Pharmacogenomics driven decision support prototype with machine learning: A framework for improving patient care

Introduction: A growing number of healthcare providers make complex treatment decisions guided by electronic health record (EHR) software interfaces. Many interfaces integrate multiple sources of data (e.g., labs, pharmacy, diagnoses) successfully, though relatively few have incorporated genetic data. Method: This study utilizes informatics methods with predictive modeling to create and validate algorithms to enable informed pharmacogenomic decision-making at the point of care in near real-time. The proposed framework integrates EHR and genetic data relevant to the patient's current medications including decision support mechanisms based on predictive modeling. We created a prototype with EHR and linked genetic data from the Department of Veterans Affairs (VA), the largest integrated healthcare system in the US. The EHR data included diagnoses, medication fills, and outpatient clinic visits for 2,600 people with HIV and matched uninfected controls linked to prototypic genetic data (variations in single or multiple positions in the DNA sequence). We then mapped the medications that patients were prescribed to medications defined in the drug-gene interaction mapping of the Clinical Pharmacogenomics Implementation Consortium's (CPIC) level A (i.e., sufficient evidence for at least one prescribing action) guidelines that predict adverse events. CPIC is a National Institute of Health funded group of experts who develop evidence based pharmacogenomic guidelines. Preventable adverse events (PAE) can be defined as a harmful outcome from an intervention that could have been prevented. For this study, we focused on potential PAEs resulting from a medication-gene interaction. Results: The final model showed AUC scores of 0.972 with an F1 score of 0.97 with genetic data as compared to 0.766 and 0.73 respectively, without genetic data integration. Discussion: Over 98% of people in the cohort were on at least one medication with CPIC level a guideline in their lifetime. We compared predictive power of machine learning models to detect a PAE between five modeling methods: Random Forest, Support Vector Machine (SVM), Extreme Gradient Boosting (XGBoost), K Nearest neighbors (KNN), and Decision Tree. We found that XGBoost performed best for the prototype when genetic data was added to the framework and improved prediction of PAE. We compared area under the curve (AUC) between the models in the testing dataset

PRN OPINION PAPER: Application of precision medicine across pharmacy specialty areas

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/149551/1/jac51107_am.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/149551/2/jac51107.pd

Geisinger’s Use of Clinical CarePaths: Impact of a Psoriasis CarePath on Process, Clinical, and Economic Outcomes

Background: CarePaths are evidence-based integrative care processes that seek to treat patients with complex diseases in a standardized manner. Herein we describe the development and implementation of the CarePath for psoriasis and present preliminary economic and clinical outcomes of this streamlined process. Methods: The CarePath for psoriasis was developed through a five-step process involving population identification, care algorithm development (synchronized to coverage determinations), IT development, patient-family engagement, and outcome monitoring utilizing input from our integrative delivery network of patients, providers and payers. Over the course of 12 months, the multidisciplinary team developed standardized data elements within the electronic health record (Epic) and a psoriasis-specific performance dashboard to ensure consistent population targeting, outcome monitoring and provider compliance tracking. Monitoring within the build allows for evaluations of the adoption of the CarePath including: body surface area tracking and completion, inclusion of psoriasis on the patient’s problem list, utilization of nonpharmacological options (eg, light therapy), and drug therapies used. A simple cost-avoidance model of selecting light therapy over alternative biologic therapy was employed to calculate savings. Results: Adoption of the psoriasis CarePath has steadily increased since its launch in July 2015. Inclusion of psoriasis in the problem list of affected patients steadily increased in the 12 months post-CarePath launch. Tracking of body surface area measurements increased from 41.7% to 76.3% over the same time period. A total of 72 patients initiated light therapy since CarePath implementation, 61 of whom were biologic candidates and 11 who switched from biologics. With an estimated 6-year single patient cost of $2,200 for one ultraviolet light or$294,000 for formulary biologic alternatives, the psoriasis CarePath is estimated to save the health system $21,009,600 ($3,501,600/year). Conclusion: Through the psoriasis CarePath, we have been able to standardize the care of patients across Geisinger Health System by providing patient-focused, evidence-based care at substantial cost savings. Lessons gleaned through the early success of the psoriasis CarePath are being applied to CarePath construction for rheumatoid arthritis, heart failure, pulmonary hypertension and other diseases

The Value of Cytochrome P450 2C19 Pharmacogenomic Information for Patients Receiving Clopidogrel Therapy Following a Major Cardiovascular Event: Evidence from Geisinger

