Meniscal allograft transplantation (MAT) combined with revision ACL reconstruction and lateral extra-articular tenodesis results in significantly improved outcomes, but patient-reported knee function is inferior compared to an isolated MAT.

PURPOSE: Meniscal allograft transplantation (MAT) is a valuable option for postmeniscectomy syndrome in young and active patients, which can successfully improve symptoms, function and quality of life. This study aimed to report outcomes and complications in patients treated with isolated MAT or MAT combined with revision anterior cruciate ligament reconstruction (ACLR) and lateral extra-articular tenodesis (LET). METHODS: In total, 18 patients who were treated with MAT using a soft tissue fixation technique were retrospectively analysed. Patients were allocated into two groups, the isolated MAT group and the MAT plus revision ACLR + LET group (MAT+ group). Both groups were assessed using the following Patient-Reported Outcome Measures (PROMs): Knee Injury and Osteoarthritis Outcome Score (KOOS), International Knee Documentation Committee (IKDC), Tegner Activity Scale, Lysholm score, EuroQol 5-Dimension 5-level (EQ-5D-5L) questionnaire and a patient acceptable symptom state (PASS) statement. RESULTS: The mean follow-up was 3.2 years and the mean age of patients was 29 (±7.6) years. Ten patients were treated with isolated MAT and eight patients were treated with MAT combined with revision ACLR and LET. No complications and no reoperations were reported. In both groups, all PROMs significantly improved postoperatively (p < 0.05), except for the Tegner score in the MAT+ group, which did not reach statistical significance (not significant [n.s.]). No significant difference was found postoperatively in PROMs between the MAT and the MAT+ group; however, differences in the KOOS Symptoms, Sports and Quality-of-life subscales reached the minimal clinically important threshold. Mean values were: IKDC 61.2 (±13.9) versus 64.7 (±23.2), KOOS Total 72.8 (±14.9) versus 68.0 (±16.7), Lysholm score 83.2 (±11) versus 84.2 (±8.7) and EQ-5D-5L 71.8 (±14.9) versus 72.2 (±22.4). Median values for the Tegner Activity Scale were 4 versus 3. PASS statement was negative in 37.5% of the MAT+ group in comparison to 20% of the MAT group (n.s.). CONCLUSION: In terms of patient-reported outcomes, MAT is an effective and safe procedure, even when combined with revision ACLR + LET. Compared to the preoperative assessment, a significant improvement of the postoperative PROMs can be expected either when MAT is performed isolated or in combination with ACLR + LET. However, clinically important differences between the two groups can be noted in favour of the isolated MAT procedure, especially regarding symptoms and sporting activity. LEVEL OF EVIDENCE: Level III.

An attention-based deep learning method for right ventricular quantification using 2D echocardiography: Feasibility and accuracy.

AIM: To test the feasibility and accuracy of a new attention-based deep learning (DL) method for right ventricular (RV) quantification using 2D echocardiography (2DE) with cardiac magnetic resonance imaging (CMR) as reference. METHODS AND RESULTS: We retrospectively analyzed images from 50 adult patients (median age 51, interquartile range 32-62 42% women) who had undergone CMR within 1 month of 2DE. RV planimetry of the myocardial border was performed in end-diastole (ED) and end-systole (ES) for eight standardized 2DE RV views with calculation of areas. The DL model comprised a Feature Tokenizer module and a stack of Transformer layers. Age, gender and calculated areas were used as inputs, and the output was RV volume in ED/ES. The dataset was randomly split into training, validation and testing subsets (35, 5 and 10 patients respectively). Mean RVEDV, RVESV and RV ejection fraction (EF) were 163 ± 70 mL, 82 ± 42 mL and 51% ± 8% respectively without differences among the subsets. The proposed method achieved good prediction of RV volumes (R2  = .953, absolute percentage error [APE] = 9.75% ± 6.23%) and RVEF (APE = 7.24% ± 4.55%). Per CMR, there was one patient with RV dilatation and three with RV dysfunction in the testing dataset. The DL model detected RV dilatation in 1/1 case and RV dysfunction in 4/3 cases. CONCLUSIONS: An attention-based DL method for 2DE RV quantification showed feasibility and promising accuracy. The method requires validation in larger cohorts with wider range of RV size and function. Further research will focus on the reduction of the number of required 2DE to make the method clinically applicable.

