Implementation of a pharmacogenetic panel-based test for pharmacotherapy-based supportive care in an adult oncology clinic.

The University of Florida Health conducted a pragmatic implementation of a pharmacogenetics (PGx) panel-based test to guide medications used for supportive care prescribed to patients undergoing chemotherapy. The implementation was in the context of a pragmatic clinical trial for patients with non-hematologic cancers being treated with chemotherapy. Patients were randomized to either the intervention arm or control arm and received PGx testing immediately or at the end of the study, respectively. Patients completed the MD Anderson Symptom Inventory (MDASI) to assess quality of life (QoL). A total of 150 patients received PGx testing and enrolled in the study. Clinical decision support and implementation infrastructure were developed. While the study was originally planned for 500 patients, we were underpowered in our sample of 150 patients to test differences in the patient-reported MDASI scores. We did observed a high completion rate (92%) of the questionnaires; however, there were few medication changes (n = 6 in the intervention arm) based on PGx test results. Despite this, we learned several lessons through this pragmatic implementation of a PGx panel-based test in an outpatient oncology setting. Most notably, patients were less willing to undergo PGx testing if the cost of the test exceeded $100. In addition, to enhance PGx implementation success, reoccurring provider education is necessary, clinical decision support needs to appear in a more conducive way to fit in with oncologists' workflow, and PGx test results need to be available earlier in treatment planning.

Feasibility of preemptive pharmacogenetic testing and improvement of medication treatment satisfaction among medically underserved patients.

Previous findings suggest that medically underserved patients are prescribed medications with pharmacogenetic (PGx) guidelines at a high frequency. Thus, underserved patients may especially benefit from PGx testing, but little evidence exists regarding the effect of testing in this population. This pilot study aimed to generate key feasibility data and explore clinical outcomes of PGx implementation in underserved populations. Black and Latino patients were recruited from an outpatient clinic and underwent PGx testing. Feasibility measures included enrollment metrics and actionable genotype frequencies. The primary clinical outcome was patient medication treatment satisfaction 6 months after testing. Implementation outcomes included the number of healthcare provider encounters and medication changes within the 6-month follow-up. Effectiveness outcomes included medication adherence, patient-perceived test value, and time spent discussing medications with providers. Ninety-nine patients completed the study. Proton-pump inhibitors were the most frequent PGx drug class prescribed at baseline (61%) followed by nonsteroidal anti-inflammatory drugs (36%). Patients with an actionable genotype constituted 96% of the population, whereas 28% had an actionable genotype related to their PGx drug. Patient treatment satisfaction significantly increased over the 6 months after PGx testing. In addition, medication adherence and the number of provider encounters significantly increased over the study period. In a pilot study, preemptive PGx testing was feasible in primary care clinics, improved patient treatment satisfaction and adherence, and increased the number of provider encounters in medically underserved patients. Future clinical trials are warranted to assess the long-term effects of PGx testing in a larger diverse patient population.

Lessons from clinical implementation of a preemptive pharmacogenetic panel as part of a testing pilot program with an employer-sponsored medical plan.

Introduction: This manuscript reports on a pilot program focused on implementing pharmacogenetic testing within the framework of an employer-sponsored medical plan at University of Florida (UF) Health. The aim was to understand the challenges associated with program implementation and to gather insights into patient attitudes towards PGx testing. Methods: The pilot program adopted a partially preemptive approach, targeting patients on current prescriptions for medications with relevant gene-drug associations. Patients were contacted via phone or through the MyChart system and offered pharmacogenetic testing with no additional direct costs. Results: Of 244 eligible patients, 110 agreed to participate. However, only 61 returned the mailed DNA collection kits. Among these, 89% had at least one potentially actionable genotype-based phenotype. Post-test follow-up revealed that while the majority viewed the process positively, 71% preferred a consultation with a pharmacogenetic specialist for better understanding of their results. Barriers to implementation ranged from fatigue with the healthcare system to a lack of understanding of the pharmacogenetic testing and concerns about privacy and potential misuse of genetic data. Conclusion: The findings underscore the need for clearer patient education on pharmacogenetic results and suggest the importance of the role of pharmacogenetic-trained pharmacists in delivering this education. They also highlight issues with relying on incomplete or inaccurate medication lists in patients' electronic health record. The implementation revealed less obvious challenges, the understanding of which could be beneficial for the success of future preemptive pharmacogenetic implementation programs. The insights from the pilot program served to bridge the information gap between patients, providers, and pharmacogenetic -specialists, with the ultimate goal of improving patient care.

Patients' Perspectives of Factors That Influence Pharmacogenetic Testing Uptake: Enhancing Patient Counseling and Results Dissemination.

Patient preferences for pharmacogenetic (PGx) counseling, testing and results dissemination are not well-established, especially in medically underserved Black and Latino populations. The aim of this study was to capture the preferences of Black and Latino patients who received PGx testing to ascertain: (1) factors enhancing their willingness to do testing and (2) preferences for the dissemination of results. Using the constant comparative method, we thematically analyzed interviews with 13 patients from medically underserved populations who had undergone PGx testing. The findings describe participants wanting better medication options, receiving a clear explanation about the testing, valuing or having an interest in science or medicine and having misconceptions about testing results as factors affecting one's willingness to undergo PGx testing. Additionally, patients confirmed preferring receiving results of PGx testing in a sharable format and described the significance of discussing results in a clinical appointment. The findings provide insight into what Black and Latino patients may prefer in terms of clinical implementation of PGx testing. These results can be utilized for tailoring future implementation of PGx testing and informing best pre- and post-test patient counseling and education practices.

