Annals of translational medicine最新文献

Background: Despite advancements in medical care, coronary artery disease (CAD) remains a leading cause of death. Previous studies have indicated a higher mortality rate for women compared to men following percutaneous coronary intervention (PCI). While both sexes share common risk factors, the magnitude of their impact may differ, and specific sex-related factors contribute to observed disparities. Although attempts have been made to reduce this gap, data concerning medical care and risk factors related to mortality remain limited. This study aims to assess the in-hospital mortality rate following PCI and to investigate sex-specific differences in associated risk factors.

Methods: We utilized a comprehensive national PCI registry comprising 19,701 patients. The patient cohort was stratified by sex. In-hospital mortality rates were reported, and risk factors related to mortality were analyzed using multivariable models.

Results: Women exhibited a higher in-hospital mortality rate (3.5%) compared to men (2.3%). They were also more significantly affected by specific risk factors that increased mortality exclusively in women: chronic kidney disease (CKD) [odds ratio (OR) 2.73, 95% confidence interval (CI): 1.90-3.93, P<0.001], cerebrovascular disease (OR 2.43, 95% CI: 1.43-4.12, P=0.001), and cardiogenic shock (CS) at the time of PCI (OR 5.6, 95% CI: 3.85-8.15, P<0.001). Furthermore, several factors demonstrated a greater association with mortality in women compared to men. Women undergoing emergency PCI exhibited a higher odds ratio (OR 14.01, 95% CI: 8.03-24.43, P<0.001) than men (OR 3.77, 95% CI: 2.19-6.50, P<0.001). Similar trends were observed for urgent PCI (OR 5.58, 95% CI: 3.08-10.09, P<0.001 vs. OR 2.74, 95% CI: 1.64-4.58, P<0.001) and new required dialysis (OR 7.10, 95% CI: 2.84-17.77, P<0.001 vs. OR 3.28, 95% CI: 1.83-5.86, P<0.001).

Conclusions: Women undergoing PCI had significantly higher in-hospital and one-year mortality rates than men. Key risk factors for increased mortality in women include a history of stroke, CS, and CKD. While emergency PCI, dialysis initiation, arrhythmias, and procedure failure affect both sexes, their impact was more pronounced in women. Improving prognosis requires increased attention to women's specific needs and enhanced awareness of appropriate peri-procedure care.

Background and objective: Artificial intelligence (AI)-driven virtual physiotherapy assistants (VPAs) are increasingly adopted in home-based rehabilitation, offering real-time feedback and personalised guidance through wearable sensors. These systems enhance treatment adherence, minimise clinic visits, and improve rehabilitation outcomes. However, challenges such as sensor accuracy, patient engagement, and affordability hinder widespread implementation. This review explores current applications, benefits, and limitations of AI-driven VPAs.

Methods: A comprehensive narrative review was conducted across PubMed, IEEE Xplore, Scopus, Google Scholar, and Web of Science databases. Search terms such as: "artificial intelligence", "virtual physiotherapy assistants", "home rehabilitation", and "wearable sensors". From 847 initially identified articles, 31 peer-reviewed publications (2018-2024) met inclusion criteria. Exclusion criteria eliminated non-English publications, conference abstracts, and studies without AI components. The review synthesised literature on sensor accuracy, AI-based monitoring algorithms, and patient engagement strategies.

Key content and findings: Analysis of 31 studies revealed that AI-driven VPAs enhance adherence and reduce in-person visits. Integrating wearable sensors and AI facilitates real-time feedback and personalised support, improving exercise accuracy. Critical limitations include inertial measurement unit drift, electromyography sensor placement variability, and optical system environmental dependencies. Challenges remain in sensor precision, user motivation, cost barriers, and technology accessibility. Novel findings highlight potential for predictive analytics, gamification strategies, and telehealth integration.

Conclusions: AI-driven VPAs offer a promising accessible, personalised home-based rehabilitation solution. Evidence demonstrates therapeutic potential, though systematic addressing of sensor accuracy, engagement strategies, and accessibility barriers is essential for implementation. Technological improvements and increased affordability are crucial for broader adoption and long-term impact on rehabilitation delivery.

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.].