Healthcare

PurposeThe objective of this study was to identify predictors of statin adherence in the primary and secondary prevention of CVD among patients in the first two years after the date of first prescription using real-world data. MethodsThe Electronic Practice Based Research Network Linked Dataset was used in this study. Statin adherence was calculated using a modified proportion of days covered (PDC) formula. Individuals with PDC [≥] 80% during the two years of observation period were considered as adherent. All analyses were performed with R software. Descriptive and multivariate logistic regression analyses were performed. Sensitivity analysis was performed using the Akaike Information Criterion model selection method. ResultsOverall, 3,432 patients accounting for 57,227 visits met the selection criteria. The mean PDC was 91.6% ({+/-}22.2%), and 72.0% of the patients were adherent to statins (PDC [≥] 80%) in the first two years after the date of first prescription. After adjusting for all other variables, statin adherence was positively associated with age (AOR 1.7, 95% CI 1.4 - 2.0), SEIFA index (AOR 1.8, 95% CI 1.2 - 2.6), polypharmacy (AOR 1.8, 95% CI 1.3 - 2.5) and comorbidities (AOR 1.4, 95% CI 1.1 - 1.7), and negatively associated with the number of statin types (AOR 0.6, 95% CI 0.5 - 0.9) and smoking status (AOR 0.7, 95% CI 0.6 - 0.9). The sensitivity analysis showed similar results as the regression model. ConclusionsStatin adherence is influenced by an aging, multimorbid population, who are exposed to polypharmacy, multiple statin options and socioeconomic diversity. Key pointsO_LIAdherence in the first two years after the first date of statin prescription was measured as proportion of days covered (PDC) C_LIO_LIThe mean PDC was 91.6% ({+/-}22.2%) C_LIO_LI72.0% of the patients were adherent to statins, with PDC [≥] 80% C_LIO_LIStatin adherence was positively associated with age, area-based social advantage and disadvantage index, polypharmacy and comorbidities C_LIO_LIStatin adherence was negatively associated with the number of statin types prescribed to the patients and the smoking status of patients C_LI Plain Language SummaryThe objective of this study was to identify predictors of statin adherence among patients in the first two years after the date of first prescription using real-world data. The dataset used was the Electronic Practice Based Research Network Linked Dataset. Statin adherence was calculated using proportion of days covered (PDC). A PDC [≥] 80% during the two years of observation period were considered as adherent. Overall, 3,432 patients were eligible for this study, and 72.0% of them were adherent to statins in the first two years after the date of first prescription. Statin adherence was positively associated with age, area-based social advantage and disadvantage index, number of medicines taken by the patient and number of chronic conditions that the patient suffered. Moreover, statin adherence was negatively associated with the number of statin types prescribed to the patients and smoking status of patients.

BackgroundArtificial intelligence chatbots (AICs) are increasingly being integrated into scholarly publishing, with the potential to automate routine editorial tasks and streamline workflows. In traditional, complementary, and integrative medicine (TCIM) publishing, editorial and peer review processes can be particularly complex due to diverse methodologies and culturally embedded knowledge systems, presenting unique opportunities and challenges for AIC adoption. MethodsAn anonymous, online cross-sectional survey was distributed to the editorial board members of 115 TCIM journals. The survey assessed familiarity and current use of AICs, perceived benefits and challenges, ethical concerns, and anticipated future roles in editorial workflows. ResultsOf 5119 invitations, 217 eligible participants completed the survey. While approximately 70% of respondents reported familiarity with AI tools, over 60% had never used AICs for editorial tasks. Editors expressed strongest support for text-focused applications, such as grammar and language checks (81.0%) and plagiarism/ethical screening (67.4%). Most respondents (82.8%) believed that AICs would be important or very important to the future of scholarly publishing; however, the majority (65.3%) reported that their journals lacked AI-specific policies and training programs to guide editors and peer reviewers. ConclusionsMost TCIM editors believe that AICs have potential to support routine editorial functions but also have limited adoption into editorial and peer review processes due to practical, ethical, and institutional barriers. Additional training and guidance are warranted by journals to direct responsible and ethical use if AICs are to be adopted in TCIM academic publishing.