Can we mitigate the psychological impacts of social isolation using behavioural activation? Long-term results of the UK BASIL urgent public health COVID-19 pilot randomised controlled trial and living systematic review.

BACKGROUND: Behavioural and cognitive interventions remain credible approaches in addressing loneliness and depression. There was a need to rapidly generate and assimilate trial-based data during COVID-19. OBJECTIVES: We undertook a parallel pilot RCT of behavioural activation (a brief behavioural intervention) for depression and loneliness (Behavioural Activation in Social Isolation, the BASIL-C19 trial ISRCTN94091479). We also assimilate these data in a living systematic review (PROSPERO CRD42021298788) of cognitive and/or behavioural interventions. METHODS: Participants (≥65 years) with long-term conditions were computer randomised to behavioural activation (n=47) versus care as usual (n=49). Primary outcome was PHQ-9. Secondary outcomes included loneliness (De Jong Scale). Data from the BASIL-C19 trial were included in a metanalysis of depression and loneliness. FINDINGS: The 12 months adjusted mean difference for PHQ-9 was -0.70 (95% CI -2.61 to 1.20) and for loneliness was -0.39 (95% CI -1.43 to 0.65).The BASIL-C19 living systematic review (12 trials) found short-term reductions in depression (standardised mean difference (SMD)=-0.31, 95% CI -0.51 to -0.11) and loneliness (SMD=-0.48, 95% CI -0.70 to -0.27). There were few long-term trials, but there was evidence of some benefit (loneliness SMD=-0.20, 95% CI -0.40 to -0.01; depression SMD=-0.20, 95% CI -0.47 to 0.07). DISCUSSION: We delivered a pilot trial of a behavioural intervention targeting loneliness and depression; achieving long-term follow-up. Living meta-analysis provides strong evidence of short-term benefit for loneliness and depression for cognitive and/or behavioural approaches. A fully powered BASIL trial is underway. CLINICAL IMPLICATIONS: Scalable behavioural and cognitive approaches should be considered as population-level strategies for depression and loneliness on the basis of a living systematic review.

How to Transition from Single-Gene Pharmacogenetic Testing to Preemptive Panel-Based Testing: A Tutorial.

There have been significant advancements in precision medicine and approaches to medication selection based on pharmacogenetic results. With the availability of direct-to-consumer genetic testing and growing awareness of genetic interindividual variability, patient demand for more precise, individually tailored drug regimens is increasing. The University of Florida (UF) Health Precision Medicine Program (PMP) was established in 2011 to improve integration of genomic data into clinical practice. In the ensuing years, the UF Health PMP has successfully implemented several single-gene tests to optimize the precision of medication prescribing across a variety of clinical settings. Most recently, the UF Health PMP launched a custom-designed pharmacogenetic panel, including pharmacogenes relevant to supportive care medications commonly prescribed to patients undergoing chemotherapy treatment, referred to as "GatorPGx." This tutorial provides guidance and information to institutions on how to transition from the implementation of single-gene pharmacogenetic testing to a preemptive panel-based testing approach. Here, we demonstrate application of the preemptive panel in the setting of an adult solid tumor oncology clinic. Importantly, the information included herein can be applied to other clinical practice settings.

Development of a Hospital Outcome Measure Intended for Use With Electronic Health Records: 30-Day Risk-standardized Mortality After Acute Myocardial Infarction.

BACKGROUND: Electronic health records (EHRs) offer the opportunity to transform quality improvement by using clinical data for comparing hospital performance without the burden of chart abstraction. However, current performance measures using EHRs are lacking. METHODS: With support from the Centers for Medicare & Medicaid Services (CMS), we developed an outcome measure of hospital risk-standardized 30-day mortality rates for patients with acute myocardial infarction for use with EHR data. As no appropriate source of EHR data are currently available, we merged clinical registry data from the Action Registry-Get With The Guidelines with claims data from CMS to develop the risk model (2009 data for development, 2010 data for validation). We selected candidate variables that could be feasibly extracted from current EHRs and do not require changes to standard clinical practice or data collection. We used logistic regression with stepwise selection and bootstrapping simulation for model development. RESULTS: The final risk model included 5 variables available on presentation: age, heart rate, systolic blood pressure, troponin ratio, and creatinine level. The area under the receiver operating characteristic curve was 0.78. Hospital risk-standardized mortality rates ranged from 9.6% to 13.1%, with a median of 10.7%. The odds of mortality for a high-mortality hospital (+1 SD) were 1.37 times those for a low-mortality hospital (-1 SD). CONCLUSIONS: This measure represents the first outcome measure endorsed by the National Quality Forum for public reporting of hospital quality based on clinical data in the EHR. By being compatible with current clinical practice and existing EHR systems, this measure is a model for future quality improvement measures.