Background: Coronary artery calcium (CAC) can be identified on non-gated chest CTs, but this finding is not consistently incorporated into care. A deep learning algorithm enables opportunistic CAC screening of non-gated chest CTs. Our objective was to evaluate the impact of notifying clinicians and patients of incidental CAC on statin initiation. Methods: NOTIFY-1 was a randomized quality improvement project in the Stanford healthcare system. Patients without known atherosclerotic cardiovascular disease (ASCVD) or prior statin prescription were screened for CAC on a prior non-gated chest CT from 2014-2019 using a validated deep learning algorithm with radiologist confirmation. Patients with incidental CAC were randomized to notification of the primary care clinician and patient versus usual care. Notification included a patient-specific image of CAC and guideline recommendations regarding statin use. The primary outcome was statin prescription within 6 months. Results: Among 2,113 patients who met initial clinical inclusion criteria, CAC was identified by the algorithm in 424 patients. After additional exclusions following chart review, a radiologist confirmed CAC among 173 of 194 patients (89.2%) who were randomized to notification or usual care. At 6 months, the statin prescription rate was 51.2% (44/86) in the notification arm versus 6.9% (6/87) with usual care (p<0.001). There was also more coronary artery disease testing in the notification arm (15.1% [13/86] vs. 2.3% [2/87], p=0.008). Conclusions: Opportunistic CAC screening of prior non-gated chest CTs followed by clinician and patient notification led to a significant increase in statin prescriptions. Further research is needed to determine whether this approach can reduce ASCVD events.

Background Statins conclusively decrease mortality in atherosclerotic cardiovascular disease (ASCVD), the leading cause of death worldwide, and are strongly recommended by guidelines. However, real-world statin utilization and persistence are low, resulting in excess mortality. Identifying reasons for statin nonuse at scale across health systems is crucial to developing targeted interventions to improve statin use. Methods We developed and validated deep learning-based natural language processing (NLP) approaches (Clinical Bidirectional Encoder Representations from Transformers [BERT]) to classify statin nonuse and reasons for statin nonuse using unstructured electronic health records (EHRs) from a diverse healthcare system. Results We present data from a cohort of 56,530 ASCVD patients, among whom 21,508 (38%) lack guideline-directed statin prescriptions and statins listed as allergies in structured EHR portions. Of these 21,508 patients without prescriptions, only 3,929 (18%) have any discussion of statin use or nonuse in EHR documentation. The NLP classifiers identify statin nonuse with an area under the curve (AUC) of 0.94 (95% CI 0.93–0.96) and reasons for nonuse with a weighted-average AUC of 0.88 (95% CI 0.86–0.91) when evaluated against manual expert chart review in a held-out test set. Clinical BERT identifies key patient-level reasons (side-effects, patient preference) and clinician-level reasons (guideline-discordant practices) for statin nonuse, including differences by type of ASCVD and patient race/ethnicity. Conclusions Our deep learning NLP classifiers can identify crucial gaps in statin nonuse and reasons for nonuse in high-risk populations to support education, clinical decision support, and potential pathways for health systems to address ASCVD treatment gaps.

Background Coronary artery calcium (CAC) scans can help reclassify risk and guide patient‐clinician shared treatment decisions for cardiovascular disease prevention. Patients increasingly access online patient educational materials (OPEMs) to guide medical decision‐making. The American Medical Association (AMA) recommends that OPEMs should be written below a 6th‐grade reading level. This study estimated the readability of commonly accessed OPEMs on CAC scans. Methods and Results The terms “coronary artery calcium scan,” “heart scan,” and “CAC score” were queried using an online search engine to identify the top 50 commonly accessed websites based on order of search results on December 17, 2019. Grade‐level readability was calculated using generalized estimating equations, with observations nested within readability metrics from each website. Results were compared with AMA‐recommended readability parameters. Overall grade‐level readability among all search terms was 10.9 (95% CI, 9.3–12.5). Average grade‐level readability of OPEMs for the search terms “coronary artery calcium scan,” “heart scan,” and “CAC score,” was 10.7 (95% CI, 9.0–12.5), 10.5 (95% CI, 8.9–12.1), and 11.9 (95% CI, 10.3–13.5), respectively. Professional society and news/media/blog websites had the highest average reading grade level of 12.6, while health system websites had the lowest average reading grade level of 10.0. Less than half of the unique websites (45.3%) included explanatory images or videos. Conclusions Current OPEMs on CAC scans are written at a higher reading level than recommended for the general public. This may lead to patient misunderstanding, which could exacerbate disparities in cardiovascular health among groups with lower health literacy.

Introduction: Female authors are underrepresented in cardiology journals, although prior work suggested improvement in reducing disparities over time. Early in the recent COVID-19 pandemic, female authorship continued to lag that of their male counterparts despite a surge in publications. The cumulative impact of the COVID-19 pandemic on authorship gender disparities remains unclear. We aimed to characterize gender disparities in COVID-19-related cardiology publications across the duration of the ongoing pandemic. Methods: We retrospectively analyzed COVID-19-related research articles published in the top 20 impact factor cardiology journals between March and June 2021. Gender representation data were extracted for any author, first authors, and senior authors.Results: We found that 841 articles were related to COVID-19, with a total of 5586 authors and an average of 42 articles per journal. Less than a third (29.9%) of the total authors from publications were women. Women represented a smaller proportion of first authors (21.3%) and senior authors (16.4%). Conclusions: Female authorship has continued to lag male authorship for the duration of the ongoing COVID-19 pandemic. The pandemic may have impeded progress in reducing gender disparities in academic cardiology publications. The low proportions of first and senior female authors may reflect the impact of the pandemic on women in cardiology in leadership domains.

