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

Primary care provider perspectives on the value of opportunistic CT screening

BACKGROUND

Clinical adoption of AI applications requires stakeholders see value in their use. AI-enabled opportunistic-CT-screening (OS) capitalizes on incidentally-detected findings within CTs for potential health benefit. This study evaluates primary care providers' (PCP) perspectives on OS.

METHODS

A survey was distributed to US Internal and Family Medicine residencies. Assessed were familiarity with AI and OS, perspectives on potential value/costs, communication of results, and technology implementation.

RESULTS

62 % of respondents (n = 71) were in Family Medicine, 64.8 % practiced in community hospitals. Although 74.6 % of respondents had heard of AI/machine learning, 95.8 % had little-to-no familiarity with OS. The majority reported little-to-no trust in AI. Reported concerns included AI accuracy (74.6 %) and unknown liability (73.2 %). 78.9 % of respondents reported that OS applications would require radiologist oversight. 53.5 % preferred OS results be included in a separate "screening" section within the Radiology report, accompanied by condition risks and management recommendations. The majority of respondents reported results would likely affect clinical management for all queried applications, and that atherosclerotic cardiovascular disease risk, abdominal aortic aneurysm, and liver fibrosis should be included within every CT report regardless of reason for examination. 70.5 % felt that PCP practices are unlikely to pay for OS. Added costs to the patient (91.5 %), the healthcare provider (77.5 %), and unknown liability (74.6 %) were the most frequently reported concerns.

CONCLUSION

PCP preferences and concerns around AI-enabled OS offer insights into clinical value and costs. As AI applications grow, feedback from end-users should be considered in the development of such technology to optimize implementation and adoption. Increasing stakeholder familiarity with AI may be a critical prerequisite first step before stakeholders consider implementation.

Current status and future directions in artificial intelligence for nuclear cardiology

INTRODUCTION

Myocardial perfusion imaging (MPI) is one of the most commonly ordered cardiac imaging tests. Accurate motion correction, image registration, and reconstruction are critical for high-quality imaging, but this can be technically challenging and has traditionally relied on expert manual processing. With accurate processing, there is a rich variety of clinical, stress, functional, and anatomic data that can be integrated to guide patient management.

AREAS COVERED

PubMed and Google Scholar were reviewed for articles related to artificial intelligence in nuclear cardiology published between 2020 and 2024. We will outline the prominent roles for artificial intelligence (AI) solutions to provide motion correction, image registration, and reconstruction. We will review the role for AI in extracting anatomic data for hybrid MPI which is otherwise neglected. Lastly, we will discuss AI methods to integrate the wealth of data to improve disease diagnosis or risk stratification.

EXPERT OPINION

There is growing evidence that AI will transform the performance of MPI by automating and improving on aspects of image acquisition and reconstruction. Physicians and researchers will need to understand the potential strengths of AI in order to benefit from the full clinical utility of MPI.

Artificial Intelligence in Cardiovascular Disease Prevention: Is it Ready for Prime Time?

PURPOSE OF REVIEW

This review evaluates how Artificial Intelligence (AI) enhances atherosclerotic cardiovascular disease (ASCVD) risk assessment, allows for opportunistic screening, and improves adherence to guidelines through the analysis of unstructured clinical data and patient-generated data. Additionally, it discusses strategies for integrating AI into clinical practice in preventive cardiology.

RECENT FINDINGS

AI models have shown superior performance in personalized ASCVD risk evaluations compared to traditional risk scores. These models now support automated detection of ASCVD risk markers, including coronary artery calcium (CAC), across various imaging modalities such as dedicated ECG-gated CT scans, chest X-rays, mammograms, coronary angiography, and non-gated chest CT scans. Moreover, large language model (LLM) pipelines are effective in identifying and addressing gaps and disparities in ASCVD preventive care, and can also enhance patient education. AI applications are proving invaluable in preventing and managing ASCVD and are primed for clinical use, provided they are implemented within well-regulated, iterative clinical pathways.

Thoracic versus coronary calcification for atherosclerotic cardiovascular disease events prediction

This study compared the prognostic value of quantified thoracic artery calcium (TAC) including aortic arch on chest CT and coronary artery calcium (CAC) score on ECG-gated cardiac CT.

METHODS

A total of 2412 participants who underwent both chest CT and ECG-gated cardiac CT at the same period were included in the Multi-Ethnic Study of Atherosclerosis Exam 5. All participants were monitored for incident atherosclerotic cardiovascular disease (ASCVD) events. TAC is defined as calcification in the ascending aorta, aortic arch and descending aorta on chest CT. The quantification of TAC was measured using the Agatston method. Time-dependent receiver-operating characteristic (ROC) curves were used to compare the prognostic value of TAC and CAC scores.

RESULTS

Participants were 69±9 years of age and 47% were male. The Spearman correlation between TAC and CAC scores was 0.46 (p<0.001). During the median follow-up period of 8.8 years, 234 participants (9.7%) experienced ASCVD events. In multivariable Cox regression analysis, TAC score was independently associated with increased risk of ASCVD events (HR 1.31, 95% CI 1.09 to 1.58) as well as CAC score (HR 1.82, 95% CI 1.53 to 2.17). However, the area under the time-dependent ROC curve for CAC score was greater than that for TAC score in all participants (0.698 and 0.641, p=0.031). This was particularly pronounced in participants with borderline/intermediate and high 10-year ASCVD risk scores.

