Development and validation of QRISK3 risk prediction algorithms to estimate future risk of cardiovascular disease: prospective cohort study

Managing Cardiovascular Risk in Patients with Autoimmune Diseases: Insights from a Nutritional Perspective

PURPOSE OF REVIEW

Autoimmune diseases manifest as an immune system response directed against endogenous antigens, exerting a significant influence on a substantial portion of the population. Notably, a leading contributor to morbidity and mortality in this context is cardiovascular disease (CVD). Intriguingly, individuals with autoimmune disorders exhibit a heightened prevalence of CVD compared to the general population. The meticulous management of CV risk factors assumes paramount importance, given the current absence of a standardized solution to this perplexity. This review endeavors to address this challenge from a nutritional perspective.

RECENT FINDINGS

Emerging evidence suggests that inflammation, a common thread in autoimmune diseases, also plays a pivotal role in the pathogenesis of CVD. Nutritional interventions aimed at reducing inflammation have shown promise in mitigating cardiovascular risk. The integration of nutritional strategies into the management plans for patients with autoimmune diseases offers a holistic approach to reducing cardiovascular risk. While conventional pharmacological treatments remain foundational, the addition of targeted dietary interventions can provide a complementary pathway to improve cardiovascular outcomes.

Genetic relations between type 1 diabetes, coronary artery disease and leukocyte counts

AIMS/HYPOTHESIS

Type 1 diabetes is associated with excess coronary artery disease (CAD) risk even when known cardiovascular risk factors are accounted for. Genetic perturbation of haematopoiesis that alters leukocyte production is a novel independent modifier of CAD risk. We examined whether there are shared genetic determinants and causal relationships between type 1 diabetes, CAD and leukocyte counts.

METHODS

Genome-wide association study summary statistics were used to perform pairwise linkage disequilibrium score regression and heritability estimation from summary statistics (ρ-HESS) to respectively estimate the genome-wide and local genetic correlations, and two-sample Mendelian randomisation to estimate the causal relationships between leukocyte counts (335,855 healthy individuals), type 1 diabetes (18,942 cases, 501,638 control individuals) and CAD (122,733 cases, 424,528 control individuals). A latent causal variable (LCV) model was performed to estimate the genetic causality proportion of the genetic correlation between type 1 diabetes and CAD.

RESULTS

There was significant genome-wide genetic correlation (rg) between type 1 diabetes and CAD (rg=0.088, p=8.60 × 10-3) and both diseases shared significant genome-wide genetic determinants with eosinophil count (rg for type 1 diabetes [rg(T1D)]=0.093, p=7.20 × 10-3, rg for CAD [rg(CAD)]=0.092, p=3.68 × 10-6) and lymphocyte count (rg(T1D)=-0.052, p=2.76 × 10-2, rg(CAD)=0.176, p=1.82 × 10-15). Sixteen independent loci showed stringent Bonferroni significant local genetic correlations between leukocyte counts, type 1 diabetes and/or CAD. Cis-genetic regulation of the expression levels of genes within shared loci between type 1 diabetes and CAD was associated with both diseases as well as leukocyte counts, including SH2B3, CTSH, MORF4L1, CTRB1, CTRB2, CFDP1 and IFIH1. Genetically predicted lymphocyte, neutrophil and eosinophil counts were associated with type 1 diabetes and CAD (lymphocyte OR for type 1 diabetes [ORT1D]=0.67, p=2.02-19, ORCAD=1.09, p=2.67 × 10-6; neutrophil ORT1D=0.82, p=5.63 × 10-5, ORCAD=1.17, p=5.02 × 10-14; and eosinophil ORT1D=1.67, p=5.45 × 10-25, ORCAD=1.07, p=2.03 × 10-4. The genetic causality proportion between type 1 diabetes and CAD was 0.36 ± 0.16 (pLCV=1.30 × 10-2), suggesting a possible intermediary causal variable.

CONCLUSIONS/INTERPRETATION

This study sheds light on shared genetic mechanisms underlying type 1 diabetes and CAD, which may contribute to their co-occurrence through regulation of gene expression and leukocyte counts and identifies cellular and molecular targets for further investigation for disease prediction and potential drug discovery.

Screening for chronic kidney disease: change of perspective and novel developments

PURPOSE OF REVIEW

Chronic kidney disease (CKD) is a serious health issue because of its rising global prevalence and its complications, such as kidney failure and cardiovascular disease (CVD). CKD is mainly diagnosed late or undiagnosed, delaying or missing the initiation of preventive interventions. Screening can prevent or delay progressive kidney function decline and CVD. This article reviews diagnostic tests and risk prediction developments for patients with CKD, highlights key evidence for targeted screening, and provides new insights into population-wide screening.

RECENT FINDINGS

Large cohort studies and clinical trial data established the strong association of albuminuria with CKD outcomes, supporting the role of albuminuria as target of CKD screening and treatment. Significant advances in both risk prediction of CKD and CVD in CKD patients and treatment options provided new evidence for the relevance and implications of CKD screening. Guidelines recommend targeted screening in high-risk patients, but evidence suggests limited adherence to guideline recommendations. More recently, population-wide screening has been investigated as another approach, showing potential effectiveness and cost-effectiveness.

SUMMARY

There is increasing evidence for the methods, implications, and effectiveness of CKD screening. Implementing and optimizing screening strategies requires enhanced awareness and understanding of the possibilities for CKD screening within different healthcare systems.

Autoimmune diseases and atherosclerotic cardiovascular disease

Autoimmune diseases are associated with a dramatically increased risk of atherosclerotic cardiovascular disease and its clinical manifestations. The increased risk is consistent with the notion that atherogenesis is modulated by both protective and disease-promoting immune mechanisms. Notably, traditional cardiovascular risk factors such as dyslipidaemia and hypertension alone do not explain the increased risk of cardiovascular disease associated with autoimmune diseases. Several mechanisms have been implicated in mediating the autoimmunity-associated cardiovascular risk, either directly or by modulating the effect of other risk factors in a complex interplay. Aberrant leukocyte function and pro-inflammatory cytokines are central to both disease entities, resulting in vascular dysfunction, impaired resolution of inflammation and promotion of chronic inflammation. Similarly, loss of tolerance to self-antigens and the generation of autoantibodies are key features of autoimmunity but are also implicated in the maladaptive inflammatory response during atherosclerotic cardiovascular disease. Therefore, immunomodulatory therapies are potential efficacious interventions to directly reduce the risk of cardiovascular disease, and biomarkers of autoimmune disease activity could be relevant tools to stratify patients with autoimmunity according to their cardiovascular risk. In this Review, we discuss the pathophysiological aspects of the increased cardiovascular risk associated with autoimmunity and highlight the many open questions that need to be answered to develop novel therapies that specifically address this unmet clinical need.

Modelling Adverse Events in Patients Receiving Chronic Oral Corticosteroids in the UK

BACKGROUND

Oral corticosteroids (OCS) are effective anti-inflammatory agents used across a range of conditions. However, substantial evidence associates their use with increased risks for adverse events (AEs), causing high burden on healthcare resources. Emerging biologics present as alternative agents, enabling the reduction of OCS use. However, current modelling approaches may underestimate their effects by not capturing OCS-sparing effects. In this study, we present a modelling approach designed to capture the health economic benefits of OCS-sparing regimens and agents.

METHODS

We developed a disease-agnostic model using a UK health technology assessment (HTA) perspective, with discounting of 3.5% for costs and outcomes, a lifetime horizon, and 4-week cycle length. The model structure included type 2 diabetes mellitus, established cardiovascular disease, and osteoporosis as key AEs and drivers of morbidity and mortality, as well as capturing transient events. Quality-adjusted life-years (QALYs), life-years, and costs were determined for OCS-only and OCS-sparing treatment arms. Outcomes were determined using baseline 50% OCS-sparing, considering several OCS average daily doses (5, 10, 15 mg).

RESULTS

A treatment regimen with 50% OCS dose-sparing led to lifetime incremental cost savings per patient of £1107 (95% confidence interval £1014-£1229) at 5 mg, £2403 (£2203-£2668) at 10 mg, and £19,501 (£748-£51,836) at 15 mg. Patients also gained 0.033 (0.030-0.036) to 0.356 (0.022-2.404) QALYs dependent on dose. The benefits of OCS sparing were long-term, plateauing after 35-40 years of treatment.

CONCLUSIONS

We present a modelling approach that captures additional long-term health economic benefits from OCS sparing that would otherwise be missed from current modelling approaches. These results may help inform future decision making for emerging OCS-sparing therapeutics by comparing them against the cost of such treatments.

