Cardiovascular diagnosis and therapy最新文献

Background and objective: Electronic health records (EHRs) have modernized care but increased documentation burden and clinician burnout. Ambient artificial intelligence (AI) scribes, combining automated speech recognition (ASR), natural language processing (NLP), and generative AI, aim to address this by capturing encounters and generating documentation. Related technologies, including virtual assistants and autonomous patient-facing systems, extend these capabilities beyond the clinician's physical presence. This narrative review synthesizes current evidence on the real-world performance, implementation, and impact of these AI tools.

Methods: A narrative literature search was conducted using PubMed, supplemented by a manual review of reference lists from key articles. The search covered studies published between January 2019 and June 2025. After screening and full-text review, 18 studies met inclusion criteria and were incorporated into this review.

Key content and findings: AI scribes consistently reduce documentation burden and cognitive load, improve workflow efficiency, save time, and enhance patient-clinician interaction by allowing greater clinician focus. However, studies also report frequent documentation omissions and occasional clinically significant hallucinations. Implementation remains a sociotechnical challenge involving workflow redesign, medico-legal considerations, and preservation of the patient-clinician relationship. In cardiology, where documentation requires precise, time-sensitive detail, AI-related errors may carry greater risk, underscoring the need for specialty-specific validation.

Conclusions: Ambient AI scribes show promise in reducing workload, improving efficiency, and decreasing burnout, but current systems still generate high omission rates and intermittent factual inaccuracies that may affect clinical decision-making. Evidence remains limited by small cohorts and methodological variability, warranting cautious interpretation. More rigorous, standardized evaluations are needed before routine clinical adoption.

Background and objective: Coronary artery bypass grafting (CABG) remains a vital treatment option for high-risk patients with advanced coronary artery disease, especially those with multivessel disease, extensive left main disease, or refractory angina. While CABG effectively lowers long-term mortality and morbidity, it is still associated with many postoperative complications that can hinder recovery and affect quality of life. This review aims to thoroughly explore risk factors, prevention, and management strategies of major postoperative complications after CABG, categorized by physiological systems.

Methods: A comprehensive literature review was conducted on PubMed and Google Scholar from January 1, 2005, to August 6, 2025, without applying filters, but only including English-language publications, to gather a wide range of studies. Full texts were chosen based on set criteria, followed by a qualitative analysis to identify common themes, results, and gaps.

Key content and findings: Post-CABG complications span neurological, cardiac, pulmonary, renal, gastrointestinal/hepatobiliary, infectious, endocrine, and psychosocial domains. Across systems, consistently identified significant risk factors include advanced age, diabetes, renal dysfunction, prolonged cardiopulmonary bypass time, prior stroke, chronic obstructive pulmonary disease (COPD), and impaired left ventricular (LV) function. Effective preventive strategies included optimized glycemic control, early mobilization and rehabilitation, targeted use of anti-inflammatory and antioxidant therapies, prophylactic amiodarone or magnesium for atrial fibrillation (AF), strict infection-control measures, renal-protective protocols, and multimodal pain management. Recently, artificial intelligence (AI)-based tools, including machine learning models for predicting acute kidney injury, delirium, stroke, arrhythmias, and surgical-site infections, are emerging as promising adjuncts for earlier risk identification and personalized postoperative care.

Conclusions: Post-CABG complications remain across organ systems, emphasizing the need for early risk identification and targeted prevention. Major risk factors include age, diabetes, renal dysfunction, and prolonged bypass time. Multidisciplinary care and emerging AI-based prediction tools may improve individualized risk assessment and postoperative outcomes.

Background: Mucopolysaccharidoses (MPS) are a group of lysosomal storage diseases. Cardiovascular pathology occurs in all types of MPS, represented by valvular defects, myocardial hypertrophy, and coronary artery disease. Cardiovascular abnormalities in parents of patients with MPS are poorly understood, which is the purpose of our work.

Methods: During January 2022 to October 2023, a cross-sectional observational study of MPS mutation carriers was conducted in the City Clinical Hospital No. 31, practice base of the Department of Propaedeutics of Internal Disease, Pirogov Russian National Research Medical University, Moscow, Russian Federation. There were 21 consecutive parents of children with MPS examined. All MPS carriers-parents underwent a standard clinical and laboratory examination, electrocardiography (ECG), echocardiography, 24-hour Holter ECG monitoring. Distributions of all parametric characteristics of the patients were not normal, non-parametric criteria were used in statistical calculations. Differences between nominal variables were compared using a Chi-squared test. Fisher's exact test was used when more than 20% of cells with expected frequencies less than 5. P value <0.05 was considered statistically significant. The analysis was performed by a biostatistician using the statistical software SPSS (version 26.0; SPSS Institute, USA) and STATISTICA (version 12.0; StatSoft, USA).

