Leveraging clinical decision support tools to improve guideline-directed medical therapy in patients with atherosclerotic cardiovascular disease at hospital discharge

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.

An initiative to promote value-based stress test selection in primary care and cardiology clinics: A mixed methods evaluation

OBJECTIVES

Exercise stress echocardiograms (stress echos) are overused, whereas exercise stress electrocardiograms (stress ECGs) can be an appropriate, lower-cost substitute. In this post hoc, mixed methods evaluation, we assessed an initiative promoting value-based, guideline-concordant ordering practices in primary care (PC) and cardiology clinics.

METHODS

Change in percent of stress ECGs ordered of all exercise stress tests (stress ECGs and echos) was calculated between three periods: baseline (January 2019-February 2020); Period 1 with reduced stress ECG report turnaround time + PC-targeted education (began June 2020); and Period 2 with the addition of electronic health record-based alternative alert (AA) providing point-of-care clinical decision support. The AA was deployed in two of five PC clinics in July 2020, two additional PC clinics in January 2021, and one of four cardiology clinics in February 2021. Nineteen primary care providers (PCPs) and five cardiologists were interviewed in Period 2.

RESULTS

Clinicians reported reducing ECG report turnaround time was crucial for adoption. PCPs specifically reported that value-based education helped change their practice. In PC, the percent of stress ECGs ordered increased by 38% ± 6% (SE) (p < 0.0001) from baseline to Period 1. Most PCPs identified the AA as the most impactful initiative, yet stress ECG ordering did not change (6% ± 6%; p = 0.34) between Periods 1 and 2. In contrast, cardiologists reportedly relied on their expertise rather than AAs, yet their stress ECGs orders increased from Period 1 to 2 to a larger degree in the cardiology clinic with the AA (12% ± 5%; p = 0.01) than clinics without the AA (6% ± 2%; p = 0.01). The percent of stress ECGs ordered was higher in Period 2 than baseline for both specialties (both p < 0.0001).

CONCLUSIONS

This initiative influenced ordering behaviour in PC and cardiology clinics. However, clinicians' perceptions of the initiative varied between specialties and did not always align with the observed behaviour change.

Clinical Decision Support Tools in the Electronic Medical Record

The integration of clinical decision support (CDS) tools into electronic medical record (EMR) systems has become common. Although there are many benefits for both patients and providers from successful integration, barriers exist that prevent consistent and effective use of these tools. Such barriers include tool alert fatigue, lack of interoperability between tools and medical record systems, and poor acceptance of tools by care providers. However, successful integration of CDS tools into EMR systems have been reported; examples of these include the Statin Choice Decision Aid, and the Kidney Failure Risk Equation (KFRE). This article reviews the history of EMR systems and its integration with CDS tools, the barriers preventing successful integration, and the benefits reported from successful integration. This article also provides suggestions and strategies for improving successful integration, making these tools easier to use and more effective for care providers.

A Web-Based Application for Risk Stratification and Optimization in Patients With Cardiovascular Disease: Pilot Study

BACKGROUND

In addition to aspirin, angiotensin-converting enzyme inhibitors, statins, and lifestyle modification interventions, novel pharmacological agents have been shown to reduce morbidity and mortality in atherosclerotic cardiovascular disease patients, including new antithrombotics, antihyperglycemics, and lipid-modulating therapies. Despite their benefits, the uptake of these guideline-directed therapies remains a challenge. There is a need to develop strategies to support knowledge translation for the uptake of secondary prevention therapies.

OBJECTIVE

The goal of this study was to test the feasibility and usability of Stratification and Optimization in Patients With Cardiovascular Disease (STOP-CVD), a point-of-care application that was designed to facilitate knowledge translation by providing individualized risk stratification and optimization guidance.

METHODS

Using the REACH (Reduction of Atherothrombosis for Continued Health) Registry trial and predictive modeling (which included 67,888 patients), we designed a free web-based secondary risk calculator. Based on demographic and comorbidity profiles, the application was used to predict an individual's 20-month risk of cardiovascular events and cardiovascular mortality and provides a comparison to an age-matched control with an optimized cardiovascular risk profile to illustrate the modifiable residual risk. Additionally, the application used the patient's risk profile to provide specific guidance for possible therapeutic interventions based on a novel algorithm. During an initial 3-month adoption phase, 1-time invitations were sent through email and telephone to 240 physicians that refer to a regional cardiovascular clinic. After 3 months, a survey of user experience was sent to all users. Following this, no further marketing of the application was performed. Google Analytics was collected postimplementation from January 2021 to December 2021. These were used to tabulate the total number of distinct users and the total number of monthly uses of the application.

RESULTS

During the 1-year pilot, 47 of the 240 invited clinicians used the application 1573 times, an average of 131 times per month, with sustained usage over time. All 24 postimplementation survey respondents confirmed that the application was functional, easy to use, and useful.

CONCLUSIONS

This pilot suggests that the STOP-CVD application is feasible and usable, with high clinician satisfaction. This tool can be easily scaled to support the uptake of guideline-directed medical therapy, which could improve clinical outcomes. Future research will be focused on evaluating the impact of this tool on clinician management and patient outcomes.

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.

The effect of discharge care plans on statin prescription rates

BACKGROUND

Stroke discharge care bundles have been proposed to address inadequate provider statin prescription rates.

