Feasibility of incorporating genomic knowledge into electronic medical records for pharmacogenomic clinical decision support.

In pursuing personalized medicine, pharmacogenomic (PGx) knowledge may help guide prescribing drugs based on a person's genotype. Here we evaluate the feasibility of incorporating PGx knowledge, combined with clinical data, to support clinical decision-making by: 1) analyzing clinically relevant knowledge contained in PGx knowledge resources; 2) evaluating the feasibility of a rule-based framework to support formal representation of clinically relevant knowledge contained in PGx knowledge resources; and, 3) evaluating the ability of an electronic medical record/electronic health record (EMR/EHR) to provide computable forms of clinical data needed for PGx clinical decision support. Findings suggest that the PharmGKB is a good source for PGx knowledge to supplement information contained in FDA approved drug labels. Furthermore, we found that with supporting knowledge (e.g. IF age <18 THEN patient is a child), sufficient clinical data exists in University of Washington's EMR systems to support 50% of PGx knowledge contained in drug labels that could be expressed as rules.

Head and neck lymph node region delineation with image registration.

BACKGROUND: The success of radiation therapy depends critically on accurately delineating the target volume, which is the region of known or suspected disease in a patient. Methods that can compute a contour set defining a target volume on a set of patient images will contribute greatly to the success of radiation therapy and dramatically reduce the workload of radiation oncologists, who currently draw the target by hand on the images using simple computer drawing tools. The most challenging part of this process is to estimate where there is microscopic spread of disease. METHODS: Given a set of reference CT images with "gold standard" lymph node regions drawn by the experts, we are proposing an image registration based method that could automatically contour the cervical lymph code levels for patients receiving radiation therapy. We are also proposing a method that could help us identify the reference models which could potentially produce the best results. RESULTS: The computer generated lymph node regions are evaluated quantitatively and qualitatively. CONCLUSIONS: Although not conforming to clinical criteria, the results suggest the technique has promise.

Content-specific auditing of a large scale anatomy ontology.

Biomedical ontologies are envisioned to be usable in a range of research and clinical applications. The requirements for such uses include formal consistency, adequacy of coverage, and possibly other domain specific constraints. In this report we describe a case study that illustrates how application specific requirements may be used to identify modeling problems as well as data entry errors in ontology building and evolution. We have begun a project to use the UW Foundational Model of Anatomy (FMA) in a clinical application in radiation therapy planning. This application focuses mainly (but not exclusively) on the representation of the lymphatic system in the FMA, in order to predict the spread of tumor cells to regional metastatic sites. This application requires that the downstream relations associated with lymphatic system components must only be to other lymphatic chains or vessels, must be at the appropriate level of granularity, and that every path through the lymphatic system must terminate at one of the two well known trunks of the lymphatic system. It is possible through a programmable query interface to the FMA to write small programs that systematically audit the FMA for compliance with these constraints. We report on the design of some of these programs, and the results we obtained by applying them to the lymphatic system. The algorithms and approach are generalizable to other network organ systems in the FMA such as arteries and veins. In addition to illustrating exact constraint checking methods, this work illustrates how the details of an application may reflect back a requirement to revise the design of the ontology itself.

Biological Model Development as an Opportunity to Provide Content Auditing for the Foundational Model of Anatomy Ontology.

Constructing a biological model using an established ontology provides a unique opportunity to perform content auditing on the ontology. We built a Markov chain model to study tumor metastasis in the regional lymphatics of patients with head and neck squamous cell carcinoma (HNSCC). The model attempts to determine regions with high likelihood for metastasis, which guides surgeons and radiation oncologists in selecting the boundaries of treatment. To achieve consistent anatomical relationships, the nodes in our model are populated using lymphatic objects extracted from the Foundational Model of Anatomy (FMA) ontology. During this process, we discovered several classes of inconsistencies in the lymphatic representations within the FMA. We were able to use this model building opportunity to audit the entities and connections in this region of interest (ROI). We found five subclasses of errors that are computationally detectable and resolvable, one subclass of errors that is computationally detectable but unresolvable, requiring the assistance of a content expert, and also errors of content, which cannot be detected through computational means. Mathematical descriptions of detectable errors along with expert review were used to discover inconsistencies and suggest concepts for addition and removal. Out of 106 organ and organ parts in the ROI, 8 unique entities were affected, leading to the suggestion of 30 concepts for addition and 4 for removal. Out of 27 lymphatic chain instances, 23 were found to have errors, with a total of 32 concepts suggested for addition and 15 concepts for removal. These content corrections are necessary for the accurate functioning of the FMA and provide benefits for future research and educational uses.

