Multistate Model Research Articles

Fibromyalgia pathogenesis explained by a neuroendocrine multistable model.

Fibromyalgia (FM) is a central disorder characterized by chronic pain, fatigue, insomnia, depression, and other minor symptoms. Knowledge about pathogenesis is lacking, diagnosis difficult, clinical approach puzzling, and patient management disappointing. We conducted a theoretical study based on literature data and computational analysis, aimed at developing a comprehensive model of FM pathogenesis and addressing suitable therapeutic targets. We started from the evidence that FM must involve a dysregulation of central pain processing, is female prevalent, suggesting a role for the hypothalamus-pituitary-gonadal (HPG) axis, and is stress-related, suggesting a role for the HP-adrenocortical (HPA) axis. Central pathogenesis was supposed to involve a pain processing loop system including the thalamic ventroposterolateral nucleus (VPL), the primary somatosensory cortex (SSC), and the thalamic reticular nucleus (TRN). For decreasing GABAergic and/or increasing glutamatergic transmission, the loop system crosses a bifurcation point, switching from monostable to bistable, and converging on a high-firing-rate steady state supposed to be the pathogenic condition. Thereafter, we showed that GABAergic transmission is positively correlated with gonadal-hormone-derived neurosteroids, notably allopregnanolone, whereas glutamatergic transmission is positively correlated with stress-induced glucocorticoids, notably cortisol. Finally, we built a dynamic model describing a multistable, double-inhibitory loop between HPG and HPA axes. This system has a high-HPA/low-HPG steady state, allegedly reached in females under combined premenstrual/postpartum brain allopregnanolone withdrawal and stress condition, driving the thalamocortical loop to the high-firing-rate steady state, and explaining the connection between endocrine and neural mechanisms in FM pathogenesis. Our model accounts for FM female prevalence and stress correlation, suggesting the use of neurosteroid drugs as a possible solution to currently unsolved problems in the clinical treatment of the disease.

Cost-effectiveness of single-pill and separate-pill administration of antihypertensive triple combination therapy: a population-based microsimulation study

BackgroundSingle-pill combination (SPC) of three antihypertensive drugs has been shown to improve adherence to therapy compared with free combinations, but little is known about its long-term costs and health consequences. This study aimed to evaluate the lifetime cost-effectiveness profile of a three-drug SPC of an angiotensin-converting enzyme inhibitor, a calcium-channel blocker, and a diuretic vs the corresponding two-pill administration (a two-drug SPC plus a third drug separately) from the Italian payer perspective.MethodsA cost-effectiveness analysis was conducted using multi-state semi-Markov modeling and microsimulation. Using the healthcare utilization database of the Lombardy Region (Italy), 30,172 and 65,817 patients aged ≥ 40 years who initiated SPC and two-pill combination, respectively, between 2015 and 2018 were identified. The observation period extended from the date of the first drug dispensation until death, emigration, or December 31, 2019. Disease and cost models were parametrized using the study cohort, and a lifetime microsimulation was applied to project costs and life expectancy for the compared strategies, assigning each of them to each cohort member. Costs and life-years gained were discounted by 3%. Probabilistic sensitivity analysis with 1,000 samples was performed to address parameter uncertainty.ResultsCompared with the two-pill combination, the SPC increased life expectancy by 0.86 years (95% confidence interval [CI] 0.61–1.14), with a mean cost differential of -€12 (95% CI -9,719–8,131), making it the dominant strategy (ICER = -14, 95% CI -€15,871–€7,113). The cost reduction associated with the SPC was primarily driven by savings in hospitalization costs, amounting to €1,850 (95% CI 17–7,813) and €2,027 (95% CI 19–8,603) for patients treated with the SPC and two-pill combination, respectively. Conversely, drug costs were higher for the SPC (€3,848, 95% CI 574–10,640 vs. €3,710, 95% CI 263–11,955). The cost-effectiveness profile did not significantly change according to age, sex, and clinical status.ConclusionsThe SPC was projected to be cost-effective compared with the two-pill combination at almost all reasonable willingness-to-pay thresholds. As it is currently prescribed to only a few patients, the widespread use of this strategy could result in benefits for both patients and the healthcare system.