ObjectiveThe objective of this study was to explore the predictors of statin intolerance in the primary and secondary prevention of CVD among patients in the first two years after the date of first prescription using real-world data. MethodsThis study used the Electronic Practice Based Research Network Linked Dataset. An algorithm, which considered the muscle symptoms and creatinine kinase of patients, was used to identify statin intolerant patients. The R software was used for all analyses. Descriptive and multivariate logistic regression analyses were performed along with sensitivity analysis which was done using the Akaike Information Criterion model selection method. ResultsOverall, 4,016 patients accounting for 60,873 visits met the selection criteria. About 3.5% of the patients were statin intolerant. After adjusting for all other variables, statin intolerance was positively associated with gender (AOR 1.5, 95% CI 1.0 - 2.2), SEIFA index (AOR 3.8, 95% CI 2.3 - 6.7), employment status (AOR 2.4, 95% CI 1.1 - 5.7), and comorbidities (AOR 7.0, 95% CI 2.2 - 19.0). A similar direction of associations was seen for the exposures of the model from the sensitivity analysis and the regression model. However, since the unrecorded employment status showed a positive association, the sensitivity analysis suggests that the relationship may be influenced by residual confounding or information bias, indicating that this finding should be interpreted with caution. ConclusionStatin intolerance within the diverse community represented in the dataset is driven by gender, employment status, area-based social advantage and disadvantage index, and comorbidities.

BackgroundAlthough ST-segment elevation myocardial infarction (STEMI) and non-STEMI (NSTEMI) are very similar regarding pathophysiology and clinical treatments, especially NSTEMI comprises a much more heterogenic group of patients and underlying diseases. We therefore aimed to assess the treatments and outcomes of both entities in a large contemporary cohort. MethodsPatients with STEMI and NSTEMI between 01/2010 to 12/2018 were identified from the largest German Health Insurance (AOK, {approx}26 million members). Patient demographics, their hospital course, adherence to guideline-directed drug therapy and overall survival were assessed. ResultsIn total 544,529 patients (mean age 74, IQR 62-82), one third of whom had a STEMI. Chronic kidney disease, peripheral arterial disease, and heart failure were more common in patients with NSTEMI. Patients with STEMI were more likely to get coronary angiograms and percutaneous coronary interventions. Although STEMI more frequently led to cardiogenic shock, the rate of serious cardiac events was lower. Mortality was higher for STEMI only within the first 30 days, whereas long-term survival rates were better. The combination of statins, angiotensin converting enzyme inhibitors /angiotensin receptor blockers, beta blockers, and oral anticoagulants or antiplatelet agents was associated with higher overall survival in patients with STEMI (hazard ratio [HR] 0.20; 95% confidence interval [95%CI] 0.18 - 0.24; p<0.001) or NSTEMI (HR 0.30; 95%CI 0.28 - 0.33; p<0.001). Nevertheless, the prescription rates decreased over time, particular in patients with NSTEMI. ConclusionClear differences between STEMI and NSTEMI were observed regarding short-and long-term survival. Guideline-recommended therapy improved long-term survival, but decreased during the follow-up period.

Neurological health score (NHS), indicating the health of brain and nervous system, helps in identifying high risk individuals, and in recommending lifestyle modifications. In the present study, we developed NHS based on genetic, lifestyle and biochemical variables associated with eight neurological disorders - dementia, stroke, Parkinsons disease, amyotrophic lateral sclerosis, schizophrenia, bipolar disorder, multiple sclerosis and migraine. UK Biobank data from Caucasian individuals was used to develop the model, and the data from individuals of Indian ethnicity was used to validate the model. Logistic regression and XGBoost algorithms were used in selecting the significant variables for the disorders. NHS developed from the selected variables was found to be very significant after adjusting for age and sex (AUC:0.6, OR: 0.95). Higher NHS was associated with a lower risk of neurological disorders and better social well-being. Highest NHS group (top 25%) showed 1.3 times lower risk compared to the rest of the individuals. Results of our study help in developing a framework for quantifying the neurological health in clinical setting.

