Snapping Complication After Partial-Thickness Quadriceps Tendon Autograft Anterior Cruciate Ligament Reconstruction: A Report of 2 Cases.

CASE: A 14-year-old adolescent girl and 18-year-old man underwent right anterior cruciate ligament (ACL) reconstruction using quadriceps tendon (QT) autografts via partial-thickness harvest. While both patients initially recovered well, later they experienced a painful snapping in their knee localized to the lateral QT, just proximal to the patella. Surgical completion of the previous partial-thickness defect with imbrication provided resolution of symptoms at 4 and 9 months postoperatively, respectively. CONCLUSION: We present a snapping QT as a rare complication of partial-thickness QT harvest for ACL reconstruction. Surgical completion of the partial-thickness defect with imbrication resolved the snapping sensation in these two cases.

Distinct risk profiles for short-term surgical complications and venous thromboembolism exist among extensor mechanism repair procedures.

PURPOSE: Extensor mechanism injuries, which comprise patella fractures, patella tendon tears and quadriceps tendon tears, are severely debilitating injuries and a common cause of traumatic knee pathology that requires surgical intervention. Risk factors for short-term surgical complications and venous thromboembolism (VTE) in this population have not been well characterised. The aim of this study was to identify perioperative risk factors associated with these short-term complications. METHODS: The National Surgical Quality Improvement Program database was used to identify patients who underwent an isolated, primary extensor mechanism repair from 2015 to 2020. Patients were stratified by injury type. Demographic data were collected and compared. A multivariate logistic regression was used to control for demographic and comorbid factors while assessing risk factors for developing short-term complications. RESULTS: A total of 8355 patients were identified for inclusion in this study. Overall, 3% of patients sustained short-term surgical complications and 1% were diagnosed with VTE within 30 days of surgery. Patella fracture fixation had a nearly twofold higher risk for surgical complications compared to quadriceps tendon repair (p = 0.004). Patella tendon repair had a twofold higher risk for VTE (p = 0.045), specifically deep vein thrombosis (p = 0.020), compared to patella fracture fixation. Increasing age, smoking and American Society of Anesthesiologists Classifications 3 and 4 were also found to be risk factors for surgical complications (p = 0.012, p = 0.004, p = 0.011 and p = 0.032, respectively). CONCLUSION: This study used a nationally representative, widely validated, peer-reviewed database to provide valuable insights into risk factors for short-term postoperative complications associated with extensor mechanism repair procedures, revealing notable differences in risk profiles among distinct surgical procedures. The results of this study will inform surgeons and patients in enhancing risk assessment, guiding procedure-specific decision-making, optimising preoperative care, improving postoperative monitoring and contributing to future research of extensor mechanism injuries. LEVEL OF EVIDENCE: Level III.

Most Publications Regarding Platelet-Rich Plasma Use in the Knee Are From Asia, Investigate Injection for Osteoarthritis, and Show Outcome Improvement: A Scoping Review.

PURPOSE: To evaluate and synthesize the available literature related to platelet-rich plasma (PRP) treatment of knee pathologies and to provide recommendations to inform future research in the field. METHODS: PubMed, CINAHL, and Scopus databases were queried on October 6, 2023. All identified citations were collated and uploaded into Covidence for screening and data extraction. Studies were included if they were human studies published in English with adult cohorts that received PRP as a procedural injection or surgical augmentation for knee pathologies with patient-reported outcome measures (PROMs) and level of evidence Levels I-IV. RESULTS: Our search yielded 2,615 studies, of which 155 studies from 2006 to 2023 met the inclusion criteria. Median follow-up was 9 months (±11.2 months). Most studies (75.5%) characterized the leukocyte content of PRP, although most studies (86%) did not use a comprehensive classification scheme. In addition, most studies were from Asia (50%) and Europe (32%) and were from a single center (96%). In terms of treatment, 74% of studies examined PRP as a procedural injection, whereas 26% examined PRP as an augmentation. Most studies (68%) examined treatment of knee osteoarthritis. Many studies (83%) documented significant improvements in PROMs, including 93% of Level III/IV evidence studies and 72% of Level I/II evidence studies, although most studies (70%) failed to include minimal clinically important difference values. The visual analog scale was the most-used PROM (58% of studies), whereas the Short Form Health Survey 36-item was the least-used PROM (5% of studies). CONCLUSIONS: Most published investigations of knee PRP are performed in Asia, investigate procedural injection for osteoarthritis, and show significant outcome improvements. In addition, this review highlights the need for better classification of PRP formulations. LEVEL OF EVIDENCE: Level IV, scoping Review of level I-IV studies.

