Annals of translational medicine最新文献

Background and objective: Rheumatoid arthritis (RA) is a chronic, autoimmune disease primarily influencing the synovial joints resulting in joint destruction. Systemic manifestations associated with RA have been implicated with recent evidence suggesting a pertinent role of RA in cardiovascular health. Epicardial adipose tissue (EAT), the layer of fat surrounding the heart, has been found to be an emerging diagnostic marker for cardiovascular conditions associated with RA given its role in proinflammatory cytokine release. However, given the novelty of EAT, its utility required further appraisal. This review aims to evaluate the role of EAT as a diagnostic and prognostic marker for cardiovascular involvement in RA and to explore its potential as a therapeutic target to mitigate cardiometabolic risk.

Methods: PubMed and EMBASE were searched from July to October 2024 yielding relevant studies that examined the role of EAT as a clinical tool for RA associated cardiometabolic diseases.

Key content and findings: Evidence has implicated greater EAT thickness and higher disease activity in RA. Elevated levels of adipokines, secreted by the adipose tissue, and found in association with EAT, play a key role in regulating inflammatory diseases such as RA. Since EAT could be promoting atherosclerosis, it could be a useful tool for early identification of cardiovascular conditions in RA and anti-inflammatory therapies controlling systemic inflammation may indirectly reduce EAT.

Conclusions: Given the clinical modifiability of EAT, it holds promise as a viable risk stratification tool and as a potential therapeutic target for reducing cardiovascular complications in RA.

Background: At present, surgical bullectomy together with pleurodesis has the highest efficacy in terms of preventing the recurrence of primary spontaneous pneumothorax (PSP). There is still debate in the type of pleurodesis. In this study, we aim to investigate the efficacy of polyglactin mesh covering comparing to the standard surgical pleurodesis.

Methods: This is a retrospective study collecting PSP patients who underwent bullectomy with pleurodesis between January 2016 and August 2023. The patients were divided into two groups as mesh and non-mesh group. Propensity score-matching analysis (1:1) was performed to balance the patient characteristics. The primary outcome was the pneumothorax recurrence after the index operation analyzed by Kaplan-Meier method. Operative and post-operative results were compared using Chi-squared test, Student's t-test and Mann-Whitney U test.

Results: There are 151 PSP patients during the study period, 84 and 67 of them were in mesh and non-mesh group respectively. After propensity matched, there were 49 patients in each group. From the Kaplan-Meier analysis with the longest follow-up time as 48 months, as the non-inferiority trial, there was no statistically significant between two groups (P=0.23). Importantly, the mesh group showed lower operative time for 15 minutes (P=0.01), lower blood loss for 10 mL (P<0.001), and shorter duration of chest tube for 1 day (P=0.002).

Conclusions: In PSP patients undergoing lung bullectomy with pleurodesis, using of polyglactin mesh coverage the entire lung facilitates less operative time, less intra-operative blood loss and shorter both length of stay and chest tube duration. Polyglactin mesh should be considered as an alternative option for surgical pleurodesis.

Background and objective: The Emergency Department (ED) is a critical, high-stakes environment where timely and accurate assessments of patient outcomes are essential for ensuring optimal care and effective resource management. This narrative review aimed to synthesise current evidence on machine learning (ML)-based predictive models used in the ED to forecast patient outcomes such as mortality, intensive care unit (ICU) admission, and discharge probability, whilst identifying key limitations and future research directions.

Methods: This narrative review synthesises recent advancements in ML-based predictive models for ED outcomes published between January 2015 and December 2024. It explores the integration of real-time and historical clinical data, focusing on key ML techniques such as regression models, decision trees, neural networks, and ensemble methods. The review also evaluates data sources, model evaluation metrics, and addresses challenges including data quality, interpretability, and ethical considerations. A comprehensive search of four major databases yielded 156 initial results, with 45 studies ultimately included after systematic screening.

Key content and findings: ML models demonstrate significant promise in processing complex, non-linear data for ED outcome prediction with area under the receiver operating characteristic curve (AUC-ROC) values typically ranging from 0.75-0.95 across different outcomes. Techniques like ensemble methods and neural networks offer strong performance, while personalized prediction models and explainable artificial intelligence (XAI) enhance precision and interpretability. However, current approaches face substantial limitations including data heterogeneity, poor model generalisability across institutions, and lack of real-world implementation studies. Emerging integration of telemedicine further broadens the applicability of predictive modeling in the ED.

Conclusions: ML is reshaping predictive modeling in the ED, offering timely, data-driven support for clinical decision-making. Despite challenges, advancements in personalized and explainable models hold the potential to increase trust and usability in clinical workflows. Critical gaps remain in addressing data quality issues, standardising evaluation metrics, and conducting multi-centre validation studies.

