Future Health Status Research Articles

Optimal investment, consumption, and life insurance decisions for households with consumption habits under the health shock risk

. This article investigates the optimal investment, consumption, and life insurance strategies for households under the impact of health shock risk. Considering the uncertainty of the future health status of family members, a non homogeneous Markov process is used to model the health status of the wage earner. Drawing upon the theory of habit formation, we investigate the influence of different consumption habits on households’ investment, consumption, and life insurance strategies. Based on whether the wage earner is alive or not, we formulate and solve the corresponding Hamilton-Jacobi-Bellman (HJB) equations for the two scenarios of wage earner survival and wage earner’s demise, respectively, and obtain explicit expressions for the optimal investment, consumption, and life insurance strategies of households. Through sensitivity analysis of important parameters, it has been shown that the presence of health shocks within households has a negative impact on investment and consumption decisions, while the formation of consumption habits increases household propensity for precautionary savings.

Evaluation of low-and middle-income countries according to cardiovascular disease risk factors by using pythagorean fuzzy AHP and TOPSIS methods

BackgroundCardiovascular disease risk factors play a crucial role in determining individuals’ future health status and significantly affect health. This paper aimed to address cardiovascular disease risk factors in low- and middle-income countries using multi-criteria decision-making methods.MethodsIn line with this objective, 22 evaluation criteria were identified. Due to the unequal importance levels of the criteria, the interval-valued Pythagorean Fuzzy AHP (PF-AHP) method was employed for weighting. The TOPSIS method was utilized to rank the countries.ResultsThe application of interval-valued PF-AHP revealed that metabolic, behavioral, and economic factors are more important in contributing to disease risk. Among adults, tobacco use prevalence was identified as the most significant risk factor. According to the TOPSIS method, Lebanon, Jordan, Solomon Islands, Serbia, and Bulgaria ranked highest, while Timor Leste, Benin, Ghana, Niger, and Ethiopia ranked lowest.ConclusionsIdentifying disease risk factors and preventing or reducing risks are crucial in combating cardiovascular diseases. Therefore, it is recommended that countries ranking higher take remedial actions to reduce disease risk.

Methodological quality assessment tools for diagnosis and prognosis research: overview and guidance

Background and ObjectivesMultiple tools exist for assessing the methodological quality of diagnosis and prognosis research. It can be challenging to decide on when to use which tool. We aimed to provide an overview of existing methodological quality assessment (QA) tools for diagnosis and prognosis studies, highlight the overlap and differences among these tools, and to provide guidance for choosing the appropriate tool. Study Design and SettingWe performed a methodological review of tools designed for assessing risk of bias, applicability, or other aspects related to methodological quality in studies investigating tests/factors/markers/models for classifying or predicting a current (diagnosis) and/or future (prognosis) health state. Tools focusing exclusively on causal research or on reporting quality were excluded. Guidance was subsequently developed to assist in choosing an appropriate QA tool. ResultsWe identified 14 QA tools, eight of which were developed for assessment of diagnosis studies, four for prognosis studies, and two addressing both. We propose a set of five questions to help guide the process of choosing a QA tool based on the purpose or question of the user: whether the focus is on (1) diagnosis, prognosis, or another domain; (2) a prediction model vs a test/factor/marker; (3) evaluating simply the performance of a test/factor/marker vs assessing its added value over other variables; (4) comparing two or more tests/factors/markers/models; and (5) whether the user aims to assess only risk of bias or also other quality aspects. ConclusionExisting QA tools for appraising diagnosis and prognosis studies vary in purpose, scope, and contents. Our guidance may help researchers, systematic reviewers, health policy makers, and guideline developers in specifying their purpose and question to select the most appropriate QA tool for their assessment. Plain Language SummaryMethodological quality assessment (QA) tools provide a set of criteria to evaluate how well a medical study was done and how trustworthy its results are. To accurately assess a study’s quality, it is important to use a QA tool that matches the type of medical study. However, with many QA tools available for different study types, choosing the right one can be challenging, especially for diagnosis and prognosis studies (ie, studies that evaluate tests, factors, markers, and models used for diagnosis and prognosis). To assist in selecting the best QA tools for diagnostic and prognostic studies, we created an overview of available tools and practical tips for choosing the most appropriate one. After searching online databases and consulting experts in the field, we identified 14 QA tools specific to diagnostic and prognostic studies. Additionally, we developed five key questions to guide users in choosing the best tool for their study. While the 14 QA tools differ in their focus and content, our guidance simplifies the process of choosing the right tool and helps users refine their research question.

