Lim, Elisabeth

Background: There is a paucity of evidence to inform the value of pharmacogenomic (PGx) results in patients after kidney transplant and how these results differ between Indigenous Americans and Whites. This study aims to identify the frequency of recommended medication changes based on PGx results and compare the pharmacogenomic (PGx) results and patients’ perceptions of the findings between a cohort of Indigenous American and White kidney transplant recipients. Methods: Thirty-one Indigenous Americans and fifty White kidney transplant recipients were studied prospectively. Genetic variants were identified using the OneOme RightMed PGx test of 27 genes. PGx pharmacist generated a report of the genetic variation and recommended changes. Pre- and post-qualitative patient surveys were obtained. Results: White and Indigenous American subjects had a similar mean number of medications at the time of PGx testing (mean 13 (SD 4.5)). In the entire cohort, 53% received beta blockers, 30% received antidepressants, 16% anticoagulation, 47% pain medication, and 25% statin therapy. Drug–gene interactions that warranted a clinical action were present in 21.5% of patients. In 12.7%, monitoring was recommended. Compared to the Whites, the Indigenous American patients had more normal CYP2C19 (p = 0.012) and CYP2D6 (p = 0.012) activities. The Indigenous American patients had more normal CYP4F2 (p = 0.004) and lower VKORC (p = 0.041) activities, phenotypes for warfarin drug dosing, and efficacy compared to the Whites. SLC6A4, which affects antidepressant metabolism, showed statistical differences between the two cohorts (p = 0.017); specifically, SLC6A4 had reduced expression in 45% of the Indigenous American patients compared to 20% of the White patients. There was no significant difference in patient perception before and after PGx. Conclusions: Kidney transplant recipients had several drug–gene interactions that were clinically actionable; over one-third of patients were likely to benefit from changes in medications or drug doses based on the PGx results. The Indigenous American patients differed in the expression of drug-metabolizing enzymes and drug transporters from the White patients.

OBJECTIVES Few medical schools incorporate formal education on human trafficking (HT) and sex trafficking (ST) into their curriculum. Our objective was to develop, implement, and evaluate education on HT and ST in the first-year medical student curriculum. METHODS The curriculum included a standardized patient (SP) experience and lecture. As part of their mandatory sexual health course, students interviewed an SP who presented with red flags for ST and then participated in a discussion led by a physician-facilitator in an observed small group setting. A multiple-choice survey to assess knowledge about HT and ST was developed and administered to students before and after the SP interview. RESULTS Of the 50 first-year medical students, 29 (58%) participated in the survey. Compared with the students’ baseline scores (according to the percentage of correct responses), scores after the educational intervention showed a significant increase in percentage correct on questions related to trafficking definition and scope (elder care, P = .01; landscaping, P = .03); victim identification ( P < .001); referral to services ( P < .001); legal issues ( P = .01); and security ( P < .001). On the basis of the feedback, a 2-hour lecture, which was adapted from the American Medical Women's Association–Physicians Against the Trafficking of Humans “Learn to Identify and Fight Trafficking” training, was presented the next year to all first-year medical students as part of their longitudinal clinical skills course and before the SP case. Curriculum objectives included learning trafficking definitions, victim/survivor identification, intersections with health care, the local impact of HT, and available resources. CONCLUSION This curriculum fulfills course objectives and could be replicated at other institutions. Further evaluation of this pilot curriculum is necessary to evaluate its effectiveness.