Dual antiplatelet therapy, combining a P2Y12 inhibitor and aspirin, is central to the management of patients with acute coronary syndromes. Clopidogrel is a commonly prescribed thienopyridine P2Y12 inhibitor used to prevent secondary events following a myocardial infarction / percutaneous coronary intervention. Clopidogrel effectiveness has been linked to proper function of metabolic enzyme cytochrome P450 2C19 (CYP2C19). Testing for CYP2C19 genetic changes that result in decreased activity has been proposed as a mechanism by which to identify clopidogrel non-responders a priori. Robust real-world studies evaluating the impact of genotype-guided antiplatelet selection on clinical, utilization and outcome metrics are lacking. Approach: We linked clinical and financial data on a cohort of 2,595 Geisinger patients who were prescribed antiplatelet therapy without pharmacogenomic insight. Leveraging post-hoc, research-generated pharmacogenomic information, we describe the potential value of pharmacogenomic information in the selection of clopidogrel as the P2Y12 inhibitor therapy. Major Results: Among patients examined, CYP2C19 loss of function did not predict the probability of follow-on adverse events, clinical utilization or total medical costs within the 12 months after clopidogrel initiation in adjusted or crude analysis. Conclusion: Universal CYP2C19 pharmacogenomic testing to inform P2Y12 selection following an MI/PCI index event within the Geisinger patient population studied would not have led to improved clinical outcomes, decreased health care utilization or lower total medical cost

ProvenCare-Psoriasis: A disease management model to optimize care

There are a variety of evidence-based treatments available for psoriasis. The transition of this evidence into practice is challenging. In this article, we describe the design of our disease management approach for Psoriasis (ProvenCare®) and present preliminary evidence of the effect of its implementation. In designing our approach, we identified three barriers to optimal care: 1) lack of a standardized and discrete disease activity measure within the electronic health record, 2) lack of a system-wide, standardized approach to care, and 3) non-uniform financial access to appropriate non-pharmacologic treatments. We implemented several solutions, which collectively form our approach. We standardized the documentation of clinical data such as body surface area (BSA), created a disease management algorithm for psoriasis, and aligned incentives to facilitate the implementation of the algorithm. This approach provides more coordinated, cost effective care for psoriasis, while being acceptable to key stakeholders. Future work will examine the effect of the implementation of our approach on important clinical and patient outcomes

Clinical characteristics and outcomes for 7,995 patients with SARS-CoV-2 infection.

ObjectiveSevere acute respiratory syndrome virus (SARS-CoV-2) has infected millions of people worldwide. Our goal was to identify risk factors associated with admission and disease severity in patients with SARS-CoV-2.DesignThis was an observational, retrospective study based on real-world data for 7,995 patients with SARS-CoV-2 from a clinical data repository.SettingYale New Haven Health (YNHH) is a five-hospital academic health system serving a diverse patient population with community and teaching facilities in both urban and suburban areas.PopulationsThe study included adult patients who had SARS-CoV-2 testing at YNHH between March 1 and April 30, 2020.Main outcome and performance measuresPrimary outcomes were admission and in-hospital mortality for patients with SARS-CoV-2 infection as determined by RT-PCR testing. We also assessed features associated with the need for respiratory support.ResultsOf the 28605 patients tested for SARS-CoV-2, 7995 patients (27.9%) had an infection (median age 52.3 years) and 2154 (26.9%) of these had an associated admission (median age 66.2 years). Of admitted patients, 2152 (99.9%) had a discharge disposition at the end of the study period. Of these, 329 (15.3%) required invasive mechanical ventilation and 305 (14.2%) expired. Increased age and male sex were positively associated with admission and in-hospital mortality (median age 80.7 years), while comorbidities had a much weaker association with the risk of admission or mortality. Black race (OR 1.43, 95%CI 1.14-1.78) and Hispanic ethnicity (OR 1.81, 95%CI 1.50-2.18) were identified as risk factors for admission, but, among discharged patients, age-adjusted in-hospital mortality was not significantly different among racial and ethnic groups.ConclusionsThis observational study identified, among people testing positive for SARS-CoV-2 infection, older age and male sex as the most strongly associated risks for admission and in-hospital mortality in patients with SARS-CoV-2 infection. While minority racial and ethnic groups had increased burden of disease and risk of admission, age-adjusted in-hospital mortality for discharged patients was not significantly different among racial and ethnic groups. Ongoing studies will be needed to continue to evaluate these risks, particularly in the setting of evolving treatment guidelines

PRN OPINION PAPER: Application of Precision Medicine across Pharmacy Specialty Areas

Clinical pharmacists have been incorporating precision medicine into practice for decades. Drug selection and dosing based on patient-specific clinical factors such as age, weight, renal function, drug interactions, plasma drug concentrations, and diet are expected as part of routine clinical practice. Newer concepts of precision medicine such as pharmacogenomics have recently been implemented into clinical care, while other concepts such as epigenetics and pharmacomicrobiomics still predominantly exist in the research area but clinical translation is expected in the future. The purpose of this paper is to describe current and emerging aspects of precision medicine as it relates to clinical pharmacy across a variety of specialty areas of practice, with perspectives from the American College of Clinical Pharmacy Practice and Research Network membership