Autonomous Decision Support Based on Artificial Intelligence Techniques for Maintenance Processes

Through the recent digitalization of the industry and the use of technologies and ideas from Industry 4.0, maintenance tasks have altered. Companies are now able to develop knowledge about the production system’s present and future health state by connecting to and talking with it, which enables more effective control over the machinery. Predictive maintenance is a technique whose objective is to minimize unscheduled downtimes and efficiently arrange maintenance tasks before faults and stoppages occur. Several Artificial Intelligence (AI) data analysis tools have been presented in recent decades to construct a prescriptive maintenance system that will aid with the autonomous choice in order to better assist this work. In order to comprehend how artificial intelligence algorithms are affecting maintenance policies and to analyze their implications in strategies, we investigate the state-of-the-art technologies in the prescriptive maintenance system in this study. The findings are compiled in a thorough database that offers illustrations of how to adopt maintenance policies based on descriptive, predictive, and prescriptive analytics using concepts and empirical evidence from the literature. The goal of this study, which is the first in-depth inquiry of these research subjects, is to provide a deeper understanding and awareness of current trends and major challenges while highlighting important aspects and barriers to the adoption of novel policies.

Rhizosphere effect: microbial and enzymatic dynamics in the rhizosphere of various shrub species

Abstract Aims The aim of our research was to compare the properties of the rhizosphere soil of different shrub species. We assumed that the physicochemical properties and microbiome of the rhizosphere differ from bulk soil and the species of shrub is important in their formation. Methods The study involved determining the abundance and diversity of microorganisms in the rhizosphere, along with assessing the basic chemical properties and enzymatic activity. Samples were collected from both rhizosphere and bulk soil in pine stands with an understory of shrubs forming biogroups. Results The enzymatic activity in the rhizosphere was significantly higher than in bulk soil. At the same time enzymatic activity in the rhizosphere differed between the shrub species tested. Both intracellular and extracellular enzymes exhibited a positive correlation with basic biochemical parameters. The number of bacteria and fungi correlated with physicochemical and biochemical properties. The number of bacteria and fungi was notably higher in the rhizosphere of the examined shrub species compared to bulk soil, at the same time, we noted a significant influence of the shrub species on microorganisms in the rhizosphere zone. Among the bacteria in the rhizosphere of shrubs, genera Edaphobaculum, Gemmatimonas, Bdellovibrio, Jorgensenbacteria, Rhodoplanes, and Acidocella were more abundant. Regarding fungi, Mortierella longigemmata, Didymella viburnicola, Trichoderma stellatum, and Phacidium pseudophacidioides were found to be more abundant in the rhizosphere. Conclusions The findings from our research may have practical applications in shaping the species composition of tree stands, with potential implications for future soil health condition.

Prediction of Health Status in Patients Undergoing Lower Extremity Intervention for Claudication

Identifying predictors of generic and disease-specific health status outcomes in patients with lower extremity peripheral artery disease who undergo femoral-popliteal peripheral vascular intervention (PVI) for claudication remains an understudied area. We aimed to identify important predictors of health status after PVI as the foundation for risk-based approaches to deploying therapies that improve patient outcomes. Baseline characteristics of 468 participants with claudication and femoral/popliteal stenosis treated with either the Stellarex paclitaxel-based drug-coated balloon or plain balloon angioplasty from 2013 to 2019 were included. The study examined 59 baseline variables and aimed to identify the most important predictors of generic (EQ-5D-3L and EQ-5D visual analog scale [VAS]) and disease-specific (Walking Impairment Questionnaire [WIQ]) health status at 1year. A random forest model was used to generate a rank variables based on their importance in predicting 1-year health status. Among included patients, mean±standard deviation age was 68.3±9years with 67.9% and 87% being male and White, respectively. Baseline mean EQ-5D-3L, EQ-5D VAS, and WIQ were 0.7±0.3, 64.6±16.0, and 30.7±22.1, respectively. The most important variables for predicting EQ-5D-3L, EQ-5D VAS, and WIQ were a history of deep venous thrombosis, chronic lung disease, and hypertension, respectively. Among patients undergoing PVI for claudication, comorbidities were the most important predictors of future health status, underscoring the need for an integrated approach for peripheral artery disease management. Utilization of machine learning approaches can facilitate development of models to inform risk-based approaches to treatment.