Deriving rules and assertions from pharmacogenomics knowledge resources in support of patient drug metabolism efficacy predictions.

OBJECTIVE: Pharmacogenomics evaluations of variability in drug metabolic processes may be useful for making individual drug response predictions. We present an approach to deriving 'phenotype scores' based on existing pharmacogenomics knowledge and a patient's genomics data. Pharmacogenomics plays an important role in the bioactivation of tamoxifen, a prodrug administered to patients for breast cancer treatment. Tamoxifen is therefore considered a model for many drugs requiring bioactivation. We investigate whether this knowledge-based approach can be applied to produce a phenotype score that is predictive of the endoxifen/N-desmethyltamoxifen (NDM) plasma concentration ratio in patients taking tamoxifen. MATERIALS AND METHODS: We implement a knowledge-based model for calculating phenotype scores from patient-specific genotype data. These data include allelic variants of genes encoding enzymes involved in the bioactivation of tamoxifen. We performed quantile linear regression to evaluate whether six phenotype scoring algorithms are predictive of patient endoxifen/NDM plasma concentration ratio, and validate our scoring methods. RESULTS: Our model illustrates a knowledge-based approach to predict drug metabolism efficacy given patient genomics data. Results showed that for one phenotype scoring algorithm, scores were weakly correlated with patient endoxifen/NDM plasma concentration ratios. This algorithm performed better than simple metrics for variation in individual and multiple genes. DISCUSSION: We discuss advantages of the model, challenges to its implementation in a personalized medicine context, and provide example future directions. CONCLUSIONS: We demonstrate the utility of our model in a tamoxifen case study context. We also provide evidence that more complicated polygenic models are needed to represent heterogeneity in clinical outcomes.

Developing a prototype system for integrating pharmacogenomics findings into clinical practice.

Findings from pharmacogenomics (PGx) studies have the potential to be applied to individualize drug therapy to improve efficacy and reduce adverse drug events. Researchers have identified factors influencing uptake of genomics in medicine, but little is known about the specific technical barriers to incorporating PGx into existing clinical frameworks. We present the design and development of a prototype PGx clinical decision support (CDS) system that builds on existing clinical infrastructure and incorporates semi-active and active CDS. Informing this work, we updated previous evaluations of PGx knowledge characteristics, and of how the CDS capabilities of three local clinical systems align with data and functional requirements for PGx CDS. We summarize characteristics of PGx knowledge and technical needs for implementing PGx CDS within existing clinical frameworks. PGx decision support rules derived from FDA drug labels primarily involve drug metabolizing genes, vary in maturity, and the majority support the post-analytic phase of genetic testing. Computerized provider order entry capabilities are key functional requirements for PGx CDS and were best supported by one of the three systems we evaluated. We identified two technical needs when building on this system, the need for (1) new or existing standards for data exchange to connect clinical data to PGx knowledge, and (2) a method for implementing semi-active CDS. Our analyses enhance our understanding of principles for designing and implementing CDS for drug therapy individualization and our current understanding of PGx characteristics in a clinical context. Characteristics of PGx knowledge and capabilities of current clinical systems can help govern decisions about CDS implementation, and can help guide decisions made by groups that develop and maintain knowledge resources such that delivery of content for clinical care is supported.

AMIA Board white paper: definition of biomedical informatics and specification of core competencies for graduate education in the discipline.

The AMIA biomedical informatics (BMI) core competencies have been designed to support and guide graduate education in BMI, the core scientific discipline underlying the breadth of the field's research, practice, and education. The core definition of BMI adopted by AMIA specifies that BMI is 'the interdisciplinary field that studies and pursues the effective uses of biomedical data, information, and knowledge for scientific inquiry, problem solving and decision making, motivated by efforts to improve human health.' Application areas range from bioinformatics to clinical and public health informatics and span the spectrum from the molecular to population levels of health and biomedicine. The shared core informatics competencies of BMI draw on the practical experience of many specific informatics sub-disciplines. The AMIA BMI analysis highlights the central shared set of competencies that should guide curriculum design and that graduate students should be expected to master.

A decision aid for intensity-modulated radiation-therapy plan selection in prostate cancer based on a prognostic Bayesian network and a Markov model.

