The Global Alliance for Genomics and Health (GA4GH) Phenopacket Schema was released in 2022 and approved by ISO as a standard for sharing clinical and genomic information about an individual, including phenotypic descriptions, numerical measurements, genetic information, diagnoses, and treatments. A phenopacket can be used as an input file for software that supports phenotype-driven genomic diagnostics and for algorithms that facilitate patient classification and stratification for identifying new diseases and treatments. There has been a great need for a collection of phenopackets to test software pipelines and algorithms. Here, we present Phenopacket Store. Phenopacket Store v.0.1.19 includes 6,668 phenopackets representing 475 Mendelian and chromosomal diseases associated with 423 genes and 3,834 unique pathogenic alleles curated from 959 different publications. This represents the first large-scale collection of case-level, standardized phenotypic information derived from case reports in the literature with detailed descriptions of the clinical data and will be useful for many purposes, including the development and testing of software for prioritizing genes and diseases in diagnostic genomics, machine learning analysis of clinical phenotype data, patient stratification, and genotype-phenotype correlations. This corpus also provides best-practice examples for curating literature-derived data using the GA4GH Phenopacket Schema.
- MeSH
- Algorithms MeSH
- Databases, Genetic MeSH
- Phenotype * MeSH
- Genomics * methods MeSH
- Humans MeSH
- Software * MeSH
- Check Tag
- Humans MeSH
- Publication type
- Journal Article MeSH
OBJECTIVES: Decision-analytic models assessing the value of emerging Alzheimer's disease (AD) treatments are challenged by limited evidence on short-term trial outcomes and uncertainty in extrapolating long-term patient-relevant outcomes. To improve understanding and foster transparency and credibility in modeling methods, we cross-compared AD decision models in a hypothetical context of disease-modifying treatment for mild cognitive impairment (MCI) due to AD. METHODS: A benchmark scenario (US setting) was used with target population MCI due to AD and a set of synthetically generated hypothetical trial efficacy estimates. Treatment costs were excluded. Model predictions (10-year horizon) were assessed and discussed during a 2-day workshop. RESULTS: Nine modeling groups provided model predictions. Implementation of treatment effectiveness varied across models based on trial efficacy outcome selection (clinical dementia rating - sum of boxes, clinical dementia rating - global, mini-mental state examination, functional activities questionnaire) and analysis method (observed severity transitions, change from baseline, progression hazard ratio, or calibration to these). Predicted mean time in MCI ranged from 2.6 to 5.2 years for control strategy and from 0.1 to 1.0 years for difference between intervention and control strategies. Predicted quality-adjusted life-year gains ranged from 0.0 to 0.6 and incremental costs (excluding treatment costs) from -US$66 897 to US$11 896. CONCLUSIONS: Trial data can be implemented in different ways across health-economic models leading to large variation in model predictions. We recommend (1) addressing the choice of outcome measure and treatment effectiveness assumptions in sensitivity analysis, (2) a standardized reporting table for model predictions, and (3) exploring the use of registries for future AD treatments measuring long-term disease progression to reduce uncertainty of extrapolating short-term trial results by health-economic models.
