Pedro J. Caraballo

Preemptive genotyping for personalized medicine: design of the right drug, right dose, right time-using genomic data to individualize treatment protocol.

OBJECTIVE To report the design and implementation of the Right Drug, Right Dose, Right Time-Using Genomic Data to Individualize Treatment protocol that was developed to test the concept that prescribers can deliver genome-guided therapy at the point of care by using preemptive pharmacogenomics (PGx) data and clinical decision support (CDS) integrated into the electronic medical record (EMR). PATIENTS AND METHODS We used a multivariate prediction model to identify patients with a high risk of initiating statin therapy within 3 years. The model was used to target a study cohort most likely to benefit from preemptive PGx testing among the Mayo Clinic Biobank participants, with a recruitment goal of 1000 patients. We used a Cox proportional hazards model with variables selected through the Lasso shrinkage method. An operational CDS model was adapted to implement PGx rules within the EMR. RESULTS The prediction model included age, sex, race, and 6 chronic diseases categorized by the Clinical Classifications Software for International Classification of Diseases, Ninth Revision codes (dyslipidemia, diabetes, peripheral atherosclerosis, disease of the blood-forming organs, coronary atherosclerosis and other heart diseases, and hypertension). Of the 2000 Biobank participants invited, 1013 (51%) provided blood samples, 256 (13%) declined participation, 555 (28%) did not respond, and 176 (9%) consented but did not provide a blood sample within the recruitment window (October 4, 2012, through March 20, 2013). Preemptive PGx testing included CYP2D6 genotyping and targeted sequencing of 84 PGx genes. Synchronous real-time CDS was integrated into the EMR and flagged potential patient-specific drug-gene interactions and provided therapeutic guidance. CONCLUSION This translational project provides an opportunity to begin to evaluate the impact of preemptive sequencing and EMR-driven genome-guided therapy. These interventions will improve understanding and implementation of genomic data in clinical practice.

Predictors of Malignancy in Patients with Cytologically Suspicious Thyroid Nodules

BACKGROUND Fine needle aspiration (FNA), although very reliable for cytologically benign and malignant thyroid nodules, has much lower predictive value in the case of suspicious or indeterminate nodules. We aimed to identify clinical predictors of malignancy in the subset of patients with suspicious FNA cytology. METHODS We reviewed the electronic medical records of 462 patients who had FNA of thyroid nodules at our institution with a suspicious cytological diagnosis, and underwent surgery at Mayo Clinic between January 2004 and September 2008. Demographic data including age, gender, history of exposure to radiation and use of thyroid hormone was collected. The presence of single versus multiple nodules by ultrasonography, nodule size, and serum thyroid-stimulating harmone (TSH) level before thyroid surgery were recorded. Analysis of the latter was limited to patients not taking thyroid hormone or antithyroid drugs at the time of FNA. RESULTS Of the 462 patients, 327 had lesions suspicious for follicular neoplasm (S-FN) or Hürthle cell neoplasm (S-HCN), 125 had cytology suspicious for papillary carcinoma (S-PC) and 10 had a variety of other suspicious lesions (medullary cancer, lymphoma and atypical). Malignancy rate for suspicious neoplastic lesions (FN+HCN) was ∼15%, whereas malignancy rate for lesions S-PC was 77%. Neither age, serum TSH level, or history of radiation exposure were associated with increased malignancy risk. The presence of multiple nodules (41.1% vs. 26.4%, p=0.0014) or smaller nodule size (2.6±1.8 cm vs. 2.9±1.6 cm, p=0.008) was associated with higher malignancy risk. In patients with cytology suspicious for neoplasm (FN, HCN) malignancy risk was higher in those receiving thyroid hormone therapy than in nonthyroid hormone users (37.7% vs. 16.5%, p=0.0004; odds ratio: 3.1), although serum TSH values did not differ significantly between thyroid hormone users and nonusers. CONCLUSION In patients with cytologically suspicious thyroid nodules, the presence of multiple nodules or smaller nodule size was associated with increased risk of malignancy. In addition, our study demonstrates for the first time, an increased risk of malignancy in patients with nodules suspicious for neoplasm who are taking thyroid hormone therapy. The reason for this association is unknown.

