Risk scores for prediction of paroxysmal atrial fibrillation after acute ischemic stroke or transient ischemic attack: A systematic review and meta-analysis.

Introduction: Detection of paroxysmal atrial fibrillation (PAF) is crucial for secondary prevention in patients with recent strokes of unknown etiology. This systematic review and meta-analysis assess the predictive power of available risk scores for detecting new PAF after acute ischemic stroke (AIS). Methods: PubMed, Embase, Scopus, and Web of Science databases were searched until September 2023 to identify relevant studies. A bivariate random effects meta-analysis model pooled data on sensitivity, specificity, and area under the curve (AUC) for each score. The QUADAS-2 tool was used for the quality assessment. Results: Eventually, 21 studies with 18 original risk scores were identified. Age, left atrial enlargement, and NIHSS score were the most common predictive factors, respectively. Seven risk scores were meta-analyzed, with iPAB showing the highest pooled sensitivity and AUC (sensitivity: 89.4%, specificity: 74.2%, AUC: 0.83), and HAVOC having the highest pooled specificity (sensitivity: 46.3%, specificity: 82.0%, AUC: 0.82). Altogether, seven risk scores displayed good discriminatory power (AUC ≥0.80) with four of them (HAVOC, iPAB, Fujii, and MVP scores) being externally validated. Conclusion: Available risk scores demonstrate moderate to good predictive accuracy and can help identify patients who would benefit from extended cardiac monitoring after AIS. External validation is essential before widespread clinical adoption.

Methodological insights into ChatGPT's screening performance in systematic reviews.

BACKGROUND: The screening process for systematic reviews and meta-analyses in medical research is a labor-intensive and time-consuming task. While machine learning and deep learning have been applied to facilitate this process, these methods often require training data and user annotation. This study aims to assess the efficacy of ChatGPT, a large language model based on the Generative Pretrained Transformers (GPT) architecture, in automating the screening process for systematic reviews in radiology without the need for training data. METHODS: A prospective simulation study was conducted between May 2nd and 24th, 2023, comparing ChatGPT's performance in screening abstracts against that of general physicians (GPs). A total of 1198 abstracts across three subfields of radiology were evaluated. Metrics such as sensitivity, specificity, positive and negative predictive values (PPV and NPV), workload saving, and others were employed. Statistical analyses included the Kappa coefficient for inter-rater agreement, ROC curve plotting, AUC calculation, and bootstrapping for p-values and confidence intervals. RESULTS: ChatGPT completed the screening process within an hour, while GPs took an average of 7-10 days. The AI model achieved a sensitivity of 95% and an NPV of 99%, slightly outperforming the GPs' sensitive consensus (i.e., including records if at least one person includes them). It also exhibited remarkably low false negative counts and high workload savings, ranging from 40 to 83%. However, ChatGPT had lower specificity and PPV compared to human raters. The average Kappa agreement between ChatGPT and other raters was 0.27. CONCLUSIONS: ChatGPT shows promise in automating the article screening phase of systematic reviews, achieving high sensitivity and workload savings. While not entirely replacing human expertise, it could serve as an efficient first-line screening tool, particularly in reducing the burden on human resources. Further studies are needed to fine-tune its capabilities and validate its utility across different medical subfields.

Efficacy and Safety of Preoperative Embolization in Glomus Jugulare Tumors: A Systematic Review and Meta-analysis of Clinical Outcomes and Complications.

PURPOSE: This study aims to provide a comprehensive review of the clinical benefits, complications, and safety profile associated with preoperative embolization in Glomus jugulare tumors (GJTs). MATERIALS AND METHODS: A comprehensive search in PubMed, Embase, and Web of Science was conducted for English articles published up to March 2023, focusing on GJTs and preoperative embolization. Included studies involved patients over 18 with GJTs. We excluded studies that explored embolization methods other than the standard endovascular approach, as well as studies involving paragangliomas that did not provide specific data related to GJTs. Key variables such as hemorrhage volume and surgical time, as well as clinical outcomes, were analyzed. Data were analyzed using a random-effects model meta-analysis, assessing heterogeneity with the I2 statistic. RESULTS: This review encompasses 19 studies with a total of 328 patients. The studies incorporated into our meta-analysis display considerable differences and inconsistencies in their data. The findings of the meta-analysis show a mean hemorrhage volume of 636 ml (95% confidence interval (CI) 473-799) following preoperative embolization, and a mean surgical duration of 487 min (95% CI 350-624). The study also notes potential complications: facial nerve deficits occurred in 20% of cases (95% CI 11-32%), and vagal nerve deficits in 22% (95% CI 13-31%). CONCLUSION: This study suggests that preoperative embolization could decrease surgery duration and blood loss, but emphasizes the importance of evaluating risks like nerve damage. However, the generalizability of these findings is restricted due to the diversity of available data.

