Manifestation Of Arterial Disease Research Articles

Estimating uncertainty when providing individual cardiovascular risk predictions: a Bayesian survival analysis

BackgroundCardiovascular disease (CVD) risk scores provide point estimates of individual risk without uncertainty quantification. The objective of the current study was to demonstrate the feasibility and clinical utility of calculating uncertainty surrounding individual CVD-risk predictions using Bayesian methods. Study Design and SettingIndividuals with established atherosclerotic CVD were included from the Utrecht Cardiovascular Cohort—Secondary Manifestations of ARTerial disease (UCC-SMART). In 8,355 individuals, followed for median of 8.2 years (IQR 4.2–12.5), a Bayesian Weibull model was derived to predict the 10-year risk of recurrent CVD events. ResultsModel coefficients and individual predictions from the Bayesian model were very similar to that of a traditional (‘frequentist’) model but the Bayesian model also predicted 95% credible intervals (CIs) surrounding individual risk estimates. The median width of the individual 95%CrI was 5.3% (IQR 3.6–6.5) and 17% of the population had a 95%CrI width of 10% or greater. The uncertainty decreased with increasing sample size used for derivation of the model. Combining the Bayesian Weibull model with sampled hazard ratios based on trial reports may be used to estimate individual estimates of absolute risk reduction with uncertainty measures and the probability that a treatment option will result in a clinically relevant risk reduction. ConclusionEstimating uncertainty surrounding individual CVD risk predictions using Bayesian methods is feasible. The uncertainty regarding individual risk predictions could have several applications in clinical practice, like the comparison of different treatment options or by calculating the probability of the individual risk being below a certain treatment threshold. However, as the individual uncertainty measures only reflect sampling error and no biases in risk prediction, physicians should be familiar with the interpretation before widespread clinical adaption.

Dietary habits and compliance with dietary guidelines in patients with established cardiovascular disease.

Unhealthy dietary habits are an important risk factor for cardiovascular disease (CVD) and adopting a healthy diet is a central recommendation in CVD prevention. This study assessed the dietary habits of patients with established CVD, their compliance to dietary guidelines, and the relationship between guideline-compliance and recurrent cardiovascular event risk. 2656 patients with established CVD from the Utrecht Cardiovascular Cohort-Secondary Manifestations of ARTerial disease (UCC-SMART) prospective cohort study, were included between 1996 and 2022. Data on dietary intake was retrospectively collected for all participants in December 2022 using a 160-item food frequency questionnaire. Compliance with dietary guidelines was quantified using an amended version of the Dutch Healthy Diet 2015 (DHD-15) index (range: 0-135). Cox proportional hazard models were used to quantify the relationship with cardiovascular events (stroke and myocardial infarction). Among 2656 CVD patients (77% male, mean age 59 ± 9 years), median energy intake was 1922 [IQR: 1536-2351] kcal/day. The median DHD-15 index was 81.7 [IQR 71.2-92.0], with high compliance scores for recommendations on legumes and fish, and low scores for recommendations on whole grains, red meat, processed meat, and dairy. A higher DHD-15 score was associated with lower stroke risk (HR 0.78, 95% CI 0.66-0.92 per 10-point increase) but not with myocardial infarction. Compliance with dietary guidelines was suboptimal in patients with established CVD. High compliance was associated with a clinically significant reduction in stroke risk in patients with established CVD, emphasizing the importance of dietary counseling.

Development and validation of risk prediction model for recurrent cardiovascular events among Chinese: the Personalized CARdiovascular DIsease risk Assessment for Chinese model.