CONCLUSION

Our study demonstrated a significant association between TAC and CAC scores but a superior prognostic value of CAC score for ASCVD events. These findings suggest TAC on chest CT provides supplementary data to estimate ASCVD risk but does not replace CAC on ECG-gated cardiac CT.

Revolutionising Acute Cardiac Care With Artificial Intelligence: Opportunities and Challenges

This article reviews the application of artificial intelligence (AI) in acute cardiac care, highlighting its potential to transform patient outcomes in the face of the global burden of cardiovascular diseases. It explores how AI algorithms can rapidly and accurately process data for the prediction and diagnosis of acute cardiac conditions. The review examines AI's impact on patient health across various diagnostic tools such as echocardiography, electrocardiography, coronary angiography, cardiac computed tomography, and magnetic resonance imaging, discusses the regulatory landscape for AI in health care, and categorises AI algorithms by their risk levels. Furthermore, it addresses the challenges of data quality, generalisability, bias, transparency, and regulatory considerations, underscoring the necessity for inclusive data and robust validation processes. The review concludes with future perspectives on integrating AI into clinical workflows and the ongoing need for research, regulation, and innovation to harness AI's full potential in improving acute cardiac care.

Artificial Intelligence for Cardiovascular Care-Part 1: Advances: JACC Review Topic of the Week

Recent artificial intelligence (AI) advancements in cardiovascular care offer potential enhancements in diagnosis, treatment, and outcomes. Innovations to date focus on automating measurements, enhancing image quality, and detecting diseases using novel methods. Applications span wearables, electrocardiograms, echocardiography, angiography, genetics, and more. AI models detect diseases from electrocardiograms at accuracy not previously achieved by technology or human experts, including reduced ejection fraction, valvular heart disease, and other cardiomyopathies. However, AI's unique characteristics necessitate rigorous validation by addressing training methods, real-world efficacy, equity concerns, and long-term reliability. Despite an exponentially growing number of studies in cardiovascular AI, trials showing improvement in outcomes remain lacking. A number are currently underway. Embracing this rapidly evolving technology while setting a high evaluation benchmark will be crucial for cardiology to leverage AI to enhance patient care and the provider experience.

Opportunistic Screening of Chronic Liver Disease with Deep Learning Enhanced Echocardiography

Importance

Chronic liver disease affects more than 1.5 billion adults worldwide, however the majority of cases are asymptomatic and undiagnosed. Echocardiography is broadly performed and visualizes the liver; but this information is not leveraged.

Objective

To develop and evaluate a deep learning algorithm on echocardiography videos to enable opportunistic screening for chronic liver disease.

Design

Retrospective observational cohorts.

Setting

Two large urban academic medical centers.

Participants

Adult patients who received echocardiography and abdominal imaging (either abdominal ultrasound or abdominal magnetic resonance imaging) with ≤30 days between tests, between July 4, 2012, to June 4, 2022.

Exposure

Deep learning model predictions from a deep-learning computer vision pipeline that identifies subcostal view echocardiogram videos and detects the presence of cirrhosis or steatotic liver disease (SLD).

Main Outcome and Measures

Clinical diagnosis by paired abdominal ultrasound or magnetic resonance imaging (MRI).

Results

A total of 1,596,640 echocardiogram videos (66,922 studies from 24,276 patients) from Cedars-Sinai Medical Center (CSMC) were used to develop EchoNet-Liver, an automated pipeline that identifies high quality subcostal images from echocardiogram studies and detects the presence of cirrhosis or SLD. In the held-out CSMC test cohort, EchoNet-Liver was able to detect the presence of cirrhosis with an AUC of 0.837 (0.789 - 0.880) and SLD with an AUC of 0.799 (0.758 - 0.837). In a separate test cohort with paired abdominal MRIs, cirrhosis was detected with an AUC of 0.704 (0.689-0.718) and SLD was detected with an AUC of 0.726 (0.659-0.790). In an external test cohort of 106 patients (n = 5,280 videos), the model detected cirrhosis with an AUC of 0.830 (0.738 - 0.909) and SLD with an AUC of 0.768 (0.652 - 0.875).

Conclusions and Relevance

Deep learning assessment of clinical echocardiography enables opportunistic screening of SLD and cirrhosis. Application of this algorithm may identify patients who may benefit from further diagnostic testing and treatment for chronic liver disease.

Artificial Intelligence in Coronary Artery Calcium Scoring

Cardiovascular disease (CVD), particularly coronary heart disease (CHD), is the leading cause of death in the US, with a high economic impact. Coronary artery calcium (CAC) is a known marker for CHD and a useful tool for estimating the risk of atherosclerotic cardiovascular disease (ASCVD). Although CACS is recommended for informing the decision to initiate statin therapy, the current standard requires a dedicated CT protocol, which is time-intensive and contributes to radiation exposure. Non-dedicated CT protocols can be taken advantage of to visualize calcium and reduce overall cost and radiation exposure; however, they mainly provide visual estimates of coronary calcium and have disadvantages such as motion artifacts. Artificial intelligence is a growing field involving software that independently performs human-level tasks, and is well suited for improving CACS efficiency and repurposing non-dedicated CT for calcium scoring. We present a review of the current studies on automated CACS across various CT protocols and discuss consideration points in clinical application and some barriers to implementation.

