A Simulation-Based Optimization Model to Study the Impact of Multiple-Region Listing and Information Sharing on Kidney Transplant Outcomes.

More than 8000 patients on the waiting list for kidney transplantation die or become ineligible to receive transplants due to health deterioration. At the same time, more than 4000 recovered kidneys from deceased donors are discarded each year in the United States. This paper develops a simulation-based optimization model that considers several crucial factors for a kidney transplantation to improve kidney utilization. Unlike most proposed models, the presented optimization model incorporates details of the offering process, the deterioration of patient health and kidney quality over time, the correlation between patients' health and acceptance decisions, and the probability of kidney acceptance. We estimate model parameters using data obtained from the United Network of Organ Sharing (UNOS) and the Scientific Registry of Transplant Recipients (SRTR). Using these parameters, we illustrate the power of the simulation-based optimization model using two related applications. The former explores the effects of encouraging patients to pursue multiple-region waitlisting on post-transplant outcomes. Here, a simulation-based optimization model lets the patient select the best regions to be waitlisted in, given their demand-to-supply ratios. The second application focuses on a system-level aspect of transplantation, namely the contribution of information sharing on improving kidney discard rates and social welfare. We investigate the effects of using modern information technology to accelerate finding a matching patient to an available donor organ on waitlist mortality, kidney discard, and transplant rates. We show that modern information technology support currently developed by the United Network for Organ Sharing (UNOS) is essential and can significantly improve kidney utilization.

Quantitative classification of vortical flows based on topological features using graph matching.

Vortical flow patterns generated by swimming animals or flow separation (e.g. behind bluff objects such as cylinders) provide important insight to global flow behaviour such as fluid dynamic drag or propulsive performance. The present work introduces a new method for quantitatively comparing and classifying flow fields using a novel graph-theoretic concept, called a weighted Gabriel graph, that employs critical points of the velocity vector field, which identify key flow features such as vortices, as graph vertices. The edges (connections between vertices) and edge weights of the weighted Gabriel graph encode local geometric structure. The resulting graph exhibits robustness to minor changes in the flow fields. Dissimilarity between flow fields is quantified by finding the best match (minimum difference) in weights of matched graph edges under relevant constraints on the properties of the edge vertices, and flows are classified using hierarchical clustering based on computed dissimilarity. Application of this approach to a set of artificially generated, periodic vortical flows demonstrates high classification accuracy, even for large perturbations, and insensitivity to scale variations and number of periods in the periodic flow pattern. The generality of the approach allows for comparison of flows generated by very different means (e.g. different animal species).

Temporal transitions in COPD severity stages within the GOLD 2017 classification system.

BACKGROUND: The stability of the new GOLD 2017 COPD staging is unknown, as well as the frequency of individual transitions in COPD stages beyond one year. METHODS: All COPD participants in the CHAIN cohort were re-analysed according to GOLD 2017 up to five years of follow-up. Their individual changes within COPD stages were aggregated into cohort-wide Markov chains; group stability was evaluated using joinpoint regression. RESULTS: At baseline, 959 COPD patients were distributed according to GOLD 2017 stages as 37.7% in A, 38.3% B, 8.2% C, and 15.7% D. The group proportion of patients in each stage was maintained from years one to five. However, we found significant changes between stages at the individual patient level, especially in the more severe stages. The probability of a patient remaining in the same GOLD 2017 COPD stage for two consecutive years ranged during the five years of follow-up for stage C from 16% to 31% per year, while for D from 23% to 43% per year, indicating substantial variation either increasing or decreasing severity for the vast majority of patients. CONCLUSIONS: We conclude that group stability observed in COPD staging according to GOLD 2017 recommendations is paired with a large variability at the individual patient level.

Cost-Effectiveness of Antibody-Based Induction Therapy in Deceased Donor Kidney Transplantation in the United States.

