtain patient characteristics, imaging characteristics, and surgical treatment and outcomes, including recurrent instability. Comparative analysis was performed to identify differences between age groups. Mean follow-up time was 11 years. Results: The incidence of first-time anterior shoulder dislocation in our study population was 28.8 per 100,000 person-years, which is higher than previously reported. Full-thickness rotator cuff tears were found in 62% of the 66 patients who underwent MRI scans. Of all patients, 26% progressed to surgery at a mean time of 1.6 years after injury; 57% of all surgical procedures involved a rotator cuff repair, and 17% included anterior labral repair. All patients who underwent a labral repair also underwent concomitant rotator cuff repair. The rate of recurrent instability for the cohort was 15% at a median of 176 days after the initial instability event. There were no instances of recurrent instability after operative intervention. At an average of 7.5 years after the initial instability event, 14% of patients developed radiographic progression of glenohumeral arthritis. The rate of surgical intervention within 1 year of initial dislocation increased from 5.1% in 1994 to 1999 to 52% in 2015 to 2016. Conclusion: The incidence of first-time anterior shoulder instability in patients aged ≥50 years was 28.8 per 100,000 person-years. Full-thickness rotator cuff tears (62%) were the most common condition associated with anterior shoulder instability, followed by Hill-Sachs lesions (56%). The rate of recurrent instability for the entire cohort was 15%, with no instances of recurrent instability after operative intervention.

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.

Purpose To investigate the incidence of anteroinferior glenoid rim fractures (AGRFs) after anterior shoulder instability (ASI) in patients aged 50 years or older, identify risk factors for surgical intervention for AGRFs, compare initial treatment strategies, and compare clinical outcomes of patients with and without associated AGRFs. Methods An established geographic medical record system was used to identify patients aged 50 years or older with ASI between 1994 and 2016. Patients with radiographic evidence of AGRFs were identified and matched 1:1 to patients without AGRFs. Outcome measures included recurrent instability, recurrent pain events, conversion to arthroplasty, and osteoarthritis graded with the Samilson‐Prieto classification for post‐instability arthritis. Results Overall, 177 patients were identified, with a mean follow‐up period of 10.8 years. Of these patients, 41 (23.2%) had AGRFs and were matched to 41 control patients without AGRFs. The average age was 58.6 and 58.2 years for the AGRF and control groups, respectively. Rates of surgical intervention (27% vs 49%), recurrent instability (12% vs 20%), progression of osteoarthritis (34% vs 39%), and conversion to arthroplasty (2% vs 5%) were similar between AGRF patients and controls. For patients with AGRFs, increased bone fragment size (odds ratio, 1.1) and increased body mass index (odds ratio, 1.2) correlated with an increased risk of surgery. The cutoff value for an increased risk of surgery in patients with AGRFs was a fragment size 33% of the glenoid width or greater. Conclusions Of patients aged 50 years or older at presentation of ASI, 23.2% presented with an associated AGRF. A fragment size 33% of the glenoid width or greater and a higher patient body mass index were significant factors for surgical intervention; however, most patients did not require surgery and still showed acceptable clinical outcomes, and the most common reason for surgical intervention was a rotator cuff tear. Overall, the presence of an AGRF did not portend a worse prognosis as treatment strategies and long‐term outcomes including recurrent instability, progression of osteoarthritis, and conversion to arthroplasty were similar to those in patients without AGRFs. Level of Evidence Level III, retrospective comparative study.

