Systematic Review of Validity Assessments of Framingham Risk Score Results in Health Economic Modelling of Lipid-Modifying Therapies in Europe

Accuracy analysis of cholesterol analyzer in detecting dyslipidemia in truck drivers

Cardiovascular diseases (CVD) are a leading cause of mortality and morbidity worldwide. Rapid diagnostic tools are crucial for timely intervention, especially in high-risk groups such as truck drivers. In Brazil, the Mission® test uniquely offers test strips for simultaneous measurement of total cholesterol (TC), high-density lipoprotein (HDL), triglycerides (TG), and low-density lipoprotein (LDL). This study evaluates the accuracy of the Mission® analyzer compared to laboratory testing for HDL-C, TG, and TC in truck drivers. A blinded cross-sectional study was conducted among truck drivers aged 30-64 in Campo Grande, Mato Grosso do Sul, Brazil. Spearman correlation, linear regression, and the Bland-Altman analyses were employed to compare lipid profile results between the Mission® analyzer and laboratory methods. A total of 108 samples were analyzed. For HDL, the Mission® analyzer showed a sensitivity of 0.88, a specificity of 0.67, and an area under the curve (AUC) of 0.77 (95% CI: 0.68-0.86). For TG, sensitivity and specificity were 0.96 and 0.98, respectively, with an AUC of 0.97 (95% CI: 0.93-1.0). For TC, the AUC was 0.87 (95% CI: 0.79-0.95). Bland-Altman analysis revealed biases of -4.5 for HDL, 12.4 for TC, and -42.8 for TG between Mission® and laboratory results. The Mission® analyzer demonstrates good accuracy for rapid dyslipidemia diagnosis and Framingham Global Risk Score calculation. It is a valuable tool for initial screening and risk assessment, confirmation with laboratory testing is recommended for definitive diagnosis and treatment planning.

Cardiovascular disease risk assessment in patients with rheumatoid arthritis: A scoping review

Patients with rheumatoid arthritis (RA) have an increased risk of developing cardiovascular disease (CVD). Identification of at-risk patients is paramount to initiate preventive care and tailor treatments accordingly. Despite international guidelines recommending all patients with RA undergo CVD risk assessment, rates remain suboptimal. The objectives of this review were to map the strategies used to conduct CVD risk assessments in patients with RA in routine care, determine who delivers CVD risk assessments, and identify what composite measures are used. The Joanna Briggs Institute methodological guidelines were used. A literature search was conducted in electronic and grey literature databases, trial registries, medical clearing houses, and professional rheumatology organisations. Findings were synthesised narratively. A total of 12 studies were included. Strategies reported in this review used various system-based interventions to support delivery of CVD risk assessments in patients with RA, operationalised in different ways, adopting two approaches: (a) multidisciplinary collaboration, and (b) education. Various composite measures were cited in use, with and without adjustment for RA. Results from this review demonstrate that although several strategies to support CVD risk assessments in patients with RA are cited in the literature, there is limited evidence to suggest a standardised model has been applied to routine care. Furthermore, extensive evidence to map how health care professionals conduct CVD risk assessments in practice is lacking. Research needs to be undertaken to establish the extent to which healthcare professionals are CVD risk assessing their patients with RA in routine care. Key Points • A limited number of system-based interventions are in use to support the delivery of CVD risk assessments in patients with RA. • Multidisciplinary team collaboration, and education are used to operationalise interventions to support Health Care Professionals in conducting CVD risk assessments in practice. • The extent to which Health Care Professionals are CVD risk assessing their patients with RA needs to be established.

Public perceptions on the application of artificial intelligence in healthcare: a qualitative meta-synthesis

OBJECTIVES

Medical artificial intelligence (AI) has been used widely applied in clinical field due to its convenience and innovation. However, several policy and regulatory issues such as credibility, sharing of responsibility and ethics have raised concerns in the use of AI. It is therefore necessary to understand the general public's views on medical AI. Here, a meta-synthesis was conducted to analyse and summarise the public's understanding of the application of AI in the healthcare field, to provide recommendations for future use and management of AI in medical practice.

DESIGN

This was a meta-synthesis of qualitative studies.

METHOD

A search was performed on the following databases to identify studies published in English and Chinese: MEDLINE, CINAHL, Web of science, Cochrane library, Embase, PsycINFO, CNKI, Wanfang and VIP. The search was conducted from database inception to 25 December 2021. The meta-aggregation approach of JBI was used to summarise findings from qualitative studies, focusing on the public's perception of the application of AI in healthcare.