Natural language processing was applied to 3,586 Australian health practitioner tribunal decisions (1999-2026) to identify patterns in professional misconduct, outcomes, and temporal trends at a scale impractical through manual analysis. A text classification approach categorised 2,428 disciplinary decisions across seven misconduct types with acceptable accuracy for the major categories (per-class F1 0.47-0.82). Boundary violations were the most prevalent misconduct type (30.2%), followed by dishonesty/fraud (29.7%) and professional conduct breaches (28.0%). Reprimand was the most common outcome (53.0%), followed by cancellation (40.2%). Significant increasing trends were identified for boundary violations, dishonesty/fraud, professional conduct breaches, and communication failures. Boundary violations were associated with higher cancellation odds (OR = 1.36, p < 0.001). Opioid medications appeared in 67% of prescribing misconduct decisions. Significant jurisdictional variation in both misconduct types and outcomes was observed, with large effect sizes between major jurisdictions. The findings provide an empirical foundation for monitoring disciplinary trends under the National Law.

BackgroundAcute type A aortic dissection (AAAD) complicated by cardiopulmonary arrest is characterized by high mortality rates, rendering the selection of surgical candidates a subject of intense debate. Despite the necessity for cardiopulmonary resuscitation (CPR) prior to the completion of a definitive intervention, the prognostic impact of CPR duration on postoperative survival and neurological outcomes remains insufficiently elucidated. This study sought to evaluate the association between pre- and intra-operative CPR duration and the incidence of early mortality and central nervous system (CNS) complications in patients undergoing emergent surgical repair for AAAD. MethodsThis retrospective, cohort study was conducted at two tertiary community hospitals in Japan. All the patients who underwent emergency surgery for AAAD between January 2014 and December 2024 were enrolled. A multilevel Cox proportional hazards model, with each patient as level 1 and institutions as level 2, was used to evaluate the association between pre-or intra-operative CPR events and early postoperative mortality and CNS complications. ResultsOf the 880 patients enrolled, 785 (89.2%), 13 (1.5%), and 82 (9.3%) were without CPR, with CPR <15 min, and with CPR [&ge;]15 min, respectively. Among them, death within 30 days post-surgery occurred in 76/785 (9.7%), 3/13 (23.1%), and 47/82 (57.3%), respectively. CNS complications within 30 days post-surgery occurred in 141/785 (18.0%), 5/13 (38.5%), and 38/82 (46.3%) without CPR, CPR <15 min, and [&ge;]15 min, respectively. In multivariable analysis, CPR lasting [&ge;]15 min was associated with mortality within 30 days post-surgery (adjusted hazard ratio, 7.66; 95% confidence interval [CI], 3.56-16.5; P<0.001). Both CPR <15 min and [&ge;]15 min were associated with an increase in the sub-hazard ratio of CNS complications within 30 days post-surgery (adjusted sub-hazard ratios, 4.49; 95% CI, 3.92-5.11; P<0.001, and 3.62; 95% CI, 2.73-4.81; P<0.001, respectively). ConclusionA preoperative CPR duration of [&ge;]15 min prior to the initiation of cardiopulmonary bypass or extracorporeal membrane oxygenation was associated with a substantial escalation in 30-day mortality compared with patients without CPR. These findings suggest that CPR duration might serve as a pivotal prognostic indicator, necessitating careful consideration for surgical indication in patients with AAAD complicated by CPR. CLINICAL PERSPECTIVEO_ST_ABSWhat is new?C_ST_ABSO_LIPre- or intra-operative cardiopulmonary resuscitation lasting [&ge;]15 min in patients with acute type A dissection is associated with a nearly seven-fold increase in 30-day postoperative mortality. C_LIO_LIBoth short (<15 min) and prolonged ([&ge;]15 min) cardiopulmonary resuscitation are associated with a higher risk of early postoperative complications in the central nervous system. C_LI What are the clinical implications?O_LIPatients with acute type A dissection who require pre- or intra-operative cardiopulmonary resuscitation [&ge;]15 min should undergo careful multidisciplinary evaluation, as the risk of early mortality is substantially elevated. C_LIO_LIEven brief cardiopulmonary resuscitation is associated with increased neurological complications, highlighting the need for early neurological monitoring and supportive care postoperatively. C_LI