Cardiovascular disease (CVD) is a leading cause of mortality, especially in developing countries. This study aimed to develop and validate a CVD risk prediction model, Personalized CARdiovascular DIsease risk Assessment for Chinese (P-CARDIAC), for recurrent cardiovascular events using machine learning technique. Three cohorts of Chinese patients with established CVD were included if they had used any of the public healthcare services provided by the Hong Kong Hospital Authority (HA) since 2004 and categorized by their geographical locations. The 10-year CVD outcome was a composite of diagnostic or procedure codes with specific International Classification of Diseases, Ninth Revision, Clinical Modification. Multivariate imputation with chained equations and XGBoost were applied for the model development. The comparison with Thrombolysis in Myocardial Infarction Risk Score for Secondary Prevention (TRS-2°P) and Secondary Manifestations of ARTerial disease (SMART2) used the validation cohorts with 1000 bootstrap replicates. A total of 48 799, 119 672 and 140 533 patients were included in the derivation and validation cohorts, respectively. A list of 125 risk variables were used to make predictions on CVD risk, of which 8 classes of CVD-related drugs were considered interactive covariates. Model performance in the derivation cohort showed satisfying discrimination and calibration with a C statistic of 0.69. Internal validation showed good discrimination and calibration performance with C statistic over 0.6. The P-CARDIAC also showed better performance than TRS-2°P and SMART2. Compared with other risk scores, the P-CARDIAC enables to identify unique patterns of Chinese patients with established CVD. We anticipate that the P-CARDIAC can be applied in various settings to prevent recurrent CVD events, thus reducing the related healthcare burden.

Generalisability of trials on antithrombotic treatment intensification in patients with cardiovascular disease

ObjectiveAssessment of generalisability of guideline-informing trials on antithrombotic treatment intensification to real-world patients with cardiovascular disease (CVD).MethodsInclusion and exclusion criteria of the Cardiovascular Outcomes for People Using Anticoagulation Strategies (COMPASS),...

VLDL cholesterol associates with higher plasmatic expression of inflammatory proteins and atherosclerotic pathways compared to LDL cholesterol

Background and aim: High cholesterol in Low-Density Lipoproteins (LDL-C) is the key target of current pharmacological treatments aimed at reducing atherosclerotic cardiovascular disease (ACVD) risk. Increased cholesterol in Very low-density lipoproteins (“VLDL-C”) is an independent predictor of ACVD. VLDL-C was previously associated with markers of inflammation (for instance C-reactive protein). We now tested the relationship between either VLDL-C or LDL-C with a large spectrum of inflammatory proteins in plasma collected from subjects at different ACVD risks. Methods: We measured 276 proteins (OlinkTM) in plasma from a primary ACVD risk prevention cohort (“PLIC” in Milan; n=656 (8.2% on statins)) and a secondary ACVD risk prevention cohort (the Second Manifestations of ARTerial disease, “SMART”, the Netherlands, n=630 (50.8% on statins)). Cohorts were divided into three groups for VLDL-C (“Normal” VLDL-C<15 mg/dL, “High” VLDL-C 15-30 mg/dL, “Very high” VLDL-C >30 mg/dL) and LDL-C (“Normal” LDL-C <115 mg/dL, “High” LDL-C 115-155 mg/dL, “Very high” LDL-C>155 mg/dL). The expression (Normalized Protein eXpression, NPX) of each protein was compared among these groups by artificial intelligence. The performance to discriminate subjects with higher VLDL-C or LDL-C was evaluated by comparing the Areas Under the Curve (AUCs) of the Receiver Operating Characteristics curve (ROC) considering proteomics on top of ACVD risk factors (“CVRFs”: age, body mass index, systolic blood pressure, glycemia, therapies), versus the AUC of the ROCs with CVRFs alone. Results: The number of plasma proteins differentially expressed increased, as a function of higher VLDL-C in PLIC, as the NPXs of 84 were higher in “High” and the NPXs of 136 were higher in “Very high” vs “Normal” VLDL-C respectively. A similar trend was found in SMART, where the NPXs of 30 proteins were higher in “High” and the NPXs of 64 were higher in “Very high” vs “Normal” VLDL-C respectively. 26 proteins were shared between the two populations and recapitulated key atherosclerotic pathways (including chemotaxis of immune cells). The relationship between LDL-C was less marked; in PLIC, 14 proteins were more expressed in “High” and 33 in “Very high” vs “Normal” LDL-C respectively, while in SMART, the NPXs of 11 proteins were higher in “High” and the NPXs of 36 were higher in “Very high” vs “Normal” LDL-C respectively. Only 4 proteins were shared between high and very high LDL-C in the two populations. Finally, none of the proteins were shared between the groups of “High”/“Very high” VLDL-C and “High”/“Very high” LDL-C in the two cohorts. Canonical CVRFs alone slightly improved the ability to identify subjects with increased VLDL-C both in PLIC and SMART (AUCs between 0.6 on average), but adding plasma proteomics markedly improved the performance to identify subjects with “High” VLDL-C, in PLIC (AUC=0.767 (0.709-0.837)) and in SMART (AUC=0.781 (0.681-0.873)), and with “Very high” VLDL-C (AUC=0.950 (0.899-0.976) in PLIC, and AUC=0.938 (0.894-0.971) in SMART). The ROC of plasma proteomics with CVRFs was also superior to the ROC of the CVRFs alone to identify subjects with “High” and “Very high” LDL-C, but, as compared to the ROCs that discriminated subjects with “High” and “Very-high” VLDL-C, the AUCs were attenuated in both cohort (for “High” LDL-C: AUC=0.665 (0.558-0.774) in PLIC and AUC=0.775 (0.704-0.842) in SMART; for “Very high” LDL-C: AUC =0.776 (0.694-0.854) in PLIC and AUC=0.882 (0.825-0.931) in SMART). Conclusion: High VLDL-C associates with a higher number of differentially expressed plasma proteins versus high LDL-C and none of the proteins were in common. Our data do not underestimate the value of LDL-C in ACVD but reinforce the concept that VLDL-C may also promote different atherosclerotic pathways involved in determining ACVD.

