Journal of Personalized Medicine

Hypermobile Ehlers-Danlos Syndrome (hEDS) is a genetic connective tissue disorder characterized by hypermobile joints, chronic pain, fatigue, brain fog, orthostatic intolerance, and GI symptoms and dysmotility. Its heterogeneous presentation contributes to poor quality of life, inappropriate interventions, and prolonged diagnostic delays, often up to 10 years. This study primarily aimed to determine if physiological signals captured by a medical-grade wrist wearable could characterize autonomic patterns in hEDS and relate them to symptoms. Individuals with hEDS (n=30) and healthy controls (n=28) wore a medical grade smartwatch for 30 days, collecting continuous heart rate variability, activity, oxygen saturation, and blood pressure, alongside initial baseline symptom and quality-of-life surveys. Individuals with hEDS showed greater instability and variability in both systolic and diastolic blood pressure as well as the HRV metric LF/HF ratio, in comparison to healthy controls (p-values: 0.04, 0.02, 0.02). During sleep, metrics of parasympathetic activity (HRV measures: HF power, pNN50, RMSSD) trended lower in hEDS than healthy in comparison. As expected, survey domains assessing physiologic symptoms and quality-of-life were significantly worse in the hEDS cohort (p-values < 0.05). Notably, autonomic metrics correlated with GI symptoms in the hEDS cohort (Spearman's {rho} range: 0.38-0.60), and psychological symptoms in the healthy cohort (Spearman's {rho} range: -0.47-0.41). Principal component analysis (PCA) of physiologic and symptom features clearly separated groups, supporting distinct physiologic profiles. Combination of GI symptom index and wearable monitoring show promise as a hybrid screening approach that could substantially shorten the time to diagnosis in this population.

ObjectiveAiming at the core problems prevalent in biomedical research, including the "translational distance", the difficulty in aligning cross-scale studies, and the lack of direct validation of single-cell systems biology models in human samples, this study aims to verify whether the results of transcriptome-wide Mendelian randomization (TWMR) based on large-scale populations are consistent with the causal inference results of deep learning combined with double machine learning (DML) using single-cell transcriptome data from human samples, to clarify whether statistical biology and systems biology can converge to the same biological truth, and provide methodological support for mechanism dissection and precision medicine research of complex diseases such as rheumatoid arthritis (RA). MethodsThis study integrated multi-omics data to conduct a two-stage causal inference and cross-scale validation analysis. In the first stage, based on the summary statistics of RA genome-wide association study (GWAS) from 456,348 individuals of European ancestry in the UK Biobank (UKB), and cis-expression quantitative trait locus (cis-eQTL) data from 31,684 individuals in the eQTLGen Consortium, a two-sample Mendelian randomization approach was adopted. Transcriptome-wide causal effect analysis was performed using the inverse-variance weighted (IVW) method, MR Egger regression, and weighted median method, and gene-level causal effect values were obtained after strict quality control and multiple testing correction. In the second stage, based on single-cell RNA sequencing (scRNA-seq) data from RA patients and healthy controls (RA group: 11 samples, 211,867 cells; Healthy control group: 38 samples, 456,631 cells), after preprocessing via the Seurat pipeline, batch effect correction, and cell type annotation, a hierarchical deep neural network was constructed to complete feature compression of high-dimensional expression data, and the DML framework was used to estimate the causal effects of genes on RA disease status. Finally, Pearson correlation analysis was performed to conduct cell type-specific cross-scale validation of gene-level causal effect values obtained by the two methods, and the validated model was used to quantify the causal effects of 16 RA-related pathways from the Reactome database. ResultsThis study confirmed that the gene causal effect values obtained from large-scale population TWMR analysis were significantly correlated with those calculated by the deep learning combined with DML model based on single-cell transcriptome data. Among them, the correlation was extremely significant (p<0.001) in core naive B cells (r=0.202, p=3.2e-05, n=414) and core naive CD4 T cells (r=0.102, p=0.037, n=412). The validated DML model successfully quantified the cell type-specific causal effect values of 16 RA-related signaling pathways. ConclusionStatistical biology and systems biology can converge to the same biological truth. The cross-scale consistency between the two can significantly shorten the "translational distance" in biomedical research, and realizes the direct validation of the single-cell systems biology causal model of human samples based on large-scale population genetic data, getting rid of the excessive dependence on animal/cell experimental models in traditional research. This research paradigm not only provides a new path for mechanism dissection and therapeutic target screening of complex diseases such as RA, but also provides a feasible solution for rare disease research to break through the limitation of GWAS sample size, and lays an important theoretical and methodological foundation for constructing standardized systems biology models of human complex diseases and promoting the development of precision medicine.

