Risk stratification in pulmonary arterial hypertension using Bayesian analysis.

BACKGROUND: Current risk stratification tools in pulmonary arterial hypertension (PAH) are limited in their discriminatory abilities, partly due to the assumption that prognostic clinical variables have an independent and linear relationship to clinical outcomes. We sought to demonstrate the utility of Bayesian network-based machine learning in enhancing the predictive ability of an existing state-of-the-art risk stratification tool, REVEAL 2.0. METHODS: We derived a tree-augmented naïve Bayes model (titled PHORA) to predict 1-year survival in PAH patients included in the REVEAL registry, using the same variables and cut-points found in REVEAL 2.0. PHORA models were validated internally (within the REVEAL registry) and externally (in the COMPERA and PHSANZ registries). Patients were classified as low-, intermediate- and high-risk (<5%, 5-20% and >10% 12-month mortality, respectively) based on the 2015 European Society of Cardiology/European Respiratory Society guidelines. RESULTS: PHORA had an area under the curve (AUC) of 0.80 for predicting 1-year survival, which was an improvement over REVEAL 2.0 (AUC 0.76). When validated in the COMPERA and PHSANZ registries, PHORA demonstrated an AUC of 0.74 and 0.80, respectively. 1-year survival rates predicted by PHORA were greater for patients with lower risk scores and poorer for those with higher risk scores (p<0.001), with excellent separation between low-, intermediate- and high-risk groups in all three registries. CONCLUSION: Our Bayesian network-derived risk prediction model, PHORA, demonstrated an improvement in discrimination over existing models. This is reflective of the ability of Bayesian network-based models to account for the interrelationships between clinical variables on outcome, and tolerance to missing data elements when calculating predictions.

Diagnostic delay in pulmonary arterial hypertension: Insights from the Australian and New Zealand pulmonary hypertension registry.

BACKGROUND AND OBJECTIVE: Early diagnosis of PAH is clinically challenging. Patterns of diagnostic delay in Australian and New Zealand PAH populations have not been explored in large-scale studies. We aimed to evaluate the magnitude, risk factors and survival impact of diagnostic delay in Australian and New Zealand PAH patients. METHODS: A cohort study of PAH patients from the PHSANZ Registry diagnosed from 2004 to 2017 was performed. Diagnostic interval was the time from symptom onset to diagnostic right heart catheterization as recorded in the registry. Factors associated with diagnostic delay were analysed in a multivariate logistic regression model. Survival rates were compared across patients based on the time to diagnosis using Kaplan-Meier method and Cox regression. RESULTS: A total of 2044 patients were included in analysis. At diagnosis, median age was 58 years (IQR: 43-69), female-to-male ratio was 2.8:1 and majority of patients were in NYHA FC III-IV (82%). Median diagnostic interval was 1.2 years (IQR: 0.6-2.7). Age, CHD-PAH, obstructive sleep apnoea and peripheral vascular disease were independently associated with diagnostic interval of ≥1 year. No improvement in diagnostic interval was seen during the study period. Longer diagnostic interval was associated with decreased 5-year survival. CONCLUSION: PAH patients experience significant diagnostic interval, which has not improved despite increased community awareness. Age, cardiovascular and respiratory comorbidities are significantly associated with longer time to diagnosis. Mortality rates appear higher in patients who experience longer diagnostic interval.

Insights into mortality patterns and causes of death through a process point of view model.