Pharmacologic Treatment of Hypertension in Older Adults

The authors conducted a review of pharmacologic therapy in older adults with hypertension. They reviewed the evidence supporting their use in older adults, understanding the physiologic changes and potential adverse drug effects associated with aging and antihypertensive medication use, exploring guideline recommendations for antihypertensive use in older adults, and evaluating the associated risks and benefits of specific classes of antihypertensive medications.

A single risk assessment for the most common diseases of ageing, developed and validated on 10 cohort studies

BACKGROUND

We aimed to develop risk tools for dementia, stroke, myocardial infarction (MI), and diabetes, for adults aged ≥ 65 years using shared risk factors.

METHODS

Data were obtained from 10 population-based cohorts (N = 41,755) with median follow-up time (years) for dementia, stroke, MI, and diabetes of 6.2, 7.0, 6.8, and 7.4, respectively. Disease-free participants at baseline were included, and 22 risk factors (sociodemographic, medical, lifestyle, laboratory biomarkers) were evaluated. Two risk tools (DemNCD and DemNCD-LR based on Fine and Gray sub-distribution and logistic regression [LR], respectively) were developed and validated. Predictive accuracies of these risk tools were assessed using Harrel's C-statistics and area under the curve (AUC) and 95% confidence interval (CI). Model calibration was conducted using Hosmer-Lemeshow goodness of fit test along calibration plots.

RESULTS

Both the DemNCD and DemNCD-LR resulted in similar predictive accuracy for each outcome. The overall AUC (95% CI) for dementia, stroke, MI, and diabetes risk tool were 0·68 (0·65, 0·70), 0·58 (0·54, 0·61), 0·65 (0·61, 0·68), and 0·68 (0·64, 0·72), respectively, for males. For females, these figures were 0·65 (0·63, 0·67), 0·55 (0·52, 0·57), 0·65 (0·62, 0·68), and 0·61 (0·57, 0·65).

CONCLUSIONS

The DemNCD is the first tool to predict both dementia and multiple cardio-metabolic diseases using comprehensive risk factors and provided similar predictive accuracy to existing risk tools. It has similar predictive accuracy as tools designed for single outcomes in this age-group. DemNCD has the potential to be used in community and clinical settings as it includes self-reported and routinely available clinical measures.

Current management of chronic kidney disease in type-2 diabetes-A tiered approach: An overview of the joint Association of British Clinical Diabetologists and UK Kidney association (ABCD-UKKA) guidelines

A growing and significant number of people with diabetes develop chronic kidney disease (CKD). Diabetes-related CKD is a leading cause of end-stage kidney disease (ESKD) and people with diabetes and CKD have high morbidity and mortality, predominantly related to cardiovascular disease (CVD). Despite advances in care over the recent decades, most people with CKD and type 2 diabetes are likely to die of CVD before developing ESKD. Hyperglycaemia and hypertension are modifiable risk factors to prevent onset and progression of CKD and related CVD. People with type 2 diabetes often have dyslipidaemia and CKD per se is an independent risk factor for CVD, therefore people with CKD and type 2 diabetes require intensive lipid lowering to reduce burden of CVD. Recent clinical trials of people with type 2 diabetes and CKD have demonstrated a reduction in composite kidney end point events (significant decline in kidney function, need for kidney replacement therapy and kidney death) with sodium-glucose co-transporter-2 (SGLT-2) inhibitors, non-steroidal mineralocorticoid receptor antagonist finerenone and glucagon-like peptide 1 receptor agonists. The Association of British Clinical Diabetologists (ABCD) and UK Kidney Association (UKKA) Diabetic Kidney Disease Clinical Speciality Group have previously undertaken a narrative review and critical appraisal of the available evidence to inform clinical practice guidelines for the management of hyperglycaemia, hyperlipidaemia and hypertension in adults with type 2 diabetes and CKD. This 2024 abbreviated updated guidance summarises the recommendations and the implications for clinical practice for healthcare professionals who treat people with diabetes and CKD in primary, community and secondary care settings.

External validation of a prognostic model to improve prediction of psychosis: a retrospective cohort study in primary care

BACKGROUND

Early detection could reduce the duration of untreated psychosis. GPs are a vital part of the psychosis care pathway, but find it difficult to detect the early features. An accurate risk prediction tool, P Risk, was developed to detect these.

AIM

To externally validate P Risk.

DESIGN AND SETTING

This retrospective cohort study used a validation dataset of 1 647 934 UK Clinical Practice Research Datalink (CPRD) primary care records linked to secondary care records.

METHOD

The same predictors (age; sex; ethnicity; social deprivation; consultations for suicidal behaviour, depression/anxiety, and substance misuse; history of consultations for suicidal behaviour; smoking history; substance misuse; prescribed medications for depression/anxiety/post-traumatic stress disorder/obsessive compulsive disorder; and total number of consultations) were used as for the development of P Risk. Predictive risk, sensitivity, specificity, and likelihood ratios were calculated for various risk thresholds. Discrimination (Harrell's C-index) and calibration were calculated. Results were compared between the development (CPRD GOLD) and validation (CPRD Aurum) datasets.

RESULTS

Psychosis risk increased with values of the P Risk prognostic index. Incidence was highest in younger age groups and, in the main, higher in males. Harrell's C was 0.79 (95% confidence interval = 0.78 to 0.79) in the validation dataset and 0.77 in the development dataset. A risk threshold of 1.0% gave sensitivity of 65.9% and specificity of 86.6%.

CONCLUSION

Further testing is required, but P Risk has the potential to be used in primary care to detect future risk of psychosis.

Home blood pressure stability score is associated with better cardiovascular prognosis: data from the nationwide prospective J-HOP study

A home blood pressure (BP)-centered strategy is emerging as the optimal approach to achieve adequate BP control in individuals with hypertension, but a simple cardiovascular risk score based on home BP level and variability is lacking. This study used prospective data from the Japan Morning Surge-Home Blood Pressure (J-HOP) extended study to develop a simple home BP stability score for the prediction of cardiovascular risk. The J-HOP extended study included 4070 participants (mean age 64.9 years) who measured home BP three times in the morning and evening for 14 days at baseline. During the mean 6.3-year follow-up, there were 260 cardiovascular events. A home BP stability score was calculated based on the average of morning and evening systolic BP (SBP; MEave), and three home BP variability metrics: average real variability (average absolute difference between successive measurements); average peak (average of the highest three SBP values for each individual), and time in therapeutic range (proportion of time spent with MEave home SBP 100-135 mmHg). There was a curvilinear association between the home BP stability score and the risk of cardiovascular events. Compared with individuals in the optimal home SBP stability score group (9-10 points), those in the very high-risk group (0 points) had significantly higher cardiovascular event risk during follow-up (adjusted hazard ratio 3.97, 95% confidence interval 2.22-7.09; p < 0.001), independent of age, sex, medication, cardiovascular risk factors, and office BP. These data show the potential for a simple home BP-based score to predict cardiovascular event risk in people with hypertension.

Large-scale plasma proteomics in the UK Biobank modestly improves prediction of major cardiovascular events in a population without previous cardiovascular disease

AIMS

Improved identification of individuals at high risk of developing cardiovascular disease would enable targeted interventions and potentially lead to reductions in mortality and morbidity. Our aim was to determine whether use of large-scale proteomics improves prediction of cardiovascular events beyond traditional risk factors (TRFs).

METHODS AND RESULTS

Using proximity extension assays, 2919 plasma proteins were measured in 38 380 participants of the UK Biobank. Both data- and literature-based feature selection and trained models using extreme gradient boosting machine learning were used to predict risk of major cardiovascular events (MACEs: fatal and non-fatal myocardial infarction, stroke, and coronary artery revascularization) during a 10-year follow-up. Area under the curve (AUC) and net reclassification index (NRI) were used to evaluate the additive value of selected protein panels to MACE prediction by Systematic COronary Risk Evaluation 2 (SCORE2) or the 10 TRFs used in SCORE2. SCORE2 and SCORE2 refitted to UK Biobank data predicted MACE with AUCs of 0.740 and 0.749, respectively. Data-driven selection identified 114 proteins of greatest relevance for prediction. Prediction of MACE was not improved by using these proteins alone (AUC of 0.758) but was significantly improved by combining these proteins with SCORE2 or the 10 TRFs (AUC = 0.771, P < 001, NRI = 0.140, and AUC = 0.767, P = 0.03, NRI 0.053, respectively). Literature-based protein selection (113 proteins from five previous studies) also improved risk prediction beyond TRFs while a random selection of 114 proteins did not.

CONCLUSION

Large-scale plasma proteomics with data-driven and literature-based protein selection modestly improves prediction of future MACE beyond TRFs.