OBJECTIVE: The prognosis of cancer patients treated with intensity-modulated radiation-therapy (IMRT) is inherently uncertain, depends on many decision variables, and requires that a physician balance competing objectives: maximum tumor control with minimal treatment complications. METHODS: In order to better deal with the complex and multiple objective nature of the problem we have combined a prognostic probabilistic model with multi-attribute decision theory which incorporates patient preferences for outcomes. RESULTS: The response to IMRT for prostate cancer was modeled. A Bayesian network was used for prognosis for each treatment plan. Prognoses included predicting local tumor control, regional spread, distant metastases, and normal tissue complications resulting from treatment. A Markov model was constructed and used to calculate a quality-adjusted life-expectancy which aids in the multi-attribute decision process. CONCLUSIONS: Our method makes explicit the tradeoffs patients face between quality and quantity of life. This approach has advantages over current approaches because with our approach risks of health outcomes and patient preferences determine treatment decisions.

Implementing a mandatory password change policy at an academic medical institution.

UW Medicine implemented a new policy requiring users to change passwords at least once every 120 days. In the first two password change cycles, many users did not take action upon notification, and their passwords expired, causing high help desk loads. Compliance and support loads improved in subsequent cycles. We conclude that policy changes requiring user behavior modification should be seen as a cultural change, and the implementation strategy should consider socio-technical factors.

A Markov model approach to predicting regional tumor spread in the lymphatic system of the head and neck.

Radiation planning for cancer therapy is becoming more and more dependent on the prediction of microscopic tumor spread to regional lymph nodes for its success. It is known that microscopic spread tends to follow established lymphatic drainage pathways in the head and neck based on the location of the primary tumor. In this paper,we propose a novel model for the prediction of regional lymphatic involvement of head and neck squamous cell carcinoma based on primary tumor location and T-stage. The proposed model operates from first anatomical principles and basic stochastic techniques, and is validated against surgical data.

Qualitative pharmacokinetic modeling of drugs.

We hypothesize that a representation of drug-drug interactions (DDIs) based on physiologic, pharmacokinetic (PK) and pharmacodynamic (PD) mechanisms will provide more accurate and useful information to clinicians than current approaches that simply tabulate and index pairwise interactions of drugs. This paper explores the strengths, weaknesses, and difficulties of modeling drug mechanisms and reports on our initial work designing and implementing a drug KB based on qualitative pharmacokinetic mechanisms.

A declarative implementation of the DICOM-3 network protocol.

We describe a new design for programs using the Digital Imaging and Communications in Medicine (DICOM) protocol, which we have implemented in a DICOM image storage server and a radiation treatment plan transfer facility for our locally developed radiation treatment planning system, Prism. This design is declarative, representing DICOM as a language for describing messages and sequencing of messages. The coding involved implementing an interpreter for this language. The DICOM protocol specifies messages, message formats, and sequencing. In our design, the specification translates almost directly into computer-readable declarative expressions that closely resemble the relevant tabulated DICOM specifications. The resulting programs are small, simple, and extensible, because most of the details of the DICOM protocol are not coded in the procedural control statements but are in the expressions and state table that the interpreter uses to perform all its functions. This approach provides a way to validate the consistency of a specification and the correctness of the implementation. The same method can be generalized to other such protocols. It may also be used to assist the design of new protocols.

A rule-based model for local and regional tumor spread.

Prediction of microscopic spread of tumor cells is becoming critically important in the decision making process in planning radiation therapy for cancer. Until recently, radiation treatment of head and neck cancer has been conservative, treating large regions to insure eradication of disease. However, if it is known that regional spread is confined, a more focused treatment can be considered, with the payoff of reducing or eliminating morbidity due to irradiating healthy tissue in the vicinity of node groups. Knowledge about the occurrence of micrometastases comes mainly from pathology reports in connection with surgery. As the data accrue, it will be possible and necessary to represent this knowledge in a symbolic computational model. Our work reports on the feasibility of modeling this knowledge using published data.

Head and neck lymph node region delineation with 3-D CT image registration.

The success of radiation therapy depends critically on accurately delineating the target volume, which is the region of known or suspected disease in a patient. Methods that can compute a contour set defining a target volume on a set of patient images will contribute greatly to the success of radiation therapy and dramatically reduce the workload of radiation oncologists, who currently draw the target by hand on the images using simple computer drawing tools. The most challenging part of this process is to estimate where there is microscopic spread of disease. We are developing methods for automatically selecting and adapting standardized regions of tumor spread based on the location of lymph nodes in a standard or reference case, together with image registration techniques. The best available image registration techniques (deformable transformations computed using "mutual information" optimization) appear promising but will need to be supplemented by anatomic knowledge-based methods to achieve a clinically acceptable match.