Process point of view (POV) models of mortality, such as the Strehler-Mildvan and stochastic vitality models, represent death in terms of the loss of survival capacity through challenges and dissipation. Drawing on hallmarks of aging, we link these concepts to candidate biological mechanisms through a framework that defines death as challenges to vitality where distal factors defined the age-evolution of vitality and proximal factors define the probability distribution of challenges. To illustrate the process POV, we hypothesize that the immune system is a mortality nexus, characterized by two vitality streams: increasing vitality representing immune system development and immunosenescence representing vitality dissipation. Proximal challenges define three mortality partitions: juvenile and adult extrinsic mortalities and intrinsic adult mortality. Model parameters, generated from Swedish mortality data (1751-2010), exhibit biologically meaningful correspondences to economic, health and cause-of-death patterns. The model characterizes the twentieth century epidemiological transition mainly as a reduction in extrinsic mortality resulting from a shift from high magnitude disease challenges on individuals at all vitality levels to low magnitude stress challenges on low vitality individuals. Of secondary importance, intrinsic mortality was described by a gradual reduction in the rate of loss of vitality presumably resulting from reduction in the rate of immunosenescence. Extensions and limitations of a distal/proximal framework for characterizing more explicit causes of death, e.g. the young adult mortality hump or cancer in old age are discussed.

Fish navigation of large dams emerges from their modulation of flow field experience.

Navigating obstacles is innate to fish in rivers, but fragmentation of the world's rivers by more than 50,000 large dams threatens many of the fish migrations these waterways support. One limitation to mitigating the impacts of dams on fish is that we have a poor understanding of why some fish enter routes engineered for their safe travel around the dam but others pass through more dangerous routes. To understand fish movement through hydropower dam environments, we combine a computational fluid dynamics model of the flow field at a dam and a behavioral model in which simulated fish adjust swim orientation and speed to modulate their experience to water acceleration and pressure (depth). We fit the model to data on the passage of juvenile Pacific salmonids (Oncorhynchus spp.) at seven dams in the Columbia/Snake River system. Our findings from reproducing observed fish movement and passage patterns across 47 flow field conditions sampled over 14 y emphasize the role of experience and perception in the decision making of animals that can inform opportunities and limitations in living resources management and engineering design.

Quantifying Intrinsic and Extrinsic Contributions to Human Longevity: Application of a Two-Process Vitality Model to the Human Mortality Database.

The rise in human life expectancy has involved declines in intrinsic and extrinsic mortality processes associated, respectively, with senescence and environmental challenges. To better understand the factors driving this rise, we apply a two-process vitality model to data from the Human Mortality Database. Model parameters yield intrinsic and extrinsic cumulative survival curves from which we derive intrinsic and extrinsic expected life spans (ELS). Intrinsic ELS, a measure of longevity acted on by intrinsic, physiological factors, changed slowly over two centuries and then entered a second phase of increasing longevity ostensibly brought on by improvements in old-age death reduction technologies and cumulative health behaviors throughout life. The model partitions the majority of the increase in life expectancy before 1950 to increasing extrinsic ELS driven by reductions in environmental, event-based health challenges in both childhood and adulthood. In the post-1950 era, the extrinsic ELS of females appears to be converging to the intrinsic ELS, whereas the extrinsic ELS of males is approximately 20 years lower than the intrinsic ELS.

The Strehler-Mildvan correlation from the perspective of a two-process vitality model.

The Strehler and Mildvan (SM) general theory of ageing and mortality provides a mechanism-based explanation of Gompertz's law and predicts a log-linear relationship between the two Gompertz coefficients, known as the SM correlation. While the SM correlation is supported by data from developed countries before the second half of the twentieth century, the recent breakdown of the correlation pattern in these countries has prompted demographers to conclude that SM theory needs to be reassessed. In this paper we use a newly developed two-process vitality model to explain the SM correlation and its breakdown in terms of asynchronous trends in acute (extrinsic) and chronic (intrinsic) mortality factors. We propose that the mortality change in the first half of the twentieth century is largely determined by the elimination of immediate hazards to death, whereas the mortality change in the second half is primarily driven by the slowdown of the deterioration rate of intrinsic survival capacity.

Resource competition induces heterogeneity and can increase cohort survivorship: selection-event duration matters.