Objectives: Older adults with comorbidities and polypharmacy have disproportionately high risk of hospitalization as well as readmission from adverse drug events (ADEs), of which 28%-71% are preventable (pADEs). This paper introduces an LLM application, CommunicADE, designed to support risk-mitigation of pADE-related readmission for the aforementioned population. We aim to evaluate CommunicADE's technical performance with OpenAI's HealthBench criteria: accuracy, completeness, communication quality, context awareness, and instruction following. Materials and Methods: Our technical validation study used an LLM (KimiK2.5) to simulate interviews between CommunicADE and nine high-fidelity synthetic patients hospitalized and at increased risk for pADE-related readmission (65+ years, comorbidities, 5+ medications). Some pADE risk mechanisms clues were visible to CommunicADE in patient H&Ps, but most mechanisms were solely discoverable in interviews. Two pharmacists evaluated CommunicADE's interview questions and EHR notes with HealthBench-informed variables. Analyzes used descriptive statistics. Results: For 35 mechanisms across 9 patients (avg=3.89 mechanisms/patient), CommunicADE's precision and recall were 0.92 and 0.63, respectively. Hallucinations were absent. Coherence and person-centeredness scored 4.28 and 4.44 on a 5-point scale (5=highest). On average, communication was at a 5th grade level and objective for 78% of patients. Most patient-reported quotes included in notes (92%) supported detected mechanisms. CommunicADE followed all instructions regarding interview length and patient approvals. Discussion: CommunicADE's strongest performance was in accuracy (precision, hallucinations), communication quality (coherence, readability), context awareness (person-centeredness). Completeness (recall) and instruction following (objectivity, pADE mechanism/quote alignment) show room for improvement. Conclusion: Findings suggest technical readiness for a feasibility pilot with real-world patients, and key areas for performance improvement.

Background: Obesity is globally recognized as a complex, multifactorial chronic disease, with biological, psychological, environmental and behavioural factors involved in both disease pathogenesis and maintenance. Although previous group-based studies demonstrated involvement of each of these factors, there is large inter-individual variability in the factors contributing to disease development as well as intervention outcomes, causing limited translatability to the individual level. This heterogeneity in treatment effectiveness might be due to differential causal and maintenance factors of obesity. To enable the transition from a one-size-fits-all approach to a more personalized approach for individuals with overweight or obesity, this study aims to investigate if and how the degree of weight loss and changes in daily life behaviour after a combined lifestyle intervention depend on individual baseline profiles comprising of person characteristics, biological, psychological, environmental and behavioural factors. Methods: This study will include 600 individuals varying in BMI, 200 participants with a healthy BMI (18.5-24.9kg/m2), 200 with overweight (BMI 25.0-29.9kg/m2), and 200 with obesity (BMI [&ge;]30.0kg/m2). For all participants, a comprehensive individual baseline profile is created, including person characteristics, biological, psychological, environmental and behavioural factors. A clustering method is applied to identify clusters of participants with similar characteristics. Next, we examine if and how these clusters are linked to bodyweight indicators measured at baseline, and how they relate to daily lifestyle behaviour, as measured by ecological momentary assessment (EMA) using a smartphone app and sensor technology (3-week measurements). Individuals with overweight or obesity will be randomized to the intensive lifestyle intervention or a lifestyle information condition, to determine if treatment response can be predicted based on cluster characteristics, how daily lifestyle behaviour changes after an intervention, and how changes in daily lifestyle behaviour relate to treatment response. Discussion: The End of Average study aims to characterize a large set of individuals varying in body weight to predict intervention effectiveness measured as changes in body weight indicators and in daily lifestyle behaviours. If reliable predictors of treatment success can be identified, these can be applied in personalized lifestyle interventions to improve lifestyle behaviour, body weight management and overall health.