- MeSH
- Alzheimer Disease * economics drug therapy MeSH
- Cost-Benefit Analysis * MeSH
- Models, Economic MeSH
- Cognitive Dysfunction * economics MeSH
- Quality-Adjusted Life Years MeSH
- Humans MeSH
- Decision Support Techniques MeSH
- Disease Progression MeSH
- Treatment Outcome MeSH
- Check Tag
- Humans MeSH
- Publication type
- Journal Article MeSH
- Comparative Study MeSH
PURPOSE: Dual velocity encoding PC-MRI can produce spurious artifacts when using high ratios of velocity encoding values (VENCs), limiting its ability to generate high-quality images across a wide range of encoding velocities. This study aims to propose and compare dual-VENC correction methods for such artifacts. THEORY AND METHODS: Two denoising approaches based on spatiotemporal regularization are proposed and compared with a state-of-the-art method based on sign correction. Accuracy is assessed using simulated data from an aorta and brain aneurysm, as well as 8 two-dimensional (2D) PC-MRI ascending aorta datasets. Two temporal resolutions (30,60) ms and noise levels (9,12) dB are considered, with noise added to the complex magnetization. The error is evaluated with respect to the noise-free measurement in the synthetic case and to the unwrapped image without additional noise in the volunteer datasets. RESULTS: In all studied cases, the proposed methods are more accurate than the Sign Correction technique. Using simulated 2D+T data from the aorta (60 ms, 9 dB), the Dual-VENC (DV) error 0.82±0.07$$ 0.82\pm 0.07 $$ is reduced to: 0.66±0.04$$ 0.66\pm 0.04 $$ (Sign Correction); 0.34±0.04$$ 0.34\pm 0.04 $$ and 0.32±0.04$$ 0.32\pm 0.04 $$ (proposed techniques). The methods are found to be significantly different (p-value <0.05$$ <0.05 $$ ). Importantly, brain aneurysm data revealed that the Sign Correction method is not suitable, as it increases error when the flow is not unidirectional. All three methods improve the accuracy of in vivo data. CONCLUSION: The newly proposed methods outperform the Sign Correction method in improving dual-VENC PC-MRI images. Among them, the approach based on temporal differences has shown the highest accuracy.
- MeSH
- Algorithms * MeSH
- Aorta * diagnostic imaging MeSH
- Artifacts * MeSH
- Phantoms, Imaging MeSH
- Image Interpretation, Computer-Assisted methods MeSH
- Intracranial Aneurysm diagnostic imaging MeSH
- Humans MeSH
- Magnetic Resonance Imaging * methods MeSH
- Brain diagnostic imaging MeSH
- Computer Simulation MeSH
- Image Processing, Computer-Assisted * methods MeSH
- Signal-To-Noise Ratio * MeSH
- Reproducibility of Results MeSH
- Check Tag
- Humans MeSH
- Publication type
- Journal Article MeSH
PURPOSE: STereotactic Arrhythmia Radioablation (STAR) showed promising results in patients with refractory ventricular tachycardia. However, clinical data are scarce and heterogeneous. The STOPSTORM.eu consortium was established to investigate and harmonize STAR in Europe. The primary goal of this benchmark study was to investigate current treatment planning practice within the STOPSTORM project as a baseline for future harmonization. METHODS AND MATERIALS: Planning target volumes (PTVs) overlapping extracardiac organs-at-risk and/or cardiac substructures were generated for 3 STAR cases. Participating centers were asked to create single-fraction treatment plans with 25 Gy dose prescriptions based on in-house clinical practice. All treatment plans were reviewed by an expert panel and quantitative crowd knowledge-based analysis was performed with independent software using descriptive statistics for International Commission on Radiation Units and Measurements report 91 relevant parameters and crowd dose-volume histograms. Thereafter, treatment planning consensus statements were established using a dual-stage voting process. RESULTS: Twenty centers submitted 67 treatment plans for this study. In most plans (75%) intensity modulated arc therapy with 6 MV flattening filter free beams was used. Dose prescription was mainly based on PTV D95% (49%) or D96%-100% (19%). Many participants preferred to spare close extracardiac organs-at-risk (75%) and cardiac substructures (50%) by PTV coverage reduction. PTV D0.035cm3 ranged from 25.5 to 34.6 Gy, demonstrating a large variety of dose inhomogeneity. Estimated treatment times without motion compensation or setup ranged from 2 to 80 minutes. For the consensus statements, a strong agreement was reached for beam technique planning, dose calculation, prescription methods, and trade-offs between target and extracardiac critical structures. No agreement was reached on cardiac substructure dose limitations and on desired dose inhomogeneity in the target. CONCLUSIONS: This STOPSTORM multicenter treatment planning benchmark study not only showed strong agreement on several aspects of STAR treatment planning, but also revealed disagreement on others. To standardize and harmonize STAR in the future, consensus statements were established; however, clinical data are urgently needed for actionable guidelines for treatment planning.
