Jurgensmeier, Kevin

Case: This case highlights the utility and outcomes of an Achilles tendon bone-block allograft in reconstructing the patellar tendon of a 44-year-old man in the setting of a high-riding, irreducible patella after a chronic patellar tendon rupture of 14 years. Conclusion: In cases of a neglected patellar tendon rupture with an irreducible high-riding patella, an Achilles tendon bone-block allograft can provide the length and tissue necessary to reconstruct the extensor mechanism. Although reconstruction may not be anatomic, patients can still experience great subjective and objective functional improvement with restoration of the extensor mechanism even after delayed reconstruction.

AbstractPurposeIdentifying 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.MethodsA 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.ResultsA 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.ConclusionsAll‐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 evidenceIII.

Background: Our understanding of the risk factors for and effectiveness of prophylactic measures against shoulder periprosthetic joint infections (PJIs) continues to evolve. This study’s objective was to study patient characteristics, procedural characteristics, and various infection prophylactic measures and their effects on the risk of shoulder PJI after primary arthroplasty. Methods: Nine hundred and ninety-eight patients in a longitudinally maintained, single-institution shoulder arthroplasty database who had at least 2 years of clinical follow-up were retrospectively reviewed. Patient and procedural characteristics were collected. Perioperative variables, including the use of intraoperative antibiotics, topical antibiotics, antibiotic-containing irrigation solution, and a postoperative drain, were collected. Patients who developed shoulder PJI were compared with those without shoulder PJI to identify any association with patient or procedural characteristics. Results: Of the 998 patients, 20 (2.0%) met the criteria for shoulder PJI. Cutibacterium was identified as the causative organism in 19 (95%) of 20 culture-positive reoperations. On univariate analysis, patients in the PJI group were more likely to be younger (p < 0.001), to be male (p = 0.014), to have commercial insurance (p = 0.003), to use alcohol (p = 0.048), and to have had a ream-and-run or hemiarthroplasty procedure (p = 0.005). On multivariable analysis, older age was independently associated with a lower risk of PJI (odds ratio [OR] per year = 0.95; 95% confidence interval [CI], 0.91 to 0.99; p = 0.014). Reverse total shoulder arthroplasty (OR, 10.32; 95% CI, 0.92 to 116.33; p = 0.059) and hemiarthroplasty (OR, 8.59; 95% CI, 0.86.30 to 85.50; p = 0.067) trended toward a higher risk of PJI. Conclusions: Younger patients and patients undergoing procedures other than anatomic total shoulder arthroplasty are at higher risk for shoulder PJI. The majority of culture-positive reoperations were a result of Cutibacterium species. Level of Evidence: Therapeutic Level III . See Instructions for Authors for a complete description of levels of evidence.

Background: Ream-and-run arthroplasty can improve pain and function in patients with glenohumeral arthritis while avoiding the complications and activity restrictions associated with a prosthetic glenoid component. However, stiffness is a known complication after ream-and-run arthroplasty and can lead to repeat procedures such as a manipulation under anesthesia (MUA) or open surgical revision. The objective of this study was to determine risk factors associated with repeat procedures indicated for postoperative stiffness after ream-and-run arthroplasty. Methods: We conducted a retrospective review of our shoulder arthroplasty database to identify patients who underwent ream-and-run arthroplasty and determined which patients underwent subsequent repeat procedures (MUA and/or open revision) indicated for postoperative stiffness. The minimum follow-up was 2 years. We collected baseline demographic information and preoperative and 2-year patient-reported outcome scores and analyzed preoperative radiographs. Univariate and multivariate analyses determined the factors significantly associated with repeat procedures to treat postoperative stiffness. Results: There were 340 patients who underwent ream-and-run arthroplasty. The mean Simple Shoulder Test (SST) scores for all patients improved from 5.0 ± 2.4 preoperatively to 10.2 ± 2.6 postoperatively (p < 0.001). Twenty-six patients (7.6%) underwent open revision for stiffness. An additional 35 patients (10.3%) underwent MUA. Univariate analysis found younger age (p = 0.001), female sex (p = 0.034), lower American Society of Anesthesiologists (ASA) class (p = 0.045), posterior decentering on preoperative radiographs (p = 0.010), and less passive forward elevation at the time of discharge after ream-and-run arthroplasty (p < 0.001) to be significant risk factors for repeat procedures. Multivariate analysis found younger age (p = 0.040), ASA class 1 compared with class 3 (p = 0.020), and less passive forward elevation at discharge (p < 0.001) to be independent risk factors for repeat procedures. Of the patients who underwent open revision for stiffness, 69.2% had multiple positive cultures for Cutibacterium. Conclusions: Younger age, ASA class 1 compared with class 3, and less passive forward elevation in the immediate postoperative period were independent risk factors for repeat procedures to treat postoperative stiffness after ream-and-run arthroplasty. Level of Evidence: Prognostic Level III . See Instructions for Authors for a complete description of levels of evidence.

Background: Distal biceps tendon ruptures commonly present in the middle-aged population resulting in deficits of strength and endurance. Operative management has shown improved functional outcome; however, there is not a clear consensus on an ideal surgical method. Indications: Distal biceps tendon repair is indicated for patients with clinical evidence and supporting magnetic resonance imaging confirmation of complete or partial rupture of the biceps tendon. Ideally, surgery is performed within 1 to 2 weeks of the injury to minimize the amount of scar tissue that is formed and the severity of the tendon retraction. Technique Description: A single incision is created on the volar surface of the forearm approximately 15 mm distal to the main flexor crease. After dissection to and retrieval of the biceps tendon, a #2 FiberTag stitch is placed distally and secured in a standard looped, locking fashion. An anchor is then placed in the most proximal aspect of the radial tuberosity and preliminarily placed around the tendon. An all-suture intramedullary cortical button is then placed in the distal aspect of the radial tuberosity. The FiberTag sutures are then shuttled through the button and tightened to anatomically reduce the distal biceps. The sutures on the proximal anchor are then used as supplemental fixation. Results: An anterior, single incision technique provides exposure necessary for dual anchor fixation and anatomic restoration of the distal biceps tendon. Utilizing this approach, patients are found to have improved flexion and pronation at 1 year, lower rates of heterotopic ossification, and lower rates of reoperation. Conclusion: Distal biceps tendon repair through an anterior, single incision provides excellent exposure for surgical repair. Utilizing a dual anchor technique, the distal button allows for anatomic fixation while the proximal suture anchor provides secondary fixation and increases the bone-tendon interface. Patient Consent Disclosure Statement: The author(s) attests that consent has been obtained from any patient(s) appearing in this publication. If the individual may be identifiable, the author(s) has included a statement of release or other written form of approval from the patient(s) with this submission for publication.

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.

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.