British journal of hospital medicine最新文献

Aims/background: Stroke recurrence remains a significant challenge in post-stroke management, with traditional prediction models often showing limited accuracy. This study aims to compare the performance of multiple machine learning (ML) algorithms that integrate routine clinical variables with imaging-derived features in predicting stroke recurrence risk, and to identify the optimal predictive model.

Methods: This retrospective cohort study enrolled 350 patients with ischemic stroke who were admitted to The Fifth People's Hospital of Jinan between January 2018 and December 2021. Patients were divided into three groups based on the time of first stroke onset: Group A (n = 110), Group B (n = 120), and Group C (n = 120). Routine clinical variables (age, gender, hypertension, and diabetes) and imaging features (infarct size and location) were collected. Four ML-based algorithms-logistic regression, random forest (RF), support vector machine (SVM), and extreme gradient boosting (XGBoost)-were used to construct predictive models. The predictive performance of these models was evaluated by area under the curve (AUC), sensitivity, specificity, and accuracy.

Results: The XGBoost model showed the superior predictive performance, achieving the highest AUC of 0.86, followed by the random forest model (0.82), support vector machine model (0.78), and logistic regression model (0.75). The most influential predictors for stroke recurrence were found to be infarct size, history of hypertension, and fasting blood glucose levels.

Conclusion: ML-based algorithms that integrate routine clinical variables with imaging-derived data can predict stroke recurrence risk effectively, with the XGBoost model demonstrating superior predictive performance, which may further support more individualized clinical decision-making.

The global prevalence of diabetes mellitus (DM) continues to rise, with type 1 diabetes mellitus (T1DM) and type 2 diabetes mellitus (T2DM) being the most common subtypes. T1DM is characterised by the autoimmune destruction of pancreatic β-cells leading to absolute insulin deficiency, whereas T2DM is associated with insulin resistance and relative insulin insufficiency, often linked to lifestyle factors. Both subtypes are frequently misdiagnosed or underdiagnosed due to insufficient screening awareness, outdated diagnostic processes, and poor patient compliance, leading to delayed interventions and increased complication risks. This review examines information-management-based blood glucose control pathways, focusing on their role in improving the diagnostic rates of newly diagnosed T1DM and T2DM. It specifically examines the applications of key technologies: electronic health records (EHRs) for integrating multi-source data (e.g., autoantibodies for T1DM, metabolic indicators for T2DM), mobile health (mHealth) applications for real-time monitoring and targeted screening reminders, artificial intelligence (AI) for developing subtype-specific risk prediction models, Internet of Things (IoT) devices for capturing subtype-specific glycemic patterns, and blockchain for secure data sharing. Furthermore, the review describes how these technologies enhance early detection by optimising screening workflows, improving patient adherence, and facilitating accurate subtype differentiation. Despite demonstrated potential, challenges include data security, technological accessibility, and system interoperability. Future research should prioritise personalised pathways for each subtype, integrate multi-omics data, refine AI algorithms for subtype-specific diagnosis, and strengthen policy support to develop a precise, efficient early screening system for DM.

Aims/background: The World Health Organization Disability Assessment Schedule 2.0 (WHODAS 2.0) is extensively used internationally for assessing disabilities across diverse cultures and health conditions; however, psychometric validation of its Arabic version remains scarce. This study aimed to evaluate the psychometric properties regarding the Arabic WHODAS 2.0 using Rasch analysis, providing the first validation evidence of reliability, validity, dimensionality, and item functioning among individuals with disabilities in Arabic-speaking populations.

Methods: This cross-sectional study included 486 Arabic-speaking participants. Rasch analysis was performed using WINSTEPS software to assess item fit, item and person reliability, unidimensionality, separation indices, differential item functioning (DIF), and category functioning.

Results: The Arabic WHODAS 2.0 showed strong person (0.80) and item (0.99) reliability, with adequate item-person targeting. Most items fit the Rasch model, except for World Health Organization Disability Assessment Schedule 5 (WHODAS5), which displayed misfit and disorder thresholds. Principal component analysis supported unidimensionality (53.4% variance explained), and no significant DIF was detected across the demographic groups.