- MeSH
- Benchmarking * MeSH
- Radiotherapy Dosage MeSH
- Tachycardia, Ventricular surgery radiotherapy MeSH
- Consensus * MeSH
- Organs at Risk * radiation effects MeSH
- Humans MeSH
- Radiotherapy Planning, Computer-Assisted * standards methods MeSH
- Radiosurgery * standards methods MeSH
- Radiotherapy, Intensity-Modulated methods standards MeSH
- Heart radiation effects MeSH
- Arrhythmias, Cardiac MeSH
- Check Tag
- Humans MeSH
- Publication type
- Journal Article MeSH
- Multicenter Study MeSH
- Geographicals
- Europe MeSH
BACKGROUND: Advancements in artificial intelligence (AI) and machine learning (ML) have revolutionized the medical field and transformed translational medicine. These technologies enable more accurate disease trajectory models while enhancing patient-centered care. However, challenges such as heterogeneous datasets, class imbalance, and scalability remain barriers to achieving optimal predictive performance. METHODS: This study proposes a novel AI-based framework that integrates Gradient Boosting Machines (GBM) and Deep Neural Networks (DNN) to address these challenges. The framework was evaluated using two distinct datasets: MIMIC-IV, a critical care database containing clinical data of critically ill patients, and the UK Biobank, which comprises genetic, clinical, and lifestyle data from 500,000 participants. Key performance metrics, including Accuracy, Precision, Recall, F1-Score, and AUROC, were used to assess the framework against traditional and advanced ML models. RESULTS: The proposed framework demonstrated superior performance compared to classical models such as Logistic Regression, Random Forest, Support Vector Machines (SVM), and Neural Networks. For example, on the UK Biobank dataset, the model achieved an AUROC of 0.96, significantly outperforming Neural Networks (0.92). The framework was also efficient, requiring only 32.4 s for training on MIMIC-IV, with low prediction latency, making it suitable for real-time applications. CONCLUSIONS: The proposed AI-based framework effectively addresses critical challenges in translational medicine, offering superior predictive accuracy and efficiency. Its robust performance across diverse datasets highlights its potential for integration into real-time clinical decision support systems, facilitating personalized medicine and improving patient outcomes. Future research will focus on enhancing scalability and interpretability for broader clinical applications.
- MeSH
- Databases, Factual MeSH
- Humans MeSH
- Neural Networks, Computer MeSH
- Patient-Centered Care * MeSH
- Machine Learning * MeSH
- Translational Science, Biomedical MeSH
- Translational Research, Biomedical MeSH
- Artificial Intelligence * MeSH
- Treatment Outcome MeSH
- Check Tag
- Humans MeSH
- Publication type
- Journal Article MeSH
BACKGROUND: Observational data on composite scores often comes with missing component information. When a complete-case (CC) analysis of composite scores is unbiased, preferable approaches of dealing with missing component information should also be unbiased and provide a more precise estimate. We assessed the performance of several methods compared to CC analysis in estimating the means of common composite scores used in axial spondyloarthritis research. METHODS: Individual mean imputation (IMI), the modified formula method (MF), overall mean imputation (OMI), and multiple imputation of missing component values (MI) were assessed either analytically or by means of simulations from available data collected across Europe. Their performance in estimating the means of the Bath Ankylosing Spondylitis Disease Activity Index (BASDAI), the Bath Ankylosing Spondylitis Functional Index (BASFI), and the Ankylosing Spondylitis Disease Activity Score based on C-reactive protein (ASDAS-CRP) in cases where component information was set missing completely at random was compared to the CC approach based on bias, variance, and coverage. RESULTS: Like the MF method, IMI uses a modified formula for observations with missing components resulting in modified composite scores. In the case of an unbiased CC approach, these two methods yielded representative samples of the distribution arising from a mixture of the original and modified composite scores, which, however, could not be considered the same as the distribution of the original score. The IMI and MF method are, thus, intrinsically biased. OMI provided an unbiased mean but displayed a complex dependence structure among observations that, if not accounted for, resulted in severe coverage issues. MI improved precision compared to CC and gave unbiased means and proper coverage as long as the extent of missingness was not too large. CONCLUSIONS: MI of missing component values was the only method found successful in retaining CC's unbiasedness and in providing increased precision for estimating the means of BASDAI, BASFI, and ASDAS-CRP. However, since MI is susceptible to incorrect implementation and its performance may become questionable with increasing missingness, we consider the implementation of an error-free CC approach a valid and valuable option. TRIAL REGISTRATION: Not applicable as study uses data from patient registries.
