Barriers to Implementing Cardiovascular Risk Calculation in Primary Care: Alignment With the Consolidated Framework for Implementation Research

Atherosclerotic cardiovascular disease risk among Ghanaians: A comparison of the risk assessment tools

Objectives

Risk stratification is a cornerstone for preventing atherosclerotic cardiovascular disease (ASCVD). Ghana has yet to develop a locally derived and validated ASCVD risk model. A critical first step towards this goal is assessing how the commonly available risk models perform in the Ghanaian population. This study compares the agreement and correlation between four ASCVD risk assessment models commonly used in Ghana.

Methods

The Ghana Heart Study collected data from four regions in Ghana (Ashanti, Greater Accra, Northern, and Central regions) and excluded people with a self-declared history of ASCVD. The 10-year fatal/non-fatal ASCVD risk of participants aged 40-74 was calculated using mobile-based apps for Pooled Cohort Equation (PCE), laboratory-based WHO/ISH CVD risk, laboratory-based Framingham risk (FRS), and Globorisk, categorizing them as low, intermediate, or high risk. The risk categories were compared using the Kappa statistic and Spearman correlation.

Results

A total of 615 participants were included in this analysis (median age 55 [Inter quartile range 46, 64]) years with 365 (59.3 %) females. The WHO/ISH risk score categorized 504 (82.0 %), 58 (9.4 %), and 53 (8.6 %) as low-, intermediate-, and high-risk, respectively. The PCE categorized 345 (56.1 %), 181 (29.4 %), and 89 (14.5 %) as low-, intermediate- and high-risk, respectively. The Globorisk categorized 236 (38.4 %), 273 (44.4 %), and 106 (17.2 %) as low-, intermediate-, and high-risk, respectively. Significant differences in the risk categorization by region of residence and age group were noted. There was substantial agreement between the PCE vs FRS (Kappa = 0.8, 95 % CI 0.7 - 0.8), PCE vs Globorisk (Kappa = 0.6; 95 % CI 0.6 - 0.7), and FRS vs Globorisk (Kappa = 0.6; 95 % CI 0.6 - 0.7). However, there was only fair agreement between the WHO vs Globorisk (Kappa = 0.3; 95 % CI 0.3-0.4) and moderate agreement between the WHO vs PCE and WHO vs FRS.

Conclusion

There are significant differences in the ASCVD risk prediction tools in the Ghanaian population, posing a threat to primary prevention. Therefore, there is a need for locally derived and validated tools.

Validation of the Novel Web-Based Application HUMTELEMED for a Comprehensive Assessment of Cardiovascular Risk Based on the 2021 European Society of Cardiology Guidelines

Background and aims: SCORE2/SCORE2-OP cardiovascular risk (CVR) charts and online calculators do not apply to patients with comorbidities, target organ damage, or atherosclerotic cardiovascular disease, for whom the assessment relies on the conventional consultation of the 2021 ESC guidelines (qualitative approach). To simplify the CVR evaluation, we developed an integrated multi-language and free-to-use web application. This study assessed the agreement between the conventional method versus our web app. Methods: A cross-sectional study was carried out on 1306 consecutive patients aged 40+ years referred to our center for the diagnosis and management of hypertension and dyslipidemia. Two double-blind operators performed the CVR assessment and classified each patient into low-moderate-, high-, and very-high-risk categories by using the conventional method (SCORE2/SCORE2-OP charts and consultation of the 2021 ESC guidelines) and the web app. The Kappa statistics were used to compare the two methods. Results: The mean age was 60.3 ± 11.9 years, with male prevalence (51.4%). Patients in primary prevention were 77.0%. According to the SCORE2/SCORE2-OP charts and 2021 ESC guideline consultation, the CVR was low-moderate in 18.6% (n° 243), high in 36.8% (n° 480), and very high in 44.6% (n° 583). According to the web app, individual CVR was low-moderate in 19.5% (n° 255), high in 35.4% (n° 462), and very high in 45.1% (n° 589). The two methods strongly agreed (Kappa = 0.960, p < 0.001), with a 97.5% concordance. Conclusions: our application has excellent reliability in a broad "real life" population and may help non-expert users and busy clinicians to assess individual CVR appropriately, representing a free-to-use, simple, time-sparing and widely available alternative to the conventional CVR evaluation using SCORE2/SCORE2-OP and 2021 ESC guideline charts.

