British journal of hospital medicine最新文献

Artificial intelligence (AI) holds significant promise in the fields of diagnostics and therapeutics, particularly in cancer management. AI has been extensively applied in various aspects of breast cancer care. Numerous studies and reviews have been published on the use of AI in breast cancer; however, many studies have focused on a single application and can be difficult to understand because of the complex nature of the jargon. This review aimed to comprehensively explore the various applications of AI in breast cancer management. AI has proven to be a valuable tool for enhancing the workflows of radiologists, pathologists, and clinicians. Although it has not yet replaced clinical decision-making, AI shows promising potential in molecular pathology, with the hope of advancing virtual biopsies. Progress in AI could also open new avenues for preventive oncology. Current clinical challenges in therapeutic practice can be addressed by utilising radiomics, which combines the clinical, imaging, and molecular characteristics of tumours to guide treatment decisions and target therapies more effectively. The potential of AI to improve patient outcomes, reduce workforce demand, and minimise postoperative complications suggests a promising future for its application in cancer care.

Aims/Background Neurogenic bladder (NB) is a common complication after spinal cord injury (SCI), significantly impacting patients' social functioning and quality of life. Urinary tract infections and neurological prognosis following SCI have been thoroughly investigated; however, studies assessing NB risk factors are limited, hindering its effective prevention. This study developed and validated a high-accuracy predictive model based on multivariate logistic regression analysis to identify key risk factors for NB after SCI and provide a basis for early intervention. Methods This study included 115 SCI patients hospitalised between January 2022 and July 2024. Patients were divided into the NB and non-NB groups. General data were collected and analysed using univariate and multivariate logistic regression methods. A prediction model for NB post-SCI was developed and validated using the Hosmer-Lemeshow test and receiver operating characteristic (ROC) curve analysis with the bootstrap resampling method. Furthermore, NB patients underwent routine rehabilitation, and improvements in urinary incontinence, single voiding volume, and micturition frequency were recorded before and after treatment. Results In this study, the incidence of NB in patients with SCI was 30.43%. Significant differences in NB incidence were observed based on age, American Spinal Cord Injury Association (ASIA) grade, urine culture results, C-reactive protein (CRP) levels, urination mode, pain scores, and sexual dysfunction (p < 0.05). Multivariate logistic regression identified old age, ASIA grade A, increased CRP levels, urinary incontinence/indwelling catheter, and higher pain scores as significant risk factors (p < 0.05). The formula is expressed as: NB prediction index after SCI = 0.058 × age + 0.045 × ASIA grade + 0.394 × CRP + 2.132 × urinary incontinence/indwelling catheter + 0.608 × pain score + 5.032. The prediction model had an overall accuracy rate of 97.14%, with a Hosmer-Lemeshow chi-square value of χ² =10.904 (p = 0.207), indicating a good fit. ROC curve analysis showed an area under the curve (AUC) of 0.904 (95% CI: 0.832-0.976, p < 0.05), with sensitivity of 88.60% and specificity of 82.50%. Post-treatment, daily urinary incontinence, and urination frequency decreased, while single voiding volume increased (p < 0.05). Conclusion The occurrence of NB post-SCI is affected by various factors, such as age, ASIA grade, CRP levels, urination modes, and pain severity. The risk prediction model showed strong predictive value, offering a promising early risk management and clinical decision-making method.

Aims/Background Gut microbiota (GM) dysbiosis may exacerbate periodontitis by impairing intestinal barrier integrity and inducing systemic inflammation. However, the causal relationships and mediating roles of inflammatory proteins remain unclear. This study aimed to clarify these causal pathways and mediating mechanisms. Methods Two-sample and multivariable Mendelian randomization (MR) analyses were conducted to evaluate the causal association between GM and periodontitis using publicly available genome-wide association study (GWAS) data and FinnGen repository. Causal estimates were obtained through inverse-variance weighted (IVW), weighted median, MR-Egger, simple mode, and weighted mode approaches. Additionally, MR-Egger regression and Cochran's Q-test were applied to detect and correct for potential pleiotropy and heterogeneity. Results Significant causal relationships were identified between GM genetics and periodontitis risk. Protective effects were observed for the class Actinobacteria (odds ratio [OR]: 0.726, 95% confidence interval [CI]: 0.574-0.918; p = 0.007), the genus Collinsella (OR: 0.655, 95% CI: 0.456-0.941; p = 0.022), the genus Ruminococcus 1 (OR: 0.692, 95% CI: 0.497-0.964; p = 0.029), the genus Sutterella (OR: 0.697, 95% CI: 0.541-0.897; p = 0.005), and the phylum Actinobacteria (OR: 0.712, 95% CI: 0.551-0.921; p = 0.010), whereas the genus Alistipes was identified as a risk factor (OR: 1.682, 95% CI: 1.240-2.280; p = 0.001). Furthermore, fractalkine potentially mediated 13.37% of the association between the phylum Actinobacteria and periodontitis. No evidence for reverse causation was found between periodontitis risk and the aforementioned six gut microbiota in the bidirectional MR analysis. Conclusion Specific GM taxa and fractalkine exert causal effects on periodontitis, supporting the existence of a gut-oral axis mediated by systemic inflammation. These findings suggest potential therapeutic strategies targeting GM dysbiosis and inflammatory pathways.

