Johnson, Quinn J.

Abstract Osteonecrosis (ON) of the femoral head is a significant complication following chronic corticosteroid treatment, especially in adolescents and young adults. While studies in the adult population suggest that early core decompression augmented with bone marrow aspirate concentrate injection (BMAC) may reduce progression, evidence in younger patients remains scarce. This study evaluates survivorship free from femoral head collapse and conversion to total hip arthroplasty following core decompression with BMAC in patients under 25 years old with corticosteroid-induced femoral head ON. A retrospective review was conducted of 13 patients (24 hips) who underwent hip core decompression with BMAC for corticosteroid-induced femoral head ON. Variables analyzed included sex, body-mass index, age at index surgery, continued corticosteroid use, concomitant labral tear, Kerboul angle/grade, and Steinberg classification of osteonecrosis. These variables were assessed for their association with survivorship free of THA, conversion to THA, and patient reported outcomes (PROs) (modified Harris Hip Score [mHHS] and Hip Disability and Osteoarthritis Outcome Score [HOOS-12]). The mean follow-up duration was 10 ± 5.4 years. At the final follow-up, 11 hips (45.8%) required reoperation, including 10 total hip arthroplasties and one femoral head resurfacing. Progression to radiographic femoral head collapse occurred in 9 hips (37.5%), resulting in a collapse-free survival rate of 62.5%. The mean survival time until reoperation was 8.34 years. Continued corticosteroid use (p=0.033), Steinberg stage >2 (p=0.044), male sex (p=0.045), and high BMI (p=0.002) were associated with increased risk for reoperation. On multivariate analysis, ongoing corticosteroid use (OR=6.4, p=0.01) and Steinberg stage >2 (OR=4.3, p=0.04) were predictors of reoperation. Patient-reported outcomes at final follow-up included a mean mHHS of 77.5 and a HOOS-12 score of 80%. Core decompression with BMAC for corticosteroid induced femoral head ON in adolescents and young adults resulted in collapse-free survival rate of 62.5% at average 10 years follow up with an average reoperation-free survival of 8.34 years. Patients with Steinberg stage ≥2 and ongoing corticosteroid use had high rates of reoperation and conversion to total hip arthroplasty. Our data highlights that careful patient selection and shared decision-making remain critical in optimizing patient outcomes in this challenging patient population.

Background: Anterior cruciate ligament reconstruction (ACLR) is one of the most common orthopaedic procedures and one of the most well studied. Despite extensive research dedicated to ACLR, there is limited understanding of how chronic inflammatory systemic diseases (CIDs) such as rheumatoid arthritis and systemic lupus erythematosus affect outcomes. Purpose: To compare the outcomes of ACLR in cohorts of patients with and without CID. Study Design: Cohort study; Level of evidence, 3. Methods: A retrospective query of a regional data set was conducted for all patients who underwent ACLR from 1990 to 2021 for traumatic ACL rupture. All patients with CID were identified and propensity matched to non-CID controls. Baseline characteristics and clinical outcomes were identified through retrospective chart review, and patients were contacted for subjective outcomes. Results: A total of 30 patients with ACLR and a diagnosis of CID were identified. These patients were propensity matched to 120 non-CID controls. Baseline demographic and surgical characteristics demonstrated no statistical differences. Follow-up duration was similar between the CID and non-CID groups (mean, 14.6 vs 14.2 years; P = .868). The CID cohort had a higher arthrofibrosis rate (16.7% vs 4.3%; P = .031), higher osteoarthritis rate (33.3% vs 16.7%; P = .041), higher total knee arthroplasty (TKA) rate (16.7% vs 3.3%; P = .016), and earlier time to TKA (14.7 vs 23.5 years; P = .032). Knee range of motion, infection rate, retear rate, time to retear, and time to osteoarthritis were not statistically different between the cohorts. The CID cohort had higher visual analog scale pain scores (mean, 2.00 vs 1.20; P = .043) but slightly higher satisfaction (mean, 3.92 vs 3.39; P = .043). There were no differences in preinjury Tegner, postoperative Tegner, change in Tegner, or IKDC score. In a univariate Cox regression model, the CID cohort had a retear hazard ratio of 1.43 (95% CI, 0.46-4.51; P = .537). Kaplan-Meier survival revealed no significant differences in retear-free survival between the CID and non-CID cohorts at 25 years (85.7% vs 87.3%; P = .53). The CID cohort had a TKA hazard ratio of 3.94 (95% CI, 1.05-14.8; P = .042). Kaplan-Meier survival demonstrated significantly decreased TKA-free survival at 25 years in the CID cohort (64.9% vs 91.2%; P = .029). Conclusion: CID increases the incidence of arthrofibrosis, osteoarthritis, and TKA in those undergoing ACLR. Patients with CID also undergo TKA significantly sooner than non-CID counterparts. Notably, the majority of patient-reported outcome measures are no worse in patients who have a CID diagnosis. Thus, ACLR constructs themselves may not necessarily fare worse in patients with CID. Nonetheless, these patients need to be cautiously counseled on the clinical outlook after their ACLR.

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: Establishing imaging registries for large patient cohorts is challenging because manual labeling is tedious and relying solely on DICOM (digital imaging and communications in medicine) metadata can result in errors. We endeavored to establish an automated hip and pelvic radiography registry of total hip arthroplasty (THA) patients by utilizing deep-learning pipelines. The aims of the study were (1) to utilize these automated pipelines to identify all pelvic and hip radiographs with appropriate annotation of laterality and presence or absence of implants, and (2) to automatically measure acetabular component inclination and version for THA images. Methods: We retrospectively retrieved 846,988 hip and pelvic radiography DICOM files from 20,378 patients who underwent primary or revision THA performed at our institution from 2000 to 2020. Metadata for the files were screened followed by extraction of imaging data. Two deep-learning algorithms (an EfficientNetB3 classifier and a YOLOv5 object detector) were developed to automatically determine the radiographic appearance of all files. Additional deep-learning algorithms were utilized to automatically measure the acetabular angles on anteroposterior pelvic and lateral hip radiographs. Algorithm performance was compared with that of human annotators on a random test sample of 5,000 radiographs. Results: Deep-learning algorithms enabled appropriate exclusion of 209,332 DICOM files (24.7%) as misclassified non-hip/pelvic radiographs or having corrupted pixel data. The final registry was automatically curated and annotated in <8 hours and included 168,551 anteroposterior pelvic, 176,890 anteroposterior hip, 174,637 lateral hip, and 117,578 oblique hip radiographs. The algorithms achieved 99.9% accuracy, 99.6% precision, 99.5% recall, and a 99.6% F1 score in determining the radiograph appearance. Conclusions: We developed a highly accurate series of deep-learning algorithms to rapidly curate and annotate THA patient radiographs. This efficient pipeline can be utilized by other institutions or registries to construct radiography databases for patient care, longitudinal surveillance, and large-scale research. The stepwise approach for establishing a radiography registry can further be utilized as a workflow guide for other anatomic areas. Level of Evidence: Diagnostic Level IV . See Instructions for Authors for a complete description of levels of evidence.