Cavallari, Larisa H.

ABSTRACT Ischemic events after contemporary percutaneous coronary intervention (PCI) are uncommon but carry high morbidity/mortality. Identifying patients at highest risk is critical to guide dual antiplatelet therapy (DAPT) intensity while minimizing bleeding. Current machine learning (ML) model‐developed risk scores do not incorporate pharmacogenetic data. To develop and externally validate ML models that integrate clinical, demographic, and CYP2C19 genetic information to predict 1‐year ischemic outcomes post‐PCI to refine clinical decision making. We analyzed 8317 patients from TAILOR‐PCI trial ( n = 4572) and the Precision PCI registry ( n = 3745). The outcome was a composite of cardiovascular death, myocardial infarction, stroke, and stent thrombosis at 1 year. Boruta feature selection identified 11 predictors. Multiple ML algorithms were trained in TAILOR‐PCI and validated in Precision PCI using cross‐validation and synthetic minority oversampling (SMOTE). Model performance was assessed by area under the receiver operating characteristic curve (AUC), sensitivity, and specificity. The best external performance was achieved with a support vector machine (SVM, polynomial kernel) (AUC 0.667; sensitivity 0.871; specificity 0.282), while XGBoost provided a more balanced profile (AUC 0.619; sensitivity 0.442; specificity 0.688). Variable importance for the SVM polynomial model demonstrated that all 11 included Boruta feature selected predictors had relatively high importance (> 75). ML models trained on large clinical trial and real‐world registry datasets can help identify the small subset of patients at high ischemic risk after PCI. This distinction is of relevance because ischemic events are rare, and most patients may safely de‐escalate DAPT to reduce bleeding risk while maintaining ischemic protection through a multimodal approach to risk stratification. Trial Registration: TAILOR‐PCI URL: https://clinicaltrials.gov/ct2/show/NCT01742117

The 2025 American College of Cardiology/American Heart Association/American College of Emergency Physicians/National Association of Emergency Medical Services Physicians/Society for Cardiovascular Angiography & Interventions acute coronary syndrome guideline focuses on strategies to reduce bleeding risk with antiplatelet therapy yet lacks any recommendation related to CYP2C19 genotyping. The impact of CYP2C19 loss‐of‐function alleles on the effectiveness of clopidogrel is well documented, and although prasugrel and ticagrelor more effectively reduce the risk for atherothrombotic events compared with clopidogrel in patients with a CYP2C19 loss‐of‐function allele, clopidogrel reduces bleeding risk without an increase in atherothrombotic events compared with prasugrel or ticagrelor in those without a loss‐of‐function allele. Accordingly, an American Heart Association Scientific Statement supports CYP2C19 genetic testing before oral P2Y 12 inhibitors are prescribed. This commentary summarizes the evidence in support of CYP2C19 ‐guided P2Y 12 inhibitor selection in the context of other 2025 acute coronary syndrome guideline recommendations and urges future guidelines to incorporate recommendations for CYP2C19 genotyping, especially for those at high bleeding risk.

Introduction: Identifying patients at risk for ischemic events after percutaneous coronary intervention (PCI) relies on traditional analysis of limited clinical and imaging variables. Machine learning (ML) has shown promise in effectively predicting cardiovascular risk in population studies. While existing ML models mainly predict mortality and incorporate clinical variables, there is a lack of tools that have utilized genetic data and that predict ischemic events. Aims: This study aims to develop and validate a ML model incorporating genotyping and clinical data to enhance prediction of ischemic outcomes for PCI patients utilizing large prospectively derived diverse datasets. Methods: Patients from the TAILOR-PCI trial (n=5302) were utilized for model development. 50% of the sample was utilized for Boruta feature selection and 50% for training and testing using cross validation. Features included demographics, medical history, medications, PCI characteristics, and genetic data (specifically, CYP2C19 *2, *3, *17 alleles). The primary endpoint was a composite of cardiovascular death, myocardial infarction, stroke, stent thrombosis, and severe recurrent ischemia at 12 months. Multiple ML classification algorithms, including Support Vector Machine (SVM) polynomial, Random Forest, Light gradient boost, XG Boost, among others, were benchmarked for their prediction performance on rare events. The top performing classifiers were externally validated on an independent dataset from the PRECISION PCI study (n=3,745). Results: Mean participant age of the training set was 64.2 ± 11.0 years, with 75.4% being male. During follow-up of 12 months, among 4,572 patients in the entire cohort 343 (7.5%) met the primary outcome. The SVM polynomial model demonstrated the highest area under the curve (AUC) of 0.67 for predicting the primary outcome with test dataset. The sensitivity, specificity, precision, and recall were 0.87, 0.28, 0.07, and 0.87 respectively (Figure). Peripheral arterial disease, body mass index, and age were among the top variables by feature importance. Conclusion: ML models incorporating both clinical and genetic data are feasible and highly promising in predicting major adverse cardiac events that may help guide use of anti-platelet drug therapy. The AUC values are reasonable given imbalances and misclassifications in datasets, and further model optimization with prospective utilization of the model will be paramount.

