simKAP: simulation framework for the kidney allocation process with decision making model.

Organ shortage is a major barrier in transplantation and rules guarding organ allocation decisions should be robust, transparent, ethical and fair. Whilst numerous allocation strategies have been proposed, it is often unrealistic to evaluate all of them in real-life settings. Hence, the capability of conducting simulations prior to deployment is important. Here, we developed a kidney allocation simulation framework (simKAP) that aims to evaluate the allocation process and the complex clinical decision-making process of organ acceptance in kidney transplantation. Our findings have shown that incorporation of both the clinical decision-making and a dynamic wait-listing process resulted in the best agreement between the actual and simulated data in almost all scenarios. Additionally, several hypothetical risk-based allocation strategies were generated, and we found that these strategies improved recipients' long-term post-transplant patient survival and reduced wait time for transplantation. The importance of simKAP lies in its ability for policymakers in any transplant community to evaluate any proposed allocation algorithm using in-silico simulation.

A field guide to cultivating computational biology.

Evolving in sync with the computation revolution over the past 30 years, computational biology has emerged as a mature scientific field. While the field has made major contributions toward improving scientific knowledge and human health, individual computational biology practitioners at various institutions often languish in career development. As optimistic biologists passionate about the future of our field, we propose solutions for both eager and reluctant individual scientists, institutions, publishers, funding agencies, and educators to fully embrace computational biology. We believe that in order to pave the way for the next generation of discoveries, we need to improve recognition for computational biologists and better align pathways of career success with pathways of scientific progress. With 10 outlined steps, we call on all adjacent fields to move away from the traditional individual, single-discipline investigator research model and embrace multidisciplinary, data-driven, team science.

Psychosocial stratification of antenatal indicators to guide population-based programs in perinatal depression.

BACKGROUND: There is increasing awareness that perinatal psychosocial adversity experienced by mothers, children, and their families, may influence health and well-being across the life course. To maximise the impact of population-based interventions for optimising perinatal wellbeing, health services can utilise empirical methods to identify subgroups at highest risk of poor outcomes relative to the overall population. METHODS: This study sought to identify sub-groups using latent class analysis within a population of mothers in Sydney, Australia, based on their differing experience of self-reported indicators of psychosocial adversity. This study sought to identify sub-groups using latent class analysis within a population of mothers in Sydney, Australia, based on their differing experience of self-reported indicators of psychosocial adversity. Subgroup differences in antenatal and postnatal depressive symptoms were assessed using the Edinburgh Postnatal Depression Scale. RESULTS: Latent class analysis identified four distinct subgroups within the cohort, who were distinguished empirically on the basis of their native language, current smoking status, previous involvement with Family-and-Community Services (FaCS), history of child abuse, presence of a supportive partner, and a history of intimate partner psychological violence. One group consisted of socially supported 'local' women who speak English as their primary language (Group L), another of socially supported 'migrant' women who speak a language other than English as their primary language (Group M), another of socially stressed 'local' women who speak English as their primary language (Group Ls), and socially stressed 'migrant' women who speak a language other than English as their primary language (Group Ms.). Compared to local and not socially stressed residents (L group), the odds of antenatal depression were nearly three times higher for the socially stressed groups (Ls OR: 2.87 95%CI 2.10-3.94) and nearly nine times more in the Ms. group (Ms OR: 8.78, 95%CI 5.13-15.03). Antenatal symptoms of depression were also higher in the not socially stressed migrant group (M OR: 1.70 95%CI 1.47-1.97) compared to non-migrants. In the postnatal period, Group M was 1.5 times more likely, while the Ms. group was over five times more likely to experience suboptimal mental health compared to Group L (OR 1.50, 95%CI 1.22-1.84; and OR 5.28, 95%CI 2.63-10.63, for M and Ms. respectively). CONCLUSIONS: The application of empirical subgrouping analysis permits an informed approach to targeted interventions and resource allocation for optimising perinatal maternal wellbeing.

Survival and Quality of Life Impact of a Risk-based Allocation Algorithm for Deceased Donor Kidney Transplantation.

BACKGROUND: To determine the incremental gains in graft and patient survival under a risk-based, deceased donor kidney allocation compared with the current Australian algorithm. METHODS: Risk-based matching algorithms were applied to first graft, kidney only recipients (n = 7513) transplanted in Australia between 1994 and 2013. Probabilistic models were used to compare the waiting time, life, and QALYs and graft years between the 8 risk-based allocation strategies against current practice. RESULTS: Compared with current practice, Kidney Donor Risk Index-Estimated Posttransplant Survival matching of the lowest 20% of scores reduced median waiting time by 0.64 years (95% confidence interval [CI], 0.52-0.73) for recipients aged 30 years or younger, but increased waiting time by 0.94 years (95% CI, 0.79-1.09) for recipients older than 60 years. Among all age groups, the greatest gains occurred if Kidney Donor Risk Index-Estimated Posttransplant Survival matching of the lowest 30% of scores was used, incurring a median overall gain of 0.63 (95% CI, 0.03-1.25) life years and 0.78 (95% CI, 0.30-1.26) graft years compared with the current practice. A median gain in survival of 1.91 years for younger recipients (aged 30-45 years) was offset by a median reduction in survival (by 0.95 life years) among the older recipients. Prioritization of lower-quality donor kidneys for older candidates reduced the waiting time for recipients older than 45 years, but no changes in graft and patient survivals were observed. CONCLUSIONS: Risk-based matching engendered a moderate, overall increase in graft and patient survivals, accrued through benefits for recipients 45 years or younger but disadvantage to recipients older than 60 years.

ClassifyR: an R package for performance assessment of classification with applications to transcriptomics.

UNLABELLED: Although a large collection of classification software packages exist in R, a new generic framework for linking custom classification functions with classification performance measures is needed. A generic classification framework has been designed and implemented as an R package in an object oriented style. Its design places emphasis on parallel processing, reproducibility and extensibility. Finally, a comprehensive set of performance measures are available to ease post-processing. Taken together, these important characteristics enable rapid and reproducible benchmarking of alternative classifiers. AVAILABILITY AND IMPLEMENTATION: ClassifyR is implemented in R and can be obtained from the Bioconductor project: http://bioconductor.org/packages/release/bioc/html/ClassifyR.html.