Identifying individuals at high risk for dementia in primary care: Development and validation of the DemRisk risk prediction model using routinely collected patient data

INTRODUCTION

Health policy in the UK and globally regarding dementia, emphasises prevention and risk reduction. These goals could be facilitated by automated assessment of dementia risk in primary care using routinely collected patient data. However, existing applicable tools are weak at identifying patients at high risk for dementia. We set out to develop improved risk prediction models deployable in primary care.

METHODS

Electronic health records (EHRs) for patients aged 60-89 from 393 English general practices were extracted from the Clinical Practice Research Datalink (CPRD) GOLD database. 235 and 158 practices respectively were randomly assigned to development and validation cohorts. Separate dementia risk models were developed for patients aged 60-79 (development cohort n = 616,366; validation cohort n = 419,126) and 80-89 (n = 175,131 and n = 118,717). The outcome was incident dementia within 5 years and more than 60 evidence-based risk factors were evaluated. Risk models were developed and validated using multivariable Cox regression.

RESULTS

The age 60-79 development cohort included 10,841 incident cases of dementia (6.3 per 1,000 person-years) and the age 80-89 development cohort included 15,994 (40.2 per 1,000 person-years). Discrimination and calibration for the resulting age 60-79 model were good (Harrell's C 0.78 (95% CI: 0.78 to 0.79); Royston's D 1.74 (1.70 to 1.78); calibration slope 0.98 (0.96 to 1.01)), with 37% of patients in the top 1% of risk scores receiving a dementia diagnosis within 5 years. Fit statistics were lower for the age 80-89 model but dementia incidence was higher and 79% of those in the top 1% of risk scores subsequently developed dementia.

CONCLUSION

Our models can identify individuals at higher risk of dementia using routinely collected information from their primary care record, and outperform an existing EHR-based tool. Discriminative ability was greatest for those aged 60-79, but the model for those aged 80-89 may also be clinical useful.

Management of a Patient with Cardiovascular Disease Should Include Assessment of Primary and Secondary Immunodeficiencies: Part 2-Secondary Immunodeficiencies

BACKGROUND

Cardiovascular diseases are among the most common chronic diseases, generating high social and economic costs. Secondary immunodeficiencies occur more often than primary ones and may result from the co-occurrence of specific diseases, treatment, nutrient deficiencies and non-nutritive bio-active compounds that result from the industrial nutrient practices.

OBJECTIVES

The aim of this article is to present selected secondary immunodeficiencies and their impact on the cardiovascular system.

RESULTS

The treatment of a patient with cardiovascular disease should include an assess-ment for immunodeficiencies, because the immune and cardiovascular systems are closely linked.

CONCLUSIONS

Immune system dysfunctions can significantly affect the course of cardiovascular diseases and their treatment. For this reason, comprehensive care for a patient with cardiovascular disease requires taking into account potential immunodeficiencies, which can have a significant impact on the patient's health.

Cytokine-Based Validation of the Inflammation-Based Risk Score in Patients with ST-Segment Elevation Myocardial Infarction

This study aimed to validate an inflammation-based risk score in patients with ST-segment elevation myocardial infarction (STEMI) by examining their cytokine profiles. Upon admission, patients were evaluated for systemic inflammation using a risk score that assigned points based on specific biomarkers: 1 point for leukocyte count ≥9.3 × 10³ cells/μL, 2 points for high-sensitivity C-reactive protein (hsCRP) ≥13.0 mg/L, and 3 points for serum albumin ≤3.6 g/dL. Patients were categorized into three groups: no inflammation (0 points, n = 13), mild inflammation (1-2 points, n = 35), and severe inflammation (3-6 points, n = 26). Serum levels of 16 key cytokines were measured. Patients with higher risk scores showed elevated interleukin (IL)-6 levels (19.6 vs. 8.5 vs. 6.8 pg/mL; P = 0.021) and decreased interferon-γ-induced protein-10 (IP-10) levels (73.4 vs. 68.8 vs. 112.2 pg/mL; P = 0.011). IL-6 was positively correlated with hsCRP (ρ 0.307) and negatively correlated with albumin (ρ -0.298), while IP-10 was negatively correlated with leukocyte count (ρ -0.301). No other cytokines showed significant association with the risk score. Higher inflammation scores were also associated with an increased incidence of major adverse cardiovascular events, particularly acute heart failure. This study underscores the association between the inflammation-based risk score and cytokine levels, specifically IL-6 and IP-10, in patients with STEMI.

Current understanding and management of cardiovascular involvement in rheumatic immune-mediated inflammatory diseases

Immune-mediated inflammatory diseases (IMIDs) are a spectrum of disorders of overlapping immunopathogenesis, with a prevalence of up to 10% in Western populations and increasing incidence in developing countries. Although targeted treatments have revolutionized the management of rheumatic IMIDs, cardiovascular involvement confers an increased risk of mortality and remains clinically under-recognized. Cardiovascular pathology is diverse across rheumatic IMIDs, ranging from premature atherosclerotic cardiovascular disease (ASCVD) to inflammatory cardiomyopathy, which comprises myocardial microvascular dysfunction, vasculitis, myocarditis and pericarditis, and heart failure. Epidemiological and clinical data imply that rheumatic IMIDs and associated cardiovascular disease share common inflammatory mechanisms. This concept is strengthened by emergent trials that indicate improved cardiovascular outcomes with immune modulators in the general population with ASCVD. However, not all disease-modifying therapies that reduce inflammation in IMIDs such as rheumatoid arthritis demonstrate equally beneficial cardiovascular effects, and the evidence base for treatment of inflammatory cardiomyopathy in patients with rheumatic IMIDs is lacking. Specific diagnostic protocols for the early detection and monitoring of cardiovascular involvement in patients with IMIDs are emerging but are in need of ongoing development. This Review summarizes current concepts on the potentially targetable inflammatory mechanisms of cardiovascular pathology in rheumatic IMIDs and discusses how these concepts can be considered for the diagnosis and management of cardiovascular involvement across rheumatic IMIDs, with an emphasis on the potential of cardiovascular imaging for risk stratification, early detection and prognostication.

Cardiovascular Risk Scores and Migraine Status

Importance

A previous cohort study in the US found that women with higher cardiovascular risk were more likely to have a history of migraine but less likely to have active migraine. Extrapolating these results to men and European individuals is crucial to understanding the complex association between migraine activity status and vascular health in other populations.

Objective

To evaluate the association pattern between a cardiovascular risk score, the most recent European version of the Systematic Coronary Risk Evaluation 2 (SCORE2) risk estimation system, and migraine activity status in Dutch men and women.

Design, Setting, and Participants

The prospective population-based Lifelines cohort consists of community-dwelling adults residing in the northern part of the Netherlands. Individuals with a terminal illness, incapacitated individuals, including those with a severe mental illness, or who were unable to visit their general practitioner or complete the questionnaires were excluded from participation within Lifelines. Participants whose data on the cardiovascular risk scores and migraine status were complete were included in the analysis. Data on baseline characteristics were collected between November 1, 2006, to December 31, 2014. Cross-sectional and follow-up analyses were conducted within the prospective cohort. Questionnaires were sent approximately every 1.5 to 2.5 years, and the last self-reported migraine assessment took place between October 1, 2019, and January 31, 2021. Data were analyzed from March 1, 2022, to August 16, 2024.

Exposures

The SCORE2 is a sex-specific European cardiovascular risk score that includes age, cholesterol levels, smoking status, diabetes, and systolic blood pressure.

Main Outcomes and Measures

The primary outcome was the association pattern between cardiovascular risk scores and migraine activity status. SCORE2 risk scores were measured once at baseline; groups of the SCORE2 (<1.0%, 1.0% to <2.5%, 2.5% to <5.0%, 5.0% to <7.5%, 7.5% to <10.0%, and ≥10.0%) were created based on the sum of points of individual risk factors. Migraine activity status was assessed using self-reported questionnaires and classified as (1) prevalent (ie, migraine at baseline), (2) incident (ie, no migraine at baseline but migraine in ≥1 follow-up), and (3) none. To evaluate the influence of age, we conducted stratified analyses of the SCORE2 by age categories (<40, 40-49, and ≥50 years).