FRAX predicts cardiovascular risk in women undergoing osteoporosis screening: the Manitoba bone mineral density registry

Osteoporosis and cardiovascular disease (CVD) are highly prevalent in older women, with increasing evidence for shared risk factors and pathogenesis. Although FRAX was developed for the assessment of fracture risk, we hypothesized that it might also provide information on CVD risk. To test the ability of the FRAX tool and FRAX-defined risk factors to predict incident CVD in women undergoing osteoporosis screening with DXA, we performed a retrospective prognostic cohort study which included women aged 50 yr or older with a baseline DXA scan in the Manitoba Bone Mineral Density Registry between March 31, 1999 and March 31, 2018. FRAX scores for major osteoporotic fracture (MOF) were calculated on all participants. Incident MOF and major adverse CV events (MACE; hospitalized acute myocardial infarction [AMI], hospitalized non-hemorrhagic cerebrovascular disease [CVA], or all-cause death) were ascertained from linkage to population-based healthcare data. The study population comprised 59 696 women (mean age 65.7 ± 9.4 yr). Over mean 8.7 yr of observation, 6021 (10.1%) had MOF, 12 277 women (20.6%) had MACE, 2274 (3.8%) had AMI, 2061 (3.5%) had CVA, and 10 253 (17.2%) died. MACE rates per 1000 person-years by FRAX risk categories low (10-yr predicted MOF <10%), moderate (10%-19.9%) and high (≥20%) were 13.5, 34.0, and 64.6, respectively. Although weaker than the association with incident MOF, increasing FRAX quintile was associated with increasing risk for MACE (all P-trend <.001), even after excluding prior CVD and adjusting for age. HR for MACE per SD increase in FRAX was 1.99 (95%CI, 1.96-2.02). All FRAX-defined risk factors (except parental hip fracture and lower BMI) were independently associated with higher non-death CV events. Although FRAX is intended for fracture risk prediction, it has predictive value for cardiovascular risk.

Deep learning-based fundus image analysis for cardiovascular disease: a review

It is well established that the retina provides insights beyond the eye. Through observation of retinal microvascular changes, studies have shown that the retina contains information related to cardiovascular disease. Despite the tremendous efforts toward reducing the effects of cardiovascular diseases, they remain a global challenge and a significant public health concern. Conventionally, predicting the risk of cardiovascular disease involves the assessment of preclinical features, risk factors, or biomarkers. However, they are associated with cost implications, and tests to acquire predictive parameters are invasive. Artificial intelligence systems, particularly deep learning (DL) methods applied to fundus images have been generating significant interest as an adjunct assessment tool with the potential of enhancing efforts to prevent cardiovascular disease mortality. Risk factors such as age, gender, smoking status, hypertension, and diabetes can be predicted from fundus images using DL applications with comparable performance to human beings. A clinical change to incorporate DL systems for the analysis of fundus images as an equally good test over more expensive and invasive procedures may require conducting prospective clinical trials to mitigate all the possible ethical challenges and medicolegal implications. This review presents current evidence regarding the use of DL applications on fundus images to predict cardiovascular disease.

Adoption of shared decision-making and clinical decision support for reducing cardiovascular disease risk in community health centers

Objective

Electronic health record (EHR)-based shared decision-making (SDM) and clinical decision support (CDS) systems can improve cardiovascular disease (CVD) care quality and risk factor management. Use of the CV Wizard system showed a beneficial effect on high-risk community health center (CHC) patients' CVD risk within an effectiveness trial, but system adoption was low overall. We assessed which multi-level characteristics were associated with system use.

Materials and Methods

Analyses included 80 195 encounters with 17 931 patients with high CVD risk and/or uncontrolled risk factors at 42 clinics in September 2018-March 2020. Data came from the CV Wizard repository and EHR data, and a survey of 44 clinic providers. Adjusted, mixed-effects multivariate Poisson regression analyses assessed factors associated with system use. We included clinic- and provider-level clustering as random effects to account for nested data.

Results

Likelihood of system use was significantly higher in encounters with patients with higher CVD risk and at longer encounters, and lower when providers were >10 minutes behind schedule, among other factors. Survey participants reported generally high satisfaction with the system but were less likely to use it when there were time constraints or when rooming staff did not print the system output for the provider.

Discussion

CHC providers prioritize using this system for patients with the greatest CVD risk, when time permits, and when rooming staff make the information readily available. CHCs' financial constraints create substantial challenges to addressing barriers to improved system use, with health equity implications.

