Rates and correlates of potentially inappropriate colorectal cancer screening in the Veterans Health Administration.

BACKGROUND: Inappropriate use of colorectal cancer (CRC) screening procedures can inflate healthcare costs and increase medical risk. Little is known about the prevalence or causes of inappropriate CRC screening. OBJECTIVE: Our aim was to estimate the prevalence of potentially inappropriate CRC screening, and its association with patient and facility characteristics in the Veterans Health Administration (VHA) . DESIGN AND PARTICIPANTS: We conducted a cross-sectional study of all VHA patients aged 50 years and older who completed a fecal occult blood test (FOBT) or a screening colonoscopy between 1 October 2009 and 31 December 2011 (n = 1,083,965). MAIN MEASURES: Measures included: proportion of patients whose test was classified as potentially inappropriate; associations between potentially inappropriate screening and patient demographic and health characteristics, facility complexity, CRC screening rates, dependence on FOBT, and CRC clinical reminder attributes. KEY RESULTS: Of 901,292 FOBT cases, 26.1 % were potentially inappropriate (13.9 % not due, 7.8 % limited life expectancy, 11.0 % receiving FOBT when colonoscopy was indicated). Of 134,335 screening colonoscopies, 14.2 % were potentially inappropriate (10.4 % not due, 4.4 % limited life expectancy). Each additional 10 years of patient age was associated with an increased likelihood of undergoing potentially inappropriate screening (ORs = 1.60 to 1.83 depending on screening mode). Compared to facilities scoring in the bottom third on a measure of reliance on FOBT (versus screening colonoscopy), facilities scoring in the top third were less likely to conduct potentially inappropriate FOBTs (OR = 0.,78) but more likely to conduct potentially inappropriate colonoscopies (OR = 2.20). Potentially inappropriate colonoscopies were less likely to be conducted at facilities where primary care providers were assigned partial responsibility (OR = 0.74) or full responsibility (OR = 0.73) for completing the CRC clinical reminder. CONCLUSIONS: A substantial number of VHA CRC screening tests are potentially inappropriate. Establishing processes that enforce appropriate screening intervals, triage patients with limited life expectancies, and discourage the use of FOBTs when a colonoscopy is indicated may reduce inappropriate testing.

Association between adverse reactions to allopurinol and exposures to high maintenance doses: implications for management of patients using allopurinol.

OBJECTIVE: The objective of this study was to estimate the association between adverse drug reactions (ADRs) and exposure to allopurinol maintenance doses higher than those in the 1984 suggested limits of Hande et al. adjusted for level of renal function. METHODS: We conducted a retrospective review of electronic health records of patients prescribed allopurinol from January 1, 2004, to June 30, 2011, to identify those who had a definite or possible ADR to allopurinol. The associations of ADRs with maintenance doses of allopurinol 1 to 1.5 times and more than 1.5 times the suggested limits of Hande et al. compared with doses within the suggested limits of Hande et al. were estimated using logistic regression models. RESULTS: Of 4755 patients prescribed allopurinol, 2946 had a serum creatinine measured within 6 months of starting allopurinol, and of these, 1268 patients' records were reviewed. Forty-eight patients had a definite ADR to allopurinol, 2 of which were allopurinol hypersensitivity syndrome. The odds ratios of definite ADRs with maintenance doses of allopurinol 1.0 to 1.5 times and more than 1.5 times suggested compared with doses within suggested limits were, respectively, 1.42 (95% confidence interval [CI], 0.66-3.04) and 2.04 (95% CI, 0.87-4.77). Among those with an allopurinol maintenance dose more than 1.5 times suggested limits, the proportion of patients with a definite ADR was 2.6% (95% CI, 1.0%-5.2%). CONCLUSIONS: There is no significant association of high maintenance doses of allopurinol with ADRs, and the absolute risk of ADRs at doses higher than 1.5 times the 1984 suggested limits of Hande et al. is low. Cautious, gradual increases in allopurinol maintenance doses above the suggested limits of Hande et al. are warranted if necessary to achieve a serum uric acid level less than 6 mg/dL.

Approaching neural net feature interpretation using stacked autoencoders: gene expression profiling of systemic lupus erythematosus patients.