Results

The total study population consisted of 140 915 individuals at baseline with a mean (SD) age of 44.4 (12.7) years, of whom 58.5% were women. In total, 25 915 individuals (18.4% of the total population) had prevalent migraine and 2224 (1.9% of the 115 000 without prevalent migraine) had incident migraine. The odds of having prevalent and incident migraine, compared with individuals with a SCORE2 category of less than 1.0%, varied and decreased with increasing SCORE2 categories, with odds ratios (ORs) for prevalent migraine ranging from 0.93 (95% CI, 0.90-0.96) for a SCORE2 category of 1.0% to less than 2.5% to 0.43 (95% CI, 0.39-0.48) for a SCORE2 category of at least 10.0% and, for incident migraine, from 0.63 (95% CI, 0.57-0.69) for a SCORE2 category of 1.0% to less than 2.5% to 0.17 (95% CI, 0.10-0.27) for a SCORE2 category of at least 10.0%. A similar pattern was observed in both sexes but more profound in women. In women, ORs for prevalent migraine ranged from 1.21 (95% CI, 1.16-1.25) to 0.70 (95% CI, 0.58-0.83) (vs 1.19 [95% CI, 1.09-1.29] to 0.84 [95% CI, 0.72-0.99] in men) and, for incident migraine, 0.72 (95% CI, 0.64-0.80) to 0.20 (95% CI, 0.07-0.43) (vs 1.18 [95% CI, 0.92-1.52] to 0.44 [95% CI, 0.22-0.78] in men). Models with incident migraine as the outcome showed lower ORs across the ascending cardiovascular risk score categories. Age stratification suggested that the association between cardiovascular risk scores and migraine activity status were unlikely to be strongly influenced by age.

Conclusions and Relevance

In this cohort study of community-dwelling Dutch adults, the odds of having prevalent or incident migraine decreased with increasing cardiovascular risk score categories. These results support the hypothesis that a relatively healthy cardiovascular system increases the probability of having active or developing migraine in the future, especially among women. Sex differences might play a pathophysiological role in the association between migraine activity and vascular health.

Absolute cardiovascular risk assessment using 'real world' clinic blood pressures compared to standardized unobserved and ambulatory methods: an observational study

Clinic blood pressure (BP) is recommended for absolute cardiovascular disease (CVD) risk assessment. However, in 'real-world' settings, clinic BP measurement is unstandardised and less reliable compared to more rigorous methods but the impact for absolute CVD risk assessment is unknown. This study aimed to determine the difference in absolute CVD risk assessment using real-world clinic BP compared to standardised BP methods. Participants were patients (n = 226, 59 ± 15 years; 58% female) with hypertension referred to a BP clinic for assessment. 'Real-world' clinic BP was provided by the referring doctor. All participants had unobserved automated office BP (AOBP) and 24-h ambulatory BP monitoring (ABPM) measured at the clinic. Absolute CVD risk was calculated (Framingham) using systolic BP from the referring doctor (clinic BP), AOBP and ABPM, with agreement assessed by Kappa statistic. Clinic systolic BP was 18 mmHg than AOBP and daytime ABPM and 22 mmHg higher than 24-h ABPM (p < 0.001). Subsequently, absolute CVD risk scores using clinic BP were higher compared to AOBP, daytime ABPM and 24-h ABPM (10.4 ± 8.1%, 7.8 ± 6.4%, 7.8 ± 6.3%, and 7.3 ± 6.1%, respectively, P < 0.001). As a result, more participants were classified as high CVD risk using clinic BP (n = 89, 40%) compared with AOBP (n = 44, 20%) daytime ABPM (n = 38, 17%) and 24-h ABPM (n = 38, 17%) (p < 0.001) with weak agreement in risk classification (κ = 0.57[0.45-0.69], κ = 0.52[0.41-0.64] and κ = 0.55[0.43-0.66], respectively). Real-world clinic BP was higher and classified twice as many participants at high CVD risk compared to AOBP or ABPM. Given the challenges to high-quality BP measurement in clinic, more rigorous BP measurement methods are needed for absolute CVD risk assessment.

Does Adding Single-Nucleotide Polymorphisms to Risk Algorithms Improve Cardiovascular Disease Risk Prediction in Rheumatoid Arthritis? An Internal and External Validation of a Clinical Risk Score

OBJECTIVE

Current risk algorithms do not accurately predict cardiovascular disease (CVD) risk in rheumatoid arthritis (RA). An area of interest is that of single-nucleotide polymorphisms (SNPs), of which several have been associated with CVD in the general population. We investigated whether these SNPs are associated with CVD in RA and whether SNPs could improve CVD risk prediction in RA.

METHODS

Sixty SNPs were genotyped in 353 patients with RA. Logistic and Cox regression analyses were performed to identify SNPs that were associated with CVD (n = 99). A prediction model with clinical variables was made. SNPs were added to investigate the additional predictive value. Both models were internally validated. External validation was done in a separate cohort (n = 297).

RESULTS

rs3184504, rs4773144, rs12190287, and rs445925 were significantly associated with new CVD. The clinical prediction model consisted of age, sex, body mass index, systolic blood pressure, high-density lipoprotein cholesterol (HDLc), and creatinine, with an area under the curve (AUC) of 0.74 (P = 0.03). Internal validation resulted in an AUC of 0.76 (P < 0.01). A new model was made including SNPs and resulted in a model with rs17011666 and rs801426, age, total cholesterol, and HDLc, which performed slightly better with an AUC of 0.77 (P < 0.01). External validation resulted in a good fit for the clinical model, but a poor fit for the SNP model.

CONCLUSION

Several SNPs were associated with CVD in RA. Risk prediction slightly improved after adding SNPs to the models, but the clinical relevance is debatable. However, larger studies are needed to determine more accurately the additional value of these SNPs to CVD risk prediction algorithms.

Oral Corticosteroids for Skin Disease in the Older Population: Minimizing Potential Adverse Effects

Corticosteroids play a crucial role as anti-inflammatory and immunomodulatory agents in dermatology and other medical specialties; however, their therapeutic benefits are accompanied by significant risks, especially in older adults. This review examines the broad spectrum of adverse effects (AEs) associated with oral corticosteroid therapy and offers strategies to prevent, monitor, and manage these issues effectively in older adults. AEs associated with systemic corticosteroids include immune suppression, gastrointestinal problems, hyperglycemia, insulin resistance, weight gain, cardiovascular complications, ocular issues, osteoporosis, osteonecrosis, muscle weakness, collagen impairment, psychiatric symptoms, and adrenal suppression. To minimize these AEs, tailored dosing and duration, frequent monitoring, and additional preventative measures can be employed to optimize corticosteroid treatment. By customizing management plans to the specific needs and risk factors associated with each patient, clinicians can promote the safe and effective use of oral corticosteroids, ultimately improving outcomes and quality of life in patients with inflammatory dermatologic disorders.

Home Blood Pressure Variability Risk Prediction Score for Cardiovascular Disease Using Data From the J-HOP Study

BACKGROUND

Home blood pressure (BP) is more closely associated with cardiovascular event risk than office BP, but cardiovascular risk prediction based on home BP variability is lacking. This study developed a simple cardiovascular event prediction score, including home BP variability data, from the J-HOP study (Japan Morning Surge-Home Blood Pressure).

METHODS

The J-HOP study extended follow-up from December 2017 to May 2018 generated the study data set (4231 patients). Cardiovascular events included fatal/nonfatal stroke (n=94), coronary heart disease (n=124), heart failure (n=42), and aortic dissection (n=8). Cox proportional hazards models were used to predict overall cardiovascular risk. Potential covariates included age, sex, body mass index, smoking, history of diabetes, statin use, history of cardiovascular disease, total cholesterol:high-density lipoprotein cholesterol ratio, office systolic BP (SBP), mean of morning-evening average (MEave), home SBP, and average real variability of MEave home SBP. A risk score and models were constructed, and model performance was assessed.

RESULTS

Model performance was best when average real variability of MEave SBP was included (C statistic, 0.760). The risk score assigns points for age (5-year bands), sex, cardiovascular disease history, high-density lipoprotein cholesterol, mean MEave home SBP, and average real variability of MEave home SBP. Estimated 10-year cardiovascular risk ranged from ≤0.6% (score ≤0) to >32% (score ≥26). Calibration 2 statistics values for the model (2.66) and risk score (5.29) indicated excellent goodness of fit.

CONCLUSIONS

This simple cardiovascular disease prediction algorithm, including day-by-day home BP variability, could be used as part of a home BP-centered approach to hypertension management in clinical practice.

Predicting cardiovascular events with fluoropyrimidine chemotherapy using a standard cardiovascular risk calculator

AIMS

Fluoropyrimidine chemotherapy is important for treatment of many solid tumours but is associated with cardiotoxicity. The relationship of fluoropyrimidine-associated cardiotoxicity (FAC) with conventional cardiovascular (CV) risk factors is poorly understood, and standard cardiovascular risk scores are not validated in this context.

