Assessment of the benefits of Discrete Conditional Survival Models in modelling ambulance response times

Many of the challenges faced in health care delivery can be informed through building models. In particular, Discrete Conditional Survival (DCS) models, recently under development, can provide policymakers with a flexible tool to assess time-to-event data. The DCS model is capable of modelling the survival curve based on various underlying distribution types and is capable of clustering or grouping observations (based on other covariate information) external to the distribution fits. The flexibility of the model comes through the choice of data mining techniques that are available in ascertaining the different subsets and also in the choice of distribution types available in modelling these informed subsets. This paper presents an illustrated example of the Discrete Conditional Survival model being deployed to represent ambulance response-times by a fully parameterised model. This model is contrasted against use of a parametric accelerated failure-time model, illustrating the strength and usefulness of Discrete Conditional Survival models.

Models of open populations with space-limited recruitment: extension of theory and application to the barnacle Chthamalus montagui.

1. Barnacles are a good model organism for the study of open populations with space-limited recruitment. These models are applicable to other species with open supply of new individuals and resource limitation. The inclusion of space in models leads to reductions in recruitment with increasing density, and thus predictions of population size and stability are possible. 2. Despite the potential generality of a demographic theory for open space-limited populations, the models currently have a narrow empirical base. In this study, a model for an open population with space-limited recruitment was extended to include size-specific survival and promotions to any size class. The assumptions of this model were tested using data from a pan-European study of the barnacle Chthamalus montagui Southward. Two models were constructed: a 6-month model and a periodic annual model. Predicted equilibria and their stabilities were compared between shores. 3. Tests of model assumptions supported the extension of the theory to include promotions to any size class. Mortality was found to be size-specific and density independent. Studied populations were open, with recruitment proportional to free space. 4. The 6-month model showed a significant interaction between time and location for equilibrium free space. This may have been due to contrasts in the timing of structuring processes (i.e. creating and filling space) between Mediterranean and Atlantic systems. Integration of the 6-month models into a periodic annual model removed the differences in equilibrium-free space between locations. 5. Model predictions show a remarkable similarity between shores at a European scale. Populations were persistent and all solutions were stable. This reflects the apparent absence of density-dependent mortality and a high adult survivorship in C. montagui. As populations are intrinsically stable, observations of fluctuations in density are directly attributable to variations in the environmental forcing of recruitment or mortality

Survival patterns among lymphoma patients with a family history of lymphoma

Purpose: Genetic factors are important in the etiology and pathogenesis of chronic lymphocytic leukemia (CLL), Hodgkin's lymphoma (HL), and non-Hodgkin's lymphoma (NHL). Only a few small studies have assessed clinical characteristics and prognosis for familial patients, with inconsistent findings. Methods: Using population-based registries from Sweden and Denmark, 7,749 patients with CLL, 7,476 patients with HL, and 25,801 patients with NHL with linkable first-degree relatives were identified. Kaplan-Meier curves were constructed to compare survival in patients with lymphoma with and without a family history of lymphoma. The risk of dying was assessed using adjusted Cox proportional hazard models. Results: We found 85 patients with CLL (1.10%), 95 patients with HL (1.28%), and 206 patients with NHL (0.80%) with a family history of any lymphoma. Five-year mortality was similar for patients with CLL (hazard ratio [HR], 1.28; 95% CI, 0.95 to 1.72), HL (HR, 0.78; 95% CI, 0.49 to 1.25), and NHL (HR, 0.91; 95% CI, 0.74 to 1.12) versus without a family history of any lymphoma. Mortality was also similar for patients with versus without a family history of the same lymphoma. T-cell/anaplastic lymphoma patients with a family history of NHL had poorer outcome 5-years after diagnosis (HR, 5.38; 95% CI, 1.65 to 17.52). Results were similar for 10 years of follow-up. Conclusion: With the exception of T-cell/anaplastic lymphoma, survival patterns for patients with CLL, HL, and NHL with a family history of lymphoma were similar to those for sporadic patients, suggesting that most familial lymphomas do not have an altered clinical course. Our findings provide no evidence to modify therapeutic strategies for patients with CLL, HL, or NHL based solely on family history. © 2008 by American Society of Clinical Oncology.


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Simulating Coxian Phase-type Distributions for Patient Survival

Coxian phase-type distributions are a special type of Markov model that can be used to represent survival times in terms of phases through which an individual may progress until they eventually leave the system completely. Previous research has considered the Coxian phase-type distribution to be ideal in representing patient survival in hospital. However, problems exist in fitting the distributions. This paper investigates the problems that arise with the fitting process by simulating various Coxian phase-type models for the representation of patient survival and examining the estimated parameter values and eigenvalues obtained. The results indicate that numerical methods previously used for fitting the model parameters do not always converge. An alternative technique is therefore considered. All methods are influenced by the choice of initial parameter values. The investigation uses a data set of 1439 elderly patients and models their survival time, the length of time they spend in a UK hospital.