Guideline-Based Cardiovascular Risk Assessment Delivered by an mHealth App: Development Study.

Identifying high-risk individuals is crucial for preventing cardiovascular diseases (CVDs). Currently, risk assessment is mostly performed by physicians. Mobile health apps could help decouple the determination of risk from medical resources by allowing unrestricted self-assessment. The respective test results need to be interpretable for laypersons. Together with a patient organization, we aimed to design a digital risk calculator that allows people to individually assess and optimize their CVD risk. The risk calculator was integrated into the mobile health app HerzFit, which provides the respective background information. To cover a broad spectrum of individuals for both primary and secondary prevention, we integrated the respective scores (Framingham 10-year CVD, Systematic Coronary Risk Evaluation 2, Systematic Coronary Risk Evaluation 2 in Older Persons, and Secondary Manifestations Of Arterial Disease) into a single risk calculator that was recalibrated for the German population. In primary prevention, an individual's heart age is estimated, which gives the user an easy-to-understand metric for assessing cardiac health. For secondary prevention, the risk of recurrence was assessed. In addition, a comparison of expected to mean and optimal risk levels was determined. The risk calculator is available free of charge. Data safety is ensured by processing the data locally on the users' smartphones. Offering a risk calculator to the general population requires the use of multiple instruments, as each provides only a limited spectrum in terms of age and risk distribution. The integration of 4 internationally recommended scores allows risk calculation in individuals aged 30 to 90 years with and without CVD. Such integration requires recalibration and harmonization to provide consistent and plausible estimates. In the first 14 months after the launch, the HerzFit calculator was downloaded more than 96,000 times, indicating great demand. Public information campaigns proved effective in publicizing the risk calculator and contributed significantly to download numbers. The HerzFit calculator provides CVD risk assessment for the general population. The public demonstrated great demand for such a risk calculator as it was downloaded up to 10,000 times per month, depending on campaigns creating awareness for the instrument.

Cardiovascular risk assessment in patients with antiphospholipid syndrome: a cross-sectional performance analysis of nine clinical risk prediction tools

ObjectivesThis study aimed to assess the performance of cardiovascular risk (CVR) prediction models reported by European Alliance of Associations for Rheumatology and European Society of Cardiology recommendations to identify high-atherosclerotic...