Emergency nursing is essential to healthcare systems worldwide. Triage plays a pivotal role in emergency nursing, prioritizing patients based on the urgency of their medical condition and focusing on rapid assessment and prioritization of patient care according to their condition and its severity. In the emergency department, the triage nurse assesses vital signs and gathers information from the patient to determine the severity of their condition. This aims to provide appropriate medical intervention quickly for life-threatening cases and minimize waiting times for less critical cases, thus contributing to the efficient allocation of scarce resources. Our study aimed to evaluate the triage knowledge, skills, and practices of emergency nurses in Mafraq, Jordan. MethodsA cross-sectional study used a previously validated questionnaire. Fifty emergency nurses from two public and one private hospital in Mafraq participated. We collected data through an online survey then analyzed in SPSS. Results92% of nurses had sufficient triage knowledge ([&ge;]60%), while 14% exhibited deficient triage skills (<60%) and 86% had moderate skills (60-80%). Regarding practices, 32% rated as "poor" (<60%) and 68% as "adequate" (>60%). Length working in emergency, hospital type significantly related to nurses triage knowledge, skills, and practices. ConclusionThe study underscores continual trainings, simulation programs and mentorships importance for enhancing emergency nurses triage knowledge, skills, especially in rural settings. Implementing clear triage protocols, continuous support and integrating triage competencies into curricula are recommended to improve overall triage competency

We developed and validated a self-administered clinical vignette platform powered by a large language model (LLM), deployed through a SurveyCTO web survey, to measure primary health care provider competencies in Vietnam. In a pilot focus group, nine physicians rated LLM-simulated patient interactions as realistic (mean 3.78/5) and user-friendly. In the validation phase, 22 providers completed 132 vignette interactions across ten clinical scenarios in Vietnamese. Essential diagnostic checklist scores (human-coded from translated transcripts) correlated with expert clinician evaluations (Pearsons{rho} = 0.55-0.60). LLM-automated coding of checklist items from translated English transcripts correlated reasonably with human coding ({rho} = 0.53), and coding directly from Vietnamese transcripts performed comparably ({rho} = 0.51), suggesting that a separate translation step may not be necessary. The total cost of 132 chatbot interactions was under USD 2. LLM-driven conversational vignettes represent a low-cost and scalable method for assessing provider competencies in respondents local language, eliminating the need for extensive enumeration staffs while preserving the open-ended format critical to vignette validity, and additionally introducing flexible feature extraction from transcripts using grading rubrics. The platform is open-source and designed for replication in other health system contexts. Author summaryMeasuring the clinical skills of healthcare providers is essential for improving the quality of care, but current survey methods are expensive and require trained enumerators to travel to health facilities in person. We developed a new approach that uses large language models (LLMs) - the technology behind tools like ChatGPT and Claude - to simulate patients in realistic clinical conversations that healthcare providers can complete on their phones or laptops over the Internet in their own language. In Vietnam, we tested this tool with 31 physicians across ten clinical scenarios. Providers found the simulated patient conversations realistic and easy to use. We also tested whether LLMs could automatically score the conversations, which showed reasonable agreement with human scoring, and performed nearly as well when scoring directly from Vietnamese, without requiring a separate translation step. When we compared these results from our tool against holistic expert physician ratings of the same conversations, the scores agreed well, suggesting that automatic transcript grading based on rubrics produces meaningful measures of clinical skill. This tool costs less than two US dollars for over a hundred consultations and required no in-person surveyors, making it potentially transformative for routine, large-scale monitoring of healthcare quality in resource-limited settings. The platform and code are openly available for adaptation.

BackgroundCardiac surgery significantly improves clinical endpoints but imposes challenges in postoperative recovery. Assessing patient-reported outcome is crucial for optimal care. However, no cardiac surgery-specific tools currently exist to adequately capture postoperative recovery experience. ObjectivesTo develop and validate a recovery scale after cardiac surgery (Fuwai-CRS). MethodsThis study was conducted from May 2023 to December 2024, comprising: (1) a qualitative study (Cohort 1) enrolling postoperative patients of cardiac surgery and medical staffs to develop the draft scale through literature review, semi-structured interview and Delphi consensus; and (2) a single-center prospective validation study (Cohort 2) to finalize the scale and evaluate psychometric properties. ResultsIn Cohort 1, a 17-item draft Fuwai-CRS was generated based on literature review, semi-structured interview (40 patients and medical staffs) and a Delphi study (15 experts). In Cohort 2 (n=500), a 9-item Fuwai-CRS was finalized by data distribution assessment, hierarchical cluster and factor analysis, and its understandability, reliability, validity and responsiveness were found acceptable. ConclusionsThe Fuwai-CRS is a concise and valid tool for recovery assessment after cardiac surgery.