Background. End-stage kidney disease patients with concomitant heart failure (HF) with reduced ejection fraction are often denied kidney transplantation. The aims of this study were to explore factors predictive of suitability for kidney transplant and to assess cardiovascular outcomes in patients with impaired left ventricular ejection fraction (LVEF) after transplant. Methods. We evaluated 109 consecutive adults with LVEF ≤40% at the time of initial kidney transplant evaluation between 2013 and 2018. Posttransplant cardiovascular outcomes were defined as nonfatal myocardial infarction (MI), admission for HF, cardiovascular death, and all-cause mortality. Results. A cardiologist participated in kidney transplant evaluation for 87% of patients and was present at 49% of transplant selection conferences. Twenty-four patients (22%) were denied by a cardiologist for kidney transplant‚ and 59 (54%) were denied by the selection committee, of whom 43 were because of cardiovascular risk. Forty-two (38%) patients were approved for kidney transplant. On univariate analysis, the variables associated with denial for kidney transplant included cardiologist denial, higher cardiac troponin T, prior coronary intervention, cardiovascular event, positive stress study, lower ejection fraction, and lower VO2 max (all P < 0.05). Cardiologist denial was the most significant predictor of denial for kidney transplant in different multivariate models. At a median follow-up of 15 mo, 5 (5%) suffered nonfatal MI, 13 (12%) were hospitalized for HF exacerbation, and 17 (16%) died. Only 22 patients, 52% of those approved, underwent kidney transplant. After kidney transplant, there was 1 death, 1 nonfatal MI, and 3 hospitalizations for HF. Median LVEF improved from 38% before listing to 55% posttransplant. Conclusions. Cardiologist denial was the primary predictor of rejection for kidney transplant. Despite careful selection, prevalence of cardiovascular events and mortality after kidney transplant was 23%. There is need for a structured multidisciplinary approach for patients with impaired LVEF.

Abstract Objective To compare the artificial intelligence‐enabled electrocardiogram (AI‐ECG) atrial fibrillation (AF) prediction model output in patients with migraine with aura (MwA) and migraine without aura (MwoA). Background MwA is associated with an approximately twofold risk of ischemic stroke. Longitudinal cohort studies showed that patients with MwA have a higher incidence of developing AF compared to those with MwoA. The Mayo Clinic Cardiology team developed an AI‐ECG algorithm that calculates the probability of concurrent paroxysmal or impending AF in ECGs with normal sinus rhythm (NSR). Methods Adult patients with an MwA or MwoA diagnosis and at least one NSR ECG within the past 20 years at Mayo Clinic were identified. Patients with an ECG‐confirmed diagnosis of AF were excluded. For each patient, the ECG with the highest AF prediction model output was used as the index ECG. Comparisons between MwA and MwoA were conducted in the overall group (including men and women of all ages), women only, and men only in each age range (18 to <35, 35 to <55, 55 to <75, ≥75 years), and adjusted for age, sex, and six common vascular comorbidities that increase risk for AF. Results The final analysis of our cross‐sectional study included 40,002 patients (17,840 with MwA, 22,162 with MwoA). The mean (SD) age at the index ECG was 48.2 (16.0) years for MwA and 45.9 (15.0) years for MwoA ( p < 0.001). The AF prediction model output was significantly higher in the MwA group compared to MwoA (mean [SD] 7.3% [15.0%] vs. 5.6% [12.4%], mean difference [95% CI] 1.7% [1.5%, 2.0%], p < 0.001). After adjusting for vascular comorbidities, the difference between MwA and MwoA remained significant in the overall group (least square means of difference [95% CI] 0.7% [0.4%, 0.9%], p < 0.001), 18 to <35 (0.4% [0.1%, 0.7%], p = 0.022), and 35 to <55 (0.5% [0.2%, 0.8%], p < 0.001), women of all ages (0.6% [0.3%, 0.8%], p < 0.001), men of all ages (1.0% [0.4%, 1.6%], p = 0.002), women 35 to <55 (0.6% [0.3%, 0.9%], p < 0.001), and men 18 to <35 (1.2% [0.3%, 2.1%], p = 0.008). Conclusions Utilizing a novel AI‐ECG algorithm on a large group of patients, we demonstrated that patients with MwA have a significantly higher AF prediction model output, implying a higher probability of concurrent paroxysmal or impending AF, compared to MwoA in both women and men. Our results suggest that MwA is an independent risk factor for AF, especially in patients <55 years old, and that AF‐mediated cardioembolism may play a role in the migraine–stroke association for some patients.