Validating the SMART2 Score in a Racially Diverse High-Risk Nationwide Cohort of Patients Receiving Coronary Artery Bypass Grafting.

Background We tested the potential of the Secondary Manifestations of Arterial Disease (SMART2) risk score for use in patients undergoing coronary artery bypass grafting. Methods and Results We conducted an external validation of the SMART2 score in a racially diverse high-risk national cohort (2010-2019) that underwent isolated coronary artery bypass grafting. We calculated the preoperative SMART2 score and modeled the 5-year major adverse cardiovascular event (cardiovascular mortality+myocardial infarction+stroke) incidence. We evaluated SMART2 score discrimination at 5 years using c-statistic and calibration with observed/expected ratio and calibration plots. We analyzed the potential clinical benefit using decision curves. We repeated these analyses in clinical subgroups, diabetes, chronic kidney disease, and polyvascular disease, and separately in White and Black patients. In 27 443 (mean age, 65 years; 10% Black individuals) US veterans undergoing coronary artery bypass grafting (2010-2019) nationwide, the 5-year major adverse cardiovascular event rate was 25%; 27% patients were in high predicted risk (>30% 5-year major adverse cardiovascular events). SMART2 score discrimination (c-statistic: 64) was comparable to the original study (c-statistic: 67) and was best in patients with chronic kidney disease (c-statistic: 66). However, it underpredicted major adverse cardiovascular event rates in the whole cohort (observed/expected ratio, 1.45) as well as in all studied subgroups. The SMART2 score performed better in White than Black patients. On decision curve analysis, the SMART2 score provides a net benefit over a wide range of risk thresholds. Conclusions The SMART2 model performs well in a racially diverse coronary artery bypass grafting cohort, with better predictive capabilities at the upper range of baseline risk, and can therefore be used to guide secondary preventive pharmacotherapy.

C-Reactive Protein and Risk of Incident Heart Failure in Patients With Cardiovascular Disease

BackgroundPatients with established cardiovascular disease (CVD) are at high risk of incident heart failure (HF), which may in part reflect the impact of systemic inflammation. ObjectivesThe goal of this study was to determine the association between C-reactive protein (CRP) and incident HF in patients with established CVD. MethodsPatients from the prospective UCC-SMART (Utrecht Cardiovascular Cohort-Second Manifestations of ARTerial disease) cohort with established CVD, but without prevalent HF were included (n = 8,089). Incident HF was defined as a first hospitalization for HF. The association between baseline CRP and incident HF was assessed using Cox proportional hazards models adjusted for established risk factors (ie, age, sex, myocardial infarction, smoking, diabetes mellitus, body mass index, blood pressure, cholesterol, and kidney function). ResultsDuring a median follow-up of 9.7 years (IQR 5.4-14.1 years), 810 incident HF cases were observed (incidence rate 1.01/100 person-years). Higher CRP was independently associated with an increased risk of incident HF: HR per 1 mg/L: 1.10 (95% CI: 1.07-1.13), and for last vs first CRP quartile: 2.22 (95% CI: 1.76-2.79). The association was significant for both HF with reduced (HR: 1.09; 95% CI: 1.04-1.14) and preserved ejection fraction (HR: 1.12; 95% CI: 1.07-1.18) (P for difference = 0.137). Additional adjustment for medication use and interim myocardial infarction did not attenuate the association, and the association remained consistent beyond 15 years after the CRP measurement. ConclusionsIn patients with established CVD, CRP is an independent risk marker of incident HF. These data support ongoing trial efforts to assess whether anti-inflammatory agents can reduce the burden of HF.

The relevance of competing risk adjustment in cardiovascular risk prediction models for clinical practice.