Operational reliability evaluation integrating prediction models for enhanced situational awareness in wind-integrated system

The extensive implementation of wind power generation (WPG) challenges power systems' flexibility and reliability during operations. Fluctuating wind speeds lead to variability in generation within wind energy systems, directly impacting operational reliability. Therefore, evaluating wind energy systems' situational awareness (SA) is imperative to forecast two-day-ahead operational reliability, facilitating effective power system planning. The foundation of SA for wind energy systems lies in monitoring the wind profile. This involves gaining insights into the system's state through perceiving wind speed and WPG, comprehending the dynamics of its state, and subsequently predicting operational reliability. To achieve this, a Simulink model of the wind system has been developed and integrated with dSPACE hardware through a real-time interface for real-time validation and implementation of a cloud-based IoT platform. This IoT platform aids in acquiring real-time data on wind profiles, enhancing the assessment of SA. The representation of the wind energy system utilizes a multi-state wind speed model, incorporating the inherent randomness of wind energy generation. A time-series-based non-linear autoregressive with exogenous input (NARX) artificial neural network (ANN) model is employed to predict wind speed and WPG for SA-based operational reliability. This model proves instrumental in forecasting the dynamic nature of wind speed and WPG. The proposed approach for operational planning is validated by implementing the wind energy system in real-time on the dSPACE platform and integrating it with IoT. This approach considers the uncertainties associated with wind generation, providing a comprehensive framework for assessing SA and optimizing power system operations.

Survival and population size of the Indo‐Pacific humpback dolphins off the eastern Leizhou Peninsula

AbstractIndo‐Pacific humpback dolphins form a resident population off the east coast of Leizhou Peninsula (LZP) in South China. The most recent study suggested ~1,500 individuals in this area, making this population one of only two populations of this species exceeding 1,000 individuals. However, demographic information regarding this population has been lacking since 2012. Using a 5‐year (2019–2023) photo‐identification data set, we examined two key demographic parameters of the LZP population, namely the apparent survival and population size, using multistate capture‐recapture analyses. Annual survival probabilities (φ) changed over time, fluctuating between 0.942 and 0.961 for adults, and between 0.787 and 0.837 for juveniles. Jolly‐Seber multistate model analyses resulted in highly comparable noncalf population size estimates from 2020 to 2023 (2020 = 479, SE = 28; 2021 = 458, SE = 29; 2022 = 481, SE = 27; 2023 = 446, SE = 31). By reviewing the available demographic information, we conclude that the population size of LZP humpback dolphins has been previously overestimated. The time‐dependent survivals seemingly reflect a dynamic development of anthropogenic disturbance during the pandemic period. We propose that the population status should be re‐evaluated, especially in the postpandemic era when the human activities came back to “normal” levels.

Estimation in Multi-State Semi-Markov Models with a Cured Fraction and Masked Causes of Deaths

Estimation in Multi-State Semi-Markov Models with a Cured Fraction and Masked Causes of Deaths

Dynamic Risk Estimation of Adverse Events in Ambulatory LVAD Patients: A MOMENTUM 3 Analysis

BackgroundHemocompatibility-related adverse events affect patients after left ventricular assist device (LVAD) implantation but are hard to predict. ObjectivesDynamic risk modeling with a multistate model can predict risk of gastrointestinal bleeding (GIB), stroke, or death in ambulatory patients. MethodsThis was a secondary analysis of the MOMENTUM 3 (Multicenter Study of MagLev Technology in Patients Undergoing Mechanical Circulatory Support Therapy with HeartMate 3) trial. HeartMate 3 LVAD recipients who survived to hospital discharge and were followed for up to 2 years. A total of 145 variables were included in the multistate model with multivariate logistic regression. Model performance was assessed with the area under the curve in a holdout validation cohort. A risk stratification tool was created by dividing patients into categories of predicted risk using the final model variables and associated OR. ResultsAmong 2,056 LVAD patients, the median age was 59.4 years (20.4% women, 28.6% Black). At 2 years, the incidence of GIB, stroke, and death was 25.6%, 6.0%, and 12.3%, respectively. The multistate model included 39 total variables to predict risk of GIB (16 variables), stroke (10 variables), and death (19 variables). When ambulatory patients were classified according to their risk category, the 30-day observed event rate in the highest risk group for GIB, stroke, or death was 26.9%, 1.8%, and 4.8%, respectively. The multistate model predicted GIB, stroke, and death at any 30-day period with an area under the curve of 0.70, 0.69, and 0.86, respectively. ConclusionsThe multistate model informs 30-day risk in ambulatory LVAD recipients and allows recalculation of risk as new patient-specific data become available. The model allows for accurate risk stratification that predicts impending adverse events and may guide clinical decision making. (MOMENTUM 3 IDE Clinical Study Protocol; NCT02224755)