Satisfactory patient-reported outcomes in patients treated with impaction bone grafting and autologous matrix-induced chondrogenesis for osteochondral knee defects.

PURPOSE: Osteochondral knee defects usually affect young, active patients and may alter knee biomechanics and progressively lead to joint degeneration. Various treatment options exist with autologous, impaction bone grafting in combination with autologous matrix-induced chondrogenesis (BG-AMIC) being a less-expensive, one-step, promising option. The purpose of this study is to evaluate the clinical and radiological mid-term outcomes of large osteochondral lesions treated with BG-AMIC, identify a possible correlation between the two and report postoperative complications and reoperation rate. METHODS: A retrospective analysis of 25 patients treated with the BG-AMIC technique for knee osteochondral lesions was performed. Patients were assessed using the following PROMs: the IKDC, the KOOS and the Lysholm score, the Tegner activity scale and a patient acceptable symptom state (PASS). The EQ-5D-5L score was used to assess health-related quality of life. Radiological assessment was performed using the MOCART 2.0 score on a 3 T MRI. RESULTS: At a mean of 3.8 (± 0.8)-year follow-up, all functional scores increased significantly (p < 0.005) when compared to the preoperative baseline. IKDC increased from 44.5 (± 15.9) to 81.4 (± 14.7), KOOS from 41.5 (± 16.1) to 91.6 (± 11.6) and Lysholm from 54.4 (± 23) to 95.2 (± 5.5) (p < 0.005). The EQ-5D-5L score also revealed a significant improvement [59.9 (± 25) to 93.4 (± 10.2), p < 0.005]. Mean Tegner score reached pre-injury levels. The PASS was positive in 100% of patients. The minimum clinically important difference was reached in all PROMs except for the KOOS Sports subscale. There were no re-operations. Morphological evaluation of the repair tissue using the MOCART 2.0 score revealed a mean total score of 52.8 (± 30.5). A statistically significant, positive correlation was found between the MOCART 2.0 score and the IKDC score, the KOOS ADL subscale and the EQ-5D-5L (p < 0.05). CONCLUSION: BG-AMIC is a safe and reliable option for treating deep, knee osteochondral lesions, providing a statistically significant and clinically important improvement in patient-reported outcomes. No complications were noticed, and no re-operations were performed after the procedure. A moderate positive correlation between the MOCART 2.0 score and the IKDC, KOOS ADL and EQ-5D-5L was noticed. However, this correlation is not necessarily clinically relevant, and excellent clinical results can be expected even in patients with low MOCART scores. LEVEL OF EVIDENCE: III.

Long-term Quality of Life in Patients After ACL Reconstruction With Concomitant Meniscal Injury Treatment: Patient-Reported Outcomes at Minimum 10-Year Follow-up.