Abstract Introduction The CYP2D6 enzyme metabolizes opioids commonly prescribed for cancer‐related pain, and CYP2D6 polymorphisms may contribute to variability in opioid response. We evaluated the feasibility of implementing CYP2D6 ‐guided opioid prescribing for patients with cancer and reported pilot outcome data. Methods Adult patients from two cancer centers were prospectively enrolled into a hybrid implementation‐effectiveness clinical trial and randomized to CYP2D6 ‐genotype‐guided opioid selection, with clinical recommendations, or usual care. Implementation metrics, including provider response, medication changes consistent with recommendations, and patient‐reported pain and symptom scores at baseline and up to 8 weeks, were assessed. Results Most (87/114, 76%) patients approached for the study agreed to participate. Of 85 patients randomized, 71% were prescribed oxycodone at baseline. The median (range) time to receive CYP2D6 test results was 10 (3–37) days; 24% of patients had physicians acknowledge genotype results in a clinic note. Among patients with CYP2D6 ‐genotype‐guided recommendations to change therapy ( n = 11), 18% had a change congruent with recommendations. Among patients who completed baseline and follow‐up questionnaires ( n = 48), there was no difference in change in mean composite pain score (−1.01 ± 2.1 vs. −0.41 ± 2.5; p = 0.19) or symptom severity at last follow‐up (3.96 ± 2.18 vs. 3.47 ± 1.78; p = 0.63) between the usual care arm ( n = 26) and genotype‐guided arm ( n = 22), respectively. Conclusion Our study revealed high acceptance of pharmacogenetic testing as part of a clinical trial among patients with cancer pain. However, provider response to genotype‐guided recommendations was low, impacting assessment of pain‐related outcomes. Addressing barriers to utility of pharmacogenetics results and clinical recommendations will be critical for implementation success.

Pharmacogenetics can improve clinical outcomes by reducing adverse drug effects and enhancing therapeutic efficacy for commonly used drugs that treat a wide range of cardiovascular diseases. One of the major barriers to the clinical implementation of cardiovascular pharmacogenetics is limited education on this field for current healthcare providers and students. The abundance of pharmacogenetic literature underscores its promise, but it can also be challenging to learn such a wealth of information. Moreover, current clinical recommendations for cardiovascular pharmacogenetics can be confusing because they are outdated, incomplete, or inconsistent. A myriad of misconceptions about the promise and feasibility of cardiovascular pharmacogenetics among healthcare providers also has halted clinical implementation. Therefore, the main goal of this tutorial is to provide introductory education on the use of cardiovascular pharmacogenetics in clinical practice. The target audience is any healthcare provider (or student) with patients that use or have indications for cardiovascular drugs. This tutorial is organized into the following 6 steps: (1) understand basic concepts in pharmacogenetics; (2) gain foundational knowledge of cardiovascular pharmacogenetics; (3) learn the different organizations that release cardiovascular pharmacogenetic guidelines and recommendations; (4) know the current cardiovascular drugs/drug classes to focus on clinically and the supporting evidence; (5) discuss an example patient case of cardiovascular pharmacogenetics; and (6) develop an appreciation for emerging areas in cardiovascular pharmacogenetics. Ultimately, improved education among healthcare providers on cardiovascular pharmacogenetics will lead to a greater understanding for its potential in improving outcomes for a leading cause of morbidity and mortality.