Opportunistic Screening for Coronary Artery Disease: An Untapped Population Health Resource

BACKGROUND

Coronary artery disease is the leading cause of death in the United States. At-risk asymptomatic adults are eligible for screening with electrocardiogram-gated coronary artery calcium (CAC) CT, which aids in risk stratification and management decision-making. Incidental CAC (iCAC) is easily quantified on chest CT in patients imaged for noncardiac indications; however, radiologists do not routinely report the finding.

OBJECTIVE

To determine the clinical significance of CAC identified incidentally on routine chest CT performed for noncardiac indications.

DESIGN

An informationist developed search strategies in MEDLINE, Embase, and SCOPUS, and two reviewers independently screened results at both the abstract and full text levels. Data extracted from eligible articles included age, rate of iCAC identification, radiologist reporting frequency, impact on downstream medical management, and association of iCAC with patient outcomes.

RESULTS

From 359 unique citations, 83 research publications met inclusion criteria. The percentage of patients with iCAC ranged from 9% to 100%. Thirty-one investigations measured association(s) between iCAC and cardiovascular morbidity and mortality, and 29 identified significant correlations, including nonfatal myocardial infarction, fatal myocardial infarction, major adverse cardiovascular event, cardiovascular death, and all-cause death. iCAC was present in 20% to 100% of the patients in these cohorts, but when present, iCAC was reported by radiologists in only 31% to 44% of cases. Between 18% and 77% of patients with iCAC were not on preventive medications in studies that reported these data. Seven studies measured the effect of reporting on guideline directed medical therapy, and 5 (71%) reported an increase in medication prescriptions after diagnosis of iCAC, with one confirming reductions in low-density lipoprotein levels. Twelve investigations reported good concordance between CAC grade on noncardiac CT and Agatston score on electrocardiogram-gated cardiac CT, and 10 demonstrated that artificial intelligence tools can reliably calculate an Agatston score on noncardiac CT.

CONCLUSION

A body of evidence demonstrates that patients with iCAC on routine chest CT are at risk for cardiovascular disease events and death, but they are often undiagnosed. Uniform reporting of iCAC in the chest CT impression represents an opportunity for radiology to contribute to early identification of high-risk individuals and potentially reduce morbidity and mortality. AI tools have been validated to calculate Agatston score on routine chest CT and hold the best potential for facilitating broad adoption.

Coronary artery calcium in 2023: Guidelines for LDL-C goals, non-statin therapies, and aspirin use

Personalizing risk assessment and treatment decisions for the primary prevention of atherosclerotic cardiovascular disease (ASCVD) rely on pooled cohort equations and increasingly coronary artery calcium (CAC) score. A growing body of evidence supports that elevated CAC scores correspond to progressively elevated ASCVD risk, and that scores of ≥100, ≥300, and ≥1000 denote risk that is equivalent to certain secondary prevention populations. This has led consensus guidelines to incorporate CAC score thresholds for guiding escalation of preventive therapy for lowering low-density lipoprotein cholesterol goals, initiation of non-statin lipid lowering medications, and use of low-dose daily aspirin. As data on CAC continues to grow, more decision pathways will incorporate CAC score cutoffs to guide management of blood pressure and cardiometabolic medications. CAC score is also being used to enrich clinical trial study populations for elevated ASCVD risk, and to screen for subclinical coronary atherosclerosis in patients who received chest imaging for other diagnostic purposes.

Measurement and Application of Incidentally Detected Coronary Calcium: JACC Review Topic of the Week

Coronary artery calcium (CAC) scoring is a powerful tool for atherosclerotic cardiovascular disease risk stratification. The nongated, noncontrast chest computed tomography scan (NCCT) has emerged as a source of CAC characterization with tremendous potential due to the high volume of NCCT scans. Application of incidental CAC characterization from NCCT has raised questions around score accuracy, standardization of methodology including the possibility of deep learning to automate the process, and the risk stratification potential of an NCCT-derived score. In this review, the authors aim to summarize the role of NCCT-derived CAC in preventive cardiovascular health today as well as explore future avenues for eventual clinical applicability in specific patient populations and broader health systems.

How will lung cancer screening and lung nodule management change the diagnostic and surgical lung cancer landscape?

INTRODUCTION

Implementation of lung cancer screening, with its subsequent findings, is anticipated to change the current diagnostic and surgical lung cancer landscape. This review aimed to identify and present the most updated expert opinion and discuss relevant evidence regarding the impact of lung cancer screening and lung nodule management on the diagnostic and surgical landscape of lung cancer, as well as summarise points for clinical practice.

METHODS

This article is based on relevant lectures and talks delivered during the European Society of Thoracic Surgeons-European Respiratory Society Collaborative Course on Thoracic Oncology (February 2023). Original lectures and talks and their relevant references were included. An additional literature search was conducted and peer-reviewed studies in English (December 2022 to June 2023) from the PubMed/Medline databases were evaluated with regards to immediate affinity of the published papers to the original talks presented at the course. An updated literature search was conducted (June 2023 to December 2023) to ensure that updated literature is included within this article.