ObjectiveTo analyze the burden, trends, health inequalities, and future projections of heart failure in Asia from 1990 to 2023 based on the Global Burden of Disease (GBD) 2023 study. MethodsAge-standardized prevalence rates (ASPR) and age-standardized Years Lived with Disability (YLDs) rates were calculated. Joinpoint regression, Age-Period-Cohort (APC) models, Das Gupta decomposition analysis, Data Envelopment Analysis (DEA), Slope Index of Inequality (SII), Concentration Index (CI), and Bayesian Age-Period-Cohort (BAPC) models were employed. ResultsIn 2023, approximately 30.29 million (95% uncertainty intervals [UI]: 23.66-37.99 million) heart failure cases occurred in Asia, with an ASPR of 602.603 per 100,000 (95% UI: 471.499-754.961). YLDs totaled 2.92 million (95% UI: 1.89-4.26 million), with an age-standardized YLDs rate of 57.970 per 100,000 (95% UI: 37.575-84.627). From 1990 to 2023, ASPR increased with an estimated annual percentage change (EAPC) of 0.252% (95% CI: 0.231-0.273) and the age-standardized YLDs rate increased with an EAPC of 0.238% (95% CI: 0.219-0.257). East Asia had the highest ASPR (674.809 per 100,000) and YLDs rate (65.802 per 100,000). Males had higher ASPR and YLDs rates than females across all subregions. Decomposition analysis showed that aging (49.04%) and population growth (41.80%) were the primary drivers of burden increase. SII deteriorated from 10.684 in 1990 to 25.003 in 2023 (134.0% increase), and CI declined from -0.077 to -0.229, indicating widening health inequalities. BAPC projections estimated that ASPR will rise to 623.351 per 100,000 and the age-standardized YLDs rate to 60.596 per 100,000 by 2038. ConclusionsHeart failure burden in Asia increased from 1990 to 2023 with marked regional and gender disparities, expanding health inequalities and is projected to continue rising through 2038.

Carsickness impairs comfort and affects a large proportion of the population. However, interventions that provide a therapeutic solution to carsickness have yet to be established. Here we introduce a wearable mindfulness meditation brain-computer interface (MM-BCI) system as a closed-loop training therapy for carsickness. The system records electroencephalographic activity, decodes meditative state in real time and delivers audiovisual neurofeedback to scaffold meditation practice. In a 10-week randomized controlled trial, 60 individuals susceptible to carsickness were assigned to practice mindfulness meditation with either real-time MM-BCI neurofeedback or sham feedback, both during real-world car riding and at home. Critically, pre-intervention, post-intervention, and one-month follow-up assessments of carsickness severity were conducted during regular car riding without any task or feedback system. Relative to the sham group, the MM-BCI group showed significantly reduced carsickness severity at post-intervention and follow-up. At baseline, carsickness-susceptible participants exhibited a reduced aperiodic exponent in occipito-parietal cortex relative to non-susceptible controls, identifying a candidate neural signature of carsickness susceptibility. MM-BCI training increased this exponent toward non-susceptible levels, and the magnitude of this neural normalization was associated with the degree of symptom improvement. This study provides the first demonstration that BCI-enhanced mindfulness meditation can induce promising treatment effect on carsickness, offering a transformative non-pharmacological approach to enhance passenger well-being in everyday transit.

