Citations

Abstract Background Pancreatic cysts (PC) are increasingly detected through abdominal imaging, prompting exploration of alternatives such as endoscopic ultrasound-guided pancreatic cyst ablation (EUS-PCA) due to the risks and costs associated with surgery. This study conducts a systematic review and meta-analysis of EUS-PCA's short-term efficacy and complications for PC management. Methods A systematic review and meta-analysis were carried out on PubMed, Ovid, Cochrane, and TRIP electronic databases. The primary outcome was cyst resolution (partial and complete) and persistence on imaging 12 months after ablation. The secondary outcome was procedure-related adverse events. Results Eight studies were eligible for analysis. Complete cyst resolution on imaging 12 months after EUS ablation was 50% [95% CI 36, 63, I2 = 85.31%]. Partial cyst resolution was 27% [95% CI 15, 41, I2 = 87.07%], and cyst persistence was 17% [95% CI 11, 24, I2 = 62.11%]. The rate of complete resolution varied depending on the treatment agent (for ethanol 29% [95% CI 10, 53], lauromacrogol 51% [95% Cl 36, 67], ethanol and paclitaxel 63% [95% CI 48, 76], paclitaxel and gemcitabine 67% [95% CI 45, 83], ethanol, paclitaxel and gemcitabine 61% [95% CI 39, 80]). Post-procedure adverse events included abdominal pain in 4% [95% CI 0, 11], pancreatitis in 3% [95% CI 1, 5], and fever in 1% [95% CI 0, 3] of all patients. Conclusion The treatment of pancreatic cysts with EUS ablation results in acceptable levels of complete resolution, and low incidence of severe adverse events. The effectiveness of this treatment is further enhanced when chemoablative agents are employed.

Background: Spontaneous coronary artery dissection (SCAD) is defined as a non-traumatic separation of the epicardial coronary artery walls that creates a false lumen. SCAD poses a difficult challenge in management, as decisions regarding revascularization and medical management seem to be tailored to the individual patient. We evaluated and compared outcomes based on cardiogenic shock in patients with SCAD utilizing Nationwide Readmissions Database (NRD) between January 1, 2016, to December 30, 2020. Methods: We utilized the NRD 2016–2019 to carry out this study. We evaluated demographics (e.g., age, gender), conventional risk factors, comorbidities present on the index admission, and in-hospital outcomes using their specific ICD-10-CM codes. The primary outcomes were In-hospital mortality and 30-day readmission, and the secondary outcome was to compare the complications in SCAD patient with cardiogenic shock (CS) compared to those without CS. Results: We analyzed 2473 individuals with SCAD, 2199 of these individuals did not have cardiogenic shock whereas 274 of these individuals did have cardiogenic shock. When comparing SCAD with cardiogenic shock to SCAD without cardiogenic shock, there was a statistically significant increased odds ratio (OR) for death (propensity matched OR 24.93 (7.49–83.05), use of mechanical circulatory support (propensity matched OR 15.30 (6.87–34.04), ventricular tachycardia (propensity matched OR 4.45 (1.92–10.34), utilization of blood transfusions (propensity matched OR 3.82 (1.86–7.87), acute kidney injury (propensity matched OR 4.02 (1.45–11.13), need for mechanical ventilation (propensity matched OR 8.87 (3.53–22.31), and respiratory failure (propensity matched OR 4.95 (1.83–13.41)))))))). There was no statistically significant difference in 30-day readmission rates between the two groups. Conclusions: SCAD is a unique condition that can lead to many complications. In our analysis, we showed that SCAD associated with cardiogenic shock compared to SCAD not associated with cardiogenic shock results in greater odds of complications including death, use of mechanical circulatory support, need for blood transfusions, ventricular tachycardia, acute kidney injury, use of mechanical ventilation, and respiratory failure. SCAD with cardiogenic shock represents a significantly critical clinical scenario that requires a multi-disciplinary approach to prevent the many potential complications associated with this disease process.

Artificial intelligence, specifically advanced language models such as ChatGPT, have the potential to revolutionize various aspects of healthcare, medical education, and research. In this review, we evaluate the myriad applications of artificial intelligence in diverse healthcare domains. We discuss its potential role in clinical decision-making, exploring how it can assist physicians by providing rapid, data-driven insights for diagnosis and treatment. We review the benefits of artificial intelligence such as ChatGPT in personalized patient care, particularly in geriatric care, medication management, weight loss and nutrition, and physical activity guidance. We further delve into its potential to enhance medical research, through the analysis of large datasets, and the development of novel methodologies. In the realm of medical education, we investigate the utility of artificial intelligence as an information retrieval tool and personalized learning resource for medical students and professionals.