RESULTS

Lung cancer screening suspicious findings are expected to increase the number of diagnostic investigations required therefore impacting on current capacity and resources. Healthcare systems already face a shortage of imaging and diagnostic slots and they are also challenged by the shortage of interventional radiologists. Thoracic surgery will be impacted by the wider lung cancer screening implementation with increased volume and earlier stages of lung cancer. Nonsuspicious findings reported at lung cancer screening will need attention and subsequent referrals where required to ensure participants are appropriately diagnosed and managed and that they are not lost within healthcare systems.

CONCLUSIONS

Implementation of lung cancer screening requires appropriate mapping of existing resources and infrastructure to ensure a tailored restructuring strategy to ensure that healthcare systems can meet the new needs.

Contemporary attitudes and beliefs on coronary artery calcium from social media using artificial intelligence

Coronary artery calcium (CAC) is a powerful tool to refine atherosclerotic cardiovascular disease (ASCVD) risk assessment. Despite its growing interest, contemporary public attitudes around CAC are not well-described in literature and have important implications for shared decision-making around cardiovascular prevention. We used an artificial intelligence (AI) pipeline consisting of a semi-supervised natural language processing model and unsupervised machine learning techniques to analyze 5,606 CAC-related discussions on Reddit. A total of 91 discussion topics were identified and were classified into 14 overarching thematic groups. These included the strong impact of CAC on therapeutic decision-making, ongoing non-evidence-based use of CAC testing, and the patient perceived downsides of CAC testing (e.g., radiation risk). Sentiment analysis also revealed that most discussions had a neutral (49.5%) or negative (48.4%) sentiment. The results of this study demonstrate the potential of an AI-based approach to analyze large, publicly available social media data to generate insights into public perceptions about CAC, which may help guide strategies to improve shared decision-making around ASCVD management and public health interventions.

Predicting mortality from AI cardiac volumes mass and coronary calcium on chest computed tomography

Chest computed tomography is one of the most common diagnostic tests, with 15 million scans performed annually in the United States. Coronary calcium can be visualized on these scans, but other measures of cardiac risk such as atrial and ventricular volumes have classically required administration of contrast. Here we show that a fully automated pipeline, incorporating two artificial intelligence models, automatically quantifies coronary calcium, left atrial volume, left ventricular mass, and other cardiac chamber volumes in 29,687 patients from three cohorts. The model processes chamber volumes and coronary artery calcium with an end-to-end time of ~18 s, while failing to segment only 0.1% of cases. Coronary calcium, left atrial volume, and left ventricular mass index are independently associated with all-cause and cardiovascular mortality and significantly improve risk classification compared to identification of abnormalities by a radiologist. This automated approach can be integrated into clinical workflows to improve identification of abnormalities and risk stratification, allowing physicians to improve clinical decision-making.

Impact of myocardial perfusion and coronary calcium on medical management for coronary artery disease

AIMS

Single-photon emission computed tomography (SPECT) myocardial perfusion imaging (MPI) remains one of the most widely used imaging modalities for the diagnosis and prognostication of coronary artery disease (CAD). Despite the extensive prognostic information provided by MPI, little is known about how this influences the prescription of medical therapy for CAD. We evaluated the relationship between MPI with computed tomography (CT) attenuation correction and prescription of acetylsalicylic acid (ASA) and statins.

METHODS AND RESULTS

We performed a retrospective analysis of consecutive patients who underwent SPECT MPI at a single centre between 2015 and 2021. Myocardial perfusion abnormalities and coronary calcium burden were assessed, with attenuation correction imaging 77.8% of patients. Medication prescriptions before and within 180 days after the test were compared. Associations between abnormal perfusion and calcium burden with ASA and statin prescription were assessed using multivariable logistic regression. In total, 9908 patients were included, with a mean age 66.8 ± 11.7 years and 5337 (53.9%) males. The prescription of statins increased more in patients with abnormal perfusion (increase of 19.2 vs. 12.0%, P < 0.001). Similarly, the presence of extensive CAC led to a greater increase in statin prescription compared with no calcium (increase 12.1 vs. 7.8%, P < 0.001). In multivariable analyses, ischaemia and coronary artery calcium were independently associated with ASA and statin prescription.

CONCLUSION

Abnormal MPI testing was associated with significant changes in medical therapy. Both calcium burden and perfusion abnormalities were associated with increased prescriptions of medical therapy for CAD.

Impact of early preeclampsia prediction on medication adherence and behavior change: a survey of pregnant and recently-delivered individuals

BACKGROUND

Behavior change and medication adherence represent potential barriers to optimal prevention of pregnancy complications including preeclampsia. We sought to evaluate baseline sentiments on pregnancy care and medication amenability, and how these measures would be impacted by early predictive testing for preeclampsia.

METHODS

We developed a digital survey to query participants' baseline sentiments on pregnancy care, knowledge about pregnancy complications, and views on a hypothetical test to predict preeclampsia. The survey was administered online to pregnant and recently-delivered individuals in the United States. Survey data were analyzed using pooled two-sample proportion z-tests with adjustment for multiple comparisons.