Many models developed for predicting the risk of cardiovascular disease (CVD), are adjusted for the competing risk of non-CVD mortality, which has been suggested to reduce potential overestimation of cumulative incidence in populations where the risk of competing events is high. The objective was to evaluate and illustrate the clinical impact of competing risk adjustment when deriving a CVD prediction model in a high-risk population. Individuals with established atherosclerotic CVD were included from the Utrecht Cardiovascular Cohort - Secondary Manifestations of ARTerial disease (UCC-SMART). In 8,355 individuals, followed for median of 8.2 years (IQR 4.2-12.5), two similar prediction models for the estimation of 10-year residual CVD risk were derived: with competing risk adjustment using a Fine and Gray model and without competing risk adjustment using a Cox proportional hazards model. On average, predictions were higher from the Cox model. The Cox model predictions overestimated the cumulative incidence ((predicted-observed ratio 1.14 [95%CI 1.09-1.20), which was most apparent in the highest risk quartiles and in older persons. Discrimination of both models was similar. When determining treatment eligibility on thresholds of predicted risks, more individuals would be treated based on the Cox model predictions. If, for example, individuals with a predicted risk >20% were considered eligible for treatment, 34% of the population would be treated according to the Fine and Gray model predictions and 44% according to the Cox model predictions. Individual predictions from the model unadjusted for competing risks were higher, reflecting the different interpretations of both models. For models aiming to accurately predict absolute risks, especially in high-risk populations, competing risk adjustment must be considered.

Proteomics and lipidomics in atherosclerotic cardiovascular disease risk prediction.

Given the limited accuracy of clinically used risk scores such as the Systematic COronary Risk Evaluation 2 system and the Second Manifestations of ARTerial disease 2 risk scores, novel risk algorithms determining an individual's susceptibility of future incident or recurrent atherosclerotic cardiovascular disease (ASCVD) risk are urgently needed. Due to major improvements in assay techniques, multimarker proteomic and lipidomic panels hold the promise to be reliably assessed in a high-throughput routine. Novel machine learning-based approaches have facilitated the use of this high-dimensional data resulting from these analyses for ASCVD risk prediction. More than a dozen of large-scale retrospective studies using different sets of biomarkers and different statistical methods have consistently demonstrated the additive prognostic value of these panels over traditionally used clinical risk scores. Prospective studies are needed to determine the clinical utility of a biomarker panel in clinical ASCVD risk stratification. When combined with the genetic predisposition captured with polygenic risk scores and the actual ASCVD phenotype observed with coronary artery imaging, proteomics and lipidomics can advance understanding of the complex multifactorial causes underlying an individual's ASCVD risk.

Cohort profile: the Utrecht Cardiovascular Cohort–Second Manifestations of Arterial Disease (UCC-SMART) Study–an ongoing prospective cohort study of patients at high cardiovascular risk in the Netherlands

PurposeThe Utrecht Cardiovascular Cohort–Second Manifestations of Arterial Disease (UCC-SMART) Study is an ongoing prospective single-centre cohort study with the aim to assess important determinants and the prognosis of cardiovascular disease...

Excess Mortality for Abdominal Aortic Aneurysms and the Potential of Strict Implementation of Cardiovascular Risk Management: A Multifaceted Study Integrating Meta-Analysis, National Registry, and PHAST and TEDY Trial Data

Previous studies imply a profound residual mortality risk following successful abdominal aorta aneurysm (AAA) repair. This excess mortality is generally attributed to increased cardiovascular risk. The aim of this study was (1) to quantify the excess residual mortality for patients with AAA, (2) to evaluate the cross sectional level of cardiovascular risk management, and (3) to estimate the potential of optimised cardiovascular risk management to reduce the excess mortality in these patients. Excess mortality was estimated through a systematic review and meta-analysis, and through data from the Swedish National Health Registry. Cardiovascular risk profiles were individually assessed during eligibility screening of patients with AAA for two multicentre pharmaceutical AAA stabilisation trials. The potential of full implementation of cardiovascular risk management was estimated through the validated Second Manifestations of ARTerial disease (SMART) risk scores algorithm. The meta-analysis showed a similarly impaired survival for patients who received early repair (small AAA) or regular repair (≥ 55 mm), and a further impaired survival for patients under surveillance for a small AAA. Excess mortality was further quantified using Swedish population data. The data revealed a more than quadrupled and doubled five year mortality rate for women and men who had their AAA repaired, respectively. Evaluation of the level of risk management of 358 patients under surveillance in 16 Dutch hospitals showed that the majority of patients with AAA did not meet therapeutic targets set for risk management in high risk populations, and indicated a more pronounced prevention gap in women. Application of the SMART risk score algorithm predicted that optimal implementation of risk management guidelines would reduce the 10 year risk of major adverse cardiovascular events from 43% to 14%. Independent of the rupture risk, AAA is associated with a worryingly compromised life expectancy with a particularly poor prognosis for women. Optimal implementation of cardiovascular risk prevention guidelines is predicted to profoundly reduce cardiovascular risk.