Conclusion: The Arabic version of the WHODAS 2.0 is a reliable and valid tool for assessing disability in Arabic-speaking populations. Overall, it demonstrated strong psychometric properties, although the WHODAS5 requires revision. This scale is suitable for clinical and research use, with recommendations for further qualitative validation and longitudinal testing.

Aims/background: Inferior vena cava (IVC) anomalies are uncommon, with absent infrarenal IVC reported as one of the rarest congenital vascular anomalies. They are associated with deep vein thrombosis (DVT) particularly in younger patients, which can have serious complications if not recognised and treated appropriately.

Case presentation: We describe the case of a 35-year-old male with absent infrarenal IVC presenting with acute DVT.

Results: Radiologists and clinicians should understand the relationship between IVC anomalies and DVT, particularly in younger patients.

Conclusion: This case highlights the importance of multi-modality diagnostic and interventional imaging techniques in assessing and treating patients effectively.

Aims/background: Increased adenosine deaminase (ADA) activity in cerebrospinal fluid (CSF) has been used as a biomarker of tuberculous meningitis and other etiologies, including viral, fungal, and neoplastic meningitis. Gout can influence inflammatory pathways, altering biochemical markers such as ADA. This report describes a unique case of varicella-zoster virus (VZV) meningitis with gout associated with elevated ADA levels.

Case presentation: A 37-year-old man presented with a one-week history of headache, fever, and chills. His medical history included gout, diagnosed three years earlier. CSF analysis from the first lumbar puncture revealed lymphocytic pleocytosis, elevated protein, mildly decreased glucose, and ADA activity of 7.4 IU/L. The patient developed worsening headache and vomiting, prompting a second lumbar puncture. Repeat CSF analysis showed persistent lymphocytosis, mildly decreased glucose, and increased ADA activity (9.1 IU/L). The patient also experienced an acute gout flare during hospitalization, which required antigout therapy. The subsequent CSF polymerase chain reaction (PCR) confirmed VZV meningitis.

Results: The patient responded well to symptomatic treatment. Serum ADA levels were also elevated (14.7 IU/L), likely reflecting systemic inflammation due to gout.

Conclusion: Systemic inflammation, which includes the meninges and joints, had been postulated to increase protein diffusion through the vascular walls and had been associated with increased ADA activity in CSF and serum. To the best of our knowledge, this is one of the few reports on VZV meningitis and gout associated with elevated ADA levels.

Asthma is a common and refractory chronic inflammatory disease of the airways, with its acute episodes governed by a multitude of factors. The postulation that skin barrier damage poses a direct threat to respiratory system health is gaining traction. In fact, asthma is closely associated with various skin barrier dysfunction-associated diseases such as atopic dermatitis and atopic eczema. Patients with atopic eczema frequently exhibit concomitant atopic dermatitis, whereas asthma is often accompanied by allergic rhinitis, constituting the classic pattern of the "allergic march". Mechanistically, these conditions share key pathological features, including genetic susceptibility (such as filaggrin (FLG) gene mutations), immune dysregulation characterized by a predominantly T helper 2 (Th2) type inflammatory response, and epithelial barrier dysfunction. Environmental factors, such as dust mite allergens, induce systemic sensitization by damaging the skin barrier, subsequently triggering airway inflammation. Targeted therapies have shown significant efficacy in both severe asthma and atopic dermatitis, highlighting a potential pathogenic pathway shared by the two conditions. In this paper, we review the relationships between asthma and skin barrier damage from a mechanistic viewpoint, thereby providing an important theoretical basis for the early prevention and precise treatment strategies of allergic diseases with overlapping pathogenic pathways.

Aims/background: The incidence of major adverse cardiovascular events (MACE) following percutaneous coronary intervention (PCI) remains high among patients with acute ST-segment elevation myocardial infarction (STEMI). The Pan-Immune-Inflammation Value (PIV), an indicator of systemic inflammatory status, has demonstrated prognostic utility across multiple diseases and may serve as an indicator of risk of STEMI patients undergoing PCI. This study aimed to investigate the predictive value of PIV for MACE within one year following PCI in patients with acute STEMI.