Background: Little is known about the benefits and outcomes of meniscal repair in patients older than 60 years. Purpose: To (1) report the clinical and radiographic outcomes of meniscal repair in patients aged ≥60 years and compare them with matched patients who underwent meniscectomy and (2) identify procedural failures. Study Design: Cohort study; Level of evidence, 2. Methods: We included 32 knees in 32 patients aged ≥60 years (20 female, 12 male; mean age, 64.5 ± 4.6 years) who underwent meniscal repair surgery at a single medical institution between 2010 and 2020. Patients were matched according to age, sex, body mass index, and meniscal tear type with a comparison cohort who underwent meniscectomy (n = 49 patients [49 knees]; 32 female, 17 male). For all patients, demographic information, clinical history, physical examination findings, treatment details, and radiographic images were reviewed and analyzed. At final follow-up (mean, 42.2 months; range, 13-128 months), patients completed the 2000 International Knee Documentation Committee (IKDC) Subjective Knee Evaluation Form, Knee injury and Osteoarthritis Outcome Score (KOOS), and Lysholm score. Clinical failure was defined as revision surgery and/or progression to total knee arthroplasty (TKA). A matched-pairs t test was used to analyze differences between the 2 treatment groups, and Kaplan-Meier analysis was used to determine the rates of knee osteoarthritis and progression to TKA. Results: The majority of patients had a medial meniscal tear (72.8%), whereas the lateral meniscus was torn in 27.2% of cases. Most tears were located in the meniscal root (56.8%), followed by the posterior horn (34.6%) and midbody (8.6%). At final follow-up, all outcome scores were higher in the repair group compared with the meniscectomy group (IKDC, 78.9 ± 13.4 vs 56.0 ± 15.4; KOOS, 86.6 ± 11.9 vs 61.7 ± 16.2; Lysholm, 88.3 ± 13.3 vs 68.7 ± 15.2, respectively; P < .001 for all). Clinical failure was observed in 22% of patients in the repair group. Conclusion: All clinical outcome scores were higher in the meniscal repair group compared with the matched meniscectomy group at final follow-up. The clinical failure rate of the repair group was 22%. These findings support meniscal repair in selected patients aged ≥60 years.

AbstractPurposeThis 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).MethodsPatients 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.ResultsA 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.ConclusionMachine 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 evidenceIII.

➢ Pathologic meniscal extrusion can compromise meniscal function, leading to increased contact forces in the tibiofemoral compartment and the acceleration of osteoarthritic changes. ➢ Extrusion is typically defined as radial displacement of ≥3 mm outside the tibial border and is best diagnosed via magnetic resonance imaging, although ultrasonography has also demonstrated encouraging diagnostic utility. ➢ Surgical management of meniscal extrusion is based on the underlying etiology, the patient’s symptom profile, the preexisting health of the articular surface, and the risk of future chondral injury and osteoarthritis.

Background: Diagnosis and treatment of posterior shoulder instability (PSI) has improved with advances in imaging and surgical technique. However, the relationship between PSI and osteoarthritis (OA) remains unclear. Purpose: To evaluate a population-based cohort to (1) determine the rate of symptomatic OA, (2) identify patient characteristic risk factors for OA, and (3) evaluate the effect of posterior capsulolabral repair on OA progression. Study Design: Case-control study; Level of evidence, 3. Methods: After review of 324 patient records, we included 115 patients (14 female, 101 male) diagnosed with PSI between January 1994 and July 2012 with an average follow-up of 12.5 years (range, 5-23 years). Medical records were reviewed for patient characteristics, injury characteristics, surgical details (if any), and radiographic progression of OA. Kaplan-Meier survival was used to estimate survival free of OA; characteristics associated with OA progression were determined via univariate Cox regression models, and associated 95% CIs and hazard ratios (HRs) are presented. Results: Overall, 14% (16/115) of patients had radiographic progression of symptomatic glenohumeral arthritis, with 5-year survival of 88.3% (95% CI, 79.7%-97.3%). Older age at the time of instability diagnosis was associated with arthritis progression (10-year unit HR, 1.95; 95% CI, 1.26-3.03). Patients who underwent surgery demonstrated increased radiographic progression of OA (HR, 4.03; 95% CI, 1.23-13.23). There was a trend of increased OA in patients treated with labral debridement compared with repair despite lower baseline levels of OA; however, this difference was not statistically significant ( P = .09). Conclusion: Symptomatic glenohumeral arthritis is not seen routinely in patients with PSI. Although uncommon, an age of at least 30 years at the time of diagnosis and surgical intervention were identified as risk factors for developing symptomatic arthritis at long-term follow-up.