BackgroundShort-video platforms have become increasingly important sources of health information for the general public. However, the informational quality and dissemination patterns of content related to specific therapeutic modalities, such as enhanced external counterpulsation (EECP), remain insufficiently characterized. This study aimed to evaluate the informational quality of EECP-related videos on a short-video platform and to examine the relationship between content quality and user engagement. MethodsA cross-sectional content analysis was conducted on EECP-related short videos identified through keyword-based searches. Informational quality was independently assessed using four validated instruments: the Global Quality Scale (GQS), the Journal of the American Medical Association (JAMA) benchmark criteria, the modified DISCERN instrument (mDISCERN), and the Video Information and Quality Index (VIQI). Video characteristics and user engagement metrics were extracted and analyzed. ResultsOverall, EECP-related videos demonstrated low-to-moderate informational quality across all assessment tools. Longer video duration was consistently associated with higher informational quality scores. In contrast, user engagement metrics, including the number of likes and comments, showed weak or negative associations with informational quality. Compared with videos addressing other coronary heart disease treatments, EECP-related videos were less frequently represented and received lower overall engagement. ConclusionsEECP-related content on short-video platforms is characterized by limited visibility and modest informational quality, with a notable misalignment between user engagement and informational value. These findings suggest that clinically relevant but complex therapies such as EECP may be structurally disadvantaged in short-video health communication environments.

IntroductionEighteenth-century medical texts document a formative period in the evolution of clinical reasoning, yet their integration into modern medical education is limited. The traditional approach to learning the history of medicine has naturally focused on passive reading, but new approaches using AI could enable learners to interrogate and simulate the historical diagnostic logic and therapeutic paradigms. More specifically, large language models (LLMs) offer an opportunity to create interactive simulations that allow experiential engagement with historical medical reasoning. MethodsWe developed a historically constrained LLM-based educational platform designed to emulate the diagnostic reasoning, language, and conceptual frameworks of an 18th-century physician. A modern GPT architecture was customized using strict instruction-based constraints and limited exclusively to a curated corpus of six foundational 17th- 18th century medical texts. Guardrails were implemented to prevent anachronistic terminology and modern medical concepts. Model outputs were evaluated qualitatively by comparing the models diagnoses and treatment plans with published diagnoses and treatment from original 18th century sources. We also applied the simulation to modern clinical vignettes for an illustrative contrast between modern and 18th century approaches. ResultsThe model generated responses that closely aligned with 18th-century medical and rhetorical style, as well as therapeutic reasoning. When presented with historical cases, the simulation demonstrated strong concordance with original diagnoses and management strategies. Secondly, when applied to modern cases, the model described period-appropriate reasoning, highlighting clear contrasts with contemporary biomedical reasoning. ConclusionsAI broadly, and more specifically LLMs configured as historically constrained simulators, can function as effective tools for learning in medical history. This approach could enable active engagement with historical clinical reasoning, fostering critical reflection on the contingent and evolving nature of medical knowledge. Such temporal simulations hold promise for medical humanities education and interdisciplinary teaching.

BackgroundElectronic Health Records(EHR) are very crucial for Clinical Decision Support Systems and for proper care to be delivered to ICU heart failure patients, there is often missing data due to monitoring device errors thus the need for robust imputation methodologies. ObjectiveTo compare and evaluate three different methodologies for imputing missing data for heart failure patients from the MIMIC-III database: Denoising Autoencoder (DAE), Self-Attention Imputation for Time Series (SAITS), and Multiple Imputation by Chained Equations (MICE) with LightGBM. MethodsAnalysis of 14,090 ICU admissions for patients with heart failure was performed using data from the MIMIC-III database. Features were selected based off of clinical relevance, and 19 clinical features were selected through a combination of Random Forest analysis, correlation analysis, and Mutual Information. The introduction of artificial missing values of 20%, 30%, and 50% was applied to the data set, and then 3 imputation methodologies were evaluated with the DAE, SAITS, and MICE+LightGBM. The performance of each imputation methodology was evaluated using Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and Normalized Root Mean Square Error (NRMSE). ResultsBoth DAE and SAITS had superior performance on the imputation of missing values across all percentages of missing values. At 20% missingness, DAE had mean MAE = 0.004967, RMSE = 0.005217, and NRMSE = 3.260893 while SAITS had mean MAE = 0.005461, RMSE = 0.005797, and NRMSE = 3.244695; thus MICE+LightGBM resulted in a higher number of errors. At 50% missingness, the SAITS methodology demonstrated the best performance followed by DAE and MICE+LightGBM methods demonstrated decreased performance. The deep learning methodologies maintained a consistent level of accuracy between the clinical variables measured. ConclusionsOur analysis indicates that deep learning-based imputation methodologies significantly outperform traditional methodologies for imputing missing values in ICU heart failure data thus supporting the implementation of these methodologies into Clinical Decision Support Systems for heart failure patients.