RESULTS

One thousand and twenty-two people completed the survey. 84% reported they were satisfied with their pregnancy care. Self-assessed knowledge about preeclampsia was high, with 75% of respondents reporting they have a "good understanding" of preeclampsia, but measured knowledge was low, with only 10% able to identify five common signs/symptoms of preeclampsia. Notably, 40% of participants with prior preeclampsia believed they were at average or below-average risk for recurrence. 91% of participants desired early pregnancy predictive testing for preeclampsia. If found to be at high risk for preeclampsia, 88% reported they would be more motivated to follow their provider's medication recommendations and 94% reported they would desire home blood pressure monitoring. Increased motivation to follow clinicians' medication and monitoring recommendations was observed across the full spectrum of medication amenability. Individuals who are more medication-hesitant still reported high rates of motivation to change behavior and adhere to medication recommendations if predictive testing showed a high risk of preeclampsia. Importantly, a high proportion of medication-hesitant individuals reported that if a predictive test demonstrated they were at high risk of preeclampsia, they would feel more motivated to take medications (83.0%) and aspirin (75.9%) if recommended.

CONCLUSION

While satisfaction with care is high, participants desire more information about their pregnancy health, would value predictive testing for preeclampsia, and report they would act on this information. Improved detection of at-risk individuals through objective testing combined with increased adherence to their recommended care plan may be an important step to remedy the growing gap in prevention.

The Clear Value of Coronary Artery Calcification Evaluation on Non-Gated Chest Computed Tomography for Cardiac Risk Stratification

To enhance risk stratification in patients suspected of coronary artery disease, the assessment of coronary artery calcium (CAC) could be incorporated, especially when CAC can be readily assessed on previously performed non-gated chest computed tomography (CT). Guidelines recommend reporting on patients' extent of CAC on these non-cardiac directed exams and various studies have shown the diagnostic and prognostic value. However, this method is still little applied, and no current consensus exists in clinical practice. This review aims to point out the clinical utility of different kinds of CAC assessment on non-gated CTs. It demonstrates that these scans indeed represent a merely untapped and underestimated resource for risk stratification in patients with stable chest pain or an increased risk of cardiovascular events. To our knowledge, this is the first review to describe the clinical utility of different kinds of visual CAC evaluation on non-gated unenhanced chest CT. Various methods of CAC assessment on non-gated CT are discussed and compared in terms of diagnostic and prognostic value. Furthermore, the application of these non-gated CT scans in the general practice of cardiology is discussed. The clinical utility of coronary calcium assessed on non-gated chest CT, according to the current literature, is evident. This resource of information for cardiac risk stratification needs no specific requirements for scan protocol, and is radiation-free and cost-free. However, some gaps in research remain. In conclusion, the integration of CAC on non-gated chest CT in general cardiology should be promoted and research on this method should be encouraged.

Recent advances in cardiovascular risk assessment: The added value of non-invasive anatomic imaging

In 2022, multiple original research studies were conducted highlighting the utility of coronary artery calcium (CAC) imaging in young individuals and provided further evidence for the role of CAC to improve atherosclerotic cardiovascular disease (ASCVD) risk assessment. Mean calcium density was shown to be a more reliable predictor than peak density in risk assessment. Additionally, in light of the ACC/AHA/Multispecialty Chest Pain Guideline's recent elevation of coronary computed tomography angiography (CCTA) to a Class I (level of evidence A) recommendation as an index diagnostic test for acute or stable chest pain, several studies support the utility of CCTA and guided future directions. This review summarizes recent studies that highlight the role of non-invasive imaging in enhancing ASCVD risk assessment across different populations.

Value Creation Through Artificial Intelligence and Cardiovascular Imaging: A Scientific Statement From the American Heart Association

Multiple applications for machine learning and artificial intelligence (AI) in cardiovascular imaging are being proposed and developed. However, the processes involved in implementing AI in cardiovascular imaging are highly diverse, varying by imaging modality, patient subtype, features to be extracted and analyzed, and clinical application. This article establishes a framework that defines value from an organizational perspective, followed by value chain analysis to identify the activities in which AI might produce the greatest incremental value creation. The various perspectives that should be considered are highlighted, including clinicians, imagers, hospitals, patients, and payers. Integrating the perspectives of all health care stakeholders is critical for creating value and ensuring the successful deployment of AI tools in a real-world setting. Different AI tools are summarized, along with the unique aspects of AI applications to various cardiac imaging modalities, including cardiac computed tomography, magnetic resonance imaging, and positron emission tomography. AI is applicable and has the potential to add value to cardiovascular imaging at every step along the patient journey, from selecting the more appropriate test to optimizing image acquisition and analysis, interpreting the results for classification and diagnosis, and predicting the risk for major adverse cardiac events.

Eradicating Atherosclerotic Events by Targeting Early Subclinical Disease: It Is Time to Retire the Therapeutic Paradigm of Too Much, Too Late

Recent decades have seen spectacular advances in understanding and managing atherosclerotic cardiovascular disease, but paradoxically, clinical progress has stalled. Residual risk of atherosclerotic cardiovascular disease events is particularly vexing, given recognized lifestyle interventions and powerful modern medications. Why? Atherosclerosis begins early in life, yet clinical trials and mechanistic studies often emphasize terminal, end-stage plaques, meaning on the verge of causing heart attacks and strokes. Thus, current clinical evidence drives us to emphasize aggressive treatments that are delayed until patients already have advanced arterial disease. I call this paradigm "too much, too late." This brief review covers exciting efforts that focus on preventing, or finding and treating, arterial disease before its end-stage. Also included are specific proposals to establish a new evidence base that could justify intensive short-term interventions (induction-phase therapy) to treat subclinical plaques that are early enough perhaps to heal. If we can establish that such plaques are actionable, then broad screening to find them in early midlife individuals would become imperative-and achievable. You have a lump in your coronaries! can motivate patients and clinicians. We must stop thinking of a heart attack as a disease. The real disease is atherosclerosis. In my opinion, an atherosclerotic heart attack is a medical failure. It is a manifestation of longstanding arterial disease that we had allowed to progress to its end-stage, despite knowing that atherosclerosis begins early in life and despite the availability of remarkably safe and highly effective therapies. The field needs a transformational advance to shift the paradigm out of end-stage management and into early interventions that hold the possibility of eradicating the clinical burden of atherosclerotic cardiovascular disease, currently the biggest killer in the world. We urgently need a new evidence base to redirect our main focus from terminal, end-stage atherosclerosis to earlier, and likely reversible, human arterial disease.