Solutes determine the temperature windows for microbial survival and growth

Microbial cells, and ultimately the Earth's biosphere, function within a narrow range of physicochemical conditions. For the majority of ecosystems, productivity is cold-limited, and it is microbes that represent the failure point. This study was carried out to determine if naturally occurring solutes can extend the temperature windows for activity of microorganisms. We found that substances known to disorder cellular macromolecules (chaotropes) did expand microbial growth windows, fungi preferentially accumulated chaotropic metabolites at low temperature, and chemical activities of solutes determined microbial survival at extremes of temperature as well as pressure. This information can enhance the precision of models used to predict if extraterrestrial and other hostile environments are able to support life; furthermore, chaotropes may be used to extend the growth windows for key microbes, such as saprotrophs, in cold ecosystems and manmade biomes.

Simulated predator extinctions: Predator identity affects survival and recruitment of oysters

The rate of species loss is increasing at a global scale, and human-induced extinctions are biased toward predator species. We examined the effects of predator extinctions on a foundation species, the eastern oyster (Crassostrea virginica). We performed a factorial experiment manipulating the presence and abundance of three of the most common predatory crabs, the blue crab (Callinectes sapidus), stone crab (Menippe mercenaria), and mud crab (Panopeus herbstii) in estuaries in the eastern United States. We tested the effects of species richness and identity of predators on juvenile oyster survival, oyster recruitment, and organic matter content of sediment. We also manipulated the density of each of the predators and controlled for the loss of biomass of species by maintaining a constant mass of predators in one set of treatments and simultaneously using an additive design. This design allowed us to test the density dependence of our results and test for functional compensation by other species. The identity of predator species, but not richness, affected oyster populations. The loss of blue crabs, alone or in combination with either of the other species, affected the survival rate of juvenile oysters. Blue crabs and stone crabs both affected oyster recruitment and sediment organic matter negatively. Mud crabs at higher than ambient densities, however, could fulfill some of the functions of blue and stone crabs, suggesting a level of ecological redundancy. Importantly, the strong effects of blue crabs in all processes measured no longer occurred when individuals were present at higher-than-ambient densities. Their role as dominant predator is, therefore, dependent on their density within the system and the density of other species within their guild (e.g., mud crabs). Our findings support the hypothesis that the effects of species loss at higher trophic levels are determined by predator identity and are subject to complex intraguild interactions that are largely density dependent. Understanding the role of biodiversity in ecosystem functioning or addressing practical concerns, such as loss of predators owing to overharvesting, remains complicated because accurate predictions require detailed knowledge of the system and should be drawn from sound experimental evidence, not based on observations or generalized models.

Discrete Conditional Phase-type models utilising classification trees: Application to modelling health service capacities

Discrete Conditional Phase-type (DC-Ph) models consist of a process component (survival distribution) preceded by a set of related conditional discrete variables. This paper introduces a DC-Ph model where the conditional component is a classification tree. The approach is utilised for modelling health service capacities by better predicting service times, as captured by Coxian Phase-type distributions, interfaced with results from a classification tree algorithm. To illustrate the approach, a case-study within the healthcare delivery domain is given, namely that of maternity services. The classification analysis is shown to give good predictors for complications during childbirth. Based on the classification tree predictions, the duration of childbirth on the labour ward is then modelled as either a two or three-phase Coxian distribution. The resulting DC-Ph model is used to calculate the number of patients and associated bed occupancies, patient turnover, and to model the consequences of changes to risk status.

Bevacizumab and rapamycin induce growth suppression in mouse models of hepatocellular carcinoma

Background/Aims: Hepatocellular carcinoma is a leading cause of global cancer mortality, with standard chemotherapy being minimally effective in prolonging survival. We investigated if combined targeting of vascular endothelial growth factor protein and expression might affect hepatocellular carcinoma growth and angiogenesis.

Out-of-field cell survival following exposure to intensity-modulated radiation fields

PURPOSE:
To determine the in-field and out-of-field cell survival of cells irradiated with either primary field or scattered radiation in the presence and absence of intercellular communication.
METHODS AND MATERIALS:
Cell survival was determined by clonogenic assay in human prostate cancer (DU145) and primary fibroblast (AGO1552) cells following exposure to different field configurations delivered using a 6-MV photon beam produced with a Varian linear accelerator.
RESULTS:
Nonuniform dose distributions were delivered using a multileaf collimator (MLC) in which half of the cell population was shielded. Clonogenic survival in the shielded region was significantly lower than that predicted from the linear quadratic model. In both cell lines, the out-of-field responses appeared to saturate at 40%-50% survival at a scattered dose of 0.70 Gy in DU-145 cells and 0.24 Gy in AGO1522 cells. There was an approximately eightfold difference in the initial slopes of the out-of-field response compared with the a-component of the uniform field response. In contrast, cells in the exposed part of the field showed increased survival. These observations were abrogated by direct physical inhibition of cellular communication and by the addition of the inducible nitric oxide synthase inhibitor aminoguanidine known to inhibit intercellular bystander effects. Additional studies showed the proportion of cells irradiated and dose delivered to the shielded and exposed regions of the field to impact on response.
CONCLUSIONS:
These data demonstrate out-of-field effects as important determinants of cell survival following exposure to modulated irradiation fields with cellular communication between differentially irradiated cell populations playing an important role. Validation of these observations in additional cell models may facilitate the refinement of existing radiobiological models and the observations considered important determinants of cell survival.