Impact of data fragmentation across healthcare centers on the accuracy of a high-throughput clinical phenotyping algorithm for specifying subjects with type 2 diabetes mellitus

OBJECTIVE To evaluate data fragmentation across healthcare centers with regard to the accuracy of a high-throughput clinical phenotyping (HTCP) algorithm developed to differentiate (1) patients with type 2 diabetes mellitus (T2DM) and (2) patients with no diabetes. MATERIALS AND METHODS This population-based study identified all Olmsted County, Minnesota residents in 2007. We used provider-linked electronic medical record data from the two healthcare centers that provide >95% of all care to County residents (ie, Olmsted Medical Center and Mayo Clinic in Rochester, Minnesota, USA). Subjects were limited to residents with one or more encounter January 1, 2006 through December 31, 2007 at both healthcare centers. DM-relevant data on diagnoses, laboratory results, and medication from both centers were obtained during this period. The algorithm was first executed using data from both centers (ie, the gold standard) and then from Mayo Clinic alone. Positive predictive values and false-negative rates were calculated, and the McNemar test was used to compare categorization when data from the Mayo Clinic alone were used with the gold standard. Age and sex were compared between true-positive and false-negative subjects with T2DM. Statistical significance was accepted as p<0.05. RESULTS With data from both medical centers, 765 subjects with T2DM (4256 non-DM subjects) were identified. When single-center data were used, 252 T2DM subjects (1573 non-DM subjects) were missed; an additional false-positive 27 T2DM subjects (215 non-DM subjects) were identified. The positive predictive values and false-negative rates were 95.0% (513/540) and 32.9% (252/765), respectively, for T2DM subjects and 92.6% (2683/2898) and 37.0% (1573/4256), respectively, for non-DM subjects. Age and sex distribution differed between true-positive (mean age 62.1; 45% female) and false-negative (mean age 65.0; 56.0% female) T2DM subjects. CONCLUSION The findings show that application of an HTCP algorithm using data from a single medical center contributes to misclassification. These findings should be considered carefully by researchers when developing and executing HTCP algorithms.

Preemptive Pharmacogenomic Testing for Precision Medicine: A Comprehensive Analysis of Five Actionable Pharmacogenomic Genes Using Next-Generation DNA Sequencing and a Customized CYP2D6 Genotyping Cascade

Significant barriers, such as lack of professional guidelines, specialized training for interpretation of pharmacogenomics (PGx) data, and insufficient evidence to support clinical utility, prevent preemptive PGx testing from being widely clinically implemented. The current study, as a pilot project for the Right Drug, Right Dose, Right Time-Using Genomic Data to Individualize Treatment Protocol, was designed to evaluate the impact of preemptive PGx and to optimize the workflow in the clinic setting. We used an 84-gene next-generation sequencing panel that included SLCO1B1, CYP2C19, CYP2C9, and VKORC1 together with a custom-designed CYP2D6 testing cascade to genotype the 1013 subjects in laboratories approved by the Clinical Laboratory Improvement Act. Actionable PGx variants were placed in patients electronic medical records where integrated clinical decision support rules alert providers when a relevant medication is ordered. The fraction of this cohort carrying actionable PGx variant(s) in individual genes ranged from 30% (SLCO1B1) to 79% (CYP2D6). When considering all five genes together, 99% of the subjects carried an actionable PGx variant(s) in at least one gene. Our study provides evidence in favor of preemptive PGx testing by identifying the risk of a variant being present in the population we studied.

Multidisciplinary model to implement pharmacogenomics at the point of care

Purpose:Despite potential clinical benefits, implementation of pharmacogenomics (PGx) faces many technical and clinical challenges. These challenges can be overcome with a comprehensive and systematic implementation model.Methods:The development and implementation of PGx were organized into eight interdependent components addressing resources, governance, clinical practice, education, testing, knowledge translation, clinical decision support (CDS), and maintenance. Several aspects of implementation were assessed, including adherence to the model, production of PGx-CDS interventions, and access to educational resources.Results:Between August 2012 and June 2015, 21 specific drug–gene interactions were reviewed and 18 of them were implemented in the electronic medical record as PGx-CDS interventions. There was complete adherence to the model with variable production time (98–392 days) and delay time (0–148 days). The implementation impacted approximately 1,247 unique providers and 3,788 unique patients. A total of 11 educational resources complementary to the drug–gene interactions and 5 modules specific for pharmacists were developed and implemented.Conclusion:A comprehensive operational model can support PGx implementation in routine prescribing. Institutions can use this model as a roadmap to support similar efforts. However, we also identified challenges that will require major multidisciplinary and multi-institutional efforts to make PGx a universal reality.Genet Med 19 4, 421–429.