Coronary and Extra-coronary Subclinical Atherosclerosis to Guide Lipid-Lowering Therapy

PURPOSE OF REVIEW

To discuss and review the technical considerations, fundamentals, and guideline-based indications for coronary artery calcium scoring, and the use of other non-invasive imaging modalities, such as extra-coronary calcification in cardiovascular risk prediction.

RECENT FINDINGS

The most robust evidence for the use of CAC scoring is in select individuals, 40-75 years of age, at borderline to intermediate 10-year ASCVD risk. Recent US recommendations support the use of CAC scoring in varying clinical scenarios. First, in adults with very high CAC scores (CAC ≥ 1000), the use of high-intensity statin therapy and, if necessary, guideline-based add-on LDL-C lowering therapies (ezetimibe, PCSK9-inhibitors) to achieve a ≥ 50% reduction in LDL-C and optimally an LDL-C < 70 mg/dL is recommended. In patients with a CAC score ≥ 100 at low risk of bleeding, the benefits of aspirin use may outweigh the risk of bleeding. Other applications of CAC scoring include risk estimation on non-contrast CT scans of the chest, risk prediction in younger patients (< 40 years of age), its value as a gatekeeper for the decision to perform nuclear stress testing, and to aid in risk stratification in patients presenting with low-risk chest pain. There is a correlation between extra-coronary calcification (e.g., breast arterial calcification, aortic calcification, and aortic valve calcification) and incident ASCVD events. However, its role in informing lipid management remains unclear. Identification of coronary calcium in selected patients is the single best non-invasive imaging modality to identify future ASCVD risk and inform lipid-lowering therapy decision-making.

Closing the Gaps in Care of Dyslipidemia: Revolutionizing Management with Digital Health and Innovative Care Models

Although great progress has been made in the diagnostic and treatment options for dyslipidemias, unawareness, underdiagnosis and undertreatment of these disorders remain a significant global health concern. Growth in digital applications and newer models of care provide novel tools to improve the management of chronic conditions such as dyslipidemia. In this review, we discuss the evolving landscape of lipid management in the 21st century, current treatment gaps and possible solutions through digital health and new models of care. Our discussion begins with the history and development of value-based care and the national establishment of quality metrics for various chronic conditions. These concepts on the level of healthcare policy not only inform reimbursements but also define the standard of care. Next, we consider the advances in atherosclerotic cardiovascular disease risk score calculators as well as evolving imaging modalities. The impact and growth of digital health, ranging from telehealth visits to online platforms and mobile applications, will also be explored. We then evaluate the ways in which machine learning and artificial intelligence-driven algorithms are being utilized to address gaps in lipid management. From an organizational perspective, we trace the redesign of medical practices to incorporate a multidisciplinary team model of care, recognizing that atherosclerotic cardiovascular disease risk is multifaceted and requires a comprehensive approach. Finally, we anticipate the future of dyslipidemia management, assessing the many ways in which atherosclerotic cardiovascular disease burden can be reduced on a population-wide scale.

Accuracy of incidental visual coronary artery calcium assessment compared with dedicated coronary artery calcium scoring

INTRODUCTION

CAC can be detected on routine chest computed tomography (CT) scans and may contribute to CVD risk estimation, but the accuracy of visual CAC scoring may be affected by the specialty of the interpreting radiologist and/or the use of contrast.

METHODS

The accuracy of visual CAC estimation on non-gated CT scans was evaluated at UT Southwestern Medical Center (UTSW) and Parkland Health and Hospital System (PHHS). All adults who underwent CAC scanning and a non-gated CT scan within 6 months were identified and the scores from the two CTs were compared overall and stratified by type of reader and whether contrast was used. Visual CAC categories of none, small, moderate, and large were compared to CAC ​= ​0, 1-99, 100-399, and ≥400, respectively.

RESULTS

From 2016 to 2021, 934 patients (mean age 60 ​± ​12 ​y, 43% male, 61% White, 34% Black, 24% Hispanic, 54% from PHHS) had both CT scans. Of these, 441 (47%) had no CAC, 278 (30%) small, 147 (16%) moderate, and 66 (7%) large CAC on non-gated CT. Visual CAC estimates were highly correlated with CAC scores (Kendalls tau-b ​= ​0.76, p ​< ​0.0001). Among those with no visual CAC, 76% had CAC ​= ​0 (72% of contrast-enhanced vs 85% of non-contrast scans, 88% of scans interpreted by CT radiologist vs 78% of those interpreted by other radiologist). In those with moderate-to-large visual CAC, 99% had CAC >0 and 88% had CAC ≥100, including 89% of those with contrast, 90% of those without contrast, 80% of those read by a CT radiologist, and 88% of those read by a non-CT radiologist.