Step-wise Lipid-Lowering Therapy: Thinking Beyond the Guidelines.

Step-wise Lipid-Lowering Therapy: Thinking Beyond the Guidelines.

Low-grade carotid artery stenosis is associated with progression of brain atrophy and cognitive decline. The SMART-MR study

Asymptomatic low-grade carotid artery stenosis (LGCS) is a common finding in patients with manifest arterial disease, however its relationship with brain MRI changes and cognitive decline is unclear. We included 902 patients (58 ± 10 years; 81% male) enrolled in the Second Manifestations of Arterial Disease – Magnetic Resonance (SMART-MR) study without a history of cerebrovascular disease. LGCS was defined as 1–49% stenosis on baseline carotid ultrasound, whereas no LGCS (reference category) was defined as absence of carotid plaque. Brain and white matter hyperintensity (WMH) volumes and cognitive function were measured at baseline and after 4 (n = 480) and 12 years (n = 222) of follow-up. Using linear mixed-effects models, we investigated associations of LGCS with progression of brain atrophy, WMH, and cognitive decline. LGCS was associated with greater progression of global brain atrophy (estimate −0.03; 95%CI, −0.06 to −0.01; p = 0.002), and a greater decline in executive functioning (estimate −0.02; 95%CI, −0.031 to −0.01; p < 0.001) and memory (estimate −0.012; 95%CI, −0.02 to −0.001; p = 0.032), independent of demographics, cardiovascular risk factors, and incident brain infarcts on MRI. No association was observed between LGCS and progression of WMH. Our results indicate that LGCS may represent an early marker of greater future brain atrophy and cognitive decline.

Baseline cardiovascular risk in subjects with type 2 diabetes and chronic kidney disease from the FLOW trial

Abstract Background Patients with chronic kidney disease and type 2 diabetes (T2D) have a high risk of progression to kidney failure as well as cardiovascular (CV) events. It is established that glucagon-like peptide-1 receptor agonists (GLP-1RAs) improve glycaemic control and reduce body weight. Some CV outcomes trials have shown that GLP-1RAs reduce CV risk in people with T2D at high CV risk; for example, the SUSTAIN 6 trial demonstrated that the GLP-1RA semaglutide significantly lowered the rate of major CV events (CV death, non-fatal myocardial infarction and non-fatal stroke) versus placebo. Some trials have also indicated that GLP-1RAs reduce albuminuria and estimated glomerular filtration rate (eGFR) decline. Based on these previous results indicating potential kidney-protective effects, the FLOW trial (NCT03819153) is evaluating once-weekly, subcutaneous semaglutide 1.0 mg versus placebo on kidney- and CV-related outcomes in participants with T2D. We describe the baseline characteristics and the calculated CV risk of this patient population using the atherosclerotic CV disease (ASCVD) and second manifestations of arterial disease (SMART) risk calculators. Methods FLOW is an ongoing, multicentre, randomised, double-blind, parallel-group, event-driven, phase 3b trial, with participants randomised 1:1 to semaglutide or placebo, each in addition to standard of care. Recruitment is complete and 3,535 participants with T2D, an eGFR ≥25–≤75 mL/min/1.73 m2, and urine albumin-to-creatinine ratio ≥100–≤5,000 mg/g have been enrolled. The primary endpoint is time to first occurrence of a kidney composite that includes ≥50% persistent eGFR reduction, kidney failure (persistent eGFR &amp;lt;15 mL/min/1.73 m2 or initiation of chronic dialysis or kidney transplantation), and kidney-related death, or CV death. Key secondary outcomes include time to first occurrence of CV death, myocardial infarction or stroke. Results The baseline clinical characteristics and demographics are shown (Table 1). Median age was 68 years, 30% were female, mean diabetes duration was 17 years, and 98% had a history of hypertension. Overall, 52% of participants had a previous CV event. The calculated 10-year risk for ASCVD events in those without prior ASCVD was 31% in males and 18% in females, and in those with previous CV events was 37–56% in males and 35–53% in females depending on the type of CV disease reported in the calculator (Table 2). Conclusions The FLOW trial has completed enrolment. Based on the ASCVD and SMART risk calculators, the enrolled population has a substantial risk for adverse CV outcomes. Event ascertainment is ongoing, and the FLOW trial will provide evidence for the potential of semaglutide to improve kidney and CV outcomes in the T2D population. Funding Acknowledgement Type of funding sources: Private company. Main funding source(s): Novo Nordisk