Background: Irritable bowel syndrome (IBS) is characterized by heterogeneous symptom trajectories and high treatment discontinuation rates. Traditional analyses examine longitudinal outcomes and time-to-event endpoints separately, potentially missing informative dropout and the association between symptom dynamics and treatment persistence. Objective: To jointly model patient-reported IBS symptom trajectories and time-to-treatment discontinuation using shared random effects, characterizing the association between individual symptom dynamics and treatment persistence in a large Canadian prospective cohort. Methods: We analyzed 2,847 adults with Rome IV diagnosed IBS enrolled in the Canadian Gut Project (2018 to 2024) across 14 gastroenterology centres in Alberta, British Columbia, and Ontario. The longitudinal submodel used linear mixed-effects regression for the IBS Severity Scoring System (IBS-SSS) measured at baseline and months 3, 6, 12, 18, and 24. The survival submodel used a Weibull proportional hazards model for time-to-treatment discontinuation. The joint model linked both processes through shared random effects (random intercept and slope), estimated via maximum likelihood with adaptive Gauss-Hermite quadrature (15 nodes). We conducted sensitivity analyses using Bayesian estimation, alternative association structures (current value, time-dependent slopes), and multiple imputation for intermittent missingness. Results: Mean baseline IBS-SSS was 298.4 (SD 72.1). Over 24 months, 1,042 participants (36.6%) discontinued treatment. The longitudinal submodel revealed a mean IBS-SSS decline of -8.7 points/month (95% CI: -10.2, -7.1) with substantial between-person heterogeneity in both intercepts (STD = 4,218.3) and slopes (STD = 12.4). The association parameter linking the shared random intercept to the hazard of discontinuation was = 0.0034 (95% CI: 0.0021, 0.0047; p < 0.001), indicating that each 10-point increase in individual-specific baseline severity increased the hazard of discontinuation by 3.5%. The shared slope association parameter was 2 = -0.187 (95% CI: -0.264, -0.110; p < 0.001), demonstrating that individuals with steeper symptom improvement had lower discontinuation hazards. IBS-D subtype (HR = 1.41; 95% CI: 1.18, 1.69), concurrent anxiety (HR = 1.28; 95% CI: 1.09, 1.50), and social media health information use (HR = 0.82; 95% CI: 0.71, 0.95) were significant predictors in the survival submodel. Conclusion: Joint longitudinal-survival modelling reveals that IBS symptom trajectories and treatment discontinuation are dynamically linked through individual-level latent processes. Higher baseline severity and slower improvement trajectories significantly predict earlier discontinuation. These findings support personalized treatment monitoring approaches that use real-time symptom trajectory data to identify patients at risk of discontinuation.

Schizophrenia (SCZ) and type 2 diabetes mellitus (T2DM) are common comorbid disorders that severely impair patient prognosis and quality of life. This study aimed to explore the association between the methylenetetrahydrofolate reductase (MTHFR) C677T gene polymorphism and MTHFR promoter methylation in patients with comorbid SCZ and T2DM. A total of 120 participants were enrolled from Liaocheng Fourth Peoples Hospital between January 2025 and June 2025, comprising 30 subjects in each of the four groups: SCZ group, T2DM group, SCZ-T2DM comorbid (SCZ+T2DM) group, and healthy control (CTL) group. Corresponding primers were designed for genetic analysis, and methylation-specific PCR (MSP) was performed to detect the methylation level of the MTHFR promoter. Genotype distribution of the MTHFR C677T polymorphism was consistent with Hardy-Weinberg equilibrium (HWE) (p>0.05). The C677T polymorphism was significantly associated with an elevated risk of SCZ and T2DM comorbidity (p<0.05). Notably, the methylation rate of the MTHFR promoter in the SCZ+T2DM group (95.00%) was not significantly higher than that in the CTL group (90.00%) (p>0.05). In conclusion, the MTHFR gene may serve as a susceptibility gene for SCZ-T2DM comorbidity, whereas MTHFR promoter methylation is not associated with the pathogenesis of this comorbid condition. These results indicate that genetic variation in MTHFR, rather than promoter methylation, contributes critically to the comorbidity of SCZ and T2DM in the Han Chinese population. Our findings may provide novel molecular insights into their shared pathophysiology and inform future clinical strategies for patients with this complex phenotype.

