BACKGROUND: Osimertinib is a third-generation epidermal growth factor receptor (EGFR) inhibitor that is currently the first-line treatment for metastatic EGFR-mutated non-small-cell lung cancer (NSCLC) due to its favorable efficacy and tolerability profile compared to previous generations of EGFR inhibitors. However, it can cause uncommon, yet serious, cardiovascular adverse effects. CASE PRESENTATION: We present the case of a 63-year-old man with EGFR-mutated NSCLC treated with osimertinib who developed new-onset non-ischemic cardiomyopathy with biventricular dysfunction and heart failure in the context of an enlarging pericardial effusion. For the first time, we demonstrate cardiac MR imaging findings associated with osimertinib-associated cardiomyopathy, including focal late gadolinium enhancement and myocardial edema. The patients biventricular function normalized after initiation of goal-directed medical therapy for heart failure and holding osimertinib. The patient was subsequently started on afatinib, a second-generation epidermal growth factor receptor-tyrosine kinase inhibitor (EGFR-TKI), without recurrence of cardiomyopathy. CONCLUSIONS: This case highlights the need to better understand osimertinib-induced cardiotoxicity and strategies to optimize oncologic care in patients who develop severe cardiac toxicities from cancer therapy. It further underlines the importance of specialized multidisciplinary care of cancer patients who develop cardiotoxicities to optimize their oncologic outcomes.

Following publication of the original article [1], the authors identified an error in Fig. 1, in which Fig. 1A and B were reversed. The correct figure is given below. (Figure presented.) Electrocardiogram (ECG) prior to (A) and after (B) pericardiocentesis. (A) Initial presenting ECG with electrical alternans, in which the direction of electrical activity flips beat-to-beat in lead V3. (B) ECG after pericardiocentesis with resolution of electrical alternans The original article [1] has been updated.

Background

Entrustable professional activities (EPAs) can form the foundation of competency-based assessment in medical training, focused on performance of discipline-specific core clinical activities.

Objective

To identify EPAs for the Internal Medicine (IM) Educational Milestones to operationalize competency-based assessment of residents using EPAs.

Methods

We used a modified Delphi approach to conduct a 2-step cross-sectional survey of IM educators at a 3-hospital IM residency program; residents also completed a survey. Participants rated the importance and appropriate year of training to reach competence for 30 proposed IM EPAs. Content validity indices identified essential EPAs. We conducted independent sample t tests to determine IM educator-resident agreement and calculated effect sizes. Finally, we determined the effect of different physician roles on ratings.

Results

Thirty-six IM educators participated; 22 completed both surveys. Twelve residents participated. Seventeen EPAs had a content validity index of 100%; 10 additional EPAs exceeded 80%. Educators and residents rated the importance of 27 of 30 EPAs similarly. Residents felt that 10 EPAs could be met at least 1 year earlier than educators had specified.

Conclusions

Internal medicine educators had a stable opinion of EPAs developed through this study, and residents generally agreed. Using this approach, programs could identify EPAs for resident evaluation, building on the initial list created via our study.

Background

Graduate medical education programs assess trainees' performance to determine readiness for unsupervised practice. Entrustable professional activities (EPAs) are a novel approach for assessing performance of core professional tasks.

Aim

To describe a pilot and feasibility evaluation of two EPAs for competency-based assessment in internal medicine (IM) residency.

Setting/participants

Post-graduate year-1 interns (PGY-1s) and attendings at a large internal medicine (IM) residency program.

Program description

Two Entrustable professional activities (EPA) assessments (Discharge, Family Meeting) were piloted.

Program feasibility evaluation

Twenty-eight out of 43 (65.1 %) PGY-1 s and 32/43 (74.4 %) attendings completed surveys about the Discharge EPA experience. Most who completed the EPA assessment (10/12, 83.8 %, PGY-1s; 9/11, 83.3 %, attendings) agreed it facilitated useful feedback discussions. For the Family Meeting EPA, 16/26 (61.5 %) PGY-1s completed surveys, and most who participated (9/12 PGY1s, 75 %) reported it improved attention to family meeting education, although only half recommended continuing the EPA assessment.

Discussion

From piloting two EPA assessments in a large IM residency, we recognized our reminder systems and time dedicated for completing EPA requirements as inadequate. Collaboration around patient safety and palliative care with relevant clinical services has enhanced implementation and buy-in. We will evaluate how well EPA-based assessment serves the intended purpose of capturing trainees' trustworthiness to conduct activities unsupervised.

Coronary angiography is the primary procedure for diagnosis and management decisions in coronary artery disease (CAD), but ad-hoc visual assessment of angiograms has high variability. Here we report a fully automated approach to interpret angiographic coronary artery stenosis from standard coronary angiograms. Using 13,843 angiographic studies from 11,972 adult patients at University of California, San Francisco (UCSF), between April 1, 2008 and December 31, 2019, we train neural networks to accomplish four sequential necessary tasks for automatic coronary artery stenosis localization and estimation. Algorithms are internally validated against criterion-standard labels for each task in hold-out test datasets. Algorithms are then externally validated in real-world angiograms from the University of Ottawa Heart Institute (UOHI) and also retrained using quantitative coronary angiography (QCA) data from the Montreal Heart Institute (MHI) core lab. The CathAI system achieves state-of-the-art performance across all tasks on unselected, real-world angiograms. Positive predictive value, sensitivity and F1 score are all ≥90% to identify projection angle and ≥93% for left/right coronary artery angiogram detection. To predict obstructive CAD stenosis (≥70%), CathAI exhibits an AUC of 0.862 (95% CI: 0.843-0.880). In UOHI external validation, CathAI achieves AUC 0.869 (95% CI: 0.830-0.907) to predict obstructive CAD. In the MHI QCA dataset, CathAI achieves an AUC of 0.775 (95%. CI: 0.594-0.955) after retraining. In conclusion, multiple purpose-built neural networks can function in sequence to accomplish automated analysis of real-world angiograms, which could increase standardization and reproducibility in angiographic coronary stenosis assessment.