- MeSH
- Spondylitis, Ankylosing MeSH
- Axial Spondyloarthritis * MeSH
- C-Reactive Protein analysis MeSH
- Data Interpretation, Statistical MeSH
- Humans MeSH
- Severity of Illness Index MeSH
- Research Design MeSH
- Bias MeSH
- Check Tag
- Humans MeSH
- Publication type
- Journal Article MeSH
- Geographicals
- Europe MeSH
Úvod: Mnohé štúdie a metaanalýzy preukázali, že telemonitorovanie krvného tlaku ako aj iných faktorov metabolického syndrómu môže zlepšiť ich manažment. Avšak mnoho pacientov nevyužíva telemonitorovanie kvôli osobným, technologickým a iným bariéram. Cieľom tejto štúdie bolo zistenie aké sú perspektívy a prekážky telemonitoringu lipitenzie na Slovensku z pohľadu pacienta. Metódy: Táto štúdia bola realizovaná ako dotazníková a mala za cieľ osloviť 2 545 pacientov. Dotazník pozostával z častí zameraných na osobné charakteristiky pacienta, návyky z hľadiska merania krvného tlaku (TK), na využívanie smart-technológií, ich predpokladané prínosy a prekážky z hľadiska pacienta ako aj na znalosť lipidového profilu a kardiovaskulárneho rizika samotným pacientom. Výsledky: Celkovo sme získali 252 odpovedí od pacientov (9,9 %). Z celkového počtu opýtaných má arteriálnu hypertenziu 67,4 %, kým nefarmakologickú terapiu užíva 7,9 %. Denne si TK meria len 21,2 % hypertonikov, signifikantne vyšší počet mužov ako žien (p = 0,011) a najčastejšie si meria TK veková kategória 31–45 rokov. Až 19,4 % využíva nositeľné zariadenia a 6,3 % tlakomery prepojené s aplikáciou. Signifikantne častejšie smart-technológie využíva kategória 31–45-ročných (p = 0,01). Závažné prekážky využitia smart-technológií neboli identifikované, väčšina si vyžadovala funkciu vzdialených konzultácií, úpravy liekov a jednoduché užívateľské rozhranie. Väčšina pacientov nevie svoju hodnotu LDL-cholesterolu a až 45,7 % tých čo vie, malo zvýšené hladiny. Záver: Celkovo prevláda záujem o využitie metód telemedicíny krvného tlaku, pri jej implementácii na Slovensku bude však nutná spolupráca pacienta a lekára.