In this study, we investigate gender bias in a Retrieval-Augmented Generation (RAG) based AI assistant developed for Finnish wellbeing services counties. We tested the system using 36 clinically relevant queries, each rendered in three gendered variants (male, female, gender-neutral), and evaluated responses using both an LLM-as-a-judge approach and a human expert panel consisting of a physician and a sociologist specializing in ethics. We observed substantial and clinically significant differences across gendered variants, including differential treatment urgency, inappropriate symptom associations, and misidentification of clinical context. Female variants disproportionately framed responses around childcare and reproductive health regardless of clinical relevance, reflecting societal stereotypes rather than medical reasoning. Bias manifested both at the LLM generation stage and the RAG retrieval stage, in several cases causing the model to hallucinate responses entirely. Some bias patterns were persistent across repeated runs, while others appeared inconsistently, highlighting the challenge of distinguishing systematic bias from stochastic variation.

Colorectal cancer (CRC) is a leading cause of cancer-related death, with incidence rising substantially among individuals under 50 years of age. Polygenic risk scores (PRS) hold promise for identifying high-risk individuals; when combined with lifestyle factors, they substantially improve prediction accuracy compared with models based on lifestyle factors alone. However, few clinical tools currently exist that facilitate this integrated, PRS-enhanced risk assessment. To bridge this gap, we developed MyGeneRisk Colon, a publicly accessible web portal that delivers individualized CRC risk prediction by incorporating genetic, demographic, family history, and lifestyle factors. This paper details the development of the underlying risk prediction model, the portal's architecture and data security, our reporting framework, and engagement with a community advisory panel. Designed as a user-friendly platform, MyGeneRisk Colon aims to effectively communicate personalized CRC risk profiles and educate users and healthcare providers about prevention strategies.

Background Cardiovascular-kidney-metabolic (CKM) syndrome integrates adiposity, metabolic risk, kidney dysfunction, and cardiovascular disease in a prevention-oriented framework. National estimates across 1999-2023 NHANES and future burden remain limited. Methods We analyzed US adults aged 20 years from 11 NHANES cycles, 1999-2000 through August 2021-August 2023. CKM stage 0-4 was assigned using harmonized examination, laboratory, medication, and questionnaire data. Prevalence was survey-weighted and standardized to the 2010 US Census adult population. Decade trends used survey-weighted logistic regression adjusted for age, sex, and race and ethnicity. Exploratory 2040 and 2050 projections combined NHANES prevalence models with US Census projections under population-aging-only, trend-continuation, and risk-improvement scenarios. Results Among 62,890 eligible adults, 62,888 had sufficient CKM data. In 2021-2023, age-standardized prevalence was 87.9% (95% CI, 86.5%-89.4%) for CKM stage 1 and 62.0% (95% CI, 60.1%-63.8%) for stages 2-4. Stage 2 accounted for 50.1% (95% CI, 48.2%-51.9%) and stages 3-4 for 11.9% (95% CI, 11.0%-12.7%). From 1999-2000 to 2021-2023, any CKM increased by 4.6 percentage points (95% CI, 2.4 to 6.9; P<0.001), whereas stages 2-4 changed by 2.1 percentage points (95% CI, 5.1 to 0.8; P=0.156). In adjusted decade models, any CKM increased (OR, 1.28; 95% CI, 1.19-1.38; P<0.001), while stages 2-4 showed no significant linear trend (OR, 0.95; 95% CI, 0.89-1.01; P=0.084). Excess adiposity and diabetes increased, dyslipidemia declined, and hypertension, chronic kidney disease, and clinical cardiovascular disease were stable. With population aging alone, projected stages 2-4 burden rose from 164.8 million adults in 2023 to 193.7 million in 2050; under risk improvement, it was 147.7 million. Conclusions CKM syndrome remained highly prevalent among US adults. Although later stages did not increase significantly, population aging may expand the absolute care burden unless broad risk improvement occurs.