Designing cancer screening trials for reduction in late-stage cancer incidence

ABSTRACT Before implementing a biomarker test for early cancer detection into routine clinical care, the test must demonstrate clinical utility, that is, the test results should lead to clinical actions that positively affect patient-relevant outcomes. Unlike therapeutical trials for patients diagnosed with cancer, designing a randomized controlled trial (RCT) to demonstrate the clinical utility of an early detection biomarker with mortality and related endpoints poses unique challenges. The hurdles stem from the prolonged natural progression of the disease and the lack of information regarding the time-varying screening effect on the target asymptomatic population. To facilitate the study design of screening trials, we propose using a generic multistate disease history model and derive model-based effect sizes. The model links key performance metrics of the test, such as sensitivity, to primary endpoints like the incidence of late-stage cancer. It also incorporates the practical implementation of the biomarker-testing program in real-world scenarios. Based on the chronological time scale aligned with RCT, our method allows the assessment of study powers based on key features of the new program, including the test sensitivity, the length of follow-up, and the number and frequency of repeated tests. The calculation tool from the proposed method will enable practitioners to perform realistic and quick evaluations when strategizing screening trials for specific diseases. We use numerical examples based on the National Lung Screening Trial to demonstrate the method.

Survival costs of reproduction are independent of energy costs in a seabird, the pelagic cormorant.

Life-history theory predicts that investment in reproduction should decrease survival (the 'cost of reproduction'). It is often assumed that energy allocation drives such trade-offs, with limited energy available for both reproduction and survival. However, the underlying mechanisms remain poorly understood, maybe because survival costs of reproduction are only apparent when resources are limited. Here, we took advantage of a natural experiment created by fluctuating environmental conditions to compare energy expenditure of a seabird, the pelagic cormorant (Phalacrocorax pelagicus), between contrasting population-scale scenarios of survival costs of reproduction. We used multi-state capture-recapture modelling across 16 years to identify which breeding seasons induced high survival costs (survival ratebreeders < survival ratenon/failed breeders) and we concomitantly estimated energy expenditure of chick-rearing males using time-energy budget models across 4 years. Daily energy expenditure (DEE) of chick-rearing pelagic cormorants varied significantly among years. However, survival costs of reproduction were observed in only 1 year, and contrary to our expectations, variation in DEE was not associated with population-level survival costs. Similarly, at the individual level, DEE in 1 year did not predict the probability of being observed again at the colony in following years (apparent survival). Finally, DEE was independent of brood size and brood age, but older individuals tended to expend less energy than younger ones. Given the lack of an apparent energetic 'cost of reproduction', lower DEE in older birds could be due to improved efficiency rather than avoidance of costs in old birds. Although future studies should account for potential sex-specific energetic constraints by including data on female energy expenditure, we conclude that a direct link between the rate of energy expenditure during breeding and subsequent survival is unlikely in this system.

Reprint of: Profiling the plight of disconnected youth in America

In a successful transition from youth to adulthood, individuals pass through a sequence of roles involving school, work, and family formation that culminate in their becoming self-sufficient adults. However, some “disconnected” youth spend extended periods of time outside of any role that constitutes an element of the pathway towards adult independence. Assisting these youth requires a systematic understanding of what “disconnection” means, how many disconnected youth there are, who these youth are, and how the scale of the problem has evolved over time. Using the National Longitudinal Surveys of Youth for 1997 and 1979, we address these issues by creating concrete definitions of “disconnection spells” using rich data on youths’ enrollment, work, and personal histories. We estimate a multi-state duration model to account for right censoring and to understand differences across salient sub-groups. Our estimates imply that in the early 2000s, almost 19% and 25% of young men and young women, respectively, experienced a disconnection spell by age 23 using our basic definition. These rates are substantially higher for certain sub-groups defined by race/ethnicity, parental education, and government aid receipt, rising as high as 30+% by age 23. Approximately 60% of youth with a disconnection spell have it last longer than a year, and close to 10% have it last longer than 4 years. However, once reconnected, a majority of youth go at least three years without a re-disconnection spell. Patterns of initial disconnection changed markedly from the 1980s to the 2000s, as young women saw a 12 percentage point decline over time. Moreover, the Black-White gap in disconnection has fallen for women, but increased for men. Our profile of disconnection experiences provides a starting point for government agencies aiming to understand where, how, and with whom to intervene to prevent lengthy disconnection spells.