BackgroundDespite recommendations in clinical guidelines, clinical experience indicates that engagement with splints and orthotics varies amongst people after stroke. ObjectivesThe aim of the study was to understand the factors that influence engagement with splints and orthotics in people after stroke. MethodsPeople after stroke who had been wearing a splint or orthotic (also known as devices) for at least 2 months under the care of one Community Neurosciences Team in the UKs National Health Service were included. Semi structured interviews based on the constructs of Banduras Social Cognitive Theory (SCT) were used to gather participants views, and a framework analysis applying the constructs of SCT was completed using NVIVO software. ResultsFour key themes were identified: 1. Self-Regulation; difficulties applying the device and aesthetic acceptability. 2. Self-Efficacy; increased confidence when wearing the device and reduced motivation to wear the device. 3. Outcomes Expectation; reduced falls risk, improved gait, improved balance, maintaining range of movement, and negative effects such as discomfort, pain, itching. 4. Social Support; support needed to apply the device and the burden on family members/carers to apply the device correctly. ConclusionsThe findings of this study highlight key factors that influence engagement with orthotics and splints. These include difficulty applying the device after stroke, device aesthetics, comfort, and the importance of continued support from carers. Manufacturers should consider how people after stroke can independently don and doff devices. Education of carers and family members also appears key to support their engagement.

BackgroundTyping in the electronic health record (EHR) takes up healthcare providers time and cognitive space and constitutes a substantial administrative burden contributing to high burnout rates in healthcare. Ambient digital scribes may improve this problem. ObjectiveTo investigate the effect of the use of Autoscriber, an ambient digital scribe, on healthcare providers administrative workload and the quality of medical notes in the EHR. MethodsA study period of 26 weeks was randomized into weeks when healthcare providers were allowed to use Autoscriber (intervention weeks) and weeks when they were not (control weeks) in a 2:1 ratio. Workload was assessed by comparing the number of characters typed in the medical note during control weeks with the number of modifications that were made to the summary produced by Autoscriber during intervention weeks. Quality of the medical note was measured by having a large language model (LLM) count the number of hallucinations, incorrect negations, context conflation errors, speculations, other inaccuracies, omissions, succinctness errors, organization errors and terminology errors per medical note. ResultsBetween 1 November 2024 and 30 April 2025, 35 healthcare providers from 14 different specialties recorded 387 consultations in intervention weeks, and 142 in control weeks. The median number of characters typed per medical note was 1079 in control weeks and the median number of modifications necessary to produce the medical note was 351 in intervention weeks, compatible with a lower workload. All types of errors occurred significantly less frequently in notes made with the support of Autoscriber than in those without, except for speculations, where the difference did not reach statistical significance. ConclusionsThe use of Autoscriber resulted in a lower workload and a higher quality of the medical note.