Automating Hierarchical Condition Category (HCC) assignment directly from unstructured electronic health record (EHR) notes remains an important but understudied problem in clinical informatics. We present HCC-Coder, an end-to-end NLP system that maps narrative documentation to 115 Centers for Medicare & Medicaid Services(CMS) HCC codes in a multi-label setting. On the test dataset, HCC-Coder achieves a macro-F1 of 0.779 and a micro-F1 of 0.756, with a macro-sensitivity of 0.819 and macro-specificity of 0.998. By contrast, Generative Pre-trained Transformer (GPT)-4o achieves the highest score of a macro-F1 of 0.735 and a micro-F1 of 0.708 under five-shot prompting. The fine-tuned model demonstrates consistent absolute improvements of 4%-5% in F1-scores over GPT-4o. To address severe label imbalance, we incorporate inverse-frequency weighting and per-label threshold calibration. These findings suggest that domain-adapted transformers provide more balanced and reliable performance than prompt-based large language models for hierarchical clinical coding and risk adjustment.

BackgroundContinuous adhesive patch electrocardiographic (ECG) wearables are increasingly prescribed. Patient experience with these devices can influence adherence, but research in this area is limited. This study aimed to explore the perceptions and experiences of patients receiving wearable cardiac monitoring technology as part of their routine care through the lens of treatment burden. MethodsThis was a qualitative study with semi-structured phone interviews conducted between February and May 2024. We recruited participants from primary care and outpatient clinics using maximum variation sampling to ensure diversity in sex, ethnicity, and education levels. Interviews were audio-recorded, transcribed, and analysed using reflexive thematic analysis. ResultsSixteen participants (mean age 51 years, 63% female) were interviewed (average duration: 33 minutes). Three themes were developed: 1) Experience using the device: Burden vs Ease of Use, which captured participants perceptions of how easily they could integrate the device in their daily lives; 2) Individual variability in responses to ECG self-monitoring covered participants emotional and cognitive response to knowing their heart rhythm was monitored; and 3) The care process shapes patient experiences reflected support preferences during the set-up and monitoring period and the uncertainty regarding timely clinical and device feedback. ConclusionsPatients valued cardiac wearables for facilitating diagnosis and felt reassured knowing they were clinically monitored. However, gaps in information provided to patients seemed to cause anxiety for some participants. These concerns could be mitigated through clearer clinician communication and patient education at the time of prescription.

BackgroundAntiphospholipid syndrome (APS) complicating pregnancy carries significant obstetric morbidity. Secondary APS, arising in the context of systemic autoimmune disease, may confer worse outcomes than primary APS due to additional inflammatory and immunological mechanisms. This study aimed to compare pregnancy outcomes between autoimmune rheumatic disease-associated secondary APS and primary APS managed at a quaternary care hospital in Chennai. MethodsA retrospective observational study analysed 82 pregnancies (secondary APS n=46; primary APS n=36) managed between January 2025 and March 2026. Outcomes including live birth rate, miscarriage, fetal death, preterm birth, pre-eclampsia, and intrauterine growth restriction (IUGR) were compared using chi-square test, Fisher exact test, and independent t-test. Multivariable logistic regression identified independent predictors of adverse outcomes. ResultsLive birth rate was significantly lower in secondary APS compared to primary APS (69.6% vs 86.1%; p=0.048). Triple antiphospholipid antibody positivity was more prevalent in secondary APS (47.8% vs 25.0%; p=0.032). On multivariable analysis, secondary APS (aOR 2.71; 95% CI 1.08-6.81; p=0.033), triple positivity (aOR 3.45; 95% CI 1.39-8.57; p=0.007), and lupus anticoagulant (aOR 2.62; 95% CI 1.01-6.76; p=0.047) independently predicted adverse outcomes. Hydroxychloroquine (aOR 0.39; p=0.038) and combination aspirin plus low-molecular-weight heparin (aOR 0.31; p=0.019) were independently protective. ConclusionSecondary APS is associated with significantly worse pregnancy outcomes than primary APS. Triple antiphospholipid positivity and lupus anticoagulant independently increase obstetric risk. Hydroxychloroquine and combination antithrombotic therapy significantly improve live birth rates. Early rheumatology referral and multidisciplinary obstetric management are essential.