Introduction: Numerous studies and meta-analyses have demonstrated that telemonitoring of blood pressure and other factors of metabolic syndrome can improve their management. However, many patients do not use telemonitoring due to personal, technological, and healthcare barriers. The aim of this study was to identify the perspectives and barriers to telemonitoring of lipid levels in Slovakia from the patient’s point of view. Methods: This study was conducted as a questionnaire-based survey targeting 2,545 patients. The questionnaire consisted of sections focused on patients’ personal characteristics, habits regarding blood pressure measurement, the use of smart technologies, their perceived benefits and barriers, as well as the patients’ knowledge of their lipid profile and cardiovascular risk. Results: A total of 252 responses were obtained (9.9 % response rate). Among the respondents, 67.4 % had hypertension, while 7.9 % were on non-pharmacological therapy. Only 21.2 % of hypertensive patients measured their blood pressure daily, with a significantly higher proportion of men compared to women (p = 0.011), and the most frequent blood pressure monitoring was observed in the 31–45 age group. A total of 19.4 % used wearable devices, and 6.3 % used blood pressure monitors connected to an app. Smart technology use was significantly more common in the 31–45 age group (p = 0.01). No severe barriers to the use of smart technologies were identified; most patients required features such as remote consultations, medication adjustments, and user-friendly interfaces. The majority of patients were unaware of their LDL-C values, and 45.7 % of those who were aware had elevated levels. Conclusion: There is a prevailing interest in implementing telemedicine methods for blood pressure monitoring. However, collaboration between patients and physicians will be necessary for its successful implementation in Slovakia.
- MeSH
- Digital Health MeSH
- Dyslipidemias prevention & control MeSH
- Hypertension * epidemiology prevention & control MeSH
- Humans MeSH
- Blood Pressure Determination methods MeSH
- Surveys and Questionnaires MeSH
- Heart Disease Risk Factors MeSH
- Statistics as Topic MeSH
- Telemedicine * methods MeSH
- Check Tag
- Humans MeSH
- Geographicals
- Slovakia MeSH
V súčasnosti, aj napriek významnému pokroku v terapii akútnych kardiovaskulárnych (KV) príhod, miera kontroly krvného tlaku a dyslipidémií, ako hlavných rizikových faktorov KV-ochorení (KVO), stagnuje a tradičné prístupy často zlyhávajú. Iniciatívy v oblasti digitálnej medicíny sa začali objavovať už predtým, ako pandémia COVID-19 zásadne ovplyvnila spôsob poskytovania zdravotnej starostlivosti. Artériová hypertenzia je ideálnym kandidátom na vzdialený manažment a digitálne riešenia v tejto oblasti rýchlo pribúdajú. Štúdie preukázali, že metódy telemedicíny signifikantne znižujú systolický, ako aj diastolický tlak pacientov a pomáhajú zlepšovať dosahovanie cieľových hodnôt tlaku a adherenciu k terapii. Bolo však taktiež preukázané, že metódy mHealth (mobile Health) museli byť spojené so súbežnou konzultáciou so zdravotníkom. Aplikácie na monitorovanie vlastného zdravia, prípadne ,,selfcouching‘‘, ktoré fungovali na pasívnom zbere dát, nepreukázali vyššie uvedené výsledky. Taktiež sa ukázalo, že existujú mnohé prekážky v implementácii týchto technológií, ako sú prístrojové (používanie validovaných tlakomerov a metód merania), finančné (úhrady zo zdravotného poistenia, náklady pre pacienta), legislatívne (hlavne ochrana osobných údajov) a taktiež neexistujúca štandardizácia v tejto oblasti. Metódy telemedicíny sa začali uplatňovať aj pri manažmente dyslipidémií, lebo sa zistilo, že vzdialený manažment pacienta so súbežným informovaním samotného pacienta o jeho KV-riziku môže výrazne dopomôcť k zlepšeniu adherencie k terapii a v dosahovaní cieľových hodnôt lipidových parametrov.
