Pericardial fluid troponin in cardiac surgery.

BACKGROUND AND OBJECTIVE: Pericardial Fluid (PF) is a rich reservoir of biologically active factors. Due to its proximity to the heart, the biochemical structure of PF may reflect the pathological changes in the cardiac interstitial environment. This manuscript aimed to determine whether the PF level of cardiac troponins changes in patients undergoing cardiac surgery. METHODS: This scoping review followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Medline, EMBASE, Cochrane, ClinicalTrials.gov, and Google Scholar databases were electronically searched for primary studies using the keywords "pericardial fluid," "troponin," and "cardiac surgery." The primary outcome of interest was changes in troponin levels within the PF preoperatively and postoperatively. Secondary outcomes of interest included comparisons between troponin level changes in the PF compared to plasma. RESULTS: A total of 2901 manuscripts were screened through a title and abstract stage by two independent blinded reviewers. Of those, 2894 studies were excluded, and the remaining seven studies underwent a full-text review. Studies were excluded if they did not provide data or failed to meet inclusion criteria. Ultimately, six articles were included that discussed cardiac troponin levels within the PF in patients who had undergone cardiac surgery. Pericardial troponin concentration increased over time after surgery, and levels were significantly higher in PF compared to serum. All studies found that the type of operation did not affect these overall observations. CONCLUSION: Our review of the literature suggest that the PF level of cardiac troponins increases in patients undergoing cardiac surgery, irrespective of the procedure type. However, these changes' exact pattern and clinical significance remain undefined.

Machine Learning as a Diagnostic and Prognostic Tool for Predicting Thrombosis in Cancer Patients: A Systematic Review.

Khorana score (KS) is an established risk assessment model for predicting cancer-associated thrombosis. However, it ignores several risk factors and has poor predictability in some cancer types. Machine learning (ML) is a novel technique used for the diagnosis and prognosis of several diseases, including cancer-associated thrombosis, when trained on specific diagnostic modalities. Consolidating the literature on the use of ML for the prediction of cancer-associated thrombosis is necessary to understand its diagnostic and prognostic abilities relative to KS. This systematic review aims to evaluate the current use and performance of ML algorithms to predict thrombosis in cancer patients. This study was conducted per Preferred Reporting Items for Systematic Reviews and Meta-Analysis guidelines. Databases Medline, EMBASE, Cochrane, and ClinicalTrials.gov, were searched from inception to September 15, 2023, for studies evaluating the use of ML models for the prediction of thrombosis in cancer patients. Search terms "machine learning," "artificial intelligence," "thrombosis," and "cancer" were used. Studies that examined adult cancer patients using any ML model were included. Two independent reviewers conducted study selection and data extraction. Three hundred citations were screened, of which 29 studies underwent a full-text review, and ultimately, 8 studies with 22,893 patients were included. Sample sizes ranged from 348 to 16,407 patients. Thrombosis was characterized as venous thromboembolism (n = 6) or peripherally inserted central catheter thrombosis (n = 2). The types of cancer included breast, gastric, colorectal, bladder, lung, esophageal, pancreatic, biliary, prostate, ovarian, genitourinary, head-neck, and sarcoma. All studies reported outcomes on the ML's predictive capacity. The extreme gradient boosting appears to be the best-performing model, and several models outperform KS in their respective datasets.

Sex differences in the outcomes after transcatheter aortic valve replacement with newer generation devices: A meta-analysis.

BACKGROUND: Transcatheter aortic valve replacement (TAVR) is a reasonable therapeutic approach among patients with symptomatic severe aortic stenosis irrespective of surgical risk. Data regarding sex-specific differences in the outcomes with newer generation valves are limited. METHODS: Electronic databases were searched for studies assessing sex differences in the outcomes of patients undergoing TAVR with newer generation valves (SAPIEN 3 or Evolut). Random effects model was constructed for summary estimates. RESULTS: Four observational studies with 4522 patients (44.8% women) were included in the meta-analysis. Women were older and had a lower prevalence of coronary artery disease and mean EuroScore. Women had a higher incidence of short-term mortality (up to 30 days) (risk ratio [RR]: 1.60, 95% confidence interval [CI]: 1.14-2.25), but no difference in 1-year mortality (RR: 0.92, 95% CI: 0.72-1.17). There was no significant difference in the incidence of major bleeding (RR: 1.16, 95% CI: 0.86-1.57), permanent pacemaker (PPM) (RR: 0.80, 95% CI: 0.62-1.04), or disabling stroke (RR: 1.16, 95% CI: 0.54-2.45). CONCLUSION: In this meta-analysis, we found that women undergoing TAVR with newer-generation devices were older but had a lower prevalence of comorbidities. Women had a higher incidence of short-term mortality but no difference in the 1-year mortality, bleeding, PPM, or stroke compared with men. Future studies are required to confirm these findings.