Modeling the HIV Cascade of Care Using Routinely Collected Clinical Data to Guide Programmatic Interventions and Policy Decisions.

The HIV care cascade is a framework to examine effectiveness of HIV programs and progress toward global targets to end the epidemic but has been conceptualized as a unidirectional process that ignores cyclical care patterns. We present a dynamic cascade that accounts for patient "churn" and apply novel analytic techniques to readily available clinical data to robustly estimate program outcomes and efficiently assess progress toward global targets. Data were assessed for 35,649 people living with HIV and receiving care at 78 clinics in East Africa between 2014 and 2020. Patients were aged ≥15 years and had ≥1 viral load measurements. We used multi-state models to estimate the probability of being in 1 of 5 states of a dynamic HIV cascade: (1) in HIV care but not on antiretroviral therapy (ART), (2) on ART, (3) virally suppressed, (4) in a gap-in-care, and (5) deceased and compared these among subgroups. To assess progress toward global targets, we summed those probabilities across patients and generated population-level proportions of patients on ART and virally suppressed in mid-2020. One year after enrollment, 2.8% of patients had not initiated ART, 86.7% were receiving ART, 57.4% were virally suppressed, 10.2% were disengaged from care, and 0.3% had died. At 5 years, the proportion on ART remained steady but viral suppression increased to 77.2%. Of those aged 15-25, >20% had disengaged from care and <60% were virally suppressed. In mid-2020, 90.1% of the cohort was on ART, 90.7% of whom had suppressed virus. Novel analytic approaches can characterize patient movement through a dynamic HIV cascade and, importantly, by capitalizing on readily available data from clinical cohorts, offer an efficient approach to estimate population-level proportions of patients on ART and virally suppressed. Significant progress toward global targets was observed in our cohort but challenges remain among younger patients.

Belatacept-based immunosuppression does not confer increased risk of BK polyomavirus-DNAemia relative to tacrolimus-based immunosuppression.

The effect of belatacept on BK polyomavirus (BKPyV) control remains largely unknown. This is a propensity matched retrospective cohort study in adult kidney transplant recipients (KTR) transplanted between 2016-2020 who received a belatacept- versus tacrolimus-based immunosuppression regimen. A continuous time multi-state Markov model was used to evaluate BKPyV replication dynamics (BKPyV-dyn). Three BKPyV-dyn states were defined: BKPyV-dyn1 (viral load <3 log10), BKPyV-dyn2 (viral load ≥ 3 log10 and ≤4 log10), and BKPyV-dyn3 (viral load >4 log10). Two hundred eighty KTR on belatacept- and 280 KTR on tacrolimus-based regimens were compared. The probability of transitioning between BKPyV-dyn states and time spent in each state in both groups was comparable. Total duration in BKPyV-dyn-1 was 632.1 days (95% CI 612.1, 648.5) for belatacept versus 615.2 days (95% CI 592.5, 635.8) for tacrolimus, BKPyV-dyn-2 was 49.2 days (95% CI 41.3, 58.4) for belatacept versus 55.6 days (95% CI 46.5, 66.8) for tacrolimus, and BKPyV-dyn-3 was 48.7 days (95% CI 37.1, 363.1) for belatacept versus 59.2 days (95% CI 45.8, 73.5) for tacrolimus. BKPyV associated nephropathy (PyVAN) occurred in 3.9% in belatacept- and 3.9% tacrolimus-treated KRT (P >.9). Compared with tacrolimus-based immunosuppression, belatacept based immunosuppression was not associated with increased risk of BKPyV-DNAemia or nephropathy.