BACKGROUND: Induction therapy in deceased donor kidney transplantation is costly, with wide discrepancy in utilization and a limited evidence base, particularly regarding cost-effectiveness. METHODS: We linked the United States Renal Data System data set to Medicare claims to estimate cumulative costs, graft survival, and incremental cost-effectiveness ratio (ICER - cost per additional year of graft survival) within 3 years of transplantation in 19 450 deceased donor kidney transplantation recipients with Medicare as primary payer from 2000 to 2008. We divided the study cohort into high-risk (age > 60 years, panel-reactive antibody > 20%, African American race, Kidney Donor Profile Index > 50%, cold ischemia time > 24 hours) and low-risk (not having any risk factors, comprising approximately 15% of the cohort). After the elimination of dominated options, we estimated expected ICER among induction categories: no-induction, alemtuzumab, rabbit antithymocyte globulin (r-ATG), and interleukin-2 receptor-antagonist. RESULTS: No-induction was the least effective and most costly option in both risk groups. Depletional antibodies (r-ATG and alemtuzumab) were more cost-effective across all willingness-to-pay thresholds in the low-risk group. For the high-risk group and its subcategories, the ICER was very sensitive to the graft survival; overall both depletional antibodies were more cost-effective, mainly for higher willingness to pay threshold (US $100 000 and US $150 000). Rabbit ATG appears to achieve excellent cost-effectiveness acceptability curves (80% of the recipients) in both risk groups at US $50 000 threshold (except age > 60 years). In addition, only r-ATG was associated with graft survival benefit over no-induction category (hazard ratio, 0.91; 95% confidence interval, 0.84-0.99) in a multivariable Cox regression analysis. CONCLUSIONS: Antibody-based induction appears to offer substantial advantages in both cost and outcome compared with no-induction. Overall, depletional induction (preferably r-ATG) appears to offer the greatest benefits.

Fast discovery and visualization of conserved regions in DNA sequences using quasi-alignment.

BACKGROUND: Next Generation Sequencing techniques are producing enormous amounts of biological sequence data and analysis becomes a major computational problem. Currently, most analysis, especially the identification of conserved regions, relies heavily on Multiple Sequence Alignment and its various heuristics such as progressive alignment, whose run time grows with the square of the number and the length of the aligned sequences and requires significant computational resources. In this work, we present a method to efficiently discover regions of high similarity across multiple sequences without performing expensive sequence alignment. The method is based on approximating edit distance between segments of sequences using p-mer frequency counts. Then, efficient high-throughput data stream clustering is used to group highly similar segments into so called quasi-alignments. Quasi-alignments have numerous applications such as identifying species and their taxonomic class from sequences, comparing sequences for similarities, and, as in this paper, discovering conserved regions across related sequences. RESULTS: In this paper, we show that quasi-alignments can be used to discover highly similar segments across multiple sequences from related or different genomes efficiently and accurately. Experiments on a large number of unaligned 16S rRNA sequences obtained from the Greengenes database show that the method is able to identify conserved regions which agree with known hypervariable regions in 16S rRNA. Furthermore, the experiments show that the proposed method scales well for large data sets with a run time that grows only linearly with the number and length of sequences, whereas for existing multiple sequence alignment heuristics the run time grows super-linearly. CONCLUSION: Quasi-alignment-based algorithms can detect highly similar regions and conserved areas across multiple sequences. Since the run time is linear and the sequences are converted into a compact clustering model, we are able to identify conserved regions fast or even interactively using a standard PC. Our method has many potential applications such as finding characteristic signature sequences for families of organisms and studying conserved and variable regions in, for example, 16S rRNA.

Analyzing taxonomic classification using extensible Markov models.

MOTIVATION: As next generation sequencing is rapidly adding new genomes, their correct placement in the taxonomy needs verification. However, the current methods for confirming classification of a taxon or suggesting revision for a potential misplacement relies on computationally intense multi-sequence alignment followed by an iterative adjustment of the distance matrix. Due to intra-heterogeneity issues with the 16S rRNA marker, no classifier is available for sub-genus level, which could readily suggest a classification for a novel 16S rRNA sequence. Metagenomics further complicates the issue by generating fragmented 16S rRNA sequences. This article proposes a novel alignment-free method for representing the microbial profiles using extensible Markov models (EMMs) with an extended Karlin-Altschul statistical framework similar to the classic alignment paradigm. We propose a log odds (LODs) score classifier based on Gumbel difference distribution that confirms correct classifications with statistical significance qualifications and suggests revisions where necessary. RESULTS: We tested our method by generating a sub-genus level classifier with which we re-evaluated classifications of 676 microbial organisms using the NCBI FTP database for the 16S rRNA. The results confirm current classification for all genera while ascertaining significance at 95%. Furthermore, this novel classifier isolates heterogeneity issues to a mere 12 strains while confirming classifications with significance qualification for the remaining 98%. The models require less memory than that needed by multi-sequence alignments and have better time complexity than the current methods. The classifier operates at sub-genus level, and thus outperforms the naive Bayes classifier of the RNA Database Project where much of the taxonomic analysis is available online. Finally, using information redundancy in model building, we show that the method applies to metagenomic fragment classification of 19 Escherichia coli strains. AVAILABILITY AND IMPLEMENTATION: Source code and binaries freely available for download at http://lyle.smu.edu/IDA/EMMSA/, implemented in JAVA and supported on MS Windows.