Currently, despite significant advances in the treatment of acute cardiovascular (CV) events, rates of blood pressure control and dyslipidemia, as major risk factors for CV disease (CVD), are stagnant and traditional approaches often fail. Initiatives in digital medicine have already started to emerge before the COVID-19 pandemic fundamentally impacted the way healthcare is delivered. Arterial hypertension is an ideal candidate for remote management, and digital solutions in this area are rapidly gaining traction. Studies have shown that telemedicine methods significantly reduce both systolic and diastolic blood pressure of patients and help improve achievement of target blood pressure values and adherence to therapy. However, it was also shown that mHealth (mobile Health) methods had to be associated with concurrent consultation with a healthcare professional. Self-monitoring or “selfcouching” apps that worked on passive data collection did not show the above results. It has also been shown that there are many barriers to the implementation of these technologies, such as instrumentation (use of validated blood pressure monitors and measurement methods), financial (health insurance reimbursement, cost to the patient), legislative (mainly privacy) and also the lack of standardization in this area. Telemedicine methods have also started to be applied in the management of dyslipidemia, where it has been found that remote patient management with simultaneous information to the patient about his/her CV risk can significantly help to improve adherence to therapy and in achieving target values of lipid parameters.
OBJECTIVES: We aimed to compare various methods for imputing disease activity in longitudinally collected observational data of patients with axial spondyloarthritis (axSpA). METHODS: We conducted a simulation study on data from 8583 axSpA patients from ten European registries. Disease activity was assessed by the Axial Spondyloarthritis Disease Activity Score (ASDAS) and the corresponding low disease activity (LDA; ASDAS<2.1) state at baseline, 6 and 12 months. We focused on cross-sectional methods which impute missing values of an individual at a particular time point based on the available information from other individuals at that time point. We applied nine single and five multiple imputation methods, covering mean, regression and hot deck methods. The performance of each imputation method was evaluated via relative bias and coverage of 95% confidence intervals for the mean ASDAS and the derived proportion of patients in LDA. RESULTS: Hot deck imputation methods outperformed mean and regression methods, particularly when assessing LDA. Multiple imputation procedures provided better coverage than the corresponding single imputation ones. However, none of the evaluated methods produced unbiased estimates with adequate coverage across all time points, with performance for missing baseline data being worse than for missing follow-up data. Predictive mean and weighted predictive mean hot deck imputation procedures consistently provided results with low bias. CONCLUSIONS: This study contributes to the available methods for imputing disease activity in observational research. Hot deck imputation using predictive mean matching exhibited the highest robustness and is thus our suggested approach.
- MeSH
- Axial Spondyloarthritis * diagnosis epidemiology MeSH
- Adult MeSH
- Data Interpretation, Statistical MeSH
- Humans MeSH
- Longitudinal Studies MeSH
- Observational Studies as Topic * MeSH
- Cross-Sectional Studies MeSH
- Registries MeSH
- Spondylarthritis diagnosis MeSH
- Severity of Illness Index * MeSH
- Check Tag
- Adult MeSH
- Humans MeSH
- Male MeSH
- Female MeSH
- Publication type
- Journal Article MeSH
- Geographicals
- Europe MeSH
BACKGROUND AND OBJECTIVE: Metabolomic interaction networks provide critical insights into the dynamic relationships between metabolites and their regulatory mechanisms. This study introduces MInfer, a novel computational framework that integrates outputs from MetaboAnalyst, a widely used metabolomic analysis tool, with Jacobian analysis to enhance the derivation and interpretation of these networks. METHODS: MInfer combines the comprehensive data processing capabilities of MetaboAnalyst with the mathematical modeling power of Jacobian analysis. This framework was applied to various metabolomic datasets, employing advanced statistical tests to construct interaction networks and identify key metabolic pathways. RESULTS: The application of MInfer revealed significant metabolic pathways and potential regulatory mechanisms across multiple datasets. The framework demonstrated high precision, sensitivity, and specificity in identifying interactions, enabling robust network interpretations. CONCLUSIONS: MInfer enhances the interpretation of metabolomic data by providing detailed interaction networks and uncovering key regulatory insights. This tool holds significant potential for advancing the study of complex biological systems.
- MeSH
- Algorithms MeSH
- Humans MeSH
- Metabolic Networks and Pathways * MeSH
- Metabolomics * MeSH
- Software MeSH
- Computational Biology MeSH
- Check Tag
- Humans MeSH
- Publication type
- Journal Article MeSH