EEG activation in preschool children: Characteristics and predictive value for current and future mental health status

BackgroundPrevious research has characterised EEG changes associated with resting activation in primary school children and adults, while task-related activation has only been considered in adults. The current study characterises physiological activation in preschool children and examines the potential value of activation indices for predicting mental health status at two time points. AimsTo investigate how resting activation and task-related activation are represented in 4- to 5-year-old preschool children and examine if these activation indices can predict current and future mental health status. Methods and proceduresFrontal EEG was recorded from 81 preschool children during eyes-closed resting, eyes-open resting, and an inhibitory control task to allow calculation of activation indices. The Child Behaviour Checklist was completed by the child’s parent at this time, and again 6–8 months later after the child’s transition to kindergarten. Outcomes and resultsResting activation was represented by reductions in frontal delta, theta, and alpha power in the eyes-open compared to eyes-closed condition, and an increase in frontal beta power. Task-related activation was represented by increases in frontal delta, theta, and alpha power and a decrease in beta power. Frontal delta and theta task-related activation significantly predicted externalising behaviours in both preschool and kindergarten, with stronger prediction in kindergarten. Conclusions and implicationsThis study characterised resting and task-related activation in preschool children, and reported similar effects to those found in older children and adults for resting activation, with novel effects for task-related activation. As task-related activation indices were predictive of externalising behaviours in both preschool and kindergarten, these results have implications for early identification of children who experience externalising behavioural problems across the transition to school period. What does this study add?This study provides new data on how the fundamental physiological processes of resting and task-related activation, both of which are theorised to contribute to “upstream” processes such as executive functions and broader behaviour, are represented in the frontal EEG of preschool aged children. We also learn that the top-down task-related activation indices for delta and theta activity were predictive of current mental health status and future status after the transition to kindergarten, while the bottom-up resting activation indices were not.

Data-Driven Generative Model Aimed to Create Synthetic Data for the Long-Term Forecast of Gas Turbine Operation

Abstract The prediction of gas turbine (GT) future health state plays a strategic role in the current energy sector. However, training an accurate prognostic model is challenging in case of limited historical data (e.g., new installation). Thus, this paper develops a Generative Adversarial Network (GAN) model aimed to generate synthetic data that can be used for data augmentation. The GAN model includes two neural networks, i.e., a generator and a discriminator. The generator aims to generate synthetic data that mimic the real data. The discriminator is a binary classification network. During the training process, the generator is optimized to fool the discriminator in distinguishing between real and synthetic data. The real data employed in this paper were taken from the literature, gathered from three GTs, and refer to two quantities, i.e., corrected power output and compressor efficiency, which are tracked during several years. Three different analyses are presented to validate the reliability of the synthetic dataset. First, a visual comparison of real and synthetic data is performed. Then, two metrics are employed to quantitively evaluate the similarity between real and synthetic data distributions. Finally, a prognostic model is trained by only using synthetic data and then employed to predict real data. The results prove the high reliability of the synthetic data, which can be thus exploited to train a prognostic model. In fact, the prediction error of the prognostic model on the real data is lower than 2.5% even in the case of long-term prediction.

Empowering Women's Health: Examining the Impact of Human Papillomavirus (HPV) Vaccination on Cervical Cancer Treatment and Beyond.