DISCUSSION

Visual CAC estimates on non-gated CT scans are concordant with Agatston score categories from cardiac CT scans. A lack of visual CAC on non-gated CT scans may not be sufficient to "de-risk" patients, particularly for contrast-enhanced scans and those read by non-CT radiologists. However, the presence of moderate-to-large CAC, including on contrasted scans and regardless of radiologist type, is highly predictive of CAC and may be used to identify high-risk patients for prevention interventions.

Incidental Coronary Artery Calcium on Chest CT in Persons Without Known Atherosclerotic Cardiovascular Disease

This cross-sectional study examines the expected prevalence of coronary artery calcium (CAC) on chest computed tomography (CT) in people without clinical atherosclerotic cardiovascular disease (ASCVD) by age, sex, and race and ethnicity.

Existing Nongated CT Coronary Calcium Predicts Operative Risk in Patients Undergoing Noncardiac Surgeries (ENCORES)

BACKGROUND

Preoperative cardiovascular risk stratification before noncardiac surgery is a common clinical challenge. Coronary artery calcium scores from ECG-gated chest computed tomography (CT) imaging are associated with perioperative events. At the time of preoperative evaluation, many patients will not have had ECG-gated CT imaging, but will have had nongated chest CT studies performed for a variety of noncardiac indications. We evaluated relationships between coronary calcium severity estimated from previous nongated chest CT imaging and perioperative major clinical events (MCE) after noncardiac surgery.

METHODS

We retrospectively identified consecutive adults age ≥45 years who underwent in-hospital, major noncardiac surgery from 2016 to 2020 at a large academic health system composed of 4 acute care centers. All patients had nongated (contrast or noncontrast) chest CT imaging performed within 1 year before surgery. Coronary calcium in each vessel was retrospectively graded from absent to severe using a 0 to 3 scale (absent, mild, moderate, severe) by physicians blinded to clinical data. The estimated coronary calcium burden (ECCB) was computed as the sum of scores for each coronary artery (0 to 9 scale). A Revised Cardiac Risk Index was calculated for each patient. Perioperative MCE was defined as all-cause death or myocardial infarction within 30 days of surgery.

RESULTS

A total of 2554 patients (median age, 68 years; 49.7% women; median Revised Cardiac Risk Index, 1) were included. The median time interval from nongated chest CT imaging to noncardiac surgery was 15 days (interquartile range, 3-106 days). The median ECCB was 1 (interquartile range, 0-3). Perioperative MCE occurred in 136 (5.2%) patients. Higher ECCB values were associated with stepwise increases in perioperative MCE (0: 2.9%, 1-2: 3.7%, 3-5: 8.0%; 6-9: 12.6%, P<0.001). Addition of ECCB to a model with the Revised Cardiac Risk Index improved the C-statistic for MCE (from 0.675 to 0.712, P=0.018), with a net reclassification improvement of 0.428 (95% CI, 0.254-0.601, P<0.0001). An ECCB ≥3 was associated with 2-fold higher adjusted odds of MCE versus an ECCB <3 (adjusted odds ratio, 2.11 [95% CI, 1.42-3.12]).

CONCLUSIONS

Prevalence and severity of coronary calcium obtained from existing nongated chest CT imaging improve preoperative clinical risk stratification before noncardiac surgery.

An Opportunity to Seize From Low Hanging Fruits: Capitalising on Incidentally Reported Coronary Artery Calcification

AIM

We investigated the prevalence of incidental coronary artery calcifications (CAC) from non-electrocardiogram (ECG)-gated computed tomography (CT) chest (both contrast and non-contrast) for inpatients. We also assessed for downstream investigation and statin prescription from the inpatient teams. Incidental CAC are frequent findings on non-ECG-gated CT chest. It is associated with adverse prognosis in multiple patient cohorts.

METHOD

All non-ECG-gated CT chest done as inpatients from a single centre referred from 1 January 2022 to 31 December 2022 with reported incidental CAC were reviewed for inclusion. Patients who had a history of known coronary artery disease, history of coronary stent or bypass, and presence of cardiac devices were excluded.

RESULTS

Total of 123 patients were included, making the prevalence 6.2% (123/1,980). The median age is 76 years (interquartile range 69-85) and predominantly male at 54.5%. The majority of CT chest done were contrasted scans (91.1%). Only 26.8% of CAC were reported on severity with visual quantification, with 7.3% each reported for both moderate and severe CAC. Only 2.4% of CAC were reported in the conclusion of the CT report. Most of these patients were asymptomatic (34.1%). A total of 20.3% of patients had further tests done. Inpatient hospital mortality was 8.1%. About 23.6% and 34.1% of patients were on aspirin and statin therapy respectively at baseline. There was only 1 patient (1.2%) who was prescribed with new statin therapy on discharge.

CONCLUSION

Incidental CAC is prevalent in inpatient settings and remains under-recognised by ordering clinicians, with low prescription rate of statin therapy. Practice-changing measures to standardise reporting of incidental CAC is needed to identify patients with subclinical coronary disease and initiate preventive interventions.

