Technical Reports

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This collection includes open access technical reports of the Vanderbilt Department of Biomedical Informatics.

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Age distribution formulas for budding yeast
(Vanderbilt University, 2008-08-05T18:15:41Z) Boczko, Erik M.; Ban, Hyunju
Yeast are an important eukaryotic model system in the study of aging. Replicative age in budding yeast can be quantitatively determined by visualizing chitanous bud scars. The dynamics of the process of growth and division effects the distribution of replicative age. How much physiological information is encoded in experimental age distributions is not fully understood. Formulas relating the stationary age distribution to the spectrum of generational and culture doubling times have been proposed by several authors over the past four decades. We discuss the assumptions upon which they rest and some natural extensions. We describe the replicative age distribution of a population growing exponentially in terms of generational flux residence times. We demonstrate the utility of this description and show that it produces excellent agreement with experimental data, and describe how it compares with previous work. We demonstrate that the age distribution in a variety of strains can be predicted by a realistic population model, and we indicate how the age distribution is altered by perturbations and control.
Conceptual Framework to Support Clinical Trial Optimization and End-to-End Enrollment Workflow
(JCO CLINICAL CANCER INFORMATICS, 2019-06-21) Jain, Neha M.; Culley, Alison; Knoop, Teresa; Micheel, Christine; Osterman, Travis; Levy, Mia
In this work, we present a conceptual framework to support clinical trial optimization and enrollment workflows and review the current state, limitations, and future trends in this space. This framework includes knowledge representation of clinical trials, clinical trial optimization, clinical trial design, enrollment workflows for prospective clinical trial matching, waitlist management, and, finally, evaluation strategies for assessing improvement. (C) 2019 by American Society of Clinical Oncology
Mapping ICD-10 and ICD-10-CM Codes to Phecodes: Workflow Development and Initial Evaluation
(JMIR Medical Informatics, 2019) Wu, Patrick; Gifford, Aliya; Meng, Xiangrui; Li, Xue; Campbell, Harry; Varley, Tim; Zhao, Jaun; Carroll, Robert; Bastarache, Lisa; Denny, Joshua C.; Theodoratou, Evropi; Wei, Wei-Qi
Background: The phecode system was built upon the International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM) for phenome-wide association studies (PheWAS) using the electronic health record (EHR). Objective: The goal of this paper was to develop and perform an initial evaluation of maps from the International Classification of Diseases, 10th Revision (ICD-10) and the International Classification of Diseases, 10th Revision, Clinical Modification (ICD-10-CM) codes to phecodes. Methods: We mapped ICD-10 and ICD-10-CM codes to phecodes using a number of methods and resources, such as concept relationships and explicit mappings from the Centers for Medicare & Medicaid Services, the Unified Medical Language System, Observational Health Data Sciences and Informatics, Systematized Nomenclature of Medicine-Clinical Terms, and the National Library of Medicine. We assessed the coverage of the maps in two databases: Vanderbilt University Medical Center (VUMC) using ICD-10-CM and the UK Biobank (UKBB) using ICD-10. We assessed the fidelity of the ICD-10-CM map in comparison to the gold-standard ICD-9-CM phecode map by investigating phenotype reproducibility and conducting a PheWAS. Results: We mapped >75% of ICD-10 and ICD-10-CM codes to phecodes. Of the unique codes observed in the UKBB (ICD-10) and VUMC (ICD-10-CM) cohorts, >90% were mapped to phecodes. We observed 70-75% reproducibility for chronic diseases and <10% for an acute disease for phenotypes sourced from the ICD-10-CM phecode map. Using the ICD-9-CM and ICD-10-CM maps, we conducted a PheWAS with a Lipoprotein(a) genetic variant, rs10455872, which replicated two known genotype-phenotype associations with similar effect sizes: coronary atherosclerosis (ICD-9-CM: P<.001; odds ratio (OR) 1.60 [95% CI 1.43-1.80] vs ICD-10-CM: P<.001; OR 1.60 [95% CI 1.43-1.80]) and chronic ischemic heart disease (ICD-9-CM: P<.001; OR 1.56 [95% CI 1.35-1.79] vs ICD-10-CM: P<.001; OR 1.47 [95% CI 1.22-1.77]). Conclusions: This study introduces the beta versions of ICD-10 and ICD-10-CM to phecode maps that enable researchers to leverage accumulated ICD-10 and ICD-10-CM data for PheWAS in the EHR.
Molecular Seismology: An inverse problem in nanobiology.
(Vanderbilt University, 2006-09-12T19:44:22Z) Boczko, Erik M.; Hinow, Peter
The density profile of an elastic fiber like DNA will change in space and time as ligands associate with it. This observation affords a new direction in single molecule studies provided that density profiles can be measured in space and time. In fact, this is precisely the objective of seismology, where the mathematics of inverse problems have been employed with success. We argue that inverse problems in elastic media can be directly applied to biophysical problems of fiber-ligand association, and demonstrate that robust algorithms exist to perform density reconstruction in the condensed phase.
Next-generation quantitative measurements to validate a model for yeast nitrogen catabolite repression in Saccharomyces cerevisiae
(Vanderbilt University, 2007-02-12T14:10:02Z) Stowers, Chris; Boczko, Erik M.