This study aims to identify the change in the health status of women, particularly in cervical cancer treatment through HPV vaccination. Thus, the research aims to measure the reduction in the incidence of cervical cancer in vaccinated women and evaluate the impact of HPV vaccination on the overall health and well-being of women treated for cervical cancer. The paper uses a research approach that involves reviewing the literature, analysing epidemiological data, and assessing the impact of the vaccination program. Major observations suggest that many developed countries' campaigns have reduced cervical cancer and enhanced treatment. Further, the study also addresses some additional effects of the intervention, both health-related with an emphasis on the decrease in healthcare costs and an enhancement of the quality of life among women, and social with a focus on the changes in women's status as a result of vaccination. The research also focusses on the community and economic points of view on HPV vaccination programs, its problems and opportunities regarding socio-economic factors, cultural disparities, and healthcare systems. This study implies that working on those barriers by implementing effective interventions, increasing awareness, and demanding relevant changes in policies could improve vaccination levels as well as outcomes. Hence, this research supports HPV vaccination as vital to the future health status of women. Through the use of survey data and the adoption of a public health perspective, the study can fill existing gaps in the literature on preventive interventions and cervical malignancies and consequently contribute to the enhancement of women's health, particularly in developing countries.

Gestational Diabetes and Subsequent Metabolic Dysfunction: An National Health and Nutrition Examination Survey Analysis (2011-2018).

Background: Gestational diabetes mellitus (GDM) complicates ∼10% of pregnancies, with the highest rates among Asian women. Evidence suggests that GDM is associated with an increased risk for future chronic health conditions, yet data for Asian women are sparse. We explored the association between prior GDM and metabolic dysfunction with nationally representative data to obtain Asian-specific estimates. Methods: For this cross-sectional study, data were drawn from the National Health and Nutrition Examination Survey for 7195 women with a prior pregnancy. GDM (yes/no) was defined using the question "During pregnancy, were you ever told by a doctor or other health professional that you had diabetes, sugar diabetes, or gestational diabetes?." Current metabolic dysfunction (yes/no) was based on having at least one of four indicators: systolic blood pressure (SBP, ≥130 mmHg), waist circumference (≥88 cm), high-density lipoprotein (HDL) cholesterol (<50 mg/dL), and glycosylated hemoglobin (HbA1c) (≥6.5%). Logistic regression estimated odds ratios (ORs) and 95% confidence intervals (CIs) for the association between prior GDM and metabolic outcomes, overall and by race. Models included sampling weights and demographic and behavioral factors. Results: Overall, women with prior GDM had 46% greater odds of high waist circumference (OR: 1.5; 95% CI: 1.1-2.0) and 200% greater odds (OR: 3.0; 95% CI: 2.1-4.2) of high HbA1c. Prior GDM was not associated with high blood pressure or low HDL cholesterol. In race-specific analyses, prior GDM was associated with increased risk of elevated HbA1c among Asian (OR: 6.6; 95% CI: 2.5-17.2), Mexican American (OR: 3.0; 95% CI: 1.5-5.8), Black (OR: 3.0; 95% CI: 1.7-5.5), and White (OR: 2.6; 95% CI: 1.5-4.6) women. Prior GDM was associated with elevated SBP among Mexican American women and low HDL among Black women. Discussion: Prior GDM is associated with elevated HbA1c among all women, yet is a stronger predictor of elevated HbA1c among Asian women than other women. Race-specific associations between prior GDM and metabolic dysfunction were observed among Mexican American and Black women. Further research is warranted to understand the observed race/ethnic-specific associations.

Finances and Future Health

First-generation college students experience disproportionate levels of stress, anxiety, and lack of preparation for undergraduate education in comparison to their multi-generational peers in relation to their levels of financial support and literacy. This impacts on their levels of resiliency, their physical and mental wellbeing, academic success, and levels of attrition. This not only has impacts on their health and wellbeing in the present, but can also negatively impact both their health and their career trajectory in the future as well. Using a 2-phase approach, researchers collected both quantitative and qualitative data related to how first-generation EOF students think about the role of financial literacy, and in particular Federal Work Study, as a component of their current and future health status and academic success. Qualitative analysis gave rise to 3 themes related to student feelings of stress, pressure/obligation, lack of preparation, and uncertainty about the role of college education in their current and future lives. Findings are shared in order to better inform and guide institutions of higher education on how to best educate and support their first-generation students, particularly in how to aid these students in improving their financial literacy and financial support to improve resiliency, wellbeing, and academic success.