Conclusion

Research is needed on improving SDM and CDS adoption in CHCs.

Trial Registration

ClinicalTrials.gov, NCT03001713, https://clinicaltrials.gov/.

Tailoring Implementation Strategies for Cardiovascular Disease Risk Calculator Adoption in Primary Care Clinics

INTRODUCTION

When implementing interventions in primary care, tailoring implementation strategies to practice barriers can be effective, but additional work is needed to understand how to best select these strategies. This study sought to identify clinicians' contributions to the process of tailoring implementation strategies to barriers in clinical settings.

METHODS

We conducted a modified nominal group exercise involving 8 implementation scientists and 26 primary care clinicians in the WWAMI region Practice and Research Network. Each group identified implementation strategies it felt would best address barriers to using a cardiovascular disease (CVD) risk calculator previously identified across 44 primary care clinics from the Healthy Hearts Northwest pragmatic trial (2015 to 2018). These barriers had been mapped beforehand to the Consolidated Framework for Implementation Research (CFIR) domains. We examined similarities and differences in the strategies that 30% or more of each group identified (agreed-on strategies) for each barrier and for barriers in each CFIR domain. We used the results to demonstrate how strategies might be tailored to individual clinics.

RESULTS

Clinicians selected 23 implementation strategies to address 1 or more of the 13 barriers; implementation scientists selected 35. The 2 groups agreed on at least 1 strategy for barriers in each CFIR domain: Inner Setting, Outer Setting, Intervention Characteristics, Characteristics of Individuals, and Process. Conducting local needs assessment and assessing for readiness/identifying barriers and facilitators were the 2 most common implementation strategies chosen only by clinicians.

CONCLUSIONS

Clinician stakeholders identified implementation strategies that augmented those chosen by implementation scientists, suggesting that codesign of implementation processes between implementation scientists and clinicians may strengthen the process of tailoring strategies to overcome implementation barriers.

Translating guidelines into practice via implementation science: an update in lipidology

PURPOSE OF REVIEW

Guidelines provide recommendations for clinicians based on the best available evidence and informed by clinical expertise. These recommendations often fail to be utilized by clinicians hindering the translation of evidence into practice. The purpose of this review is to describe novel ways in which implementation science has been used to improve translation of guidelines into clinical practice in the field of lipidology.

RECENT FINDINGS

We searched PubMed for articles related to guideline implementation in lipidology published in 2021 and 2022. Identified articles were categorized into three domains: first, poor uptake of guideline recommendations in practice; second, implementation science as a solution to improve care; and third, examples of how implementation science can be incorporated into guidelines.

SUMMARY

The field of lipidology has identified that many guideline recommendations fail to be translated into practice and has started to utilize methods from implementation science to assess ways to shrink this gap. Future work should focus on deploying tools from implementation science to address current gaps in guideline development. Such as, developing a systematic approach to restructure guideline recommendations so they are implementable in practice and aid in clinicians' ability to easily translate them into practice.

Melanoma risk assessment and management: a qualitative study among Australian general practitioners

Background

Preventive guidelines for melanoma recommend that patients at high risk of melanoma receive targeted screening; however, this requires careful selection of those at high risk. To the authors’ knowledge, there has been no previous research into how all physicians approach the selection and management of high-risk individuals. Melanoma risk-prediction models are available to assist in the identification of high-risk patients but are not routinely used clinically.

Aim

To examine how GPs assessed and managed melanoma risk, and the opportunities for using melanoma risk-prediction models in primary care.

Design and setting

Semi-structured telephone interviews were conducted with 20 Australian GPs.

Method

GPs who had completed a cross-sectional online questionnaire study on melanoma risk were purposively sampled and recruited. Semi-structured telephone interviews were conducted with Australian GPs between 9 July and 10 September 2019. Interviews were audiorecorded, professionally transcribed, and analysed using grounded theory.

Results

Melanoma risk assessment and its management can be understood as a linear workflow consisting of five clinical process domains with patient selection as the entry point. There was variation between GPs on the identification of melanoma risk factors, melanoma risk estimation, management, and patient education because of intuitive and analytical processes guiding risk assessment, and the influence of patient factors. GPs were largely receptive towards melanoma risk-prediction models, sharing facilitators for and barriers to their potential implementation.

Conclusion

Further primary care interventions sensitive to existing workflow arrangements may be required to standardise melanoma risk-assessment and management processes.