Diabetic Foot Ulcers: Insights into Management and Prevention

Diabetic foot ulcer (DFU) is one of the greatest thoughtful difficulties of diabetes, negatively affecting the patient's health and socioeconomic status. Around the world, diabetes prevalence is increasing in both developing and developed countries. There are several measures in place in most countries to limit diabetes complications. This review summarizes the pathogenic mechanisms that lead to diabetic foot and focuses on prevention and management. It may be possible to prevent diabetic foot ulcers and thus amputation risk by increasing physicians' awareness and ability to identify risky feet. Diabetes neuropathy, peripheral artery disease, and immune dysfunction are the three major contributing factors. In order to treat diabetic foot disease, a detailed history and physical examination are necessary. Diabetic neuropathy and peripheral arterial disease manifestations, such as diabetic foot ulcers and infections, should be examined during this examination. Prevention approaches should integrate a multidisciplinary method centered on patient education. Preventive efforts must, however, be sustained for a long time for them to be effective. Keywords: Diabetic foot; Ulceration; Neuropathy; Pathogenesis; Peripheral arterial disease

Estimation of recurrent atherosclerotic cardiovascular event risk in patients with established cardiovascular disease: the updated SMART2 algorithm.

AimsThe 10-year risk of recurrent atherosclerotic cardiovascular disease (ASCVD) events in patients with established ASCVD can be estimated with the Secondary Manifestations of ARTerial disease (SMART) risk score, and may help refine clinical management. To broaden generalizability across regions, we updated the existing tool (SMART2 risk score) and recalibrated it with regional incidence rates and assessed its performance in external populations.Methods and resultsIndividuals with coronary artery disease, cerebrovascular disease, peripheral artery disease, or abdominal aortic aneurysms were included from the Utrecht Cardiovascular Cohort-SMART cohort [n = 8355; 1706 ASCVD events during a median follow-up of 8.2 years (interquartile range 4.2–12.5)] to derive a 10-year risk prediction model for recurrent ASCVD events (non-fatal myocardial infarction, non-fatal stroke, or cardiovascular mortality) using a Fine and Gray competing risk-adjusted model. The model was recalibrated to four regions across Europe, and to Asia (excluding Japan), Japan, Australia, North America, and Latin America using contemporary cohort data from each target region. External validation used data from seven cohorts [Clinical Practice Research Datalink, SWEDEHEART, the international REduction of Atherothrombosis for Continued Health (REACH) Registry, Estonian Biobank, Spanish Biomarkers in Acute Coronary Syndrome and Biomarkers in Acute Myocardial Infarction (BACS/BAMI), the Norwegian COgnitive Impairment After STroke, and Bialystok PLUS/Polaspire] and included 369 044 individuals with established ASCVD of whom 62 807 experienced an ASCVD event. C-statistics ranged from 0.605 [95% confidence interval (CI) 0.547–0.664] in BACS/BAMI to 0.772 (95% CI 0.659–0.886) in REACH Europe high-risk region. The clinical utility of the model was demonstrated across a range of clinically relevant treatment thresholds for intensified treatment options.ConclusionThe SMART2 risk score provides an updated, validated tool for the prediction of recurrent ASCVD events in patients with established ASCVD across European and non-European populations. The use of this tool could allow for a more personalized approach to secondary prevention based upon quantitative rather than qualitative estimates of residual risk.