RESULTS

Of the 5128 studies screened, 12 met the inclusion criteria, hence were incorporated into analysis. Three synthesised findings were used as the basis of our conclusions, including advantages of medical AI from the public's perspective, ethical and legal concerns about medical AI from the public's perspective, and public suggestions on the application of AI in medical field.

CONCLUSION

Results showed that the public acknowledges the unique advantages and convenience of medical AI. Meanwhile, several concerns about the application of medical AI were observed, most of which involve ethical and legal issues. The standard application and reasonable supervision of medical AI is key to ensuring its effective utilisation. Based on the public's perspective, this analysis provides insights and suggestions for health managers on how to implement and apply medical AI smoothly, while ensuring safety in healthcare practice.

PROSPERO REGISTRATION NUMBER

CRD42022315033.

Performance of the pooled cohort equation in South Asians: insights from a large integrated healthcare delivery system

South Asian ethnicity is associated with increased atherosclerotic cardiovascular disease (ASCVD) risk and has been identified as a "risk enhancer" in the 2018 American College of Cardiology/American Heart Association Guidelines. Risk estimation and statin eligibility in South Asians is not well understood; we studied the accuracy of 10-years ASCVD risk prediction by the pooled cohort equation (PCE), based on statin use, in a South Asian cohort. This is a retrospective cohort study of Kaiser Permanente Northern California South Asian members without existing ASCVD, age range 30-70, and 10-years follow up. ASCVD events were defined as myocardial infarction, ischemic stroke, and cardiovascular death. The cohort was stratified by statin use during the study period: never; at baseline and during follow-up; and only during follow-up. Predicted probability of ASCVD, using the PCE was calculated and compared to observed ASCVD events for low < 5.0%, borderline 5.0 to < 7.5%, intermediate 7.5 to < 20.0%, and high ≥ 20.0% risk groups. A total of 1835 South Asian members were included: 773 never on statin, 374 on statins at baseline and follow-up, and 688 on statins during follow-up only. ASCVD risk was underestimated by the PCE in low-risk groups: entire cohort: 1.8 versus 4.9%, p < 0.0001; on statin at baseline and follow-up: 2.58 versus 8.43%, p < 0.0001; on statin during follow-up only: 2.18 versus 7.77%, p < 0.0001; and never on statin: 1.37 versus 2.09%, p = 0.12. In this South Asian cohort, the PCE underestimated risk in South Asians, regardless of statin use, in the low risk ASCVD risk category.

Metabolic age correlates better than chronological age with waist-to-height ratio, a cardiovascular risk index

OBJECTIVES

Chronological age confers an increased risk for cardiovascular disease; however, chronological age does not reflect the subject's current health status. Therefore, we assessed whether Metabolic age (Met-age), based on free fat mass, is a predictor of cardiovascular risk (CVR).

METHODS

Subjects attending either IMSS UMF-2 or CUSC-1 were asked to participate. CVR was assessed using the waist-to-height ratio (WHtR), whereas Met-age was determined using the TANITA bio-analyser (model: BC-545F Fitscan). The strengthen of association was determined by calculating Pearson's r and predictability was determined by the area-under-a-receiver-operating characteristic curve (AUC).

RESULTS

284 subjects participated in this study, of which 61.6% had increased CVR. As expected, the chronological age was significantly higher in the CVR(+) group than the CVR(-) group (47.3±14.4 v. 35.2±12.7, respectively, p<.001) as well as Met-age (59.3±15.5 v. 34.3±14.3, respectively, p<.001). There was a strong association between WHtR and Met-age (r=.720, p<.001) and a moderate association for chronological age (r=.407 p<.001); however, the correlation between WHtR and Met-age was significantly better than chronological age (Z=-5.91, p<.01). Met-age was a good predictor of CVR (AUC=.88, 95%CI: .83-.92, p<.001), whereas chronological age was a fair predictor (AUC=.72, 95%CI: .66-.78, p<.001). However, Met-age showed a higher discriminatory capacity for CVR than chronological age (z=-4.597, p<.001).

CONCLUSIONS

Here, we determined that Met-age correlated with a CVR index, WHtR, and was able to predict subjects with increased CVR better than chronological age.

Prediction of coronary heart disease in rural Chinese adults: a cross sectional study

Background

Coronary heart disease (CHD) is a common cardiovascular disease with high morbidity and mortality in China. The CHD risk prediction model has a great value in early prevention and diagnosis.