Background: Long-term studies of patients after anterior cruciate ligament (ACL) reconstruction with or without concomitant meniscal tear treatment are limited. Purposes: To (1) report postoperative outcomes after anatomic ACL reconstruction with a hamstring autograft, (2) investigate how concomitant treatment of meniscal injury could affect these outcomes, and (3) evaluate the association between quality of life and activity levels at a minimum 10-year follow-up. Study Design: Cohort study; Level of evidence, 3. Methods: Patients treated with a unilateral, anatomic ACL reconstruction between 2005 and 2011 were investigated. The following patient-reported outcome measures (PROMs) were reported for the overall sample as well as a subsample of patients with meniscal injury: International Knee Documentation Committee Subjective Knee Form (IKDC-SKF), Knee injury and Osteoarthritis Outcome Score (KOOS), Lysholm knee score, Tegner activity scale, 5-level EQ-5D (EQ-5D-5L), and patient satisfaction. Sex, age, body mass index (BMI), and meniscal injury treatment (meniscectomy vs meniscal repair) were examined as patient-specific risk factors regarding long-term activity and quality of life. Results: Overall, 106 patients, 90 men (85%) and 16 women (15%), were enrolled in the study, with a mean follow-up of 13.2 years. The ACL retear rate was 2.8%. The mean scores were 80.6 ± 16.7 (IKDC-SKF), 87.4 ± 15.0 (KOOS), 90.5 ± 11.5 (Lysholm), 5.6 ± 1.9 (Tegner), and 91.8 ± 14.5 (EQ-5D-5L). The majority (90.6%) of patients considered their knee state satisfactory during follow-up. When compared with patients who underwent meniscal repair, patients who underwent meniscectomy had statistically significantly lower scores on all PROMs except for the Tegner and EQ-5D-5L (P < .05 for all). The mean difference between the 2 groups was ≥7 points on all PROM scores. Patient sex, age, and BMI did not affect PROM scores. There was a statistically significant, strong positive correlation between quality of life and activity. Conclusion: Patients had few or no symptoms and considered their knee state satisfactory 13.2 years after anatomic ACL reconstruction. Patients with concomitant meniscal tears having undergone meniscal repair had improved PROMs compared with those treated with meniscectomy. Finally, participation in activities of daily living and sports was interrelated with quality of life and was not affected by patient age, sex, or BMI.

Epidemiological Profile among Greek CrossFit Practitioners.

CrossFit (CF) is a popular and rapidly expanding training program in Greece and worldwide. However, there is a lack of scientific evidence on the risk of musculoskeletal injuries related to CF in the Greek population. A self-administered survey of 1224 Greek CF practitioners aged 18 to 59 was conducted and analyzed using the Statistical Package for Social Sciences (SPSS) software. The highest percentage of the participants (34%) practiced 5 days per week for 60 min (42.2%) and had 2 days per week of rest (41.7%). A total of 273 individuals (23%) participated in CF competitions and 948 (77%) did not. The results showed that the most common injuries were muscle injuries (51.3%), followed by tendinopathies (49.6%) and joint injuries (26.6%). The shoulders (56.6%; n = 303), knees (31.8%; n = 170), and lumbar spine (33.1%; n = 177) were the most commonly injured locations. The logistic regression model showed that participation in competitions (p = 0.001), rest per week (p = 0.01), duration of training per session (p = 0.001), and frequency of training per week (p = 0.03) were statistically significant factors for injury. Training level was not a statistically significant factor for injury (p = 0.43). As CF continues to gain popularity on a global scale and the number of athletes gradually increases, it is important to monitor the safety of practitioners. Clinicians should consider participation in competitions, rest, training duration, and frequency in order to make CF safer.

Machine learning-based prediction of mortality after heart transplantation in adults with congenital heart disease: A UNOS database analysis.

BACKGROUND: Machine learning (ML) is increasingly being applied in Cardiology to predict outcomes and assist in clinical decision-making. We sought to develop and validate an ML model for the prediction of mortality after heart transplantation (HT) in adults with congenital heart disease (ACHD). METHODS: The United Network for Organ Sharing (UNOS) database was queried from 2000 to 2020 for ACHD patients who underwent isolated HT. The study cohort was randomly split into derivation (70%) and validation (30%) datasets that were used to train and test a CatBoost ML model. Feature selection was performed using SHapley Additive exPlanations (SHAP). Recipient, donor, procedural, and post-transplant characteristics were tested for their ability to predict mortality. We additionally used SHAP for explainability analysis, as well as individualized mortality risk assessment. RESULTS: The study cohort included 1033 recipients (median age 34 years, 61% male). At 1 year after HT, there were 205 deaths (19.9%). Out of a total of 49 variables, 10 were selected as highly predictive of 1-year mortality and were used to train the ML model. Area under the curve (AUC) and predictive accuracy for the 1-year ML model were .80 and 75.2%, respectively, and .69 and 74.2% for the 3-year model, respectively. Based on SHAP analysis, hemodialysis of the recipient post-HT had overall the strongest relative impact on 1-year mortality after HΤ, followed by recipient-estimated glomerular filtration rate, age and ischemic time. CONCLUSIONS: ML models showed satisfactory predictive accuracy of mortality after HT in ACHD and allowed for individualized mortality risk assessment.