Targeted proteomics improves cardiovascular risk prediction in secondary prevention.

AimsCurrent risk scores do not accurately identify patients at highest risk of recurrent atherosclerotic cardiovascular disease (ASCVD) in need of more intensive therapeutic interventions. Advances in high-throughput plasma proteomics, analysed with machine learning techniques, may offer new opportunities to further improve risk stratification in these patients.Methods and resultsTargeted plasma proteomics was performed in two secondary prevention cohorts: the Second Manifestations of ARTerial disease (SMART) cohort (n = 870) and the Athero-Express cohort (n = 700). The primary outcome was recurrent ASCVD (acute myocardial infarction, ischaemic stroke, and cardiovascular death). Machine learning techniques with extreme gradient boosting were used to construct a protein model in the derivation cohort (SMART), which was validated in the Athero-Express cohort and compared with a clinical risk model. Pathway analysis was performed to identify specific pathways in high and low C-reactive protein (CRP) patient subsets. The protein model outperformed the clinical model in both the derivation cohort [area under the curve (AUC): 0.810 vs. 0.750; P < 0.001] and validation cohort (AUC: 0.801 vs. 0.765; P < 0.001), provided significant net reclassification improvement (0.173 in validation cohort) and was well calibrated. In contrast to a clear interleukin-6 signal in high CRP patients, neutrophil-signalling-related proteins were associated with recurrent ASCVD in low CRP patients.ConclusionA proteome-based risk model is superior to a clinical risk model in predicting recurrent ASCVD events. Neutrophil-related pathways were found in low CRP patients, implying the presence of a residual inflammatory risk beyond traditional NLRP3 pathways. The observed net reclassification improvement illustrates the potential of proteomics when incorporated in a tailored therapeutic approach in secondary prevention patients.

Epicardial adipose tissue is a robust measure of increased risk of myocardial infarction - a meta-analysis on over 6600 patients and rationale for the EPIC-ACS study.

Background:Epicardial adipose tissue (EAT) surrounds the heart and the coronary vessels. EAT produces pro- and anti-inflammatory cytokines. Several studies have already documented the association of EAT and cardiovascular risk factors as well as coronary artery disease manifestations. Currently computed tomography (CT) is considered the gold standard for measurement of 3-dimensional volume of EAT. In addition, echocardiography might be an easy accessible alternative in particular in an emergency setting.Methods:We performed a metaanalysis of existing studies describing the differences of EAT in patients with and without myocardial infarction. We used established databases and were searching for “epicardial adipose tissue” or “pericardial adipose tissue” and “myocardial infarction”, “coronary events”, or “acute coronary syndrome”. We included over 6600 patients from 7 studies. Random effect models were calculated and all analyses were performed by using the Review Manager 5.3.Results:Patients with myocardial infarction had 37% (confidence interval [0.21-0.54], P value <.001)] higher measures of EAT compared to patients without myocardial infarction. Comparing studies using echocardiography vs CT for assessment of EAT thickness, similar relative differences in EAT with wide overlap of confidence intervals were observed (for echocardiography: 0.4 [0.04-0.76], for CT: 0.36 [0.16-0.57], P value <.001 for both).Conclusions:Patients with myocardial infarction have more EAT as compared to patients without myocardial infarction independently of the used imaging modality. Further prospective studies are needed to evaluate, how quantification of EAT in clinical routine can improve patients management.