METHODS AND RESULTS

Single-centre retrospective study of patients treated with fluoropyrimidine chemotherapy using electronic health records for cardiovascular risk factors (and calculation of QRISK3 score), cancer treatment, and clinical outcomes. FAC was defined by cardiovascular events during or within 3 months of fluoropyrimidine treatment, and Cox regression was used to assess associations of CV risk and cancer treatment with FAC. One thousand eight hundred ninety-eight patients were included (45% male; median age 64 years), with median follow up 24.5 (11.5-48.3 months); 52.7% of patients were at moderate or high baseline CV risk (QRISK3 score >10%) Cardiovascular events occurred in 3.1% (59/1898)-most commonly angina (64.4%, 38/59) and atrial fibrillation (13.6%, 8/59), with 39% events during cycle one of treatment. In univariable analysis, QRISK3 score >20% was significantly associated with incident FAC (HR 2.25, 95% CI 1.11-4.93, P = 0.03). On multivariable analysis, beta-blocker use (HR 1.04, 95% CI 1.00-1.08, P = 0.04) and higher BMI (HR 2.33, 95% CI 1.04-5.19, P = 0.04) were independently associated with incident CV events. Thirty-two of the 59 patients with FAC were subsequently rechallenged with fluoropyrimidine chemotherapy, with repeat CV events in 6% (2/32). Incident FAC did not affect overall survival (P = 0.50).

CONCLUSIONS

High BMI and use of beta-blockers are associated with risk of CV events during fluoropyrimidine chemotherapy. QRISK3 score may also play a role in identifying patients at high risk of CV events during fluoropyrimidine chemotherapy. Re-challenge with further fluoropyrimidine chemotherapy can be considered in patients following CV events during prior treatment.

Use of Polygenic Risk Score for Prediction of Heart Failure in Cancer Survivors

Background

The risk for heart failure (HF) is increased among cancer survivors, but predicting individual HF risk is difficult. Polygenic risk scores (PRS) for HF prediction summarize the combined effects of multiple genetic variants specific to the individual.

Objectives

The aim of this study was to compare clinical HF prediction models with PRS in both cancer and noncancer populations.

Methods

Cancer and HF diagnoses were identified using International Classification of Diseases-10th Revision codes. HF risk was calculated using the ARIC (Atherosclerosis Risk in Communities) HF score (ARIC-HF). The PRS for HF (PRS-HF) was calculated according to the Global Biobank Meta-analysis Initiative. The predictive performance of the ARIC-HF and PRS-HF was compared using the area under the curve (AUC) in both cancer and noncancer populations.

Results

After excluding 2,644 participants with HF prior to consent, 440,813 participants without cancer (mean age 57 years, 53% women) and 43,720 cancer survivors (mean age 60 years, 65% women) were identified at baseline. Both the ARIC-HF and PRS-HF were significant predictors of incident HF after adjustment for chronic kidney disease, overall health rating, and total cholesterol. The PRS-HF performed poorly in predicting HF among cancer (AUC: 0.552; 95% CI: 0.539-0.564) and noncancer (AUC: 0.561; 95% CI: 0.556-0.566) populations. However, the ARIC-HF predicted incident HF in the noncancer population (AUC: 0.804; 95% CI: 0.800-0.808) and provided acceptable performance among cancer survivors (AUC: 0.748; 95% CI: 0.737-0.758).

Conclusions

The prediction of HF on the basis of conventional risk factors using the ARIC-HF score is superior compared to the PRS, in cancer survivors, and especially among the noncancer population.

Prediction model for recommending coronary artery calcium score screening (CAC-prob) in cardiology outpatient units: A development study

Despite the well-established significance of the CAC score as a cardiovascular risk marker, the timing of using CAC score in routine clinical practice remains unclear. We aim to develop a prediction model for patients visiting outpatient cardiology units, which can recommend whether CAC score screening is necessary. A prediction model using retrospective cross-sectional design was conducted. Patients who underwent CAC score screening were included. Eight candidate predictors were preselected, including age, gender, DM or primary hypertension, angina chest pain, LDL-C (≥130 mg/dl), presence of low HDL-C, triglyceride (≥150 mg/dl), and eGFR. The outcome of interest was the level of CAC score (CAC score 0, CAC score 1-99, CAC score ≥100). The model was developed using ordinal logistic regression, and model performance was evaluated in terms of discriminative ability and calibration. A total of 360 patients were recruited for analysis, comprising 136 with CAC score 0, 133 with CAC score 1-99, and 111 with CAC score ≥100. The final predictors identified were age, male gender, presence of hypertension or DM, and low HDL-C. The model demonstrated excellent discriminative ability (Ordinal C-statistics of 0.81) with visually good agreement on calibration plots. The implementation of this model (CAC-prob) has the potential to enhance precision in recommending CAC screening. However, external validation is necessary to assess its robustness in new patient cohorts.

Coronary Artery Disease, Family History, and Screening Perspectives: An Up-to-Date Review

Family history for CAD (coronary artery disease) is an established cardiovascular (CV) risk factor and it is progressively acquiring importance in patients' CV risk stratification. Numerous studies have demonstrated that individuals with a first-degree relative affected by CAD have a significantly higher risk of developing the condition themselves; in particular, when CAD occurs at an early age in relatives. Indeed, recently published CCS (chronic coronary syndrome) ESC (European Society of Cardiology) guidelines include family history (FH) as a risk factor to consider when calculating pre-test risk for CAD. ESC guidelines on preventive cardiology (2021) only suggested CV risk assessment in the presence of a positive FH for CV disease, not considering it in the actual risk scores. Evidence suggests that positive anamnesis for relatives affected by CAD correlates with ACS (acute coronary syndrome) and CAD, with slight differences in relative risk as far as the degree of kinship is concerned. Genetic factors contribute to this correlation by influencing key processes that affect heart health, such as cholesterol metabolism, blood pressure regulation, and inflammatory responses. New technologies in the genetics field are increasing the availability of genome sequencing, and new polymorphism panels are being tested as predictive for CAD, objectifying familiarity. Advances in imaging techniques allow the assessment of coronary atherosclerosis and its composition, and these are acquiring strength in evidence and recommendations in ESC guidelines as a way to define coronary disease in low and low-to-intermediate risk patients and to guide medical therapy and interventional procedures. Use of these emerging tools to guide screening is likely to be extended, beyond high CV risk patients, to individuals with FH for early CAD and/or specific genetic profiles, as recent evidence in the literature is suggesting.

Development and Internal Validation of Machine Learning to Predict Postoperative Worse Functional Status after Surgical Treatment for Thoracic Spinal Stenosis

BACKGROUND The objective of this study was to develop and validate machine learning (ML) algorithms to predict the 30-day and 6-month risk of deteriorating functional status following surgical treatment for thoracic spinal stenosis (TSS). We aimed to provide surgeons with tools to identify patients with TSS who have a higher risk of postoperative functional decline. MATERIAL AND METHODS The records of 327 patients with TSS who completed both follow-up visits were analyzed. Our primary endpoint was the dichotomized change in the perioperative Japanese Orthopedic Association (JOA) score, categorized based on whether it deteriorated or not. The models were developed using Naïve Bays, LightGBM, XGBoost, logistic regression, and random forest classification models. The model performance was assessed by accuracy and the c-statistic. ML algorithms were trained, optimized, and tested. RESULTS The best-performing algorithms for predicting functional decline at 30 days and 6 months after TSS surgery were XGBoost (accuracy=88.17%, c-statistic=0.83) and Naïve Bays (accuracy=86.03%, c-statistic=0.80). Both algorithms presented good calibration and discrimination in our testing data. We identified several significant predictors, including poor quality of intraoperative SSEP/MEP baseline, poor quality of preoperative SSEP, duration of symptoms, operated level, and motor dysfunction of the lower extremity. CONCLUSIONS The best-performing algorithms for predicting functional decline at 30 days and 6 months after TSS surgery were XGBoost (accuracy=88.17%, c-statistic=0.83) and Naïve Bays (accuracy=86.03%, c-statistic=0.80). Both algorithms presented good calibration and discrimination in our testing data. We identified several significant predictors, including poor quality of intraoperative SSEP/MEP baseline, poor quality of preoperative SSEP, duration of symptoms, operated level, and motor dysfunction of the lower extremity.

Proceedings of PRINCETON IV: PDE5 inhibitors and cardiac health symposium

INTRODUCTION

Prior consensus meetings have addressed the relationship between phosphodiesterase type 5 (PDE5) inhibition and cardiac health. Given significant accumulation of new data in the past decade, a fourth consensus conference on this topic was convened in Pasadena, California, on March 10 and 11, 2023.

OBJECTIVES

Our meeting aimed to update existing knowledge, assess current guidelines, and make recommendations for future research and practice in this area.

METHODS

An expert panel reviewed existing research and clinical practice guidelines.