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Background Lipoprotein(a) (Lp(a)) is a highly proatherogenic lipid fraction that is a clinically significant risk modifier. Patients wanting to learn more about Lp(a) are likely to use online patient educational materials (OPEMs). However, the readability of OPEMs may exceed the health literacy of the public. Objective This study aims to assess the readability of OPEMs related to Lp(a). We hypothesized that the readability of these online materials would exceed the sixth grade level recommended by the American Medical Association. Methods Using an online search engine, we queried the top 20 search results from 10 commonly used Lp(a)-related search terms to identify a total of 200 websites. We excluded duplicate websites, advertised results, research journal articles, or non–patient-directed materials, such as those intended only for health professionals or researchers. Grade level readability was calculated using 5 standard readability metrics (automated readability index, SMOG index, Coleman-Liau index, Gunning Fog score, Flesch-Kincaid score) to produce robust point (mean) and interval (CI) estimates of readability. Generalized estimating equations were used to model grade level readability by each search term, with the 5 readability scores nested within each OPEM. Results A total of 27 unique websites were identified for analysis. The average readability score for the aggregated results was a 12.2 (95% CI 10.9798-13.3978) grade level. OPEMs were grouped into 6 categories by primary source: industry, lay press, research foundation and nonprofit organizations, university or government, clinic, and other. The most readable category was OPEMs published by universities or government agencies (9.0, 95% CI 6.8-11.3). The least readable OPEMs on average were the ones published by the lay press (13.0, 95% CI 11.2-14.8). All categories exceeded the sixth grade reading level recommended by the American Medical Association. Conclusions Lack of access to readable OPEMs may disproportionately affect patients with low health literacy. Ensuring that online content is understandable by broad audiences is a necessary component of increasing the impact of novel therapeutics and recommendations regarding Lp(a).

Hispanic populations generally experience more adverse socioeconomic conditions yet demonstrate lower mortality compared with Non-Hispanic White (NHW) populations in the US. This finding of a mortality advantage is well-described as the “Hispanic paradox.” The Coronavirus Disease 2019 (COVID-19) pandemic has disproportionately affected Hispanic populations. To quantify these effects, we evaluated US national and county-level trends in Hispanic versus NHW mortality from 2011 through 2020. We found that a previously steady Hispanic mortality advantage significantly decreased in 2020, potentially driven by COVID-19-attributable Hispanic mortality. Nearly 16% of US counties experienced a reversal of their pre-pandemic Hispanic mortality advantage such that their Hispanic mortality exceeded NHW mortality in 2020. An additional 50% experienced a decrease in a pre-pandemic Hispanic mortality advantage. Our work provides a quantitative understanding of the disproportionate burden of the pandemic on Hispanic health and the Hispanic paradox and provides a renewed impetus to tackle the factors driving these concerning disparities.

Introduction Diverse race and ethnicity representation remains lacking in science and technology (S&T) careers in the United States (US). Due to systematic barriers across S&T training stages, there may be sequential loss of diverse representation leading to low representation, often conceptualized as a leaky pipeline. We aimed to quantify the contemporary leaky pipeline of S&T training in the US. Methods We analyzed US S&T degree data, stratified by sex and then by race or ethnicity, obtained from survey data the National Science Foundation and the National Center for Science and Engineering Statistics. We assessed changes in race and ethnicity representation in 2019 at two major S&T transition points: bachelor to doctorate degrees (2003–2019) and doctorate degrees to postdoctoral positions (2010–2019). We quantified representation changes at each point as the ratio of representation in the later stage to earlier stage (representation ratio [RR]). We assessed secular trends in the representation ratio through univariate linear regression. Results For 2019, the survey data included for bachelor degrees, 12,714,921 men and 10.612,879 women; for doctorate degrees 14,259 men and 12,860 women; and for postdoctoral data, 11,361 men and 8.672 women. In 2019, we observed that Black, Asian, and Hispanic women had comparable loss of representation among women in the bachelor to doctorate transition (RR 0.86, 95% confidence interval [CI] 0.81–0.92; RR 0.85, 95% CI 0.81–0.89; and RR 0.82, 95% CI 0.77–0.87, respectively), while among men, Black and Asian men had the greatest loss of representation (Black men RR 0.72, 95% CI 0.66–0.78; Asian men RR 0.73, 95% CI 0.70–0.77)]. We observed that Black men (RR 0.60, 95% CI 0.51–0.69) and Black women (RR 0.56, 95% CI 0.49–0.63) experienced the greatest loss of representation among men and women, respectively, in the doctorate to postdoctoral transition. Black women had a statistically significant decrease in their representation ratio in the doctorate to postdoctoral transition from 2010 to 2019 (p-trend = 0.02). Conclusion We quantified diverse race and ethnicity representation in contemporary US S&T training and found that Black men and women experienced the most consistent loss in representation across the S&T training pipeline. Findings should spur efforts to mitigate the structural racism and systemic barriers underpinning such disparities.