Association of Coronary Artery Calcium Detected by Routine Ungated CT Imaging With Cardiovascular Outcomes

BACKGROUND

Coronary artery calcium (CAC) is a strong predictor of cardiovascular events across all racial and ethnic groups. CAC can be quantified on nonelectrocardiography (ECG)-gated computed tomography (CT) performed for other reasons, allowing for opportunistic screening for subclinical atherosclerosis.

OBJECTIVES

The authors investigated whether incidental CAC quantified on routine non-ECG-gated CTs using a deep-learning (DL) algorithm provided cardiovascular risk stratification beyond traditional risk prediction methods.

METHODS

Incidental CAC was quantified using a DL algorithm (DL-CAC) on non-ECG-gated chest CTs performed for routine care in all settings at a large academic medical center from 2014 to 2019. We measured the association between DL-CAC (0, 1-99, or ≥100) with all-cause death (primary outcome), and the secondary composite outcomes of death/myocardial infarction (MI)/stroke and death/MI/stroke/revascularization using Cox regression. We adjusted for age, sex, race, ethnicity, comorbidities, systolic blood pressure, lipid levels, smoking status, and antihypertensive use. Ten-year atherosclerotic cardiovascular disease risk was calculated using the pooled cohort equations.

RESULTS

Of 5,678 adults without ASCVD (51% women, 18% Asian, 13% Hispanic/Latinx), 52% had DL-CAC >0. Those with DL-CAC ≥100 had an average 10-year ASCVD risk of 24%; yet, only 26% were on statins. After adjustment, patients with DL-CAC ≥100 had increased risk of death (HR: 1.51; 95% CI: 1.28-1.79), death/MI/stroke (HR: 1.57; 95% CI: 1.33-1.84), and death/MI/stroke/revascularization (HR: 1.69; 95% CI: 1.45-1.98) compared with DL-CAC = 0.

CONCLUSIONS

Incidental CAC ≥100 was associated with an increased risk of all-cause death and adverse cardiovascular outcomes, beyond traditional risk factors. DL-CAC from routine non-ECG-gated CTs identifies patients at increased cardiovascular risk and holds promise as a tool for opportunistic screening to facilitate earlier intervention.

Artificial intelligence in clinical workflow processes in vascular surgery and beyond

In the past decade, artificial intelligence (AI)-based applications have exploded in health care. In cardiovascular disease, and vascular surgery specifically, AI tools such as machine learning, natural language processing, and deep neural networks have been applied to automatically detect underdiagnosed diseases, such as peripheral artery disease, abdominal aortic aneurysms, and atherosclerotic cardiovascular disease. In addition to disease detection and risk stratification, AI has been used to identify guideline-concordant statin therapy use and reasons for nonuse, which has important implications for population-based cardiovascular disease health. Although many studies highlight the potential applications of AI, few address true clinical workflow implementation of available AI-based tools. Specific examples, such as determination of optimal statin treatment based on individual patient risk factors and enhancement of intraoperative fluoroscopy and ultrasound imaging, demonstrate the potential promise of AI integration into clinical workflow. Many challenges to AI implementation in health care remain, including data interoperability, model bias and generalizability, prospective evaluation, privacy and security, and regulation. Multidisciplinary and multi-institutional collaboration, as well as adopting a framework for integration, will be critical for the successful implementation of AI tools into clinical practice.

Highlights of Cardiovascular Disease Prevention Studies Presented at the 2022 American Heart Association Scientific Sessions

PURPOSE OF REVIEW

Summarize selected late-breaking science on cardiovascular (CV) disease prevention presented at the 2022 scientific session of the American Heart Association (AHA).

RECENT FINDINGS

The PROMINENT trial compared pemafibrate to a placebo in patients with type 2 diabetes mellitus (DM) and mild-to-moderate hypertriglyceridemia and high-density lipoprotein cholesterol (HDL-C)<40 mg/dL who were already on guideline-directed statin therapy. The RESPECT-EPA trial compared purified eicosapentaenoic acid (EPA) and statin therapy to statin therapy alone for secondary prevention of atherosclerotic CV disease (ASCVD). SPORT compared the efficacy of low-dose statin therapy with a placebo and six commonly used dietary supplements on lipid and inflammatory markers. Data from long-term follow-up of the FOURIER-OLE study was presented to evaluate the efficacy of very low low-density lipoprotein cholesterol (LDL-C) levels with proprotein convertase subtilisin/kexin type 9 (PCSK9) inhibitors. Patient-level meta-analyses evaluated the association of statin therapy with new-onset DM and worse glycemic control. PROMPT-LIPID evaluated if automated electronic alerts to physicians with guideline-based recommendations improved the management of hyperlipidemia in patients at very high risk. NOTIFY-1 trial evaluated if notifying physicians and patients about coronary artery calcium (CAC) scores in non-ECG gated computed tomography scans led to increased prescription of statin therapy for primary ASCVD prevention. The DCP trial compared hydrochlorothiazide and chlorthalidone for blood pressure control and CV outcomes in hypertension. The CRHCP study compared the effectiveness of a village doctor for hypertension management and CV outcomes in rural areas of China. The QUARTET USA trial compared the effectiveness and safety of 4 antihypertensive medications in ultra-low doses with angiotensin-receptor blocker monotherapy. The late-breaking science presented at the 2022 scientific session of the AHA paves the way for future pragmatic trials and provides meaningful information to guide management strategies in cardiovascular disease prevention.