Electrochemical Circuit Model Based State of Health Prognostics for Evaluation of Reusability of Lithium-Ion Batteries from Electric Vehicle.

For the purpose of predicting the state of health of already used lithium-ion batteries from 85 kWh electric vehicles, a simplified equivalent circuit model is utilized to estimate the electrochemical time constant from constant current discharge profiles. The grading process among as-obtained LIB cells is classified into three level types according to the remaining capacity and direct current resistance. Theoretically, the logarithmic equation describing cycling behavior is derived and utilized in the prediction of the state of health of the used cells. After the selection of the electrochemical time constant obtained from the best-fitting results in constant current discharge data, the suitable cycle number until the 20th cycle was selected for the prediction of the state of health after the 250th cycling data, which revealed that a narrow error range below 5% was for high and medium battery grades. Also, this error range became abruptly wider in lowest grade batteries, indicating that our proposed model for cycling behavior was highly useful in the prediction of the future state of health of the used batteries.

A hierarchical Bayesian approach for parameter identification in structural deterioration models using probabilistic surrogate models

Most structural systems currently in operation are subjected to potentially multiple deterioration processes, for which different models exist to describe their evolution in time. The quantities of interest (QoIs) involved in them are often directly unobservable. However, advances in Structural Health Monitoring (SHM) have enabled gathering structural response data which can be used to indirectly make inferences on unobservable (state) quantities through Bayesian inference. Obtaining updated estimates of deterioration model parameters, given observed data, enables them to be used to predict future health states of interest for the structure, which constitutes a core tenet of condition-based maintenance (CBM). Key to obtaining robust parameter estimates is accounting for different uncertain quantities involved in the model that transforms unobservable quantities to observable ones, which in a Bayesian context is achieved through the likelihood function. This work presents an offline, flexible hierarchical framework to tackle this problem that employs probabilistic reduced order models (ROMs) in the construction of the likelihood function, that enable marginalizing out these uncertain quantities. The approximate likelihood function is then used within a typical Markov Chain Monte Carlo (MCMC) setting for parameter estimation. The framework is demonstrated in applications related to deterioration processes common to ship structures.

Predicting the effect of hydro-climatic and land-use dynamic variables on watershed health status.

This study aimed to predict the impact of changing hydro-climatic variables and land use changes on the future health status of the Safaroud Watershed, northern Iran. It also sought to explore the significance of hydro-climatic and land use variables in prioritizing sub-watersheds based on the watershed health index. The study involved extracting key characteristics related to anthropogenetic, climatic, and hydrological factors for pressure, state, and response indicators. The current watershed health index was calculated, followed by predictions of watershed health based on dynamic hydro-climatic and land-use variables for the next 10 and 20 years. The Safaroud Watershed health assessment and zoning showed that the average value and standard deviation of the current pressure index were equal to 0.573 and 0.185, respectively. The lowest value of this index was around 0.290 and related to sub-watershed 5, and the highest value was around 0.840 and related to sub-watershed 11. The initial evaluation of the classification indicated the prevalence of moderate and high-pressure conditions with a range of about 79%. Finally, the physical factors of sub-watersheds (time of concentration with 15.72%) had the lowest role. In general, among the criteria used to calculate the pressure index in the current period, anthropogenetic and climatic factors showed the highest percentage of participation in determining the pressure index. The quantification of the current watershed health status and the 10- and 20-year-forecast periods showed that the values of the watershed health index were similar. However, the changes in the health index in the sub-watersheds at the beginning of the study period ranged from relatively unhealthy favorable conditions to moderately positive and moderately negative conditions.

