Incidental Coronary Artery Calcium: Opportunistic Screening of Previous Nongated Chest Computed Tomography Scans to Improve Statin Rates (NOTIFY-1 Project).

BACKGROUND: Coronary artery calcium (CAC) can be identified on nongated chest computed tomography (CT) scans, but this finding is not consistently incorporated into care. A deep learning algorithm enables opportunistic CAC screening of nongated chest CT scans. Our objective was to evaluate the effect of notifying clinicians and patients of incidental CAC on statin initiation. METHODS: NOTIFY-1 (Incidental Coronary Calcification Quality Improvement Project) was a randomized quality improvement project in the Stanford Health Care System. Patients without known atherosclerotic cardiovascular disease or a previous statin prescription were screened for CAC on a previous nongated chest CT scan from 2014 to 2019 using a validated deep learning algorithm with radiologist confirmation. Patients with incidental CAC were randomly assigned 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 2113 patients who met initial clinical inclusion criteria, CAC was identified by the algorithm in 424 patients. After chart review and additional exclusions were made, a radiologist confirmed CAC among 173 of 194 patients (89.2%) who were randomly assigned 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] versus 2.3% [2/87]; P=0.008). CONCLUSIONS: Opportunistic CAC screening of previous nongated chest CT scans 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 atherosclerotic cardiovascular disease events. REGISTRATION: URL: https://www. CLINICALTRIALS: gov; Unique identifier: NCT04789278.

Online Patient Education Materials Related to Lipoprotein(a): Readability Assessment.

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).

Underrepresentation of Women in Reduced Ejection Heart Failure Clinical Trials With Improved Mortality or Hospitalization.

BACKGROUND: There are established sex-specific differences in heart failure with reduced ejection fraction (HFrEF) outcomes. Randomized clinical trials (RCTs) based on cardiovascular outcome benefits, typically either reduced cardiovascular mortality or hospitalization for heart failure (HHF), influence current guidelines for therapy. OBJECTIVES: The authors evaluate the representation of women in HFrEF RCTs that observed reduced all-cause or cardiovascular mortality or HHF. METHODS: We queried Cumulative Index to Nursing and Allied Health Literature, Excerpta Medica dataBASE, Medical Literature Analysis and Retrieval System Online, and PubMed for HFrEF RCTs that reported a statistically significant benefit of intervention resulting in improved mortality or HHF published from 1980 to 2021. We estimated representation using the participation-to-prevalence ratio (PPR). A PPR of 0.8 to 1.2 was considered representative. RESULTS: The final analysis included 33 RCTs. Women represented only 23.2% of all enrolled participants (n = 24,366/104,972), ranging from 11.4% to 40.1% per trial. Overall PPR was 0.58, with per-trial PPR estimates ranging from 0.29 to 1.00. Only 5 trials (15.2%) had a PPR of women representative of the disease population. Representation did not change significantly over time. The proportion of women in North American trials was significantly greater than trials conducted in Europe (P = 0.03). The proportion of women was greater in industry trials compared to government-funded trials (P = 0.05). CONCLUSIONS: Women are underrepresented in HFrEF RCTs that have demonstrated mortality or HHF benefits and influence current guidelines. Representation is key to further delineation of sex-specific differences in major trial results. Sustained efforts are warranted to ensure equitable and appropriate inclusion of women in HFrEF trials.

The leaky pipeline of diverse race and ethnicity representation in academic science and technology training in the United States, 2003-2019.

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.

Identifying Reasons for Statin Nonuse in Patients With Diabetes Using Deep Learning of Electronic Health Records.

Background Statins are guideline-recommended medications that reduce cardiovascular events in patients with diabetes. Yet, statin use is concerningly low in this high-risk population. Identifying reasons for statin nonuse, which are typically described in unstructured electronic health record data, can inform targeted system interventions to improve statin use. We aimed to leverage a deep learning approach to identify reasons for statin nonuse in patients with diabetes. Methods and Results Adults with diabetes and no statin prescriptions were identified from a multiethnic, multisite Northern California electronic health record cohort from 2014 to 2020. We used a benchmark deep learning natural language processing approach (Clinical Bidirectional Encoder Representations from Transformers) to identify statin nonuse and reasons for statin nonuse from unstructured electronic health record data. Performance was evaluated against expert clinician review from manual annotation of clinical notes and compared with other natural language processing approaches. Of 33 461 patients with diabetes (mean age 59±15 years, 49% women, 36% White patients, 24% Asian patients, and 15% Hispanic patients), 47% (15 580) had no statin prescriptions. From unstructured data, Clinical Bidirectional Encoder Representations from Transformers accurately identified statin nonuse (area under receiver operating characteristic curve [AUC] 0.99 [0.98-1.0]) and key patient (eg, side effects/contraindications), clinician (eg, guideline-discordant practice), and system reasons (eg, clinical inertia) for statin nonuse (AUC 0.90 [0.86-0.93]) and outperformed other natural language processing approaches. Reasons for nonuse varied by clinical and demographic characteristics, including race and ethnicity. Conclusions A deep learning algorithm identified statin nonuse and actionable reasons for statin nonuse in patients with diabetes. Findings may enable targeted interventions to improve guideline-directed statin use and be scaled to other evidence-based therapies.

Readability and reliability of online patient education materials about statins.