Determining when resource competition increases survivorship can reveal processes underlying population dynamics and reinforce the importance of heterogeneity among individuals in conservation. We ran an experiment mimicking the effects of competition in a growing season on survivorship during a selection event (e.g., overwinter starvation, drought). Using a model fish species (Poecilia reticulata), we studied how food availability and competition affect mass in a treatment stage, and subsequently survivorship in a challenge stage of increased temperature and starvation. The post-treatment mean mass was strongly related to the mean time to mortality and mass at mortality at all levels of competition. However, competition increased variance in mass and extended the right tail of the survivorship curve, resulting in a greater number of individuals alive beyond a critical temporal threshold ([Formula: see text]) than without competition. To realize the benefits from previously experienced competition, the duration of the challenge ([Formula: see text]) following the competition must exceed the critical threshold [Formula: see text] (i.e., competition increases survivorship when [Formula: see text]). Furthermore, this benefit was equivalent to increasing food availability by 20 % in a group without competition in our experiment. The relationship of [Formula: see text] to treatment and challenge conditions was modeled by characterizing mortality through mass loss in terms of the stochastic rate of loss of vitality (individual's survival capacity). In essence, when the duration of a selection event exceeds [Formula: see text], competition-induced heterogeneity buffers against mortality through overcompensation processes among individuals of a cohort. Overall, our study demonstrates an approach to quantify how early life stage heterogeneity affects survivorship.

Pulmonary Hypertension Definition, Classification, and Epidemiology in Asia.

Pulmonary hypertension (PH) is caused by a range of conditions and is important to recognize as it is associated with increased mortality. Pulmonary arterial hypertension refers to a group of PH subtypes affecting the distal pulmonary arteries for which effective treatment is available. The hemodynamic definition of pulmonary arterial hypertension has recently changed which may lead to greater case recognition and earlier treatment. The prevalence of specific PH etiologies may differ depending on geographic region. PH caused by left heart disease is the most common cause of PH worldwide. In Asia, there is greater proportion of congenital heart disease- and connective tissue disease- (especially systemic lupus erythematosus) related PH relative to the West. This review summarizes the definition, classification, and epidemiology of PH as it pertains to Asia.

Telomere-length dependent T-cell clonal expansion: A model linking ageing to COVID-19 T-cell lymphopenia and mortality.

BACKGROUND: Severe COVID-19 T-cell lymphopenia is more common among older adults and entails poor prognosis. Offsetting the decline in T-cell count during COVID-19 demands fast and massive T-cell clonal expansion, which is telomere length (TL)-dependent. METHODS: We developed a model of TL-dependent T-cell clonal expansion capacity with age and virtually examined the relation of T-cell clonal expansion with COVID-19 mortality in the general population. FINDINGS: The model shows that an individual with average hematopoietic cell TL (HCTL) at age twenty years maintains maximal T-cell clonal expansion capacity until the 6th decade of life when this capacity rapidly declines by more than 90% over the next ten years. The collapse in the T-cell clonal expansion capacity coincides with the steep increase in COVID-19 mortality with age. INTERPRETATION: Short HCTL might increase vulnerability of many older adults, and some younger individuals with inherently short HCTL, to COVID-19 T-cell lymphopenia and severe disease. FUNDING: A full list of funding bodies that contributed to this study can be found in the Acknowledgements section.

Short Telomeres and a T-Cell Shortfall in COVID-19: The Aging Effect.