Methods

In this study, CHD risk prediction models among rural residents in Xinxiang County were constructed using Random Forest (RF), Support Vector Machine (SVM), and the least absolute shrinkage and selection operator (LASSO) regression algorithms with identified 16 influencing factors.

Results

Results demonstrated that the CHD model using the RF classifier performed best both on the training set and test set, with the highest area under the curve (AUC = 1 and 0.9711), accuracy (one and 0.9389), sensitivity (one and 0.8725), specificity (one and 0.9771), precision (one and 0.9563), F1-score (one and 0.9125), and Matthews correlation coefficient (MCC = one and 0.8678), followed by the SVM (AUC = 0.9860 and 0.9589) and the LASSO classifier (AUC = 0.9733 and 0.9587). Besides, the RF model also had an increase in the net reclassification index (NRI) and integrated discrimination improvement (IDI) values, and achieved a greater net benefit in the decision curve analysis (DCA) compared with the SVM and LASSO models.

Conclusion

The CHD risk prediction model constructed by the RF algorithm in this study is conducive to the early diagnosis of CHD in rural residents of Xinxiang County, Henan Province.

Legal and Regulatory Framework for AI Solutions in Healthcare in EU, US, China, and Russia: New Scenarios after a Pandemic

The COVID-19 crisis has exposed some of the most pressing challenges affecting healthcare and highlighted the benefits that robust integration of digital and AI technologies in the healthcare setting may bring. Although medical solutions based on AI are growing rapidly, regulatory issues and policy initiatives including ownership and control of data, data sharing, privacy protection, telemedicine, and accountability need to be carefully and continually addressed as AI research requires robust and ethical guidelines, demanding an update of the legal and regulatory framework all over the world. Several recently proposed regulatory frameworks provide a solid foundation but do not address a number of issues that may prevent algorithms from being fully trusted. A global effort is needed for an open, mature conversation about the best possible way to guard against and mitigate possible harms to realize the potential of AI across health systems in a respectful and ethical way. This conversation must include national and international policymakers, physicians, digital health and machine learning leaders from industry and academia. If this is done properly and in a timely fashion, the potential of AI in healthcare will be realized.

Risk of cardiovascular disease according to the Framingham score in patients with high blood pressure from Píllaro, Ecuador. 2017-2018

Introduction: Cardiovascular disease (CVD) is the main cause of morbidity and mortality worldwide. The use of the Framingham Risk Score is of great importance for predicting CVD risk. Objective: To estimate the 10-year CVD risk in adult patients diagnosed high blood pressure (HBP) who visited the outpatient service of the San Miguelito de Píllaro Health Center, in Tungurahua, Ecuador, using the Framingham Risk Score (2008). Materials and methods: Cross-sectional, observational, prospective and descriptive study conducted in 120 HBP patients aged 30 to 74 years who visited the outpatient service between January and October 2017. Data were obtained from the review of medical records, which were in turn updated during the execution of the study. The Framingham risk score was used to calculate the 10-year CVD risk. A descriptive analysis of the data was performed in Epi Info 7, using absolute frequencies and percentages. Results: Of the 120 patients, 59.17% were women. Furthermore, 15% of the participants had been diagnosed with type 2 diabetes mellitus, 13.33% had a history of smoking, 47.50% had elevated systolic blood pressure, and 39.17% had hypercholesterolemia. CVD risk was low (≤ 1% Framingham score), intermediate (10-19%), and high (≥ 20%) in 15%, 29.16%, and 59.16% of participants, respectively. None of them had a very low CVD risk (≤1%). Conclusion: The Framingham risk score was useful to estimate CVD risk in the study population treated in the primary health care setting. Consequently, more extensive use of this instrument in different health units is recommended to obtain better estimates of CVD risk and, as a result, achieve the implementation of health prevention and health care actions that improve the prognosis in the medium and long term, and thus the quality of life of these patients.

Artificial intelligence in oncology: From bench to clinic

In the past few years, Artificial Intelligence (AI) techniques have been applied to almost every facet of oncology, from basic research to drug development and clinical care. In the clinical arena where AI has perhaps received the most attention, AI is showing promise in enhancing and automating image-based diagnostic approaches in fields such as radiology and pathology. Robust AI applications, which retain high performance and reproducibility over multiple datasets, extend from predicting indications for drug development to improving clinical decision support using electronic health record data. In this article, we review some of these advances. We also introduce common concepts and fundamentals of AI and its various uses, along with its caveats, to provide an overview of the opportunities and challenges in the field of oncology. Leveraging AI techniques productively to provide better care throughout a patient's medical journey can fuel the predictive promise of precision medicine.