Automated diagnosis and prognosis of COVID-19 pneumonia from initial ER chest X-rays using deep learning.

BACKGROUND: Airspace disease as seen on chest X-rays is an important point in triage for patients initially presenting to the emergency department with suspected COVID-19 infection. The purpose of this study is to evaluate a previously trained interpretable deep learning algorithm for the diagnosis and prognosis of COVID-19 pneumonia from chest X-rays obtained in the ED. METHODS: This retrospective study included 2456 (50% RT-PCR positive for COVID-19) adult patients who received both a chest X-ray and SARS-CoV-2 RT-PCR test from January 2020 to March of 2021 in the emergency department at a single U.S. INSTITUTION: A total of 2000 patients were included as an additional training cohort and 456 patients in the randomized internal holdout testing cohort for a previously trained Siemens AI-Radiology Companion deep learning convolutional neural network algorithm. Three cardiothoracic fellowship-trained radiologists systematically evaluated each chest X-ray and generated an airspace disease area-based severity score which was compared against the same score produced by artificial intelligence. The interobserver agreement, diagnostic accuracy, and predictive capability for inpatient outcomes were assessed. Principal statistical tests used in this study include both univariate and multivariate logistic regression. RESULTS: Overall ICC was 0.820 (95% CI 0.790-0.840). The diagnostic AUC for SARS-CoV-2 RT-PCR positivity was 0.890 (95% CI 0.861-0.920) for the neural network and 0.936 (95% CI 0.918-0.960) for radiologists. Airspace opacities score by AI alone predicted ICU admission (AUC = 0.870) and mortality (0.829) in all patients. Addition of age and BMI into a multivariate log model improved mortality prediction (AUC = 0.906). CONCLUSION: The deep learning algorithm provides an accurate and interpretable assessment of the disease burden in COVID-19 pneumonia on chest radiographs. The reported severity scores correlate with expert assessment and accurately predicts important clinical outcomes. The algorithm contributes additional prognostic information not currently incorporated into patient management.

Whole-book recognition.

Whole-book recognition is a document image analysis strategy that operates on the complete set of a book's page images using automatic adaptation to improve accuracy. We describe an algorithm which expects to be initialized with approximate iconic and linguistic models--derived from (generally errorful) OCR results and (generally imperfect) dictionaries--and then, guided entirely by evidence internal to the test set, corrects the models which, in turn, yields higher recognition accuracy. The iconic model describes image formation and determines the behavior of a character-image classifier, and the linguistic model describes word-occurrence probabilities. Our algorithm detects "disagreements" between these two models by measuring cross entropy between 1) the posterior probability distribution of character classes (the recognition results resulting from image classification alone) and 2) the posterior probability distribution of word classes (the recognition results from image classification combined with linguistic constraints). We show how disagreements can identify candidates for model corrections at both the character and word levels. Some model corrections will reduce the error rate over the whole book, and these can be identified by comparing model disagreements, summed across the whole book, before and after the correction is applied. Experiments on passages up to 180 pages long show that when a candidate model adaptation reduces whole-book disagreement, it is also likely to correct recognition errors. Also, the longer the passage operated on by the algorithm, the more reliable this adaptation policy becomes, and the lower the error rate achieved. The best results occur when both the iconic and linguistic models mutually correct one another. We have observed recognition error rates driven down by nearly an order of magnitude fully automatically without supervision (or indeed without any user intervention or interaction). Improvement is nearly monotonic, and asymptotic accuracy is stable, even over long runs. If implemented naively, the algorithm runs in time quadratic in the length of the book, but random subsampling and caching techniques speed it up by two orders of magnitude with negligible loss of accuracy. Whole-book recognition has potential applications in digital libraries as a safe unsupervised anytime algorithm.