BACKGROUNDVisit-to-visit blood pressure variability (VVV BPV) is an important yet underutilised risk factor for cardiovascular disease (CVD) risk prediction. Incorporating VVV BPV in the model predicting CVD could improve its performance. This study aims to incorporate VVV BPV into a CVD risk prediction model and to evaluate its performance by comparing the discrimination and calibration of models using a single BP measurement versus those incorporating VVV BPV METHODSThis prospective cohort study included data from the electronic practice-based research network (ePBRN) in Southwestern Sydney, focusing on patients aged 18-55 years with at least five BP readings, excluding those with incomplete data or no follow-up after 55. VVV BPV measured by standard deviation (SD) and coefficient of variation (CV). The main outcome is the first occurrence of CVD. We developed the models using Cox proportional hazards regression with 10-fold cross-validation on all imputed datasets. Model performance was evaluated for discrimination and calibration. Discrimination was assessed using Harrells C-index and time-varying AUC for five-year CVD prediction. Calibration was assessed using calibration slopes and Brier scores, which were also evaluated annually. RESULTSThe study involved 3,065 patients, with 45.41% women. Incorporating VVV BPV improved the prediction of CVD risk in people aged 55 years. The model with a single systolic blood pressure (SBP) measurement had a Harrel C-Index of 0.716 (95% CI: 0.658 - 0.775), while those using SD and CV scored higher at 0.833 (95% CI: 0.804 - 0.862) and 0.837 (95% CI: 0.810 - 0.864), respectively. Five years AUC for SBP was 0.852 (95% CI: 0.820 - 0.885) for SD and 0.856 (95% CI: 0.824 - 0.888) for CV. In contrast, the single SBP model had a lower AUC of 0.757 (95% CI: 0.700 - 0.815). No significant difference was observed in calibration slopes and Brier scores between the model using single BP and VVV BPV. CONCLUSIONSThis study developed a model for CVD risk estimation using VVV BPV instead of a single blood pressure measurement. Replacing a single BP measure with VVV BPV significantly enhanced the models predictive accuracy.

IntroductionClinicians and patients are likely to increasingly use Large Language Models (LLMs) for diagnostic support. Use of LLMs mostly created in North America and Europe, could lead to a High-Income Country bias if used in Low- and Middle-Income Country (LMIC) healthcare settings. We aimed to explore if diagnostic suggestions made by LLMs are relevant in LMIC settings. MethodsFive short respiratory clinical vignettes were produced. For each vignette, a group of doctors from one of 5 countries (Ghana, India, Jordan and Brazil and the UK) independently gave the 4 most likely diagnoses. 4 LLMs (ChatGPT, Claude Sonnet, Google Gemini and Microsoft Copilot) were prompted with the same vignettes. The top 4 diagnoses for each case was requested. A Virtual Private Network (VPN) was used to access the LLM from each of the 4 countries, and in a second experiment the LLM was given the same vignettes but also informed of the country in which the case was based in the prompt. The diagnoses presented by the LLMs was compared with the doctors diagnoses for the LMICs and also compared to the UK. Results106 unique diagnoses were offered by 21 doctors, and 53 by LLMs with a VPN. The LLMs proposed fewer of the doctors diagnoses in LMICs versus in the UK - 50% (95% CI 32.6 to 67.4%) in the UK compared to 32.0% (95% CI 23.1 to 42.3%) in LMICs. This effect persisted when the LLM was informed of the location of the doctor in the prompt. Overall, LLMs performed worse in the LMIC setting (Chi-squared p = 0.028). ConclusionDoctors working in LMICs consider a wider range of diagnoses than LLMs, even when LLMs are queried from that country, or informed that they are in that country. LLMs appear to show a bias when considering likely diagnosis likely related to the epidemiology of high income countries.

The objective of this systematic review and meta-analysis was to identify the interventions used to manage intolerance in patients receiving statins for primary prevention of CVD and to determine the effectiveness of these interventions. This study was conducted according to the PRISMA checklist. The electronic databases MEDLINE (PubMed), SCOPUS, EMBASE, and CINAHL were searched for studies published until June 2025. Based on the NLA definition of statin intolerance, the outcomes were split into adverse effects caused by statins and statin discontinuation. In total, 1,238 studies were identified and screened. Nine studies were eligible for systematic review, and six studies were eligible for meta-analysis. The identified intervention strategies were adjuvant therapy, statin titration, replacing statins with other lipid-lowering agents and switching to different statin. The meta-analysis showed that the pooled risk ratio (RR) relative to control was 0.97 (95% CI, 0.86-1.08) in randomized controlled trials and 0.94 (95% CI, 0.63-1.42) in overall, with point estimates in favour of intervention arms. Moderate to substantial heterogeneity was observed, with I2 between 27% to 57%. Due to the smaller number of studies, no clear conclusions can be drawn regarding how the implemented interventions may affect statin discontinuation. This study showed no strong evidence that the implemented interventions reduced statin intolerance. PROSPERO registration numberCRD42024587573 HighlightsThis study found that the intervention strategies used to manage intolerance in patients receiving statins for the primary prevention of cardiovascular diseases were adjuvant therapy, statin titration, replacing statins with other lipid-lowering agents and switching to different statin. O_LIThis study showed no strong evidence that the implemented interventions reduced statin intolerance C_LIO_LIDue to the smaller number of studies, no clear conclusions can be drawn regarding how the implemented interventions may affect statin discontinuation C_LI