Cardiovascular Risk Scores in Women Undergoing Stress Myocardial Perfusion Scan and Comparison with Scan-Predicted Risk

Background:

Death due to cardiovascular disease is a major concern in the field of noncommunicable disease. Assessment of cardiovascular risk score using Framingham score and WHO/ISH score is a noninvasive, easier method of predicting the adverse cardiovascular event in the general population.

Aims and Objectives:

The aim of the study was to assess the cardiovascular risk using Framingham score and WHO/ISH in women undergoing stress myocardial perfusion imaging (MPI) and comparison with scan-predicted risk.

Materials and Methods:

Adult females with suspected coronary artery disease referred to the department of nuclear medicine for 2 months were included in the study. Data pertaining to the risk score assessment were collected, and the risk scores were calculated. Subsequently, the patients underwent scheduled Tc-99m methoxy-isobutyl-isonitrile myocardial stress imaging, and scan-predicted risks were calculated. Then, the risk score of Framingham and WHO/ISH methods were compared with stress myocardial perfusion score using Cohen's kappa statistic.

Results:

The mean age of the sample was 52 years (standard deviation: 11). Framingham and WHO/ISH risk scores predicted low, intermediate, and high risk in 62.2%, 28.9%, and 8.9% and 68.9%, 22.1%, and 8.89% of the population. The two scoring methods showed moderate agreement (κ =0.59). However, the scores showed only slight and fair agreement, respectively, with risk predicted by stress MPI.

Conclusion:

Although the risk scores have been shown to benefit in screening general population, they may not perform well in symptomatic patients with suspected angina. Out of the two methods, WHO/ISH fares better than Framingham score in this population.

Obesity, cardiovascular risk and healthcare resource utilization in the UK

Aims

Obesity and cardiovascular diseases (CVDs) often co-occur, likely increasing the intensity of healthcare resource utilization (HCRU). This retrospective, observational database study examined the joint effect of obesity and cardiovascular risk status on HCRU and compared HCRU between body mass index (BMI) categories and CVD-risk categories in the UK.

Methods

Patient demographics and data on CVD and BMI were obtained from the UK Clinical Practice Research Datalink. Cardiovascular risk status, calculated using the Framingham Risk Equation, was used to categorize people into high-risk and low-risk groups, while a CVD diagnosis was used to define the established CVD group. Patients were split into BMI categories using the standard World Health Organization classifications. For each CVD and BMI category, mean number and costs of general practitioner contacts, hospital admissions and prescriptions were estimated.

Results

The final study population included 1,600,709 patients. Data on CVD status were available on just over one-quarter of the sample (28.6%) and BMI data for just less than half (43.2%). The number of general practitioner contacts and prescriptions increased with increasing BMI category for each of the three CVD-risk groups. The group with established CVD had the greatest utilization of all components of healthcare resource, followed by high CVD risk then low CVD-risk groups.

Conclusion

Increasing BMI category and CVD-risk status both affected several HCRU components. These findings highlight the importance of timely obesity management and treatment of CVD-risk factors as a means of preventing increasing HCRU.

The association between job strain, depressive symptoms, and cardiovascular disease risk: results from a cross-sectional population-based study in Québec, Canada

PURPOSE

Job strain (high psychological demands and low decision control) has been associated with cardiovascular disease (CVD). It is unclear if job strain is associated with CVD risk score independently of depression, an established risk factor for CVD. This study investigated whether there is an association between job strain and CVD risk score, when depressive symptoms are controlled for. Sex differences were examined.

METHODS

Data came from the CARTaGENE study, a community health survey of adults in Québec, Canada (n = 7848). Participants were working adults aged 40-69 years. CVD risk was estimated using the Framingham risk score. Job strain was measured as the ratio of job demands to control using the Job Content Questionnaire. Depressive symptoms were assessed using the Patient Health Questionnaire (PHQ-9). Regression analyses were conducted to examine the association between job strain and CVD risk score controlling for depressive symptoms. There was no interaction effect between job strain and depressive symptoms in the association with CVD risk score.

RESULTS

High job strain was reported in approximately 21% of participants, high Framingham risk score was observed in approximately 9%. Job strain was associated with the Framingham risk score (B = 0.73, p < 0.001, adjusted for age, sex, and education) and controlling for depressive symptoms did not significantly change the association (B = 0.59, p < 0.001).