Systemic lupus erythematosus (SLE) is a rare, autoimmune disorder known to affect most organ sites. Complicating clinical management is a poorly differentiated, heterogenous SLE disease state. While some small molecule drugs and biologics are available for treatment, additional therapeutic options are needed. Parsing complex biological signatures using powerful, yet human interpretable approaches is critical to advancing our understanding of SLE etiology and identifying therapeutic repositioning opportunities. To approach this goal, we developed a semi-supervised deep neural network pipeline for gene expression profiling of SLE patients and subsequent characterization of individual gene features. Our pipeline performed exemplar multinomial classification of SLE patients in independent balanced validation (F1=0.956) and unbalanced, under-powered testing (F1=0.944) cohorts. A stacked autoencoder disambiguated individual feature representativeness by regenerating an input-like(A ') feature matrix. A to A' comparisons suggest the top associated features to be key features in gene expression profiling using neural nets.

Electronic Health Record Phenotypes for Precision Medicine: Perspectives and Caveats From Treatment of Breast Cancer at a Single Institution.

Precision medicine is at the forefront of biomedical research. Cancer registries provide rich perspectives and electronic health records (EHRs) are commonly utilized to gather additional clinical data elements needed for translational research. However, manual annotation is resource-intense and not readily scalable. Informatics-based phenotyping presents an ideal solution, but perspectives obtained can be impacted by both data source and algorithm selection. We derived breast cancer (BC) receptor status phenotypes from structured and unstructured EHR data using rule-based algorithms, including natural language processing (NLP). Overall, the use of NLP increased BC receptor status coverage by 39.2% from 69.1% with structured medication information alone. Using all available EHR data, estrogen receptor-positive BC cases were ascertained with high precision (P = 0.976) and recall (R = 0.987) compared with gold standard chart-reviewed patients. However, status negation (R = 0.591) decreased 40.2% when relying on structured medications alone. Using multiple EHR data types (and thorough understanding of the perspectives offered) are necessary to derive robust EHR-based precision medicine phenotypes.

Integrated machine learning pipeline for aberrant biomarker enrichment (i-mAB): characterizing clusters of differentiation within a compendium of systemic lupus erythematosus patients.

Clusters of differentiation (CD) are cell surface biomarkers that denote key biological differences between cell types and disease state. CD-targeting therapeutic monoclonal antibodies (mABs) afford rich trans-disease repositioning opportunities. Within a compendium of systemic lupus erythematous (SLE) patients, we applied the Integrated machine learning pipeline for aberrant biomarker enrichment (i-mAB) to profile de novo gene expression features affecting CD20, CD22 and CD30 gene aberrance. First, a novel Relief-based algorithm identified interdependent features(p=681) predicting treatment-naïve SLE patients (balanced accuracy=0.822). We then compiled CD-associated expression profiles using regularized logistic regression and pathway enrichment analyses. On an independent general cell line model system data, we replicated associations (in silico) of BCL7A (padj=1.69e-9) and STRBP(padj=4.63e-8) with CD22; NCOA2(padj=7.00e-4), ATN1 (padj=1.71e-2), and HOXC4(padj=3.34e-2) with CD30; and PHOSPHO1, a phosphatase linked to bone mineralization, with both CD22(padj=4.37e-2) and CD30(padj=7.40e-3). Utilizing carefully aggregated secondary data and leveraging a priori hypotheses, i-mAB fostered robust biomarker profiling among interdependent biological features.

Considerations for automated machine learning in clinical metabolic profiling: Altered homocysteine plasma concentration associated with metformin exposure.