Results: The median (25th and 75th percentiles) of age was 36 [33; 37] years. There were no confirmed myocardial and brain infarctions, nor diabetes mellitus in the examined carriers (81% female). A decrease in left ventricular (LV) ejection fraction <40% was found in 1 (4.8%), up to 40-50% in 2 (9.5%) carriers. LV wall thickness ≥1.5 cm was detected in 14 (66.7%) carriers, asymmetric LV hypertrophy in 18 (85.7%). Thickening of the mitral valve leaflets was detected in 16 (76.2%) carriers. Hydropericardium was detected in 5 (23.8%) carriers. Atrial flutter was registered in 1 (4.8%), paroxysmal supraventricular tachycardia in 7 (33.3%), sinus bradycardia in 3 (14.3%); conduction disorders in 15 (71.4%) carriers. A short PR interval was detected on the ECG in 2 (9.5%) carriers. A prolonged QT interval was registered in 3 (14.3%) of carriers, transient ST-segment depression in 10 (47.6%), ST-segment elevation in 3 (14.3%) carriers.

Conclusions: Our results suggested the possibility of clinical manifestations of cardiac involvement in MPS carriers. Further comparative studies are required in larger populations to assess the rate of progression of the identified abnormalities and the effectiveness of drug therapy in these patients.

Background and objective: Catheter-directed interventions (CDI) for pulmonary embolism (PE) have emerged as alternatives to systemic thrombolysis for patients requiring reperfusion, enabling thrombus burden reduction through low-dose local thrombolysis or mechanical thrombectomy. Despite encouraging hemodynamic and clinical outcomes, procedure-specific complications are poorly characterized in the literature. This narrative review aims to synthesize the reported complications of CDI in acute PE and provide practical management strategies.

Methods: A PubMed search covering 2014-2025, limited to articles published in English or Spanish, complemented by reference screening, identified randomized clinical trials and prospective or retrospective registries of CDI in acute PE reporting safety outcomes. These studies were used to construct a comprehensive overview of CDI-related complications, while additional registries, case reports, and case series were examined to describe rare events not captured by primary studies, as well as expert insights when evidence was insufficient or unavailable.

Key content and findings: Complications of CDI in acute PE encompass a broad and heterogeneous spectrum including access-site complications, right-heart injury, pulmonary arterial lesions, catheter malfunction and mechanical complications, and hemodynamic deterioration. Their incidence varies considerably across studies, and the absence of standardized definitions or classifications limits accurate assessment of the true frequency and clinical impact of those adverse events.

Conclusions: Based on the available evidence, this narrative expert review examines the spectrum of CDI-related complications in acute PE. It seeks to bridge the gap between innovation and safety by equipping clinicians and interventionalists with practical guidance for the recognition, prevention, and management of these adverse events.

Background and objective: Artificial intelligence (AI) is rapidly transforming cardiology through advancements in diagnostic accuracy, prognostication, and treatment personalization. While evidence for algorithmic performance is robust, its true impact on patient-centered outcomes remains unclear. This review aims to evaluate how AI applications influence patient outcomes in cardiology and identify current limitations and future directions.

Methods: A targeted literature search was conducted in PubMed, Scopus, Embase, and Cochrane databases on May 9 and 23, 2025, using a combination of terms related to AI, cardiology, and patient outcomes. Filters were applied to include human studies, English language, and studies published between January 2015 and May 2025. Two reviewers independently screened articles, and three reviewers reached consensus for final inclusion. A total of 11 studies met inclusion criteria.

Key content and findings: AI tools have demonstrated potential benefits across multiple domains, including clinical decision support, cardiac imaging, remote patient monitoring, and patient engagement. Evidence suggests AI can enhance diagnostic accuracy, procedural efficiency, and patient self-management. However, most studies report surrogate or process-related endpoints rather than hard clinical outcomes. Large-scale randomized trials remain scarce, and improvements in mortality, hospitalization, and quality of life (QoL) are inconsistently demonstrated. Ethical considerations, implementation challenges, and cost-effectiveness concerns persist.

Conclusions: AI in cardiology shows promise for improving patient care, but robust evidence linking its adoption to improved clinical outcomes is limited. By synthesizing available findings, this review highlights critical evidence gaps and provides guidance for future research, which should prioritize prospective trials focused on patient-centered endpoints and address barriers to implementation, transparency, and equity.