The slow pace of global vaccination and the rapid emergence of SARS-CoV-2 variants suggest recurrent waves of COVID-19 in coming years. Therefore, understanding why deaths from COVID-19 are highly concentrated among older adults is essential for global health. Severe COVID-19 T-cell lymphopenia is more common among older adults, and it entails poor prognosis. Much about the primary etiology of this form of lymphopenia remains unknown, but regardless of its causes, offsetting the decline in T-cell count during SARS-CoV-2 infection demands fast and massive T-cell clonal expansion, which is telomere length (TL)-dependent. We have built a model that captures the effect of age-dependent TL shortening in hematopoietic cells and its effect on T-cell clonal expansion capacity. The model shows that an individual with average hematopoietic cell TL (HCTL) at age twenty years maintains maximal T-cell clonal expansion capacity until the 6th decade of life when this capacity plummets by more than 90% over the next ten years. The collapse coincides with the steep increase in COVID-19 mortality with age. HCTL metrics may thus explain the vulnerability of older adults to COVID-19. That said, the wide inter-individual variation in HCTL across the general population means that some younger adults with inherently short HCTL might be at risk of severe COVID-19 lymphopenia and mortality from the disease. SIGNIFICANCE STATEMENT: Declining immunity with advancing age is a general explanation for the increased mortality from COVID-19 among older adults. This mortality far exceeds that from viral illnesses such as the seasonal influenza, and it thus requires specific explanations. One of these might be diminished ability with age to offset the development of severe T-cell lymphopenia (a low T-cell count in the blood) that often complicates COVID-19. We constructed a model showing that age-dependent shortening of telomeres might constrain the ability of T-cells of some older COVID-19 patients to undertake the massive proliferation required to clear the virus that causes the infection. The model predicts that individuals with short telomeres, principally seniors, might be at a higher risk of death from COVID-19.

Ratio- and predator-dependent functional forms for predators optimally foraging in patches.

Functional forms of predator-prey interactions are developed for predators optimally foraging on prey distributed in patches. The model uses mean free-path-length theory to develop functional forms for two idealized behaviors of prey in patches. For congregating prey that maintain a fixed density, for example, fish schools, the predation rate has a ratio-dependent form, and predator interference depends only on predator density. For sessile prey, which maintain a fixed patch size, a new predator-dependent form emerges in which predator interference depends on both prey and predator densities. The Beddington-DeAngelis equation is a special case of the sessile form. The model provides behavioral and biological criteria with which to select the functional form and ranges of coefficients appropriate for a particular food web. Finally, the model illustrates that behavior is an essential factor in predator-prey dynamics.

Step-patterned survivorship curves: Mortality and loss of equilibrium responses to high temperature and food restriction in juvenile rainbow trout (Oncorhynchus mykiss).

While survivorship curves typically exhibit smooth declines over time, step-patterned curves can occur with multiple stressors within a life stage. To explore this process, we examined the effects of heat (24°C) and food restriction on juvenile rainbow trout (Oncorhynchus mykiss Walbaum) in challenge experiments. We observed step-patterned survivorship curves determined by mortality and loss of equilibrium (LOE) endpoints. To examine the cause of heterogeneity in the stress responses from early to late mortality and LOE, we measured indices of energetic reserves. The step transition in the survivorship curves, the peak mortality rates, and start of when individuals reached a critical energetic threshold (14% dry mass; 4.0 kJ·g-1 energy) all occurred at around days 10-15 of the challenge. The coherence in these temporal patterns suggest heterogeneity in the cohort stress responses, in which an early subgroup died from heat stress and a late subgroup died from starvation. Thus, their endpoint sensitivities resulted in step-patterned survivorship curves. We discuss the implications of the study for understanding effects of multiple stressors on population heterogeneity and note the possible significance of stress response selection under climate change in which heat stress and food limitations occur in concert.

Retrospective Validation of the REVEAL 2.0 Risk Score With the Australian and New Zealand Pulmonary Hypertension Registry Cohort.