RESULTS

Key findings and clinical recommendations are the following: First, erectile dysfunction (ED) is a risk marker and enhancer for cardiovascular (CV) disease. For men with ED and intermediate levels of CV risk, coronary artery calcium (CAC) computed tomography should be considered in addition to previous management algorithms. Second, sexual activity is generally safe for men with ED, although stress testing should still be considered for men with reduced exercise tolerance or ischemia. Third, the safety of PDE5 inhibitor use with concomitant medications was reviewed in depth, particularly concomitant use with nitrates or alpha-blockers. With rare exceptions, PDE5 inhibitors can be safely used in men being treated for hypertension, lower urinary tract symptoms and other common male disorders. Fourth, for men unresponsive to oral therapy or with absolute contraindications for PDE5 inhibitor administration, multiple treatment options can be selected. These were reviewed in depth with clinical recommendations. Fifth, evidence from retrospective studies points strongly toward cardioprotective effects of chronic PDE5-inhibitor use in men. Decreased rates of adverse cardiac outcomes in men taking PDE-5 inhibitors has been consistently reported from multiple studies. Sixth, recommendations were made regarding over-the-counter access and potential risks of dietary supplement adulteration. Seventh, although limited data exist in women, PDE5 inhibitors are generally safe and are being tested for use in multiple new indications.

CONCLUSION

Studies support the overall cardiovascular safety of the PDE5 inhibitors. New indications and applications were reviewed in depth.

Sex inequalities in cardiovascular risk prediction

AIMS

Evaluate sex differences in cardiovascular disease (CVD) risk prediction, including use of (i) optimal sex-specific risk predictors and (ii) sex-specific risk thresholds.

METHODS AND RESULTS

Prospective cohort study using UK Biobank, including 121 724 and 182 632 healthy men and women, respectively, aged 38-73 years at baseline. There were 11 899 (men) and 9110 (women) incident CVD cases (hospitalization or mortality) with a median of 12.1 years of follow-up. We used recalibrated pooled cohort equations (PCEs; 7.5% 10-year risk threshold as per US guidelines), QRISK3 (10% 10-year risk threshold as per UK guidelines), and Cox survival models using sparse sex-specific variable sets (via LASSO stability selection) to predict CVD risk separately in men and women. LASSO stability selection included 12 variables in common between men and women, with 3 additional variables selected for men and 1 for women. C-statistics were slightly lower for PCE than QRISK3 and models using stably selected variables, but were similar between men and women: 0.67 (0.66-0.68), 0.70 (0.69-0.71), and 0.71 (0.70-0.72) in men and 0.69 (0.68-0.70), 0.72 (0.71-0.73), and 0.72 (0.71-0.73) in women for PCE, QRISK3, and models using stably selected variables, respectively. At current clinically implemented risk thresholds, test sensitivity was markedly lower in women than men for all models: at 7.5% 10-year risk, sensitivity was 65.1 and 68.2% in men and 24.0 and 33.4% in women for PCE and models using stably selected variables, respectively; at 10% 10-year risk, sensitivity was 53.7 and 52.3% in men and 16.8 and 20.2% in women for QRISK3 and models using stably selected variables, respectively. Specificity was correspondingly higher in women than men. However, the sensitivity in women at 5% 10-year risk threshold increased to 50.1, 58.5, and 55.7% for PCE, QRISK3, and models using stably selected variables, respectively.

CONCLUSION

Use of sparse sex-specific variables improved CVD risk prediction compared with PCE but not QRISK3. At current risk thresholds, PCE and QRISK3 work less well for women than men, but sensitivity was improved in women using a 5% 10-year risk threshold. Use of sex-specific risk thresholds should be considered in any re-evaluation of CVD risk calculators.

Feasibility of multiorgan risk prediction with routinely collected diagnostics: a prospective cohort study in the UK Biobank

OBJECTIVES

Despite rising rates of multimorbidity, existing risk assessment tools are mostly limited to a single outcome of interest. This study tests the feasibility of producing multiple disease risk estimates with at least 70% discrimination (area under the receiver operating curve, AUROC) within the time and information constraints of the existing primary care health check framework.

DESIGN

Observational prospective cohort study SETTING: UK Biobank.

PARTICIPANTS

228 240 adults from the UK population.

INTERVENTIONS

None.

MAIN OUTCOME MEASURES

Myocardial infarction, atrial fibrillation, heart failure, stroke, all-cause dementia, chronic kidney disease, fatty liver disease, alcoholic liver disease, liver cirrhosis and liver failure.

RESULTS

Using a set of predictors easily gathered at the standard primary care health check (such as the National Health Service Health Check), we demonstrate that it is feasible to simultaneously produce risk estimates for multiple disease outcomes with AUROC of 70% or greater. These predictors can be entered once into a single form and produce risk scores for stroke (AUROC 0.727, 95% CI 0.713 to 0.740), all-cause dementia (0.823, 95% CI 0.810 to 0.836), myocardial infarction (0.785, 95% CI 0.775 to 0.795), atrial fibrillation (0.777, 95% CI 0.768 to 0.785), heart failure (0.828, 95% CI 0.818 to 0.838), chronic kidney disease (0.774, 95% CI 0.765 to 0.783), fatty liver disease (0.766, 95% CI 0.753 to 0.779), alcoholic liver disease (0.864, 95% CI 0.835 to 0.894), liver cirrhosis (0.763, 95% CI 0.734 to 0.793) and liver failure (0.746, 95% CI 0.695 to 0.796).

CONCLUSIONS

Easily collected diagnostics can be used to assess 10-year risk across multiple disease outcomes, without the need for specialist computing or invasive biomarkers. Such an approach could increase the utility of existing data and place multiorgan risk information at the fingertips of primary care providers, thus creating opportunities for longer-term multimorbidity prevention. Additional work is needed to validate whether these findings would hold in a larger, more representative cohort outside the UK Biobank.

A plasma proteomic signature for atherosclerotic cardiovascular disease risk prediction in the UK Biobank cohort

Background

While risk stratification for atherosclerotic cardiovascular disease (ASCVD) is essential for primary prevention, current clinical risk algorithms demonstrate variability and leave room for further improvement. The plasma proteome holds promise as a future diagnostic and prognostic tool that can accurately reflect complex human traits and disease processes. We assessed the ability of plasma proteins to predict ASCVD.

Method

Clinical, genetic, and high-throughput plasma proteomic data were analyzed for association with ASCVD in a cohort of 41,650 UK Biobank participants. Selected features for analysis included clinical variables such as a UK-based cardiovascular clinical risk score (QRISK3) and lipid levels, 36 polygenic risk scores (PRSs), and Olink protein expression data of 2,920 proteins. We used least absolute shrinkage and selection operator (LASSO) regression to select features and compared area under the curve (AUC) statistics between data types. Randomized LASSO regression with a stability selection algorithm identified a smaller set of more robustly associated proteins. The benefit of plasma proteins over standard clinical variables, the QRISK3 score, and PRSs was evaluated through the derivation of Δ AUC values. We also assessed the incremental gain in model performance using proteomic datasets with varying numbers of proteins. To identify potential causal proteins for ASCVD, we conducted a two-sample Mendelian randomization (MR) analysis.

Result

The mean age of our cohort was 56.0 years, 60.3% were female, and 9.8% developed incident ASCVD over a median follow-up of 6.9 years. A protein-only LASSO model selected 294 proteins and returned an AUC of 0.723 (95% CI 0.708-0.737). A clinical variable and PRS-only LASSO model selected 4 clinical variables and 20 PRSs and achieved an AUC of 0.726 (95% CI 0.712-0.741). The addition of the full proteomic dataset to clinical variables and PRSs resulted in a Δ AUC of 0.010 (95% CI 0.003-0.018). Fifteen proteins selected by a stability selection algorithm offered improvement in ASCVD prediction over the QRISK3 risk score [Δ AUC: 0.013 (95% CI 0.005-0.021)]. Filtered and clustered versions of the full proteomic dataset (consisting of 600-1,500 proteins) performed comparably to the full dataset for ASCVD prediction. Using MR, we identified 11 proteins as potentially causal for ASCVD.

Conclusion

A plasma proteomic signature performs well for incident ASCVD prediction but only modestly improves prediction over clinical and genetic factors. Further studies are warranted to better elucidate the clinical utility of this signature in predicting the risk of ASCVD over the standard practice of using the QRISK3 score.

Value of Glycemic Dispersion Index in Predicting Major Adverse Cardiovascular Events in Diabetic Patients with Concomitant Acute Coronary Syndrome

Aim

This investigation aims to assess the predictive value of the glycemic dispersion index (GDI), calculated by incorporating glycated hemoglobin, fasting plasma glucose, and 2-hour postprandial plasma glucose, in predicting major adverse cardiovascular events (MACE) within a 12-month timeframe for diabetic patients with concomitant acute coronary syndrome (ACS).