CONCLUSION

The results suggest that the job strain is associated with CVD risk score and that this association is not explained by depressive symptoms. Similar associations were observed for males and females.

Noninvasive sublingual microvascular imaging reveals sex-specific reduction in glycocalyx barrier properties in patients with coronary artery disease

BACKGROUND

Risk factors for coronary artery disease (CAD) have been associated with endothelial dysfunction and degradation of the endothelial glycocalyx. This study was designed to compare sublingual microvascular perfusion and glycocalyx barrier properties in CAD patients and controls using noninvasive side stream darkfield imaging.

METHODS

Imaging of the sublingual microvasculature was performed in 52 case subjects (CAD confirmed by left heart catheterization) and 63 controls (low Framingham risk score). Red blood cell (RBC) filling percentage and functional microvascular density, measures of microvascular perfusion, and perfused boundary region (PBR), an index of glycocalyx barrier function, were measured in microvessels with a diameter ranging from 5-25 µm.

RESULTS

RBC filling percentage was lower in patients with CAD compared to controls (p < .001). Functional microvascular density did not differ between groups. The overall PBR was marginally greater in the CAD group compared to the control group (p = .08). PBR did not differ between male CAD cases and controls (p = .17). However, PBR was greater in females with CAD compared with female controls (p = .04), indicating reduced glycocalyx barrier function. This difference became more pronounced after adjusting for potential confounders.

CONCLUSIONS

Our data suggest that patients with CAD are characterized by a reduction in percentage of time microvessels are occupied by RBCs. In addition, CAD is significantly associated with impaired sublingual microvascular glycocalyx barrier function in women but not men. More research is needed to determine the significance of peripheral microvascular dysfunction in the pathophysiology of CAD, and how this may differ by sex.

Economic Modelling of Chronic Kidney Disease: A Systematic Literature Review to Inform Conceptual Model Design

BACKGROUND

Chronic kidney disease (CKD) is a progressive condition that leads to irreversible damage to the kidneys and is associated with an increased incidence of cardiovascular events and mortality. As novel interventions become available, estimates of economic and clinical outcomes are needed to guide payer reimbursement decisions.

OBJECTIVE

The aim of the present study was to systematically review published economic models that simulated long-term outcomes of kidney disease to inform cost-effectiveness evaluations of CKD treatments.

METHODS

The review was conducted across four databases (MEDLINE, Embase, the Cochrane library and EconLit) and health technology assessment agency websites. Relevant information on each model was extracted. Transition and mortality rates were also extracted to assess the choice of model parameterisation on disease progression by simulating patient's time with end-stage renal disease (ESRD) and time to ESRD/death. The incorporation of cardiovascular disease in a population with CKD was qualitatively assessed across identified models.

RESULTS

The search identified 101 models that met the criteria for inclusion. Models were classified into CKD models (n = 13), diabetes models with nephropathy (n = 48), ESRD-only models (n = 33) and cardiovascular models with CKD components (n = 7). Typically, published models utilised frameworks based on either (estimated or measured) glomerular filtration rate (GFR) or albuminuria, in line with clinical guideline recommendations for the diagnosis and monitoring of CKD. Generally, two core structures were identified, either a microsimulation model involving albuminuria or a Markov model utilising CKD stages and a linear GFR decline (although further variations on these model structures were also identified). Analysis of parameter variability in CKD disease progression suggested that mean time to ESRD/death was relatively consistent across model types (CKD models 28.2 years; diabetes models with nephropathy 24.6 years). When evaluating time with ESRD, CKD models predicted extended ESRD survival over diabetes models with nephropathy (mean time with ESRD 8.0 vs. 3.8 years).

DISCUSSION

This review provides an overview of how CKD is typically modelled. While common frameworks were identified, model structure varied, and no single model type was used for the modelling of patients with CKD. In addition, many of the current methods did not explicitly consider patient heterogeneity or underlying disease aetiology, except for diabetes. However, the variability of individual patients' GFR and albuminuria trajectories perhaps provides rationale for a model structure designed around the prediction of individual patients' GFR trajectories. Frameworks of future CKD models should be informed and justified based on clinical rationale and availability of data to ensure validity of model results. In addition, further clinical and observational research is warranted to provide a better understanding of prognostic factors and data sources to improve economic modelling accuracy in CKD.