BACKGROUND: Pulmonary arterial hypertension (PAH) prognosis has improved with targeted therapies; however, the long-term outlook remains poor. Objective multiparametric risk assessment is recommended to identify patients at risk of early morbidity and mortality, and for optimization of treatment. The US Registry to Evaluate Early and Long-Term PAH Disease Management (REVEAL) 2.0 risk score is a new model proposed for the follow-up of patients with PAH but has not been externally validated. METHODS: The REVEAL 2.0 risk score was applied to a mixed prevalent and incident cohort of patients with PAH (n = 1,011) from the Pulmonary Hypertension Society of Australia and New Zealand (PHSANZ) Registry. Kaplan-Meier survival was estimated for each REVEAL 2.0 risk score strata and for a simplified three-category (low, intermediate, and high risk) model. Sensitivity analysis was performed on an incident-only cohort. RESULTS: The REVEAL 2.0 model effectively discriminated risk in the large external PHSANZ Registry cohort, with a C statistic of 0.74 (both for full eight-tier and three-category models). When applied to incident cases only, the C statistic was 0.73. The three-category REVEAL 2.0 model demonstrated robust separation of 12- and 60-month survival estimates (all risk category comparisons P < .001). Although the full eight-tier REVEAL 2.0 model separated patients at low, intermediate, and high risk, survival estimates overlapped within some of the intermediate- and high-risk strata. CONCLUSIONS: The REVEAL 2.0 risk score was validated in a large external cohort from the PHSANZ Registry. The REVEAL 2.0 model can be applied for risk assessment of patients with PAH at follow-up. The simplified three-category model may be preferred for clinical use and for future comparison with other prognostic models.

Continuous models of population-level heterogeneity inform analysis of animal dispersal and migration.

Behavioral heterogeneity among individuals is a universal feature of natural populations. Most diffusion-based models of animal dispersal, however, implicitly assume homogeneous movement parameters within a population. Recent attempts to consider the effect of heterogeneous populations on dispersal distributions have been somewhat limited by the high number of parameters required to subdivide a population into several groups. A solution to this problem is to characterize the value of a movement parameter as continuously distributed within a population. We present several cases in which this method is useful and tractable, applying the framework both to spatial distribution data and closely related first passage times. The resulting models allow ecologists to identify the extent to which the variability in dispersal distributions can be attributed to population-level heterogeneity as opposed to intrinsic randomness. We apply the formulation to two very different cases of dispersal: resident organisms in a stream (freshwater chub Nocomis leptocephalus) and migrating organisms (juvenile salmonids Oncorhynchus spp.). In both cases, the application of heterogeneity-explicit models provides insights into the behavioral mechanisms of movement.

The vitality model: a way to understand population survival and demographic heterogeneity.

A four-parameter model describing mortality as the first passage of an abstract measure of survival capacity, vitality, is developed and used to explore four classic problems in demography: (1) medfly demographic paradox, (2) effect of diet restriction on longevity, (3) cross-life stage effects on survival curves and (4) mortality plateaus. The model quantifies the sources of mortality in these classical problems into vitality-dependent and independent parts, and characterizes the vitality-dependent part in terms of initial and evolving heterogeneities. Three temporal scales express the balance of these factors: a time scale of death from senescence, a time scale of accidental mortality and a crossover time between evolving vs. initial heterogeneity. The examples demonstrate how the first-passage approach provides a unique and informative perspective into the processes that shape the survival curves of populations.

Oceanic, riverine, and genetic influences on spring chinook salmon migration timing.

Migrating salmonids often return to their spawning habitats in overlapping timing patterns of multiple stocks (populations) collectively called a run that varies in its genetic makeup across and within years. Managers, tasked with developing harvest strategies on these runs, may have preseason estimates of total run size but little information on run timing. Without both it is difficult to assess a run's status in real time. Consequently, to avoid overharvest, managers tend to control the timing of harvest. However, this strategy may inadvertently affect the component stocks disproportionately and therefore the run's diversity. Thus, accurate estimates of run timing are needed to improve management. We developed a model that includes genetic and environmental factors to predict the mean run timing of chinook salmon (Oncorhynchus tshawytscha) at Bonneville Dam on the Columbia River, Oregon, USA. The model predicted mean runtiming (P < 0.00001, r2 = 0.78) by characterizing genetic run timing components from the arrival timing of precocious males returning one year prior to the remainder of the adults and environmental influences of oceanic and riverine flows that impede or advance the run timing. Variations in the relative abundances of the populations in the run explain 62% of the interannual variation in mean run timing while the oceanic and riverine factors combined account for 15.5%. We suggest that when genetic run timing characteristics are preserved in species with multiple maturation strategies the information can be used to improve run time predictions and maintain genetic diversity of harvested species.