[The HEARTS app: a clinical tool for cardiovascular risk and hypertension management in primary health careO aplicativo HEARTS: uma ferramenta clínica para o gerenciamento de risco cardiovascular e hipertensão na atenção primária à saúde]

HEARTS en las Américas es la adaptación regional de la iniciativa Global HEARTS de la Organización Mundial de la Salud, que será el modelo para el manejo del riesgo de las enfermedades cardiovasculares (ECV) en la atención primaria de salud en la Región de las Américas para el año 2025. Ya se ha implementado en 21 países y 1045 centros de atención primaria de salud en toda América Latina y el Caribe. Se ha adoptado un enfoque de salud pública y de sistemas de salud para introducir sistemáticamente intervenciones simplificadas en el nivel de la atención primaria de salud que se centran en el control de la hipertensión como punto de entrada clínico. En este artículo se presenta una aplicación nueva y mejorada cuyo componente principal es la calculadora de riesgo de ECV y de manejo de la hipertensión. Se resume el enfoque de evaluación del riesgo y la metodología utilizada por la Organización Mundial de la Salud para actualizar sus tablas de riesgo cardiovascular del 2019; se describe la aplicación, su uso, su funcionalidad y su proceso de validación; y se presenta un conjunto de recomendaciones prácticas para optimizar el manejo del riesgo de ECV y de la hipertensión, mediante el uso de la aplicación en la práctica clínica. La aplicación HEARTS es una herramienta sólida para mejorar la calidad de la atención prestada en los centros de atención primaria. La creación y difusión de la aplicación HEARTS es un paso esencial en el camino hacia la eliminación de las ECV prevenibles en la Región de las Américas.

The HEARTS app: a clinical tool for cardiovascular risk and hypertension management in primary health care

HEARTS in the Americas is the regional adaptation of the World Health Organization’s Global Hearts Initiative, which will be the model for risk management for cardiovascular disease (CVD) in primary health care in the Region of the Americas by 2025. It has already been implemented in 21 countries and 1045 primary health care centers throughout Latin America and the Caribbean. It takes a public health and health systems approach to systematically introduce simplified interventions at the primary health care level and focuses on hypertension as a clinical entry point. This paper introduces a new, improved application (app), the main component of which is the calculator for CVD risk and hypertension management. The paper summarizes the risk assessment approach and the methodology used by the World Health Organization to update its cardiovascular risk charts in 2019; describes the app, its use, functionality and validation process; and provides a set of practical recommendations for optimizing CVD risk and hypertension management by using the app in clinical practice. The HEARTS app is a powerful tool to improve the quality of care provided in primary health settings. The creation and dissemination of the HEARTS app is an essential step in the journey towards eliminating preventable CVD in the Americas.

"Patients would probably be more compliant to therapy if encouraged by those around them": a qualitative study exploring primary care physicians' perceptions on barriers to CVD risk management

Background

Public health prevention strategies have been developed to overcome the disease burden. Despite all the available resources, there have been several challenges in keeping up with the practices in Cardiovascular Diseases (CVD) risk management. The aim of this study is to explore Primary Care Physicians’ (PCP) perceptions on barriers to CVD risk management and strategies to overcome these barriers in Suva, Fiji.

Methods

This is a qualitative study conducted in the Suva Medical area among 7 health centers from August 1st to September 30th, 2021. Purposive sampling was used to recruit participants who provided in-depth and detailed information. From those physicians who worked in the Suva medical area as Primary Health Care (PHC) physicians, those who had at least 6 months experience and those who had some Special Outpatients Department (SOPD) exposure, in-depth interview was conducted using semi-structured questionnaire over the telephone and recorded in a mobile app. The interview content was then transcribed and thematic analysis was done.

Results

This study included 25 PHC physicians. From the thematic analysis, 2 major themes were developed including perceived barriers to CVD risk management and some of the strategies to overcome these barriers. Some of the barriers identified include patient factors such as non-compliance, physician factors such as time-constraints and lack of training, and health system factors such as poor medical records system and lack of basic resource. The strategic support systems include patient expectations, physician’s encouragement, utilization of resources, laws and legislations and continuing evidence-based medicine.

Conclusions

Physicians’ perceptions on the barriers and the strategies to overcome those barriers in CVD risk management plays an important role. The barriers include those of physician factors, patient factors and the health system as a whole. It is suggested to encourage shared-decision making in CVD management, enhance physician support and reinforce policies and research to bring about positive change and improvements in the quality of care.