Methods: A retrospective analysis was conducted on 200 patients diagnosed with acute STEMI who underwent PCI at the Hainan General Hospital between January 2022 and December 2023. Based on the occurrence of MACE within one year post-PCI, patients were categorized into a MACE group (n = 148) and a non-MACE group (n = 52). Univariate and multivariate logistic regression analyses were conducted to identify factors influencing MACE occurrence in STEMI patients following PCI, and the predictive value of PIV was assessed using receiver operating characteristic (ROC) curve analysis.

Results: There were no statistically significant differences between groups in gender, smoking status, body mass index (BMI), red and white blood cell counts, Left Atrium Diameter (LAD), Left Ventricular End-Diastolic Diameter (LVDd), total protein, albumin, triglycerides (TG), and total cholesterol (TC) (p > 0.05). However, significant differences were observed in age, heart rate, left ventricular ejection fraction (LVEF), and PIV (p < 0.05). Multivariate logistic regression analysis identified age, heart rate, PIV, and LVEF as independent predictors of MACE within one year post-PCI in patients with STEMI (p < 0.05). ROC curve analysis showed that the predicted area under the curve (AUC) for PIV in predicting the occurrence of MACE was 0.866 (p < 0.001) (95% confidence interval (CI): 0.809-0.922), with a standard error of 0.029, and a Youden index of 0.62. At this threshold, the sensitivity was 77.70%, and the specificity was 84.60%.

Conclusion: PIV demonstrates high predictive value for MACE within one year after PCI in patients with STEMI. It may serve as a valuable biomarker for assessing the risk of percutaneous coronary atherosclerosis events in this population. Further multi-center studies are warranted to validate its clinical utility.

Aims/background: The detection rate of isolated terminal ileal ulcers (ITIU) is increasing during colonoscopy. However, its clinical significance remains unknown. This study aimed to explore the detection rate and risk factors of ITIU in individuals undergoing colonoscopy and evaluate the outcomes of patients with nonspecific ITIU.

Methods: Overall, 11,504 consecutive individuals who underwent colonoscopy at Department of Gastroenterology, General Hospital of Northern Theater Command between 1 July 2021 and 31 December 2022 were retrospectively screened. Among the individuals who completed terminal ileum (TI) intubation, the detection rate of ITIU was calculated. Furthermore, among the individuals with complete baseline data, logistic regression analyses were performed to identify the independent factors associated with ITIU. At least one-year follow-up outcome after the index colonoscopy was evaluated in patients with nonspecific ITIU who received and did not receive empiric therapy.

Results: Overall, 9649 individuals completed TI intubation with an ITIU detection rate of 1.3% (123/9649). Among them, 1709 individuals had complete baseline data and were further selected in the risk factor analysis. Compared with the non-ITIU group, the ITIU group was significantly younger (56 vs. 46, p = 0.010). Multivariate logistic regression analyses also demonstrated that age ≤50 years (adjusted odds ratio [aOR] = 1.947, 95% confidence interval [CI] = 1.056-3.587, p = 0.033) was independently associated with ITIU. There was no significant difference in clinical symptoms (p = 1.000) or colonoscopic findings (p = 0.560) between patients with nonspecific ITIU who received empiric therapy and observational follow-up.

Conclusion: ITIU is not rare during colonoscopy. Young individuals may have a higher probability of suffering from ITIU. Empiric therapy may not be necessary for patients with nonspecific ITIU.

Falls and high fall risk in older adults are key signals of unmet underlying health issues as well as further fall recurrence and injury. With reference to contemporary National Institute of Health and Care Excellence (NICE) guidance, World Guidelines and National Audit of Inpatient Falls (NAIF) reporting data, this commentary presents an analysis of current progress, opportunity and forward challenge in service implementation and delivery for two focused key UK National Health Service (NHS) hospital-based groups-(1) Hospital inpatients and (2) Accident and Emergency (A&E) attendees-target groups with UK evidence of preventative and health benefit from prompt comprehensive assessment and management. For inpatients, the National Audit of Inpatient Falls (NAIF) is an operational evidence-based hospital service resource delivering and monitoring progress at the NHS national and trust level. For hospital A&E attendees with a fall, the basis for a comparable, consistent, cost-effective NHS falls prevention service design and audit process is demonstrable from UK research evidence, but unresolved challenges in service implementation remain, and a standardised audit system is still lacking. Forward strategies involving clear leadership and audit are proposed.