Methods

A retrospective study was conducted on 3261 diabetic patients with ACS who were hospitalized in the Department of Cardiology, the Sixth Affiliated Hospital of Kunming Medical University, from January 2016 to July 2022. Based on the inclusion and exclusion criteria, 512 patients were ultimately enrolled in the study. Their general information and laboratory test indicators were collected, and the occurrence of MACE within 12 months after admission was followed up and recorded for the enrolled patients, With the last follow-up having been concluded on July 31, 2023. The enrolled patients were stratified into four groups (Q1, Q2, Q3, Q4) based on their GDI values, from the lowest to the highest. Cox proportional hazards regression analysis and Kaplan-Meier survival analysis were employed to investigate the risk factors associated with MACE occurrence across these groups and to assess the cumulative risk of MACE over time within each group.

Results

The percentages of enrolled patients experiencing MACE in groups Q1 to Q4 were 10.16%, 12.50%, 15.63%, and 16.41%, respectively. GDI independently predicted the hazards for MACE in enrolled patients. The cumulative risk of MACE over time was considerably more significant in those with a GDI>4.21 than those with a GDI≤4.21.

Conclusion

The elevated GDI is correlated with an augmented risk of MACE in diabetic patients with concomitant ACS, thereby serving as an early indicator for assessing the unfavorable clinical prognosis of patients. This study offers novel insights into glycemic variability monitoring, enhancing prevention and treatment strategies for cardiovascular disease in people with diabetes.

Prevalence of cardiovascular events in a population-based registry of patients with systemic lupus erythematosus

BACKGROUND

The Manhattan Lupus Surveillance Program (MLSP), a population-based retrospective registry of patients with systemic lupus erythematosus (SLE), was used to investigate the prevalence of cardiovascular disease events (CVE) and compare rates among sex, age and race/ethnicity to population-based controls.

METHODS

Patients with prevalent SLE in 2007 aged ≥ 20 years in the MLSP were included. CVE required documentation of a myocardial infarction or cerebrovascular accident. We calculated crude risk ratios and adjusted risk ratios (ARR) controlling for sex, age group, race and ethnicity, and years since diagnosis. Data from the 2009-2010 National Health and Nutrition Examination Survey (NHANES) and the 2013-2014 NYC Health and Nutrition Examination Survey (NYC HANES) were used to calculate expected CVE prevalence by multiplying NHANES and NYC HANES estimates by strata-specific counts of patients with SLE. Crude prevalence ratios (PRs) using national and NYC estimates and age standardized prevalence ratios (ASPRs) using national estimates were calculated.

RESULTS

CVE occurred in 13.9% of 1,285 MLSP patients with SLE, and risk was increased among men (ARR:1.7, 95%CI:1.2-2.5) and older adults (age > 60 ARR:2.5, 95%CI:1.7-3.8). Compared with non-Hispanic Asian patients, CVE risk was elevated among Hispanic/Latino (ARR:3.1, 95%CI:1.4-7.0) and non-Hispanic Black (ARR:3.5, 95%CI1.6-7.9) patients as well as those identified as non-Hispanic and in another or multiple racial groups (ARR:4.2, 95%CI:1.1-15.8). Overall, CVE prevalence was higher among patients with SLE than nationally (ASPR:3.1, 95%CI:3.0-3.1) but did not differ by sex. Compared with national race and ethnicity-stratified estimates, CVE among patients with SLE was highest among Hispanics/Latinos (ASPR:4.3, 95%CI:4.2-4.4). CVE was also elevated among SLE registry patients compared with all NYC residents. Comparisons with age-stratified national estimates revealed PRs of 6.4 (95%CI:6.2-6.5) among patients aged 20-49 years and 2.2 (95%CI:2.1-2.2) among those ≥ 50 years. Male (11.3, 95%CI:10.5-12.1), Hispanic/Latino (10.9, 95%CI:10.5-11.4) and non-Hispanic Black (6.2, 95%CI:6.0-6.4) SLE patients aged 20-49 had the highest CVE prevalence ratios.

CONCLUSIONS

These population-based estimates of CVE in a diverse registry of patients with SLE revealed increased rates among younger male, Hispanic/Latino and non-Hispanic Black patients. These findings reinforce the need to appropriately screen for CVD among all SLE patients but particularly among these high-risk patients.

Beyond lung cancer screening, an opportunity for early detection of chronic obstructive pulmonary disease and cardiovascular diseases

BACKGROUND

Lung cancer screening programs concern smokers at risk for cardiovascular diseases (CVDs) and chronic obstructive pulmonary disease (COPD). The LUMASCAN (LUng Cancer Screening, MArkers and low-dose computed tomography SCANner) study aimed to evaluate the acceptability and feasibility of screening for these 3 diseases in a community population with centralized organization and to determine low-dose computed tomography (CT) markers associated with each disease.

METHODS

This cohort enrolled participants meeting National Comprehensive Cancer Network criteria (v1.2014) in an organized lung cancer-screening program including low-dose CT scans; spirometry; evaluations of coronary artery calcifications (CACs); and a smoking cessation plan at inclusion, 1, and 2 years; then telephone follow-up. Outcomes were the participation rate and the proportion of participants affected by lung cancer, obstructive lung disease, or CVD events. Logistic-regression models were used to identify radiological factors associated with each disease.

RESULTS

Between 2016 and 2019, a total of 302 participants were enrolled: 61% men; median age 58.8 years; 77% active smoker; 11% diabetes; 38% hypertension; and 27% taking lipid-lowering agents. Inclusion, 1-year, and 2-year participation rates were 99%, 81%, 79%, respectively. After a median follow-up of 5.81 years, screenings detected 12 (4%) lung cancer, 9 of 12 via low-dose CT (78% localized) and 3 of 12 during follow-up (all stage IV), 83 (27%) unknown obstructive lung disease, and 131 (43.4%) moderate to severe CACs warranting a cardiology consultation. Preexisting COPD and moderate to severe CACs were associated with major CVD events with odds ratios of 1.98 (95% confident interval [CI] = 1.00 to 3.88) and 3.27 (95% CI = 1.72 to 6.43), respectively.

CONCLUSION

The LUMASCAN study demonstrated the feasibility of combined screening for lung cancer, COPD, and CVD in a community population. Its centralized organization enabled high participation and coordination of healthcare practitioners.

Breastfeeding duration is associated with favorable body composition and lower glycoprotein acetyls in later life

OBJECTIVE

The aim of the present study was to investigate associations between lifetime breastfeeding behaviors and cardiovascular risk in later reproductive years.

METHOD

This was a prospective 10-year longitudinal cohort study of 168 parous women. Health, lifestyle and infant feeding questionnaires, blood samples, anthropometry and body composition were collected. Cardiovascular risk was estimated using QRISK®3 and hierarchical multiple linear regression analysis performed.

RESULTS

Mean age was 42.4 years (SD 3.8; range 31-50) and 98.7% (n = 156/158) were premenopausal. Ever breastfeeding rates were 72.6% (n = 122/168) and 37.5% (n = 63/168) lifetime ≥12 months breastfeeding duration. Median durations were 5.5 weeks for exclusive breastfeeding (IQR 35.8; range 0-190) and 30.5 weeks for any breastfeeding (IQR 84.0; range 0-488). Breastfeeding duration was not associated with QRISK®3 scores in adjusted models. Lower glycoprotein acetyls were associated with ever breastfeeding (P = 0.03), and lifetime breastfeeding ≥12 months (P = 0.001). Lifetime breastfeeding ≥12 months and longer exclusive breastfeeding were associated with lower fat mass index (P = 0.03, P = 0.01), tissue percentage fat (P = 0.02, P = 0.009) and visceral adipose tissue volume (P = 0.04, P = 0.025) after correcting for confounders including body mass index.

CONCLUSION

Longer breastfeeding is associated with favorable body composition and lower glycoprotein acetyls, a novel inflammatory biomarker associated with cardiometabolic risk. Breastfeeding is a low-cost, health promoting behavior for women and infants. Pregnant women, especially those at higher risk of cardiovascular disease, should be counseled about the potential benefits of exclusive and longer breastfeeding duration.

Development and external validation of a head and neck cancer risk prediction model

BACKGROUND

Head and neck cancer (HNC) incidence is on the rise, often diagnosed at late stage and associated with poor prognoses. Risk prediction tools have a potential role in prevention and early detection.

METHODS

The IARC-ARCAGE European case-control study was used as the model development dataset. A clinical HNC risk prediction model using behavioral and demographic predictors was developed via multivariable logistic regression analyses. The model was then externally validated in the UK Biobank cohort. Model performance was tested using discrimination and calibration metrics.