Extending Association Rule Summarization Techniques to Assess Risk of Diabetes Mellitus

Early detection of patients with elevated risk of developing diabetes mellitus is critical to the improved prevention and overall clinical management of these patients. We aim to apply association rule mining to electronic medical records (EMR) to discover sets of risk factors and their corresponding subpopulations that represent patients at particularly high risk of developing diabetes. Given the high dimensionality of EMRs, association rule mining generates a very large set of rules which we need to summarize for easy clinical use. We reviewed four association rule set summarization techniques and conducted a comparative evaluation to provide guidance regarding their applicability, strengths and weaknesses. We proposed extensions to incorporate risk of diabetes into the process of finding an optimal summary. We evaluated these modified techniques on a real-world prediabetic patient cohort. We found that all four methods produced summaries that described subpopulations at high risk of diabetes with each method having its clear strength. For our purpose, our extension to the Buttom-Up Summarization (BUS) algorithm produced the most suitable summary. The subpopulations identified by this summary covered most high-risk patients, had low overlap and were at very high risk of diabetes.

Participant-perceived understanding and perspectives on pharmacogenomics: the Mayo Clinic RIGHT protocol (Right Drug, Right Dose, Right Time)

Purpose:To examine predictors of understanding preemptive CYP2D6 pharmacogenomics test results and to identify key features required to improve future educational efforts of preemptive pharmacogenomics testing.Methods:One thousand ten participants were surveyed after receiving preemptive CYP2D6 pharmacogenomics test results.Results:Eighty-six percent (n = 869) of patients responded. Of the responders, 98% were white and 55% were female; 57% had 4 years or more of post-secondary education and an average age of 58.9 ± 5.5 years. Twenty-six percent said that they only somewhat understood their results and 7% reported they did not understand them at all. Only education predicted understanding. The most common suggestion for improvement was the use of layperson’s terms when reporting results. In addition, responders suggested that results should be personalized by referring to medications that they were currently using. Of those reporting imperfect drug adherence, most (91%) reported they would be more likely to use medication as prescribed if pharmacogenomic information was used to help select the drug or dose.Conclusion:Despite great efforts to simplify pharmacogenomic results (or because of them), approximately one-third of responders did not understand their results. Future efforts need to provide more examples and tailor results to the individual. Incorporation of pharmacogenomics is likely to improve medication adherence.Genet Med advance online publication 05 January 2017

Electronic Medical Record‐Integrated Pharmacogenomics and Related Clinical Decision Support Concepts

Advances in pharmacogenomics (PGx) have the potential to transform healthcare by allowing precision medicine to become a reality. However, PGx knowledge is new, complex, and evolving, and relying on the cognition of clinicians alone is insufficient for clinical implementation. Integrating clinical decision support (CDS) tools in the electronic health record (EHR) is critical for translating PGx into clinical practice. Herein, we review current strategies to implement PGx using EHR‐CDS functionalities.

Evaluation of the use of clinical decision support and online resources for pharmacogenomics education

AIM To assess impact and value of using clinical decision support (CDS) to drive providers toward online pharmacogenomics education. MATERIALS & METHODS CDS was used to target prescribers of codeine/tramadol, send an educational email, display alert/inbox and provide links to an online resource. Providers were surveyed to assess impact. RESULTS Of the methods used to target providers, educational email was more effective (7.2%). Survey response rate was 29.2% (n = 528/1817). Of respondents, 57.4% reported opening the email and 27.1% accessed the online resource. Of those accessing the resource, 89% found it useful and learned something new about pharmacogenomics. CONCLUSION The impact of using CDS to target pharmacogenomics education was limited. However, providers accessing the online resource found it useful and educational.

An Automated Clinical Alert System for Newly-Diagnosed Atrial Fibrillation

Objective Clinical decision support systems that notify providers of abnormal test results have produced mixed results. We sought to develop, implement, and evaluate the impact of a computer-based clinical alert system intended to improve atrial fibrillation stroke prophylaxis, and identify reasons providers do not implement a guideline-concordant response. Materials and Methods We conducted a cohort study with historical controls among patients at a tertiary care hospital. We developed a decision rule to identify newly-diagnosed atrial fibrillation, automatically notify providers, and direct them to online evidence-based management guidelines. We tracked all notifications from December 2009 to February 2010 (notification period) and applied the same decision rule to all patients from December 2008 to February 2009 (control period). Primary outcomes were accuracy of notification (confirmed through chart review) and prescription of warfarin within 30 days. Results During the notification period 604 notifications were triggered, of which 268 (44%) were confirmed as newly-diagnosed atrial fibrillation. The notifications not confirmed as newly-diagnosed involved patients with no recent electrocardiogram at our institution. Thirty-four of 125 high-risk patients (27%) received warfarin in the notification period, compared with 34 of 94 (36%) in the control period (odds ratio, 0.66 [95% CI, 0.37–1.17]; p = 0.16). Common reasons to not prescribe warfarin (identified from chart review of 151 patients) included upcoming surgical procedure, choice to use aspirin, and discrepancy between clinical notes and the medication record. Conclusions An automated system to identify newly-diagnosed atrial fibrillation, notify providers, and encourage access to management guidelines did not change provider behaviors.