Growth and feed efficiency of Nordic Red Dairy Cattle, Holstein, and their F1 crossbreeds when limiting feed energy concentration in prepubertal heifers

Milk production and overall dairy farm economics depend on rearing dairy heifers. This study investigated the presence of a genotype by environment interaction in Holstein (HOL), Nordic Red Dairy Cattle (RDC), and their F1 crossbreeds (HOLxRDC) when provided different feed rations. The aim of our study was to assess how different energy concentrations in feed rations affect growth, body condition scores, feed intake, and feed efficiency in the 3 groups during the prepubertal period. The 3 breed groups were randomly allocated to receive either a standard or a low energy feed ration. HOL heifers exhibited reduced growth and a lower body condition score when they were fed the low energy feed ration. In contrast, the RDC heifers demonstrated similar growth rates with the different feed rations and maintained similar body condition scores irrespective of feed energy concentration. HOLxRDC crossbred heifers performed as an intermediate between the HOL and RDC groups. There were significant differences in dry matter intake and energy intake in the HOL and HOLxRDC groups depending on feed ration treatment. The RDC heifers had similar feed intake irrespective of treatment. There were no significant differences in the feed conversion ratio between breeds and feed treatments. These results indicate the presence of a genotype by environment interaction in prepubertal HOL and RDC heifers in response to differences in feed ration treatment. Due to the influence of prepubertal growth on future milk production, reproduction, and health status, it is important to be aware of breed-specific requirements during the prepubertal period, particularly in mixed-breed and crossbred groups, to optimize growth rates and production potential.

Exploring Functional Products and Early-Life Dynamics of Gut Microbiota.

Research on the microbiome has progressed from identifying specific microbial communities to exploring how these organisms produce and modify metabolites that impact a wide range of health conditions, including gastrointestinal, metabolic, autoimmune, and neurodegenerative diseases. This review provides an overview of the bacteria commonly found in the intestinal tract, focusing on their main functional outputs. We explore biomarkers that not only indicate a well-balanced microbiota but also potential dysbiosis, which could foreshadow susceptibility to future health conditions. Additionally, it discusses the establishment of the microbiota during the early years of life, examining factors such as gestational age at birth, type of delivery, antibiotic intake, and genetic and environmental influences. Through a comprehensive analysis of current research, this article aims to enhance our understanding of the microbiota's foundational development and its long-term implications for health and disease management.

Longitudinal Associations Between Enjoyment of Physical Education, Cardiorespiratory Fitness, and Muscular Fitness Among Finnish Adolescents.

Students' experiences in physical education (PE) can shape future physical activity (PA) behaviors. PE enjoyment is associated with PA; however, the relationship between PE enjoyment and fitness has not been extensively investigated. The aim of this study was to examine if changes in PE enjoyment were associated with changes in cardiorespiratory fitness (CRF) and muscular fitness (MF) among Finnish adolescents. Study participants were students (n = 1147; 11.27 [±0.32] years at baseline) attending public schools in Finland. Data were collected yearly (2017-2021). The 20 m shuttle run assessed CRF, curl-up/push-up tests assessed MF, and the enjoyment subscale of the Sport Commitment Questionnaire-2 measured PE enjoyment. The random intercept cross-lagged panel model, including repeated measures (within-level) and latent levels (between-level) of PE enjoyment, CRF and MF, was tested. Sex, body mass index, moderate to vigorous PA, and peak height velocity were included as covariates in the analysis. Over 5 years, PE enjoyment decreased, CRF increased until Timepoint 3, and MF remained stable. Positive associations between PE enjoyment and fitness were observed, indicating the greater the PE enjoyment, the higher the fitness. For PE enjoyment, CRF and MF repeated measures were positively associated with measurement of the next year. PE enjoyment was positively related to CRF and MF the years thereafter. Our findings highlight the importance of quality PE experiences for enjoyment and fitness gains during the transition from primary to secondary school. These findings are important given youth fitness levels are associated with future health status.

Processing of clinical notes for efficient diagnosis with feedback attention-based BiLSTM.