Quantifying the abundance and survival rates of island-associated spinner dolphins using a multi-state open robust design model

Spinner dolphins (Stenella longirostris subsp.) occupy the nearshore waters of several Hawaiian Islands. Due to their constrained behavioral pattern and genetic isolation, they are vulnerable to anthropogenic threats. Their occurrence and behavior are well-described, yet a lack of data on their abundance and survival rates hinders optimal conservation action. Using design-based photo-identification surveys, this study estimated the abundance, apparent survival, and emigration of spinner dolphins off the Waiʻanae Coast of Oʻahu through multi-state open robust design (MSORD) and POPAN modelling. Eight seasonal field seasons, (two winter, spring, summer, and autumn) each comprised of six surveys of the study area, were completed during two consecutive years. Seasonal abundance estimates derived from the best fitting model ranged from 140 (± 36.8 SE, 95% CI 84–232) to 373 (± 60.0, 95% CI 273–509) individuals and were lowest during winter seasons. The MSORD estimated a survival rate of 0.95 (± 0.02 SE) and a Markovian pattern of temporary emigration. POPAN modelling estimated a super-population size of 633 (± 78 SE, 95% CI 492–798), reflecting the total number of individual dolphins that used the study area during the entire study period. Additional research on circum- and inter-island dolphin movements around and between Oʻahu and the Maui Nui region may shed light on both seasonal movement patterns and overall abundance for the Oʻahu/4-Islands stock. This work represents the first systematic mark-recapture effort to assess the abundance and survival rates of these highly exposed dolphins, providing valuable insights for conservation and management.

Cross-Sectional and Longitudinal Association of Clinical and Neurocognitive Factors With Apathy in Patients With Parkinson Disease.

A robust understanding of the natural history of apathy in Parkinson disease (PD) is foundational for developing effective clinical management tools. However, large longitudinal studies are lacking while the literature is inconsistent about even cross-sectional associations. We aimed to determine the longitudinal predictors of apathy development in a large cohort of people with PD and its cross-sectional associations and trajectories over time, using sophisticated Bayesian modeling techniques. People with PD followed up in the longitudinal New Zealand Parkinson's progression project were included. Apathy was defined using the neuropsychiatric inventory subscale ≥4, and analyses were also repeated using a less stringent cutoff of ≥1. Both MoCA and comprehensive neuropsychological testing were used as appropriate to the model. Depression was assessed using the hospital anxiety and depression scale. Cross-sectional Bayesian regressions were conducted, and a multistate predictive model was used to identify factors that predict the initial onset of apathy in nonapathetic PD, while also accounting for the competing risk of death. The relationship between apathy presence and mortality was also investigated. Three hundred forty-six people with PD followed up for up to 14 years across a total of 1,392 sessions were included. Apathy occurrence did not vary significantly across the disease course (disease duration odds ratio [OR] = 0.55, [95% CI 0.28-1.12], affecting approximately 11% or 22% of people at any time depending on the NPI cutoff used. Its presence was associated with a significantly higher risk of death after controlling for all other factors (hazard ratio [HR] = 2.92 [1.50-5.66]). Lower cognition, higher depression levels, and greater motor severity predicted apathy development in those without motivational deficits (HR [cognition] = 0.66 [0.48-0.90], HR [depression] = 1.45 [1.04-2.02], HR [motor severity] = 1.37 [1.01-1.86]). Cognition and depression were also associated with apathy cross-sectionally, along with male sex and possibly lower dopaminergic therapy level, but apathy still occurred across the full spectrum of each variable (OR [cognition] = 0.58 [0.44-0.76], OR [depression] = 1.43 [1.04-1.97], OR [female sex] = 0.45 [0.22-0.92], and OR [levodopa equivalent dose] = 0.78 [0.59-1.04]. Apathy occurs across the PD time course and is associated with higher mortality. Depressive symptoms and cognitive impairment in particular predict its future development in those with normal motivation.