Knee Injury Detection Using Deep Learning on MRI Studies: A Systematic Review.

The improved treatment of knee injuries critically relies on having an accurate and cost-effective detection. In recent years, deep-learning-based approaches have monopolized knee injury detection in MRI studies. The aim of this paper is to present the findings of a systematic literature review of knee (anterior cruciate ligament, meniscus, and cartilage) injury detection papers using deep learning. The systematic review was carried out following the PRISMA guidelines on several databases, including PubMed, Cochrane Library, EMBASE, and Google Scholar. Appropriate metrics were chosen to interpret the results. The prediction accuracy of the deep-learning models for the identification of knee injuries ranged from 72.5-100%. Deep learning has the potential to act at par with human-level performance in decision-making tasks related to the MRI-based diagnosis of knee injuries. The limitations of the present deep-learning approaches include data imbalance, model generalizability across different centers, verification bias, lack of related classification studies with more than two classes, and ground-truth subjectivity. There are several possible avenues of further exploration of deep learning for improving MRI-based knee injury diagnosis. Explainability and lightweightness of the deployed deep-learning systems are expected to become crucial enablers for their widespread use in clinical practice.

Migraine in transient global amnesia: a meta-analysis of observational studies.

BACKGROUND: PURPOSE: Although many studies have investigated the relationship between transient global amnesia (TGA) and migraine, to date, no meta-analysis has confirmed the existence and size of their association. METHODOLOGY: Literature search involved MEDLINE, EMBASE, CENTRAL and PsycINFO. Observational controlled studies including TGA patients (Caplan, Hodges and Warlow) were retrieved. Quality evaluation was based on the Newcastle-Ottawa scale. The prevalence of migraine was compared in TGA patients vs. healthy controls (HC), as well as in TGA against TIA individuals. Data from case-control, cross-sectional and cohort studies were pooled separately. RESULTS: Literature search yielded 1178 articles, 12 of which were included in the present meta-analysis. Results from case-control (ten), cohort (one) and cross-sectional (one) studies were compatible with an association between TGA and migraine. The nationwide inpatient cross-sectional study was of lesser value due to its inpatient orientation. The high-quality, population-based, retrospective cohort (158,301 participants per group) determined a higher relative-risk (RR) of TGA for migraine vs. non-migraine individuals [RR = 2.48, 95%confidence-interval (95% CI) = (1.32, 4.87)]. Sensitivity testing based on stricter diagnostic criteria strengthened the estimated association [RR = 3.84, 95% CI = (1.57, 9.38)]. Additionally, pooled data from eight case-control studies (700 TGA, 746 HC) yielded similar results [Odds-Ratio, OR = 2.51, 95% CI = (1.85, 3.41)], with the association mainly driven by the three high-quality studies, rather than the five articles of moderate quality. Finally, pooled findings from four case-control studies of moderate-quality revealed a higher prevalence of migraine among TGA compared to TIA patients [OR = 1.82, 95% CI = (1.22, 2.73)]. CONCLUSIONS: A significant association between TGA and migraine was established. The underlying connecting mechanism remains undetermined, yet.

Identifying Gait-Related Functional Outcomes in Post-Knee Surgery Patients Using Machine Learning: A Systematic Review.

Modern lifestyles require new tools for determining a person's ability to return to daily activities after knee surgery. These quantitative instruments must feature high discrimination, be non-invasive, and be inexpensive. Machine learning is a revolutionary approach that has the potential to satisfy the aforementioned requirements and bridge the knowledge gap. The scope of this study is to summarize the results of a systematic literature review on the identification of gait-related changes and the determination of the functional recovery status of patients after knee surgery using advanced machine learning algorithms. The current systematic review was conducted using multiple databases in accordance with the PRISMA guidelines, including Scopus, PubMed, and Semantic Scholar. Six out of the 405 articles met our inclusion criteria and were directly related to the quantification of the recovery status using machine learning and gait data. The results were interpreted using appropriate metrics. The results demonstrated a recent increase in the use of sophisticated machine learning techniques that can provide robust decision-making support during personalized post-treatment interventions for knee-surgery patients.