Predicting a patient's future health state through the analysis of their clinical records is an emerging area in the field of intelligent medicine. It has the potential to assist healthcare professionals in prescribing treatments safely, making more accurate diagnoses, and improving patient care. However, clinical notes have been underutilized due to their complexity, high dimensionality, and sparsity. Nevertheless, these clinical records hold significant promise for enhancing clinical decision. To tackle these problems, a novel feedback attention-based bidirectional long short-term memory (FABiLSTM) model has been proposed for more effective diagnosis using clinical records. This model incorporates PubMedBERT for filtering irrelevant information, enhances global vector word embeddings for numerical representations and K-means clustering, and performs to explore term frequency and inverse document frequency intricacies. The proposed approach excels in capturing information, aiding accurate disease prediction. The predictive capability is further enhanced with the help of a billiards-inspired optimization algorithm. The effectiveness of the FABiLSTM method has been assessed with the MIMIC-III dataset, yielding impressive results in accuracy, precision, F1 score, and recall score of 98.52%, 98%, 98.2%, and 98.2% individually. These results reveal ways in which the proposed technique excels in comparison with current practices.

Applying human-centered design to adapt the Jumpstart Guide for goals-of-care discussions in persons living with dementia.

Goals-of-care discussions (GOCD) are uncommon in persons living with dementia (PLWD) despite the likelihood of eventual loss of decisional capacity in the population. The Jumpstart Guide, an existing serious illness communication priming tool, can improve GOCD in certain populations, but has not previously been adapted for use among PLWD and their caregivers. Using human-centered design (HCD), we adapted the Jumpstart Guide for use with PLWD and their caregivers. We conducted qualitative interviews with clinicians and caregivers of PLWD. Six team members conducted qualitative rapid analysis of interviews leading to the development of summary templates and integrative matrices. Four iterations of the Jumpstart Guide led to the final version. Thirteen clinicians and 11 caregivers were interviewed. Interviews provided key insights into the unique barriers PLWD and their caregivers face during GOCD, including discomfort with accepting a dementia diagnosis and concern with using "serious illness" to describe dementia, as is commonly done in palliative care. Clinicians described differences in GOCD with PLWD compared to other serious illnesses, and the challenge of getting patients and families to think about future health states. Interviews led to Jumpstart Guide adaptations in the following domains: (1) format and structure, (2) content, and (3) specific language. Suggested changes included prioritizing naming a decision-maker, changing conversation prompts to improve accessibility and understandability, ensuring the Jumpstart Guide could be used with patients as well as their caregivers, and altering language to avoid references to "serious illness" and "abilities." Using HCD yielded valuable insights from clinicians and caregivers about the unique barriers to conducting GOCD among PLWD and their caregivers. These insights were used to adapt the Jumpstart Guide for use with PLWD and their caregivers, which is currently being tested in a pragmatic randomized controlled trial in outpatient clinics.

A digital twin model incorporating generalized metabolic fluxes to identify and predict chronic kidney disease in type 2 diabetes mellitus

We have developed a digital twin-based CKD identification and prediction model that leverages generalized metabolic fluxes (GMF) for patients with Type 2 Diabetes Mellitus (T2DM). GMF digital twins utilized basic clinical and physiological biomarkers as inputs for identification and prediction of CKD. We employed four diverse multi-ethnic cohorts (n = 7072): a Singaporean cohort (EVAS, n = 289) and a North American cohort (NHANES, n = 1044) for baseline CKD identification, and two multi-center Singaporean cohorts (CDMD, n = 2119 and SDR, n = 3627) for 3-year CKD prediction and risk stratification. We subsequently conducted a comprehensive study utilizing a single dataset to evaluate the clinical utility of GMF for CKD prediction. The GMF-based identification model performed strongly, achieving an AUC between 0.80 and 0.82. In prediction, the GMF generated with complete parameters attained high performance with an AUC of 0.86, while with incomplete parameters, it achieved an AUC of 0.75. The GMF-based prediction model utilizing complete inputs is the standard implementation of our algorithm: HealthVector Diabetes®. We have established the GMF digital twin-based model as a robust clinical tool capable of predicting and stratifying the risk of future CKD within a 3-year time horizon. We report the correlation of GMF with basic input parameters, their ability to differentiate between future health states and medication status at baseline, and their capability to quantify CKD progression rates. This holistic methodology provides insights into patients’ health states and CKD progression rates based on GMF metabolic profile differences, enabling personalized care plans.