Gray-market retatrutide use is increasing, but patient safety experiences remain poorly characterized. This cross-sectional analysis examined Reddit posts and comments from retatrutide-specific and broader peptide or weight-management communities through December 2025. A validated large language model classified self-reported retatrutide use and extracted author-attributed symptoms mapped to MedDRA Preferred Terms. Among 13,589 users reporting current use, 7,823 had at least one mapped symptom after exclusions. Unlike phase 2 trial findings dominated by gastrointestinal events, Reddit reports most often described appetite increase, fatigue, increased energy, nausea, food craving, insomnia, and elevated heart rate. Findings are hypothesis-generating and warrant pharmacovigilance attention.

The 1.8 million residents of Region Hovedstaden (Denmarks Capital Region) currently lack a secure, standardized pathway for integrating continuous wearable health data into Sundhed.dk, the national electronic health record. Consumer wearables such as Apple Watch, Oura Ring, and Garmin generate longitudinal physiological data relevant to chronic disease management, yet existing workflows rely on manual, non-standardized exports incompatible with FHIR DK v6.0.2 profiles and GDPR Article 25 privacy-by-design requirements. This paper presents a conceptual five-layer microservice architecture for secure wearable data sharing, employing MitID national authentication, National Service Infrastructure (NSI) integration, and Zero Trust security controls. Requirements were derived from a mixed-methods study including surveys of 47 Danish stakeholders and systematic benchmarking of existing platforms. Results show 51.1% conditional willingness to share wearable data under secure conditions, with audit transparency and non-medical misuse identified as central trust factors. Fourteen MoSCoW-prioritized requirements (F1-F7, NF1-NF7) are mapped to architecture components, providing a traceable blueprint for closing the interoperability gap in Danish public healthcare.

Background: Dihydropyridine calcium channel blockers (DHP-CCB) are widely prescribed antihypertensives whose adverse effects may trigger unnecessary prescribing of additional medications, termed prescribing cascades (PC). We aimed to identify potential DHP-CCB-induced PCs using high-throughput sequence symmetry analysis (HTSSA). Methods: Using Medicare claims data (2011-2020), we identified new users aged [&ge;]66 years with continuous enrollment [&ge;]360 days before and [&ge;]180 days after DHP-CCB initiation. We screened for initiation of 446 "marker" drug classes within {+/-}90 days of DHP-CCB initiation. Sequence ratios compared marker drug initiation after versus before DHP-CCB initiation. Adjusted sequence ratios (aSR), accounting for prescribing trends over time, were calculated with 95% CIs >1 considered statistically significant. Clinical experts classified statistically significant signals as potential PCs through consensus. Results: Among 388,862 DHP-CCB initiators (mean age 76.6 {+/-} 7.5 years; 62.5% women, 92.3% with hypertension), 82 of 446 marker drug classes had significantly elevated aSRs, of which 24 were classified as potential PCs. Strongest signals ranked by highest aSR included other systemic hemostatics (aSR 2.99; 95% CI, 1.10-8.16), other nasal preparations (aSR 1.99; 95% CI, 1.47-2.70), and drugs used in erectile dysfunction (aSR 1.85; 95% CI, 1.27-2.70). Other clinically relevant signals, ranked by number needed to harm (lowest to highest), included sulfonamides (NNTH 104; 95% CI, 98-111), electrolyte solutions (NNTH 216; 95% CI, 196-241), and osmotically acting laxatives (NNTH 710; 95% CI, 540-1056). Conclusion: Potential PCs identified in this Medicare cohort reflected known and underrecognized adverse effects of DHP-CCBs. Further studies are needed to evaluate the clinical consequences of these PCs.