Machine Learning for Predicting Postoperative Atrial Fibrillation After Cardiac Surgery: A Scoping Review of Current Literature.

Postoperative atrial fibrillation (POAF) occurs in up to 20% to 55% of patients who underwent cardiac surgery. Machine learning (ML) has been increasingly employed in monitoring, screening, and identifying different cardiovascular clinical conditions. It was proposed that ML may be a useful tool for predicting POAF after cardiac surgery. An electronic database search was conducted on Medline, EMBASE, Cochrane, Google Scholar, and ClinicalTrials.gov to identify primary studies that investigated the role of ML in predicting POAF after cardiac surgery. A total of 5,955 citations were subjected to title and abstract screening, and ultimately 5 studies were included. The reported incidence of POAF ranged from 21.5% to 37.1%. The studied ML models included: deep learning, decision trees, logistic regression, support vector machines, gradient boosting decision tree, gradient-boosted machine, K-nearest neighbors, neural network, and random forest models. The sensitivity of the reported ML models ranged from 0.22 to 0.91, the specificity from 0.64 to 0.84, and the area under the receiver operating characteristic curve from 0.67 to 0.94. Age, gender, left atrial diameter, glomerular filtration rate, and duration of mechanical ventilation were significant clinical risk factors for POAF. Limited evidence suggest that machine learning models may play a role in predicting atrial fibrillation after cardiac surgery because of their ability to detect different patterns of correlations and the incorporation of several demographic and clinical variables. However, the heterogeneity of the included studies and the lack of external validation are the most important limitations against the routine incorporation of these models in routine practice. Artificial intelligence, cardiac surgery, decision tree, deep learning, gradient-boosted machine, gradient boosting decision tree, k-nearest neighbors, logistic regression, machine learning, neural network, postoperative atrial fibrillation, postoperative complications, random forest, risk scores, scoping review, support vector machine.

Artificial Intelligence in Neurosurgery: A State-of-the-Art Review from Past to Future.

In recent years, there has been a significant surge in discussions surrounding artificial intelligence (AI), along with a corresponding increase in its practical applications in various facets of everyday life, including the medical industry. Notably, even in the highly specialized realm of neurosurgery, AI has been utilized for differential diagnosis, pre-operative evaluation, and improving surgical precision. Many of these applications have begun to mitigate risks of intraoperative and postoperative complications and post-operative care. This article aims to present an overview of the principal published papers on the significant themes of tumor, spine, epilepsy, and vascular issues, wherein AI has been applied to assess its potential applications within neurosurgery. The method involved identifying high-cited seminal papers using PubMed and Google Scholar, conducting a comprehensive review of various study types, and summarizing machine learning applications to enhance understanding among clinicians for future utilization. Recent studies demonstrate that machine learning (ML) holds significant potential in neuro-oncological care, spine surgery, epilepsy management, and other neurosurgical applications. ML techniques have proven effective in tumor identification, surgical outcomes prediction, seizure outcome prediction, aneurysm prediction, and more, highlighting its broad impact and potential in improving patient management and outcomes in neurosurgery. This review will encompass the current state of research, as well as predictions for the future of AI within neurosurgery.

Lipoprotein(a) in clinical practice: A guide for the clinician.

Cardiovascular disease (CVD) remains the leading cause of death worldwide. Serum lipoprotein(a) (Lp(a)) has been shown to be an independent and causative risk factor for atherosclerotic CVD and calcific aortic valvular disease. Lp(a) continues to be studied, with emerging insights into the epidemiology of CVD with respect to Lp(a), pathogenic mechanisms of Lp(a) and strategies to mitigate disease. There have been novel insights into genetic polymorphisms of the LPA gene, interactions between concomitant risk factors and Lp(a) based on real-world data, and metabolic pathway targets for Lp(a) reduction. This review highlights these recent advances in our understanding of Lp(a) and discusses management strategies as recommended by cardiovascular professional societies, emerging therapies for lowering Lp(a), and future directions in targeting Lp(a) to reduce CVD.