Abstract Aims Guidelines recommend the use of potent P2Y12 inhibitors over clopidogrel for the reduction of ischaemic events in patients with acute coronary syndrome (ACS). However, this comes at the expense of increased bleeding. A guided selection of P2Y12 inhibiting therapy has the potential to overcome this limitation. We aimed at evaluating the comparative safety and efficacy of guided vs. routine selection of potent P2Y12 inhibiting therapy in patients with ACS. Methods and results We performed a network meta-analysis of randomized controlled trials (RCTs) comparing different oral P2Y12 inhibitors currently recommended for the treatment of patients with ACS (clopidogrel, prasugrel, and ticagrelor). RCTs including a guided approach (i.e. platelet function or genetic testing) vs. standard selection of P2Y12 inhibitors among patients with ACS were also included. Incidence rate ratios (IRR) and associated 95% confidence intervals (CIs) were estimated. P-scores were used to estimate hierarchies of efficacy and safety. The primary efficacy endpoint was major adverse cardiovascular events (MACE) and the primary safety endpoint was all bleeding. A total of 61 898 patients from 15 RCTs were included. Clopidogrel was used as reference treatment. A guided approach was the only strategy associated with reduced MACE (IRR: 0.80, 95% CI: 0.65–0.98) without any significant trade-off in all bleeding (IRR: 1.22, 95% CI: 0.96–1.55). A guided approach and prasugrel were associated with reduced myocardial infarction. A guided approach, prasugrel, and ticagrelor were associated with reduced stent thrombosis. Ticagrelor was also associated with reduced total and cardiovascular mortality. Prasugrel was associated with increased major bleeding. Prasugrel and ticagrelor were associated with increased minor bleeding. The incidence of stroke did not differ between treatments. Conclusion In patients with an ACS, compared with routine selection of potent P2Y12 inhibiting therapy (prasugrel or ticagrelor), a guided selection of P2Y12 inhibiting therapy is associated with the most favourable balance between safety and efficacy. These findings support a broader adoption of guided approach for the selection of P2Y12 inhibiting therapy in patients with ACS. Study registration number This study is registered in PROSPERO (CRD42021258603). Key Question A guided selection of P2Y12 inhibiting therapy using platelet function or genetic testing improves outcomes among patients undergoing percutaneous coronary intervention. Nevertheless, the comparative safety and efficacy of a guided versus routine selection of potent P2Y12-inhibiting therapy in acute coronary syndrome has not been explored. Key Finding In a comprehensive network meta-analysis including the totality of available evidence and using clopidogrel as treatment reference, a guided approach was the only strategy associated with reduced major adverse cardiovascular events without any significant trade-off in bleeding. Prasugrel and ticagrelor increased bleeding and only ticagrelor reduced mortality. Take Home Message A guided selection of P2Y12-inhibiting therapy represents the strategy associated with the most favourable balance between safety and efficacy. These findings support a broader adoption of guided P2Y12 inhibiting therapy in patients with acute coronary syndrome.

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.

Response to a drug often differs widely among individual patients. This variability is frequently observed not only with respect to effective responses but also with adverse drug reactions. Matching patients to the drugs that are most likely to be effective and least likely to cause harm is the goal of effective therapeutics. Pharmacogenomics (PGx) holds the promise of precision medicine through elucidating the genetic determinants responsible for pharmacological outcomes and using them to guide drug selection and dosing. Here we survey the US landscape of research programs in PGx implementation, review current advances and clinical applications of PGx, summarize the obstacles that have hindered PGx implementation, and identify the critical knowledge gaps and possible studies needed to help to address them.

Numerous pharmacogenetic clinical guidelines and recommendations have been published, but barriers have hindered the clinical implementation of pharmacogenetics. The Translational Pharmacogenetics Program (TPP) of the National Institutes of Health (NIH) Pharmacogenomics Research Network was established in 2011 to catalog and contribute to the development of pharmacogenetic implementations at eight US healthcare systems, with the goal to disseminate real‐world solutions for the barriers to clinical pharmacogenetic implementation. The TPP collected and normalized pharmacogenetic implementation metrics through June 2015, including gene–drug pairs implemented, interpretations of alleles and diplotypes, numbers of tests performed and actionable results, and workflow diagrams. TPP participant institutions developed diverse solutions to overcome many barriers, but the use of Clinical Pharmacogenetics Implementation Consortium (CPIC) guidelines provided some consistency among the institutions. The TPP also collected some pharmacogenetic implementation outcomes (scientific, educational, financial, and informatics), which may inform healthcare systems seeking to implement their own pharmacogenetic testing programs.