Since the release of the first ChatGPT model in 2022, large language models (LLMs) have evolved significantly, and an increasing number of users now turn to these generative information systems for inquiries as sensitive and consequential as those related to health. The primary objective is to identify the main strengths and weaknesses of generative AI systems when responding to information needs as critical as those arising in the health domain. The study was structured using a question-answer format, in which each question corresponded to a user query and each answer represented the output generated by a model in response. The study employed a human evaluation framework involving two distinct panels of clinical experts from different specialties. The evaluation criteria encompassed three dimensions: adherence to medical consensus; presence or absence of inappropriate or incorrect information; and the potential to cause harm to users. GPT-4o mini, Llama 3, and MedLlama 3 were selected as three representative systems for the experiments. This study presents a detailed analysis of the performance of widely used contemporary large language models in addressing common health-related queries posed by online users. The results reinforce the potential of LLMs as tools for online health information seeking among non-expert users. However, the performance limitations identified underscore the need for further studies to monitor the future development of these models. Among them, performance issues have been identified in areas where users may be more vulnerable, leading to the retrieval of clinically incorrect information, particularly in matters relating to rare diseases. Furthermore, it has been noted that these models can become trapped in obsolete medical knowledge due to continuous scientific progress.

Background Gastric cancer management is heterogeneous, and although the treating surgeon leads decisions across the pathway, surgeon level outcome variation remains poorly quantified. This study assessed surgeon identity as an independent predictor of survival after risk adjustment, introducing the Surgical Assessment and Healthcare (SAH) Index. Methods This single institution retrospective study (Ruijin Hospital, Shanghai Jiao Tong University; NCT07180966) included 692 patients undergoing curative-intent resection for gastric adenocarcinoma (pStage I ,II, III) in 2019 by eight consultant surgeons. Overall survival was modelled by multivariable Cox regression (primary model, 199 events, EPV 16.6; complete-case sensitivity model, N = 647). The SAH Index expressed surgeon * stage observed-to-expected ratios for five-year mortality and major morbidity (Clavien Dindo [&ge;] IIIa). Median follow up was 74.3 months. Results Independent predictors of survival were tumour stage (HR 2.979/step), age (HR 1.030/year), and non-distal gastrectomy (HR 1.498; all p [&le;] .006). After full adjustment, surgeon identity remained significant (Wald = 14.58, df = 7, p = .042): two surgeons carried roughly double the reference hazard S6 (HR 2.219, p = .003) and S8 (HR 2.034, p = .031) both with the cohort's lowest neoadjuvant chemotherapy rates (3.0% and 7.0% versus 17.6%), implicating pre-operative pathway decisions. The effect persisted in the sensitivity model (MSI also prognostic, HR 3.162, p = .007). Morbidity benchmarking flagged no surgeon for excess complications (no Tier 2 flags) and one survival-outlier cell (S6, Stage II; Tier 3). Conclusion Surgeon identity is independently associated with survival in gastric cancer beyond measurable case mix. The SAH Index offers a reproducible tool for institutional and inter-hospital benchmarking, with tier assignments stable across all four prespecified weighting scenarios confirming tier classification is independent of weight specification.

The significant challenges associated with rare diseases in the medical and research domains include the scarcity of information, which is often confined to unstructured formats. Although existing approaches provide valuable insights, there is a need to develop effective methods to identify information pertinent to rare diseases for advancing rare disease research. We identified mentions of rare diseases in relevant texts and assessed their relevance using derived scores, the confidence score and semantic similarity from a fine-tuned BioMedBERT encoder. This encoder was fine-tuned using rare disease related text from Online Mendelian Inheritance in Man (OMIM), Orphanet, a manually validated dataset, and STS benchmark datasets. The process of identifying meaningful rare disease mentioned was presented through two case studies that retrieved relevant NIH-funded projects, utilizing a generated knowledge graph in Neo4j to host data on 2,067 GARD diseases with over 320,000 NIH funded projects. Through various case studies with NIH-funded projects related to rare diseases, we demonstrated the effectiveness of our approach in systematically providing rare disease related data to enhance our understanding of rare diseases for future investigations.