Linking growth, survival, and heterogeneity through vitality.

We model the cross-stage effect of juvenile growth on future cohort survival with vitality, a single stochastic measure of an organism's survival capacity that results in death when it reaches 0. In this construct, the distribution of vitality at the end of a growth treatment stage, which is a measure of survival capacity heterogeneity, determines a cohort's susceptibility to starvation in a subsequent challenge stage. The model predicts that the treatment-stage duration and mass gain determine the mean and variance of the initial vitality distribution of the challenge stage, which in turn determine the effect of a challenge-stage stressor on survival. Studies linking the effect of juvenile growth on time to starvation for chinook salmon and yellow perch are compared to model predictions. The feasibility of predicting survival and heterogeneity in overwintering fish populations from first-year growth is considered. Some limitations and potential extensions of the model to other scenarios are discussed.

The relationship of mammal survivorship and body mass modeled by metabolic and vitality theories.

A model describes the relationship between mammal body mass and survivorship by combining replicative senescence theory postulating a cellular basis of aging, metabolic theory relating metabolism to body mass, and vitality theory relating survival to vitality loss and extrinsic mortality. In the combined framework, intrinsic mortality results from replicative senescence of the hematopoietic stem cells and extrinsic mortality results from environmental challenges. Because the model expresses the intrinsic and extrinsic rates with different powers of body mass, across the spectrum of mammals, survivorship changes from Type I to Type II curve shapes with decreasing body mass. Fitting the model to body mass and maximum lifespan data of 494 nonvolant mammals yields allometric relationships of body mass to the vitality parameters, from which full survivorship profiles were generated from body mass alone. Because maximum lifespan data is predominantly derived from captive populations, the generated survivorship curves were dominated by intrinsic mortality. Comparison of the mass-derived and observed survivorship curves provides insights into how specific populations deviate from the aggregate of populations observed under captivity.

Conservation planning for freshwater-marine carryover effects on Chinook salmon survival.

Experiences of migratory species in one habitat may affect their survival in the next habitat, in what is known as carryover effects. These effects are especially relevant for understanding how freshwater experience affects survival in anadromous fishes. Here, we study the carryover effects of juvenile salmon passage through a hydropower system (Snake and Columbia rivers, northwestern United States). To reduce the direct effect of hydrosystem passage on juveniles, some fishes are transported through the hydrosystem in barges, while the others are allowed to migrate in-river. Although hydrosystem survival of transported fishes is greater than that of their run-of-river counterparts, their relative juvenile-to-adult survival (hereafter survival) can be less. We tested for carryover effects using generalized linear mixed effects models of survival with over 1 million tagged Chinook salmon, Oncorhynchus tshawytscha (Walbaum) (Salmonidae), migrating in 1999-2013. Carryover effects were identified with rear-type (wild vs. hatchery), passage-type (run-of-river vs. transported), and freshwater and marine covariates. Importantly, the Pacific Decadal Oscillation (PDO) index characterizing cool/warm (i.e., productive/nonproductive) ocean phases had a strong influence on the relative survival of rear- and passage-types. Specifically, transportation benefited wild Chinook salmon more in cool PDO years, while hatchery counterparts benefited more in warm PDO years. Transportation was detrimental for wild Chinook salmon migrating early in the season, but beneficial for later season migrants. Hatchery counterparts benefited from transportation throughout the season. Altogether, wild fish could benefit from transportation approximately 2 weeks earlier during cool PDO years, with still a benefit to hatchery counterparts. Furthermore, we found some support for hypotheses related to higher survival with increased river flow, high predation in the estuary and plume areas, and faster migration and development-related increased survival with temperature. Thus, pre- and within-season information on local- and broad-scale conditions across habitats can be useful for planning and implementing real-time conservation programs.

Correction: A Twin Protection Effect? Explaining Twin Survival Advantages with a Two-Process Mortality Model.

[This corrects the article DOI: 10.1371/journal.pone.0154774.].