Degradation modeling and remaining life prediction of multi-state long-life systems under random environmental influences

Abstract For large-scale systems such as bridges, which have long operating lifetimes, the operating states are usually categorized into multiple levels, and they are also subjected to various random environmental influences during operation. However, due to the significant granularity in the categorization of system states, it is difficult to assess the system state transitions influenced by random environmental factors, which compromises the accuracy of remaining life predictions. In this study, we focus on long-life systems with multiple states and investigate the degradation modeling and remaining life prediction considering the impact of random environmental factors. The system degradation process, based on the semi-Markov process and multi-state modeling, was decomposed into states using the sub-exponential approximation method. A state transition probability model considering exponential environmental influences was constructed. Furthermore, based on the developed model for calculating the distribution of sojourn times in multiple states, a reliability and remaining life prediction model for the system was derived. By taking the bridge deck as a case study, the verification and analysis of remaining life prediction for the bridge deck were conducted under the influences of average daily traffic volume and bridge age. The results indicate that both the average daily traffic volume and bridge age have a significant impact on the degradation of the bridge deck. The relative error of the predicted results considering the above effects falls within the range of 1.77%–12.18%.

The association between dynamic changes in vitamin D and frailty alterations: A prospective analysis of UK Biobank participants.

Frailty is a common geriatric syndrome associated with reduced reserves and increased vulnerability to stressors among older adults. Vitamin D deficiency has been implicated in frailty, as it is essential for maintaining musculoskeletal functions. The relationship between dynamic changes in vitamin D levels and frailty over time has not been extensively studied. This study utilized data from the UK Biobank. Baseline and longitudinal changes in vitamin D levels were measured. Frailty status was assessed using both the frailty phenotype and frailty index approaches and classified as robust, pre-frail, or frail. Changes in frailty status were assessed by frailty phenotype at baseline (2006-2010) and the follow-up (2012-2013). Mixed effect model was performed to examine the association between vitamin D levels and frailty status. Using multistate transition models, we assessed the impact of increasing vitamin D levels on the probabilities of transitioning between robust, pre-frail, and frail states. Based on the frailty phenotype, 287926 individuals (64.8%) were identified as having various degrees of frailty (median age 58.00 [51.00, 64.00] years, 55.9% females). Using the frailty index approach, 250566 individuals (56%) were found to have different levels of frailty (median age 59.00 [51.00, 64.00] years, 55.3% females). Baseline vitamin D levels were found to be significantly associated with frailty status (frailty phenotype: ORfrail 0.78, 95% CI [0.76, 0.79], P<0.001; frailty index: ORfrail 0.80, 95% CI [0.78, 0.81], P<0.001). Dynamic changes in vitamin D levels were also found to be associated with changes in frailty over time. Furthermore, increasing vitamin D levels were associated with a transition from frailty to a healthier status. A higher degree of vitamin D (estimated at 1nmol/L) was associated with a lower risk of transitioning from robust to prefrail (HR 0.997, 95% CI [0.995, 0.999]) and from prefrail to frail (HR 0.992, 95% CI [0.988, 0.995]). This study highlights the importance of vitamin D in the context of frailty. Low vitamin D levels are associated with increased frailty risk, while increasing vitamin D levels may contribute to improving frailty status. Recognizing the relationship between vitamin D levels and frailty can inform personalized management and early interventions for frail individuals. Further research is needed to explore the potential effects of vitamin D interventions on frailty and deepen our understanding of the biological connections between vitamin D and frailty.

Bayesian survival analysis with INLA.

This tutorial shows how various Bayesian survival models can be fitted using the integrated nested Laplace approximation in a clear, legible, and comprehensible manner using the INLA and INLAjoint R-packages. Such models include accelerated failure time, proportional hazards, mixture cure, competing risks, multi-state, frailty, and joint models of longitudinal and survival data, originally presented in the article "Bayesian survival analysis with BUGS." In addition, we illustrate the implementation of a new joint model for a longitudinal semicontinuous marker, recurrent events, and a terminal event. Our proposal aims to provide the reader with syntax examples for implementing survival models using a fast and accurate approximate Bayesian inferential approach.

Lessons learned: avoiding bias via multi-state analysis of patients' trajectories in real-time.