[This corrects the article DOI: 10.21037/atm-21-3583.].

[This corrects the article DOI: 10.21037/atm-20-5881.].

Hypertension is a widespread global health issue that disproportionately affects certain populations, including self-identified Blacks, the older persons, patients with chronic kidney disease (CKD), and kidney transplant recipients. Hypertension disproportionately affects self-identified Black individuals, with a prevalence of 57.1% compared to 43.6% in non-Hispanic White individuals. This disparity is linked to social determinants of health. Furthermore, APOL1 genetic variants found in self-identified Black individuals increase their susceptibility to kidney injury and CKD, which can subsequently contribute to hypertension. Although in the past thiazide diuretics and calcium channel blockers (CCBs) were suggested to be more effective in Black adults, combination therapy is now generally required, with comparable efficacy across populations. In the older persons, hypertension affects approximately 70% of individuals over the age of 65 years, often manifesting as isolated systolic hypertension (ISH). Trials like the SPRINT study (Systolic Blood Pressure Intervention Trial) have demonstrated the benefits of lowering systolic blood pressure (SBP) to less than 120 mmHg; however, treatment must take into account factors like orthostatic hypotension and frailty. Patients with CKD have a hypertension prevalence of 80-85%. The KDIGO (Kidney Disease: Improving Global Outcomes) 2021 guidelines recommend maintaining an SBP of less than 120 mmHg based on the SPRINT trial, although this goal may increase the risk of acute kidney injury (AKI). Renin-angiotensin-aldosterone system (RAAS) blockers are typically preferred for those with proteinuric CKD. Kidney transplant recipients also experience high rates of hypertension, with approximately 85% affected. The KDIGO 2021 guidelines suggest a blood pressure (BP) target of less than 130/80 mmHg in kidney transplant patients, with a focus on promoting graft survival. Dihydropyridine CCBs and angiotensin receptor blockers are commonly preferred treatments in kidney transplant patients, especially for patients with proteinuric kidney disease. This review synthesizes current evidence regarding the unique challenges and management strategies for hypertension in these specific groups. It examines the prevalence, underlying mechanisms, and treatment considerations while emphasizing the importance of individualized care to achieve optimal BP control and reduce cardiovascular risk.

Background: Stroke is the second leading cause of death worldwide, with carotid stenosis being a primary contributor. Therefore, stroke prevention would benefit from accessible carotid stenosis screening tools. Historically, acoustic stethoscopes were used to listen to the carotid artery, but this method is now outdated due to its subjectivity and inconsistent sensitivity and specificity in detecting stenosis. In contrast, electronic stethoscopes record audio, enabling precise and objective analysis. To overcome traditional auscultation limitations, our study introduces a signal analysis scheme to evaluate the electronic stethoscope as a potential screening tool for carotid plaques and severe stenosis.

Methods: We included 94 patients undergoing duplex ultrasound (DUS) for recent transient ischemic attack (TIA) or pre-operative assessment for carotid endarterectomy. DUS served as the clinical reference for determining plaque presence and estimating carotid stenosis. Participants held their breath during electronic stethoscope measurements at two points along each carotid artery: (I) proximal, on the common carotid; and (II) distal, near the bifurcation. From these recordings, we extracted 10 spectral features and utilized multivariable binary logistic regression for predicting plaques and severe stenosis, applying 10-fold cross-validation for internal validation. We constructed the receiver operating characteristic (ROC) curve by plotting the true positive rate against the false positive rate at various cutoff settings. We reported the area under the curve (AUC), along with sensitivity and specificity, which were determined using a single optimal cutoff point.

Results: For detecting >70% stenosis using distal location recordings, the analysis yielded training and testing AUCs of 0.87 and 0.79, sensitivity of 84.9% and 78.6%, and specificity of 73.6% and 72.1%, respectively. Using proximal location recordings, training and testing AUCs were 0.84 and 0.73, with sensitivities of 79.8% and 60.7%, and specificities of 76.0% and 75.6%, respectively. For detecting the presence of plaques, proximal location measurements showed training and testing AUCs of 0.79 and 0.7, sensitivities of 54.9% and 51.9%, and specificities of 91.9% and 78.8%, respectively.

Conclusions: Our findings demonstrate that the electronic stethoscope with spectral analysis is promising for identifying severe stenosis but has limited sensitivity for detecting any plaque. The performance obtained with this approach is superior to that attainable with conventional auscultation. This approach could serve as a promising, user-friendly screening tool, particularly in resource-limited settings.