With the maturation of metabolomics science and proliferation of biobanks, clinical metabolic profiling is an increasingly opportunistic frontier for advancing translational clinical research. Automated Machine Learning (AutoML) approaches provide exciting opportunity to guide feature selection in agnostic metabolic profiling endeavors, where potentially thousands of independent data points must be evaluated. In previous research, AutoML using high-dimensional data of varying types has been demonstrably robust, outperforming traditional approaches. However, considerations for application in clinical metabolic profiling remain to be evaluated. Particularly, regarding the robustness of AutoML to identify and adjust for common clinical confounders. In this study, we present a focused case study regarding AutoML considerations for using the Tree-Based Optimization Tool (TPOT) in metabolic profiling of exposure to metformin in a biobank cohort. First, we propose a tandem rank-accuracy measure to guide agnostic feature selection and corresponding threshold determination in clinical metabolic profiling endeavors. Second, while AutoML, using default parameters, demonstrated potential to lack sensitivity to low-effect confounding clinical covariates, we demonstrated residual training and adjustment of metabolite features as an easily applicable approach to ensure AutoML adjustment for potential confounding characteristics. Finally, we present increased homocysteine with long-term exposure to metformin as a potentially novel, non-replicated metabolite association suggested by TPOT; an association not identified in parallel clinical metabolic profiling endeavors. While warranting independent replication, our tandem rank-accuracy measure suggests homocysteine to be the metabolite feature with largest effect, and corresponding priority for further translational clinical research. Residual training and adjustment for a potential confounding effect by BMI only slightly modified the suggested association. Increased homocysteine is thought to be associated with vitamin B12 deficiency - evaluation for potential clinical relevance is suggested. While considerations for clinical metabolic profiling are recommended, including adjustment approaches for clinical confounders, AutoML presents an exciting tool to enhance clinical metabolic profiling and advance translational research endeavors.

An informatics research agenda to support precision medicine: seven key areas.

The recent announcement of the Precision Medicine Initiative by President Obama has brought precision medicine (PM) to the forefront for healthcare providers, researchers, regulators, innovators, and funders alike. As technologies continue to evolve and datasets grow in magnitude, a strong computational infrastructure will be essential to realize PM's vision of improved healthcare derived from personal data. In addition, informatics research and innovation affords a tremendous opportunity to drive the science underlying PM. The informatics community must lead the development of technologies and methodologies that will increase the discovery and application of biomedical knowledge through close collaboration between researchers, clinicians, and patients. This perspective highlights seven key areas that are in need of further informatics research and innovation to support the realization of PM.

Studying the Confounding Effects of Socio-Ecological Conditions in Retrospective Clinical Research: A Use Case of Social Stress.

Socio-ecological Conditions (SECs) are important to include in clinical research models as they have been known to impact the health of patients. However, current clinical research models account for these factors only in an unsatisfyingly rudimentary way. In this study, we developed an SEC Index that captured the latent and direct effects of social stress, one of the many kinds of SEC, on patients' general health as measured by the Charlson Comorbidity Index. We demonstrated that the above SEC Index had a significant effect in a clinical model, a patient-level model with the specific clinical outcome of breast cancer prevalence. Further, we demonstrated that including the SEC Index of social stress into the clinical models significantly increased their performance. Our study demonstrated a viable approach that is interchangeable to include any SEC of interest, to more appropriately account for SECs in clinical research models.

Leveraging an Electronic Health Record-Linked Biorepository to Generate a Metformin Pharmacogenomics Hypothesis.

Metformin is a first-line antihyperglycemic agent commonly prescribed in type 2 diabetes mellitus (T2DM), but whose pharmacogenomics are not clearly understood. Further, due to accumulating evidence highlighting the potential for metformin in cancer prevention and treatment efforts it is imperative to understand molecular mechanisms of metformin. In this electronic health record(EHR)-based study we explore the potential association of the flavin-containing monooxygenase(FMO)-5 gene, a biologically plausible biotransformer of metformin, and modifying glycemic response to metformin treatment. Using a cohort of 258 T2DM patients who had new metformin exposure, existing genetic data, and longitudinal electronic health records, we compared genetic variation within FMO5 to change in glycemic response. Gene-level and SNP-level analysis identified marginally significant associations for FMO5 variation, representing an EHR-driven pharmacogenetics hypothesis for a potential novel mechanism for metformin biotransformation. However, functional validation of this EHR-based hypothesis is necessary to ascertain its clinical and biological significance.

Using EHR-Linked Biobank Data to Study Metformin Pharmacogenomics.

Metformin is a commonly prescribed diabetes medication whose mechanism of action is poorly understood. In this study we utilized EHR-linked biobank data to elucidate the impact of genomic variation on glycemic response to metformin. Our study found significant gene- and SNP-level associations within the beta-2 subunit of the heterotrimeric adenosine monophosphate-activated protein kinase complex. Using EHR phenotypes where were able to add additional clarity to ongoing metformin pharmacogenomic dialogue.