New-onset postoperative atrial fibrillation after pulmonary endarterectomy is associated with adverse outcomes.

Background: New-onset postoperative atrial fibrillation (POAF) is a common complication after pulmonary thromboendarterectomy (PEA), yet the risk factors and their impact on prognosis remain poorly understood. This study aims to investigate the risk factors associated with new-onset POAF after PEA and elucidate its underlying connection with adverse postoperative outcomes. Methods: A retrospective analysis included 129 consecutive chronic thromboembolic pulmonary hypertension (CTEPH) patients and 16 sarcoma patients undergoing PEA. Univariate and multivariate analyses were conducted to examine the potential effects of preoperative and intraoperative variables on new-onset POAF following PEA. Propensity score matching (PSM) was then employed to adjust for confounding factors. Results: Binary logistic regression revealed that age (odds ratio [OR] = 1.041, 95% confidence interval [CI] = 1.008-1.075, p = 0.014) and left atrial diameter[LAD] (OR = 1.105, 95% CI = 1.025-1.191, p = 0.009) were independent risk factors for new-onset POAF after PEA. The receiver operating characteristic (ROC) curve indicated that the predictive abilities of age and LAD for new-onset POAF were 0.652 and 0.684, respectively. Patients with new-onset POAF, compared with those without, exhibited a higher incidence of adverse outcomes (in-hospital mortality, acute heart failure, acute kidney insufficiency, reperfusion pulmonary edema). Propensity score matching (PSM) analyses confirmed the results. Conclusion: Advanced age and LAD independently contribute to the risk of new-onset POAF after PEA. Patients with new-onset POAF are more prone to adverse outcomes. Therefore, heightened vigilance and careful monitoring of POAF after PEA are warranted.

Untargeted plasma metabolome identifies biomarkers in patients with extracranial arteriovenous malformations.

Objective: This study aimed to investigate the plasma metabolic profile of patients with extracranial arteriovenous malformations (AVM). Method: Plasma samples were collected from 32 AVM patients and 30 healthy controls (HC). Ultra-high performance liquid chromatography-mass spectrometry (UHPLC-MS) was employed to analyze the metabolic profiles of both groups. Metabolic pathway enrichment analysis was performed through Kyoto Encyclopedia of Genes and Genomes (KEGG) database and MetaboAnalyst. Additionally, machine learning algorithms such as Least Absolute Shrinkage and Selection Operator (LASSO) and random forest (RF) were conducted to screen characteristic metabolites. The effectiveness of the serum biomarkers for AVM was evaluated using a receiver-operating characteristics (ROC) curve. Result: In total, 184 differential metabolites were screened in this study, with 110 metabolites in positive ion mode and 74 metabolites in negative mode. Lipids and lipid-like molecules were the predominant metabolites detected in both positive and negative ion modes. Several significant metabolic pathways were enriched in AVMs, including lipid metabolism, amino acid metabolism, carbohydrate metabolism, and protein translation. Through machine learning algorithms, nine metabolites were identify as characteristic metabolites, including hydroxy-proline, L-2-Amino-4-methylenepentanedioic acid, piperettine, 20-hydroxy-PGF2a, 2,2,4,4-tetramethyl-6-(1-oxobutyl)-1,3,5-cyclohexanetrione, DL-tryptophan, 9-oxoODE, alpha-Linolenic acid, and dihydrojasmonic acid. Conclusion: Patients with extracranial AVMs exhibited significantly altered metabolic patterns compared to healthy controls, which could be identified using plasma metabolomics. These findings suggest that metabolomic profiling can aid in the understanding of AVM pathophysiology and potentially inform clinical diagnosis and treatment.

Extraction of Medication-Effect Relations in Twitter Data with Neural Embedding and Recurrent Neural Network.

Recently, an active area of research in pharmacovigilance is to use social media such as Twitter as an alternative data source to gather patient-generated information pertaining to medication use. Most of thr published work focuses on identifying mentions of adverse effects in social media data but rarely investigating the relationship between a mentioned medication and any mentioned effect expressions. In this study, we treated this relation extraction task as a classification problem, and represented the Twitter text with neural embedding which was fed to a recurrent neural network classifier. The classification performance of our method was investigated in comparison with 4 baseline word embedding methods on a corpus of 9516 annotated tweets.