Concurrent adrenal and extra-adrenal myelolipoma: A case report.

INTRODUCTION: Myelolipoma, a benign tumor characterized by mature fat cells and hematopoietic cells, is predominantly found in the adrenal glands, accounting for 6-16 % of all adrenal tumors. These tumors are often asymptomatic and discovered incidentally during imaging. We present a rare case of concurrent adrenal and extra-adrenal myelolipomas, contributing to the limited research in this area. CASE PRESENTATION: A 65-year-old female with a history of Steven-Johnson syndrome presented with epigastric pain, initially diagnosed with emphysematous cholecystitis. Imaging revealed unexpected lesions near the left kidney. During surgery for presumed cholecystitis, significant hemorrhaging occurred following an attempted biopsy of the left adrenal lesion. This complication necessitated a complete adrenalectomy. Pathological examination confirmed the presence of myelolipomas in the left adrenal gland, para-aortic, and left para-iliac regions. DISCUSSION: The simultaneous occurrence of adrenal and extra-adrenal myelolipomas is exceptionally rare, posing diagnostic and management challenges. This case highlights the complexity of managing patients with multiple comorbidities and the critical importance of differentiating myelolipomas from other fat-containing retroperitoneal masses. The incidental discovery of these tumors and their potential for significant intraoperative complications, as seen in our case, underscores the need for careful surgical planning and thorough preoperative assessment. CONCLUSION: This case emphasizes the diagnostic challenges and management complexities in patients with incidental findings of myelolipoma, particularly when accompanied by significant medical histories. The occurrence of unexpected intraoperative complications highlights the importance of cautious decision-making in surgical interventions. This report provides valuable insights into the unpredictable nature of medical practice and the management of rare pathologies.

Artificial Intelligence and Acute Appendicitis: A Systematic Review of Diagnostic and Prognostic Models.

BACKGROUND: To assess the efficacy of artificial intelligence (AI) models in diagnosing and prognosticating acute appendicitis (AA) in adult patients compared to traditional methods. AA is a common cause of emergency department visits and abdominal surgeries. It is typically diagnosed through clinical assessments, laboratory tests, and imaging studies. However, traditional diagnostic methods can be time-consuming and inaccurate. Machine learning models have shown promise in improving diagnostic accuracy and predicting outcomes. MAIN BODY: A systematic review following the PRISMA guidelines was conducted, searching PubMed, Embase, Scopus, and Web of Science databases. Studies were evaluated for risk of bias using the Prediction Model Risk of Bias Assessment Tool. Data points extracted included model type, input features, validation strategies, and key performance metrics. RESULTS: In total, 29 studies were analyzed, out of which 21 focused on diagnosis, seven on prognosis, and one on both. Artificial neural networks (ANNs) were the most commonly employed algorithm for diagnosis. Both ANN and logistic regression were also widely used for categorizing types of AA. ANNs showed high performance in most cases, with accuracy rates often exceeding 80% and AUC values peaking at 0.985. The models also demonstrated promising results in predicting postoperative outcomes such as sepsis risk and ICU admission. Risk of bias was identified in a majority of studies, with selection bias and lack of internal validation being the most common issues. CONCLUSION: AI algorithms demonstrate significant promise in diagnosing and prognosticating AA, often surpassing traditional methods and clinical scores such as the Alvarado scoring system in terms of speed and accuracy.

Plasma cell leukemia presenting as spontaneous tumor lysis syndrome with hypercalcemia.

Tumor lysis syndrome (TLS) is an oncologic emergency in which tumor cells undergo lysis either spontaneously or due to the initiation of cancer therapy typically presenting with hypocalcemia. We described a 62-year-old male patient with spontaneous TLS and hypercalcemia without a known malignancy, who is later discovered to have plasma cell leukemia.

Delayed-onset heparin-induced thrombocytopenia complicated with saddle embolus.

Delayed-onset heparin-induced thrombocytopenia is a rare complication of heparin in which thrombocytopenia and thrombosis occur several days after heparin cessation. We describe a 47-year-old female patient with a history of melanoma and multiple surgeries presented to the emergency department with acute dyspnea and chest pain on the eighth day after discharge.