A growing body of research has begun to link exposure to environmental contaminants, such as heavy metals, with a variety of negative health outcomes. In this paper, we sought to review the current research describing the impact of certain common contaminant metals on cardiovascular (CV) health. We reviewed ten metals: lead, barium, nickel, chromium, cadmium, arsenic, mercury, selenium, zinc, and copper. After a literature review, we briefly summarized the routes of environmental exposure, pathophysiological mechanisms, CV health impacts, and exposure prevention and/or mitigation strategies for each metal. The resulting article discloses a broad spectrum of pathological significance, from relatively benign substances with little to no described effects on CV health, such as chromium and selenium, to substances with a wide-ranging and relatively severe spectrum of CV pathologies, such as arsenic, cadmium, and lead. It is our hope that this article will provide clinicians with a practical overview of the impact of these common environmental contaminants on CV health as well as highlight areas that require further investigation to better understand how these metals impact the incidence and progression of CV diseases.

Bifurcation lesions account for 20% of all percutaneous coronary interventions and represent a complex subset which are associated with lower procedural success and higher rates of restenosis [...]

AbstractMeta‐analysis is commonly used to combine results from multiple clinical trials, but traditional meta‐analysis methods do not refer explicitly to a population of individuals to whom the results apply and it is not clear how to use their results to assess a treatment's effect for a population of interest. We describe recently‐introduced causally interpretable meta‐analysis methods and apply their treatment effect estimators to two individual‐participant data sets. These estimators transport estimated treatment effects from studies in the meta‐analysis to a specified target population using the individuals' potentially effect‐modifying covariates. We consider different regression and weighting methods within this approach and compare the results to traditional aggregated‐data meta‐analysis methods. In our applications, certain versions of the causally interpretable methods performed somewhat better than the traditional methods, but the latter generally did well. The causally interpretable methods offer the most promise when covariates modify treatment effects and our results suggest that traditional methods work well when there is little effect heterogeneity. The causally interpretable approach gives meta‐analysis an appealing theoretical framework by relating an estimator directly to a specific population and lays a solid foundation for future developments.

The utilization of transcatheter aortic valve implantation (TAVI) has become the treatment of choice in patients with severe aortic stenosis (AS) with intermediate-to-high surgical risk for surgical aortic valve replacement (SAVR) [...]

Abstract Background Early-stage breast cancer is among the most common cancer diagnoses. Adjuvant radiotherapy is an essential component of breast-conserving therapy, and several options exist for tailoring its extent and duration. This study assesses the comparative effectiveness of partial-breast irradiation (PBI) compared with whole-breast irradiation (WBI). Methods A systematic review was completed to identify relevant randomized clinical trials and comparative observational studies. Independent reviewers working in pairs selected studies and extracted data. Randomized trial results were pooled using a random effects model. Prespecified main outcomes were ipsilateral breast recurrence (IBR), cosmesis, and adverse events (AEs). Results Fourteen randomized clinical trials and 6 comparative observational studies with 17 234 patients evaluated the comparative effectiveness of PBI. PBI was not statistically significantly different from WBI for IBR at 5 years (RR = 1.34, 95% CI = 0.83 to 2.18; high strength of evidence [SOE]) and 10 years (RR = 1.29, 95% CI = 0.87 to 1.91; high SOE). Evidence for cosmetic outcomes was insufficient. Statistically significantly fewer acute AEs were reported with PBI compared with WBI, with no statistically significant difference in late AEs. Data from subgroups according to patient, tumor, and treatment characteristics were insufficient. Intraoperative radiotherapy was associated with higher IBR at 5, 10, and over than 10 years (high SOE) compared with WBI. Conclusions Ipsilateral breast recurrence was not statistically significantly different between PBI and WBI. Acute AEs were less frequent with PBI. This evidence supports the effectiveness of PBI among selected patients with early-stage, favorable-risk breast cancer who are similar to those represented in the included studies.

Primary care has the potential to be transformed by artificial intelligence (AI) and, in particular, machine learning (ML). This review summarizes the potential of ML and its subsets in influencing two domains of primary care: pre-operative care and screening. ML can be utilized in preoperative treatment to forecast postoperative results and assist physicians in selecting surgical interventions. Clinicians can modify their strategy to reduce risk and enhance outcomes using ML algorithms to examine patient data and discover factors that increase the risk of worsened health outcomes. ML can also enhance the precision and effectiveness of screening tests. Healthcare professionals can identify diseases at an early and curable stage by using ML models to examine medical pictures, diagnostic modalities, and spot patterns that may suggest disease or anomalies. Before the onset of symptoms, ML can be used to identify people at an increased risk of developing specific disorders or diseases. ML algorithms can assess patient data such as medical history, genetics, and lifestyle factors to identify those at higher risk. This enables targeted interventions such as lifestyle adjustments or early screening. In general, using ML in primary care offers the potential to enhance patient outcomes, reduce healthcare costs, and boost productivity.