Many studies have attempted to determine the disease severity and patterns of COVID-19. However, at the beginning of the pandemic, the complex patients' trajectories were only descriptively reported, and many analyses were worryingly prone to time-dependent-, selection-, and competing risk biases. Multi-state models avoid these biases by jointly analysing multiple clinical outcomes while taking into account their time dependency, including current cases, and modelling competing events. This paper uses a publicly available data set from the first wave in Israel as an example to demonstrate the benefits of analysing hospital data via multi-state methodology. We compared the outcome of the data analysis using multi-state models with the outcome obtained when various forms of bias are ignored. Furthermore, we used Cox regression to model the transitions among the states in a multi-state model. This allowed for the comparison of the covariates' influence on transition rates between the two states. Lastly, we calculated expected lengths of stay and state probabilities based on the multi-state model and visualised it using stacked probability plots. Compared to standard methods, multi-state models avoid many biases in the analysis of real-time disease developments. The utility of multi-state models is further highlighted through the use of stacked probability plots, which visualise the results. In addition, by stratification of disease patterns by subgroups and visualisation of the distribution of possible outcomes, these models bring the data into an interpretable form. To accurately guide the provision of medical resources, this paper recommends the real-time collection of hospital data and its analysis using multi-state models, as this method eliminates many potential biases. By applying multi-state models to real-time data, the gained knowledge allows rapid detection of altered disease courses when new variants arise, which is essential when informing medical and political decision-makers as well as the general population.

Transition between cardiometabolic conditions and body weight among women: which paths increase the risk of diabetes and cardiovascular diseases?

Previous studies investigated the association of body weight and hypertension with risk of incident cardiometabolic multimorbidity. Our aim was to estimate the risk of diabetes and cardiovascular disease later in life for subjects with different progression patterns of overweight, obesity, and hypertension in mid-life. This was a prospective cohort study in which data from 12,784 participants in the Australian Longitudinal Study on Women’s Health were used. Multistate model was used to study the progression pattern of overweight, obesity, hypertension, diabetes, and cardiovascular disease over the life course. The cumulative incidence of diabetes and cardiovascular disease up to the age of 73 was estimated for women with different patterns of other conditions. The six most common paths and corresponding cumulative incidences for diabetes were overweight 5.1%, obesity 11.5%, hypertension 6.9%, progression from overweight to obesity 8.2%, overweight and hypertension 12.1%, and obesity and hypertension 36.8%. For women with diabetes and other conditions, the cumulative incidence of cardiovascular disease (heart disease or stroke) as the next immediate condition was 22.4%. The corresponding figure for women who only had a report of diabetes but did not have high body weight or hypertension was 8.3%. The higher risk of transition from healthy state to a cardiometabolic condition was associated with low education, income stress, smoking, not drinking alcohol (compared to low drinkers), physical inactivity, and high perceived stress. Women with obesity and hypertension in middle-age had a substantially higher risk of developing diabetes and cardiovascular disease than women without these potentially preventable conditions.

MSGene: a multistate model using genetic risk and the electronic health record applied to lifetime risk of coronary artery disease

Coronary artery disease (CAD) is the leading cause of death among adults worldwide. Accurate risk stratification can support optimal lifetime prevention. Current methods lack the ability to incorporate new information throughout the life course or to combine innate genetic risk factors with acquired lifetime risk. We designed a general multistate model (MSGene) to estimate age-specific transitions across 10 cardiometabolic states, dependent on clinical covariates and a CAD polygenic risk score. This model is designed to handle longitudinal data over the lifetime to address this unmet need and support clinical decision-making. We analyze longitudinal data from 480,638 UK Biobank participants and compared predicted lifetime risk with the 30-year Framingham risk score. MSGene improves discrimination (C-index 0.71 vs 0.66), age of high-risk detection (C-index 0.73 vs 0.52), and overall prediction (RMSE 1.1% vs 10.9%), in held-out data. We also use MSGene to refine estimates of lifetime absolute risk reduction from statin initiation. Our findings underscore our multistate model’s potential public health value for accurate lifetime CAD risk estimation using clinical factors and increasingly available genetics toward earlier more effective prevention.

Single-fraction radiation retreatment for bone metastases: role of a rapid access clinic

ObjectivesThis study investigates retreatment rates in single-fraction radiation therapy (SFRT) for painful bone metastasis in patients with limited life expectancy. We compared retreatment-free survival (RFS) in patients from a rapid...