Our work is motivated by the desire to quantitatively measure biological dynamical systems. Our agenda is to describe and understand emergent behavior and to explain the observed super robustness of biological dynamics. The specific system that provides the focus for our work is an ostensibly simple stress response circuit in baker's yeast, Saccharomyces cerevisiae, that regulates the organisms' genetic response to nitrogen limitation called nitrogen catabolite repression (NCR). The circuitry of the network has been well studied for the last 40 years and comparatively much is known about its function, however, little is known about its dynamics. In order to study the dynamics at the same level of sophistication at which we formulate and reason with mathematical models, we require quantitative biophysical and biochemical techniques that are accurate at molecular dimensions on physiological timescales. Such techniques are currently in their infancy. The overall goal is to further develop the tools and techniques to measure the quantitative biological behavior of the NCR circuit well enough to refine a current NCR model and understand how to apply the model to other regulatory networks leading to advances in biology, control theory and beyond. The broader impact of our effort reaches far beyond understanding the molecular physiology of a simple fungal stress response towards a deeper understanding of the underpinnings of why some circuits persist while others do not.
Significant and Distinctive n-Grams in Oncology Notes: A Text-Mining Method to Analyze the Effect of OpenNotes on Clinical Documentation
(JCO Clinical Cancer Informatics, 2019-06-11) Rahimian, Maryam; Warner, Jeremy L.; Jain, Sandeep K.; Davis, Roger B.; Zerillo, Jessica A.; Joyce, Robin M.
PURPOSE OpenNotes is a national movement established in 2010 that gives patients access to their visit notes through online patient portals, and its goal is to improve transparency and communication. To determine whether granting patients access to their medical notes will have a measurable effect on provider behavior, we developed novel methods to quantify changes in the length and frequency of use of n-grams (sets of words used in exact sequence) in the notes. METHODS We analyzed 102,135 notes of 36 hematology/oncology clinicians before and after the OpenNotes debut at Beth Israel Deaconess Medical Center. We applied methods to quantify changes in the length and frequency of use of sequential co-occurrence of words (n-grams) in the unstructured content of the notes by unsupervised hierarchical clustering and proportional analysis of n-grams. RESULTS The number of significant n-grams averaged over all providers did not change, but for individual providers, there were significant changes. That is, all significant observed changes were provider specific. We identified eight providers who were late note signers. This group significantly reduced its late signing behavior after OpenNotes implementation. CONCLUSION Although the number of significant n-grams averaged over all providers did not change, our text-mining method detected major content changes in specific providers' documentation at the n-gram level. The method successfully identified a group of providers who decreased their late note signing behavior. (C) 2019 by American Society of Clinical Oncology
Structured override reasons for drug-drug interaction alerts in electronic health records
(Journal of the American Medical Informatics Association, 2019-10) McCoy, Allison B.
Objective: The study sought to determine availability and use of structured override reasons for drug-drug interaction (DDI) alerts in electronic health records. Materials and Methods: We collected data on DDI alerts and override reasons from 10 clinical sites across the United States using a variety of electronic health records. We used a multistage iterative card sort method to categorize the override reasons from all sites and identified best practices. Results: Our methodology established 177 unique override reasons across the 10 sites. The number of coded override reasons at each site ranged from 3 to 100. Many sites offered override reasons not relevant to DDIs. Twelve categories of override reasons were identified. Three categories accounted for 78% of all overrides: "will monitor or take precautions," "not clinically significant," and "benefit outweighs risk." Discussion: We found wide variability in override reasons between sites and many opportunities to improve alerts. Some override reasons were irrelevant to DDIs. Many override reasons attested to a future action (eg, decreasing a dose or ordering monitoring tests), which requires an additional step after the alert is overridden, unless the alert is made actionable. Some override reasons deferred to another party, although override reasons often are not visible to other users. Many override reasons stated that the alert was inaccurate, suggesting that specificity of alerts could be improved. Conclusions: Organizations should improve the options available to providers who choose to override DDI alerts. DDI alerting systems should be actionable and alerts should be tailored to the patient and drug pairs.
Viral flight data recorder for systems biology applications
(Vanderbilt University, 2008-08-13) Boczko, Erik M.
The paper briefly describes the construction of self organizing nanoparticles that can be designed to detect and record arbitrary intracellular events. The basic design captures nucleic acids. The design utilizes a single viral coat protein to nucleate a capsid if and only if a quantum of specific intracellular events occur.

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