Objective: Statins are the cornerstone for the prevention and treatment of cardiovascular disease. Patients often consult online patient education materials (OPEMs) to inform medical decision-making. We therefore aimed to assess the readability and reliability of OPEMs related to statins. Methods: A total of 17 statin-related terms were queried using an online search engine to identify the top 20 search results for each statin-related term. Each OPEM was then grouped into the following categories based on 2 independent reviewers: government OPEMs (national, state, or local government agencies); healthcare/nonprofit OPEMs (major health systems and nonprofit organizations with a specific cardiovascular health focus); industry/commercial OPEMs (pharmaceutical manufacturers and online pharmacies); lay press OPEMs (healthcare-oriented news organizations); and dictionary/encyclopedia OPEMs. Grade-level readability for each OPEM was calculated using 5 standard readability metrics and compared with AMA-recommended readability recommendations. Reliability of each OPEM was evaluated using the JAMA benchmark criteria for online health information and certification from Health on the Net (HONCode). Results: A total of 340 websites were identified across the 17 statin search terms. There were 211 statin OPEMs after excluding non-OPEM results; 172 OPEMs had unique content. Statin OPEM readability exceeded the recommended 6th grade AMA reading level (average reading grade level of 10.9). The average JAMA benchmark criteria score was 2.13 (on a scale of 0-4, with higher scores indicating higher reliability), and only 60% of statin OPEMs were HONCode-certified. There was an inverse association between readability and reliability. The most readable results were from industry and commercial sources, while the most reliable sites were from lay press sources. Conclusions: Statin OPEMs are written at an overall averaging reading grade level of 10.9. There was an inverse association between readability and reliability. Lack of accessible, high-quality online health information may contribute to statin nonadherence.

Natural language processing to identify reasons for sex disparity in statin prescriptions.

Background: Statins are the cornerstone of treatment of patients with atherosclerotic cardiovascular disease (ASCVD). Despite this, multiple studies have shown that women with ASCVD are less likely to be prescribed statins than men. The objective of this study was to use Natural Language Processing (NLP) to elucidate factors contributing to this disparity. Methods: Our cohort included adult patients with two or more encounters between 2014 and 2021 with an ASCVD diagnosis within a multisite electronic health record (EHR) in Northern California. After reviewing structured EHR prescription data, we used a benchmark deep learning NLP approach, Clinical Bidirectional Encoder Representations from Transformers (BERT), to identify and interpret discussions of statin prescriptions documented in clinical notes. Clinical BERT was evaluated against expert clinician review in 20% test sets. Results: There were 88,913 patients with ASCVD (mean age 67.8±13.1 years) and 35,901 (40.4%) were women. Women with ASCVD were less likely to be prescribed statins compared with men (56.6% vs 67.6%, p <0.001), and, when prescribed, less likely to be prescribed guideline-directed high-intensity dosing (41.4% vs 49.8%, p <0.001). These disparities were more pronounced among younger patients, patients with private insurance, and those for whom English is their preferred language. Among those not prescribed statins, women were less likely than men to have statins mentioned in their clinical notes (16.9% vs 19.1%, p <0.001). Women were less likely than men to have statin use reported in clinical notes despite absence of recorded prescription (32.8% vs 42.6%, p <0.001). Women were slightly more likely than men to have statin intolerance documented in structured data or clinical notes (6.0% vs 5.3%, p=0.003). Conclusions: Women with ASCVD were less likely to be prescribed guideline-directed statins compared with men. NLP identified additional sex-based statin disparities and reasons for statin non-prescription in clinical notes of patients with ASCVD.

Trends in national and county-level Hispanic mortality in the United States, 2011-2020.

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.

Using deep learning-based natural language processing to identify reasons for statin nonuse in patients with atherosclerotic cardiovascular disease.

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.

A natural experiment identifies an impending ecological trap for a neotropical amphibian in response to extreme weather events.

Extreme weather events are predicted to increase as a result of climate change, yet amphibian responses to extreme disturbance events remain understudied, especially in the Neotropics. Recently, an unprecedented windstorm within a protected Costa Rican rainforest opened large light gaps in sites where we have studied behavioral responses of diurnal strawberry poison frogs (Oophaga pumilio) to ultraviolet radiation for nearly two decades. Previous studies demonstrate that O. pumilio selects and defends perches where ultraviolet radiation (UV-B) is relatively low, likely because of the lethal and sublethal effects of UV-B. In this natural experiment, we quantified disturbance to O. pumilio habitat, surveyed for the presence of O. pumilio in both high-disturbance and low-disturbance areas of the forest, and assessed UV-B levels and perch selection behavior in both disturbance levels. Fewer frogs were detected in high-disturbance habitat than in low-disturbance habitat. In general, frogs were found vocalizing at perches in both disturbance levels, and in both cases, in significantly lower UV-B levels relative to ambient adjacent surroundings. However, frogs at perches in high-disturbance areas were exposed to UV-B levels nearly 10 times greater than males at perches in low-disturbance areas. Thus, behavioral avoidance of UV-B may not reduce the risks associated with elevated exposure under these novel conditions, and similarly, if future climate and human-driven land-use change lead to sustained analogous environments.

Gender Disparities in Cardiology-Related COVID-19 Publications.

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.

Readability of Online Patient Educational Materials for Coronary Artery Calcium Scans and Implications for Health Disparities.

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.