I.AO_SCPLOWBSTRACTC_SCPLOWCoronary Artery Disease (CAD) is a leading cause of cardiovascular-related mortality and affects 20.5 million people in the United States and approximately 315 million people worldwide in 2022. The asymptomatic and progressive nature of CAD presents challenges for early diagnosis and timely intervention. Traditional diagnostic methods such angiography and stress tests are known to be resource-intensive and prone to human error. This calls for a need for automated and time-effective detection methods. In this paper, this paper introduces a novel approach to the diagnosis of CAD based on a Convolutional Neural Network (CNN) with a temporal attention mechanism. The model will be developed on an architecture that will automatically extract and emphasize critical features from sequential medical imaging data from coronary angiograms, allowing subtle signs of CAD to be easily spotted, which could not have been detected by convention. The temporal attention mechanism strengthens the ability of a model to focus on relevant temporal patterns, thus improving sensitivity and robustness in detecting CAD for various stages of the disease. Experimental validation on a large and diverse dataset demonstrates the efficacy of the proposed method, with significant improvements in both detection accuracy and processing time compared to traditional CNN architectures. The results of this study propose a scalable solution system for the diagnosis of CAD. This proposed system can be integrated into clinical workflows to assist healthcare professionals. Ultimately, this research contributes to the field of AI-driven healthcare solutions and has the potential to reduce the global burden of CAD through early automated detection.

BackgroundScreening for atherosclerosis focuses on identifying Standard Modifiable Risk Factors (SMuRFs), including diabetes, hypertension, hyperlipidaemia, and smoking. PurposeTo compare the extracardiac thoracoabdominal atherosclerotic plaque burden, as measured by computed tomography angiography (CTA), among heart transplant candidates with ischemic or non-ischemic cardiomyopathy (ICM, NICM), and evaluate potential associations between plaque burden and SMuRFs. MethodsThis retrospective study identified heart transplant candidates with ICM or NICM matched for age and sex, undergoing thoracoabdominal CTA. Patients were classified as with SMuRFs or SMuRF-less. Extracardiac thoracoabdominal non-calcified and calcified atherosclerotic plaque was classified as present or absent across 78 arterial segments per patient. ResultsAmong included patients (n=167, median [interquartile range] age 58 [53-63] years, 16% female, 51% NICM), 40 patients (24%) were SMuRF-less (ICM: 16/82 (20%), NICM: 24/85 (28%), age 56 [50-67] years). Overall, out of 13,026 arterial segments analysed, 1,746 (13%) were affected by atherosclerotic plaque (9 [4-15] segments per patient). ICM had a higher total plaque burden than NICM (11 [7-18] vs 6 [3-11] segments per patient, p<0.001). SMuRF-less patients showed no difference in non-calcified, calcified, or total plaque burden compared to patients with SMuRFs, among all patients (ICM+NICM, p>0.17) and within the ICM and NICM groups, respectively (p>0.30). ConclusionsThe burden of extracardiac thoracoabdominal atherosclerotic plaque is higher among heart transplant candidates with ICM. However, it does not differ between SMuRF-less or those with SMuRFs, regardless of underlying ICM or NICM. The prevalence of SMuRFs is not an effective marker to determine the need to screen for extracardiac atherosclerotic plaque among heart transplant candidates. GRAPHICAL ABSTRACT O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=134 SRC="FIGDIR/small/26347056v1_ufig1.gif" ALT="Figure 1"> View larger version (51K): org.highwire.dtl.DTLVardef@1aff6b1org.highwire.dtl.DTLVardef@16cfb07org.highwire.dtl.DTLVardef@1d4894corg.highwire.dtl.DTLVardef@81e9d3_HPS_FORMAT_FIGEXP M_FIG C_FIG