LOCAL PROBLEM

Discontinuation of statins has been associated with a 37% relative risk increase in mortality in patients with a stroke diagnosis. The project site had a statin prescription rate of 86.2%.

METHODS

The project was initiated at a 641-bed regional community teaching medical center. Statin prescription rates upon discharge on patients with the diagnosis of transient ischemic attack or stroke were evaluated and noted to be below the benchmark of 95%. Possible interventions to improve this benchmark were discussed with key stakeholders such as the information technology team, stroke care outcomes team, and neurology service providers. The proposed intervention was incorporated into the electronic health record. Provider prescription rates were tracked monthly along with the use of the proposed intervention. A one-sided z-test was used to analyze the data collected.

INTERVENTIONS

A stroke discharge power plan within an electronic health record was modified to increase the rate of statin prescriptions. The key modification included checking off the prescription of a statin on discharge. Reinforcement of its use was done through monthly reminders.

RESULTS

Use of discharge care plan yielded 100% compliance. Overall compliance was 9.7%. The null hypothesis of the one-sided z-test was 89%. The p-value for all tests was <0.05.

CONCLUSION

The use of a stroke discharge care plan within an electronic health record can positively affect secondary stroke prevention by increasing statin prescription rates.

Trends in guideline implementation: an updated scoping review

Background

Guidelines aim to support evidence-informed practice but are inconsistently used without implementation strategies. Our prior scoping review revealed that guideline implementation interventions were not selected and tailored based on processes known to enhance guideline uptake and impact. The purpose of this study was to update the prior scoping review.

Methods

We searched MEDLINE, EMBASE, AMED, CINAHL, Scopus, and the Cochrane Database of Systematic Reviews for studies published from 2014 to January 2021 that evaluated guideline implementation interventions. We screened studies in triplicate and extracted data in duplicate. We reported study and intervention characteristics and studies that achieved impact with summary statistics.

Results

We included 118 studies that implemented guidelines on 16 clinical topics. With regard to implementation planning, 21% of studies referred to theories or frameworks, 50% pre-identified implementation barriers, and 36% engaged stakeholders in selecting or tailoring interventions. Studies that employed frameworks (n=25) most often used the theoretical domains framework (28%) or social cognitive theory (28%). Those that pre-identified barriers (n=59) most often consulted literature (60%). Those that engaged stakeholders (n=42) most often consulted healthcare professionals (79%). Common interventions included educating professionals about guidelines (44%) and information systems/technology (41%). Most studies employed multi-faceted interventions (75%). A total of 97 (82%) studies achieved impact (improvements in one or more reported outcomes) including 10 (40% of 25) studies that employed frameworks, 28 (47.45% of 59) studies that pre-identified barriers, 22 (52.38% of 42) studies that engaged stakeholders, and 21 (70% of 30) studies that employed single interventions.

Conclusions

Compared to our prior review, this review found that more studies used processes to select and tailor interventions, and a wider array of types of interventions across the Mazza taxonomy. Given that most studies achieved impact, this might reinforce the need for implementation planning. However, even studies that did not plan implementation achieved impact. Similarly, even single interventions achieved impact. Thus, a future systematic review based on this data is warranted to establish if the use of frameworks, barrier identification, stakeholder engagement, and multi-faceted interventions are associated with impact.

Trial registration

The protocol was registered with Open Science Framework (https://osf.io/4nxpr) and published in JBI Evidence Synthesis.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13012-022-01223-6.

Sociotechnical Intervention for Improved Delivery of Preventive Cardiovascular Care to Rural Communities: Participatory Design Approach

Background

Clinical practice guidelines recommend antiplatelet and statin therapies as well as blood pressure control and tobacco cessation for secondary prevention in patients with established atherosclerotic cardiovascular diseases (ASCVDs). However, these strategies for risk modification are underused, especially in rural communities. Moreover, resources to support the delivery of preventive care to rural patients are fewer than those for their urban counterparts. Transformative interventions for the delivery of tailored preventive cardiovascular care to rural patients are needed.

Objective

A multidisciplinary team developed a rural-specific, team-based model of care intervention assisted by clinical decision support (CDS) technology using participatory design in a sociotechnical conceptual framework. The model of care intervention included redesigned workflows and a novel CDS technology for the coordination and delivery of guideline recommendations by primary care teams in a rural clinic.

Methods

The design of the model of care intervention comprised 3 phases: problem identification, experimentation, and testing. Input from team members (n=35) required 150 hours, including observations of clinical encounters, provider workshops, and interviews with patients and health care professionals. The intervention was prototyped, iteratively refined, and tested with user feedback. In a 3-month pilot trial, 369 patients with ASCVDs were randomized into the control or intervention arm.

Results

New workflows and a novel CDS tool were created to identify patients with ASCVDs who had gaps in preventive care and assign the right care team member for delivery of tailored recommendations. During the pilot, the intervention prototype was iteratively refined and tested. The pilot demonstrated feasibility for successful implementation of the sociotechnical intervention as the proportion of patients who had encounters with advanced practice providers (nurse practitioners and physician assistants), pharmacists, or tobacco cessation coaches for the delivery of guideline recommendations in the intervention arm was greater than that in the control arm.

Conclusions

Participatory design and a sociotechnical conceptual framework enabled the development of a rural-specific, team-based model of care intervention assisted by CDS technology for the transformation of preventive health care delivery for ASCVDs.