Artificial Intelligence (AI) has the potential to enhance patient care in the UK's increasingly pressured healthcare system. As AI's applications in healthcare are expanding, healthcare professionals should understand the processes underpinning how AI tools translate from research to clinical application. There are several stages: (1) training and validation on healthcare data, (2) generation of evidence demonstrating performance and safety, (3) regulatory compliance, (4) AI product procurement, (5) implementation in clinical settings, and (6) ongoing monitoring and oversight of deployed AI. Each step presents unique challenges and opportunities that can influence successful integration. Clinicians should understand AI's capabilities and limitations to ensure its appropriate and effective use in practice. This review aims to provide a structured overview of the AI adoption pathway in healthcare, with a view to supporting clinicians in critically appraising its potential and limitations, optimising its integration into clinical practice, and engaging with AI in an informed manner.

As female participation in sports, traditionally dominated by males continues to grow, a notable trend has emerged: female athletes are disproportionately affected by soft tissue injuries, with anterior cruciate ligament (ACL) injury having an incidence rate up to 9 times more frequently than in their male counterparts. The burden of soft tissue injuries in female athletes such as hamstring injury, ankle sprain, and ACL injury is exacerbated by the underrepresentation of elite female athletes in sports medicine, and therefore suboptimal prevention and rehabilitative methods. This manuscript delves into the anatomical, hormonal, and training-related factors that contribute to this disparity. It examines common injury patterns including knee, ankle, and hamstring injuries, elucidating the pathophysiological mechanisms involved. The severe impact of these injuries can jeopardise the careers of elite sportswomen and prompt the need for a paradigm shift in sports medicine. This paper provides a current overview of injury management and discusses essential changes in prevention and rehabilitation strategies to improve outcomes for female athletes. By examining the existing body of knowledge, this review focuses on proposing future interventions and prevention strategies that are tailored to the unique needs of female athletes.

2024 has been a remarkable year in the field of coronary artery disease. There have been key breakthroughs at every stage of a patient's journey, from investigation to medical treatment and intervention, and many new findings are already changing practice. In this review article, we appraise and summarise the most important new evidence published in 2024 from five of the highest impact factor cardiology journals. Topics discussed include acute myocardial infarction in the elderly, antiplatelet de-escalation after percutaneous coronary intervention, the role of beta-blockers after myocardial infarction and efficacy of the microaxial flow pump in cardiogenic shock, alongside many more. We refer to the new changes in the 2024 European Society of Cardiology Chronic Coronary Syndrome guidelines in parallel to provide the reader with a clinically relevant and up-to-date review of coronary artery disease.

Falls are a common presentation to the acute medical take, particularly with the increasing prevalence of frailty. Polypharmacy is an important risk factor for falls, and also commonly coexists with frailty. Polypharmacy review should be undertaken whenever a patient presents to hospital with a fall in order to address treatable factors and reduce readmission rate. Pharmacokinetics and pharmacodynamics alter with age and may have implications for the suitability of medications in older patients; some drugs may be less effective, whilst others may have more potent side effects, altering the risk/benefit profile considerably when compared to the standard population. A variety of tools are available to aid appropriate deprescribing. We consider some of the real-world barriers to deprescribing and highlight the importance of at least beginning the process in hospital, with a recommendation for robust communication with community services for continuation.

Aims/Objective Pediatric vasovagal syncope (VVS) is prone to relapse and affects the quality of patients' lives. The current study aims to assess the predictive value of the index of cardio-electrophysiological balance (iCEB) for both diagnosis and recurrence of pediatric VVS, to enhance precision in clinical management. Methods This retrospective cohort study analysed 199 syncope patients treated at the First Hospital of Jilin University between January 2019 and December 2023. Participants were stratified into VVS (n = 79) and non-VVS (n = 120) groups. Univariate analysis and binary logistic regression were performed to identify predictors for VVS, while correlations between variables were assessed using Spearman/Pearson/point-biserial correlation coefficients. Predictive value was assessed using receiver operating characteristic (ROC) curve analysis. Results Family syncope history, prolonged standing triggers, iCEB, QT interval (QT), and QRS duration (QRS) showed significant differences between the two groups (p < 0.05). iCEB correlated negatively with QRS (r = -0.612, p < 0.001), and positively with QT (r = 0.403) and prolonged standing triggers (r = 0.164) (both p < 0.05). Logistic regression identified iCEB (odds ratio [OR] = 1.251, 95% confidence interval [CI]: 1.136-1.378, per 0.1 units, p < 0.001) and family syncope history (OR = 2.369, 95% CI: 1.192-4.708, p = 0.014) as independent predictors. ROC curve analysis demonstrated that iCEB has superior accuracy in predicting the recurrence of VVS (area under the curve [AUC] = 0.710, 95% CI: 0.634-0.786; sensitivity 65.83%, specificity 70.89% at cutoff 4.086). The high-iCEB patients (≥4.086, n = 56) exhibited a 2.36-fold higher 1-year recurrence rate than their low-iCEB counterparts (41.07% vs. 17.39%, p = 0.044). Conclusion iCEB demonstrates strong predictive value in diagnosing pediatric VVS, and higher iCEB levels are linked to an increased risk of recurrence within one year, making it a useful reference indicator for recurrence.