Background Electronic medical records ( EMR s) allow identification of disease‐specific patient populations, but varying electronic cohort definitions could result in different populations. We compared the characteristics of an electronic medical record –derived atrial fibrillation ( AF ) patient population using 5 different electronic cohort definitions. Methods and Results Adult patients with at least 1 AF billing code from January 1, 2010, to December 31, 2017, were included. Based on different electronic cohort definitions, we trained 5 different logistic regression models using a labeled training data set (n=786). Each model yielded a predicted probability; patients were classified as having AF if the probability was higher than a specified cut point. Test characteristics were calculated for each model. These models were then applied to the full cohort and resulting characteristics were compared. In the training set, the comprehensive model (including demographics, billing codes, and natural language processing results) performed best, with an area under the curve of 0.89, sensitivity of 0.90, and specificity of 0.87. Among a candidate population (n=22 000), the proportion of patients identified as having AF varied from 61% in the model using diagnosis or procedure International Classification of Diseases ( ICD ) billing codes to 83% in the model using natural language processing of clinical notes. Among identified AF patients, the proportion of patients with a CHA 2 DS 2 ‐ VAS c score ≥2 varied from 69% to 85%; oral anticoagulant treatment rates varied from 50% to 66% depending on the model. Conclusions Different electronic cohort definitions result in substantially different AF study samples. This difference threatens the quality and reproducibility of electronic medical record–based research and quality initiatives.

Purpose of Review The purpose of this review is to highlight the past impact and current role of the Appropriate Use Criteria (AUC) for echocardiography in value-based healthcare, and to address future implications in light of the recent mandate from the Centers for Medicare and Medicaid Services to incorporate AUC for other imaging modalities. Recent Findings Several studies have proven that the AUC effectively stratify the clinical practice of echocardiography as they predict important echo abnormalities and impact optimal patient care. Recent investigations have tested new technologies and demonstrated the feasibility and scalability of the application of the AUC for echocardiography at the point of care. Summary The AUC for echocardiography has accomplished their core mission, as utilization has moderated over the last decade and mandatory implementation at the point of care for echocardiography remains rare. While a new mandate signals another wave of focus on appropriate utilization, echocardiography stands ready.

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Introduction: Left ventricular assist devices (LVAD) may lead to left ventricular (LV) recovery in patients with heart failure with reduced ejection fraction (HFrEF) via LV offloading and subsequent positive remodeling. Current echocardiographic markers of LV recovery in LVAD patients are not well defined. The peak systolic slope, also known as systolic acceleration, of the outflow cannula has recently been shown to be a marker of underlying LV contractility and a tool to assess for LV recovery. We hypothesized that variations in the systolic slope would predict heart failure (HF) admissions. Methods: A total of 63 unique patients with LVAD at The University of Chicago Medical Center had HeartMate 3 (HM3) outflow tract Doppler signals obtained during routine transthoracic echocardiography (TTE) of suitable quality between 2015 and 2022. Systolic acceleration, systolic deceleration, diastolic acceleration, and the presence of flow reversal were measured. Mortality and HF admissions were recorded up to one year from the date of the TTE. Results: Increased systolic acceleration through the HM3 outflow cannula was associated with a decreased 1-year HF admission risk (352.8 [241.9, 515.8] cm/sec 2 vs 249.2 [164.0, 316.5] cm/sec 2 among readmitted patients, p = 0.03; Figure 1A). Systolic deceleration also predicted 1-year HF admission (-318.6 [-477.9, -244.0] cm/sec 2 vs -217.8 [-304.5, -188.8] cm/sec 2 among readmitted patients, p = 0.04; Figure 1B). Other variables, including the presence of flow reversal and diastolic acceleration, were not significantly associated with HF admission risk. Conclusions: Systolic acceleration, which reflects LV contractility, and systolic deceleration, which represents LV relaxation, are predictive of HF admission in patients with the HM3 LVAD.

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