RESULTS

1926 HNC cases and 2043 controls were used for the development of the model. The development dataset model including sociodemographic, smoking, and alcohol variables had moderate discrimination, with an area under curve (AUC) value of 0.75 (95% CI, 0.74-0.77); the calibration slope (0.75) and tests were suggestive of good calibration. 384 616 UK Biobank participants (with 1177 HNC cases) were available for external validation of the model. Upon external validation, the model had an AUC of 0.62 (95% CI, 0.61-0.64).

CONCLUSION

We developed and externally validated a HNC risk prediction model using the ARCAGE and UK Biobank studies, respectively. This model had moderate performance in the development population and acceptable performance in the validation dataset. Demographics and risk behaviors are strong predictors of HNC, and this model may be a helpful tool in primary dental care settings to promote prevention and determine recall intervals for dental examination. Future addition of HPV serology or genetic factors could further enhance individual risk prediction.

Cardiovascular Risk in Rheumatoid Arthritis: Considerations on Assessment and Management

In the context of holistic therapeutic practices, the cardiovascular risk of patients with rheumatoid arthritis (RA) needs to be addressed as a major factor of compromised disease prognosis and increased mortality. The elevated prevalence of cardiovascular disease (CVD) by more than twofold in RA has been attributed, inter alia, to chronic inflammation exacerbating arterial stiffness, increased onset of hypertension, dyslipidaemia and diabetes mellitus, sedentary lifestyle, and antirheumatic drug complications. CVD risk in RA can be currently assessed by practitioners through accessible adapted calculators, but it remains problematic as their diagnostic accuracy is not superior to calculators designed for the general population. Implementation of guideline-oriented personalised interventions remains the cornerstone for cardiovascular risk management in RA. Remarkably, there is lack of a consortium that brings together different health care providers engaged in the care of patients with RA (e.g., rheumatologists, cardiologists, general practitioners, etc), to guide cardiovascular risk assessment and management. This narrative review aims at providing an overview of current CVD risk assessment and management options, highlighting their pivotal role in the comprehensive treatment of RA patients.

Cardiovascular disease risk in people of African ancestry with HIV in the United Kingdom

OBJECTIVES

Our objective was to describe the prevalence of cardiovascular disease (CVD) risk factors in people of African ancestry with HIV in the UK.

METHODS

We conducted a cross-sectional analysis of CVD risk factors in Black people with HIV aged ≥40 years and estimated the 10-year CVD risk using QRISK®3-2018. Correlations between body mass index (BMI) and CVD risk factors were described using Pearson correlation coefficients, and factors associated with 10-year CVD risk ≥5% were described using logistic regression.

RESULTS

We included 833 Black people with HIV and a median age of 54 years; 54% were female, 50% were living with obesity (BMI ≥30 kg/m2), 61% had hypertension, and 19% had diabetes mellitus. CVD risk >5% ranged from 2% in female participants aged 40-49 years to 99% in men aged ≥60 years, and use of statins ranged from 7% in those with CVD risk <2.5% to 64% in those with CVD risk ≥20%. BMI was correlated (R2 0.1-0.2) with triglycerides and diastolic blood pressure in women and with glycated haemoglobin, systolic and diastolic blood pressure, and total:high-density lipoprotein (HDL) cholesterol ratio in men. In both female and male participants, older age, blood pressure, diabetes mellitus, and kidney disease were strongly associated with CVD risk ≥5%, whereas obesity, total:HDL cholesterol, triglycerides, and smoking status were variably associated with CVD risk ≥5%.

CONCLUSIONS

We report a high burden of CVD risk factors, including obesity, hypertension, and diabetes mellitus, in people of African ancestry with HIV in the UK. BMI-focused interventions in these populations may improve CVD risk while also addressing other important health issues.

Ancestry-aligned polygenic scores combined with conventional risk factors improve prediction of cardiometabolic outcomes in African populations

BACKGROUND

Cardiovascular diseases (CVD) are a major health concern in Africa. Improved identification and treatment of high-risk individuals can reduce adverse health outcomes. Current CVD risk calculators are largely unvalidated in African populations and overlook genetic factors. Polygenic scores (PGS) can enhance risk prediction by measuring genetic susceptibility to CVD, but their effectiveness in genetically diverse populations is limited by a European-ancestry bias. To address this, we developed models integrating genetic data and conventional risk factors to assess the risk of developing cardiometabolic outcomes in African populations.

METHODS

We used summary statistics from a genome-wide association meta-analysis (n = 14,126) in African populations to derive novel genome-wide PGS for 14 cardiometabolic traits in an independent African target sample (Africa Wits-INDEPTH Partnership for Genomic Research (AWI-Gen), n = 10,603). Regression analyses assessed relationships between each PGS and corresponding cardiometabolic trait, and seven CVD outcomes (CVD, heart attack, stroke, diabetes mellitus, dyslipidaemia, hypertension, and obesity). The predictive utility of the genetic data was evaluated using elastic net models containing multiple PGS (MultiPGS) and reference-projected principal components of ancestry (PPCs). An integrated risk prediction model incorporating genetic and conventional risk factors was developed. Nested cross-validation was used when deriving elastic net models to enhance generalisability.

RESULTS

Our African-specific PGS displayed significant but variable within- and cross- trait prediction (max.R2 = 6.8%, p = 1.86 × 10-173). Significantly associated PGS with dyslipidaemia included the PGS for total cholesterol (logOR = 0.210, SE = 0.022, p = 2.18 × 10-21) and low-density lipoprotein (logOR =  - 0.141, SE = 0.022, p = 1.30 × 10-20); with hypertension, the systolic blood pressure PGS (logOR = 0.150, SE = 0.045, p = 8.34 × 10-4); and multiple PGS associated with obesity: body mass index (max. logOR = 0.131, SE = 0.031, p = 2.22 × 10-5), hip circumference (logOR = 0.122, SE = 0.029, p = 2.28 × 10-5), waist circumference (logOR = 0.013, SE = 0.098, p = 8.13 × 10-4) and weight (logOR = 0.103, SE = 0.029, p = 4.89 × 10-5). Elastic net models incorporating MultiPGS and PPCs significantly improved prediction over MultiPGS alone. Models including genetic data and conventional risk factors were more predictive than conventional risk models alone (dyslipidaemia: R2 increase = 2.6%, p = 4.45 × 10-12; hypertension: R2 increase = 2.6%, p = 2.37 × 10-13; obesity: R2 increase = 5.5%, 1.33 × 10-34).

CONCLUSIONS

In African populations, CVD and associated cardiometabolic trait prediction models can be improved by incorporating ancestry-aligned PGS and accounting for ancestry. Combining PGS with conventional risk factors further enhances prediction over traditional models based on conventional factors. Incorporating data from target populations can improve the generalisability of international predictive models for CVD and associated traits in African populations.

DNA methylation in cardiovascular disease and heart failure: novel prediction models?

BACKGROUND

Cardiovascular diseases (CVD) affect over half a billion people worldwide and are the leading cause of global deaths. In particular, due to population aging and worldwide spreading of risk factors, the prevalence of heart failure (HF) is also increasing. HF accounts for approximately 36% of all CVD-related deaths and stands as the foremost cause of hospitalization. Patients affected by CVD or HF experience a substantial decrease in health-related quality of life compared to healthy subjects or affected by other diffused chronic diseases.

MAIN BODY

For both CVD and HF, prediction models have been developed, which utilize patient data, routine laboratory and further diagnostic tests. While some of these scores are currently used in clinical practice, there still is a need for innovative approaches to optimize CVD and HF prediction and to reduce the impact of these conditions on the global population. Epigenetic biomarkers, particularly DNA methylation (DNAm) changes, offer valuable insight for predicting risk, disease diagnosis and prognosis, and for monitoring treatment. The present work reviews current information relating DNAm, CVD and HF and discusses the use of DNAm in improving clinical risk prediction of CVD and HF as well as that of DNAm age as a proxy for cardiac aging.

CONCLUSION

DNAm biomarkers offer a valuable contribution to improving the accuracy of CV risk models. Many CpG sites have been adopted to develop specific prediction scores for CVD and HF with similar or enhanced performance on the top of existing risk measures. In the near future, integrating data from DNA methylome and other sources and advancements in new machine learning algorithms will help develop more precise and personalized risk prediction methods for CVD and HF.

Ten-year cardiovascular disease risk and related factors in lifetime marijuana use with comorbid methamphetamine-associated psychotic disorder: a QRISK®3 study

BACKGROUND

Methamphetamine use and related direct and indirect problems are increasing all over the world. The coexistence of lifetime marijuana use (LMU) and methamphetamine use disorder (MUD) may also be accompanied by psychotic symptoms (MAP). Methamphetamine and marijuana use are known to pose risks for cardiovascular diseases (CVDs). However, ten-year CVD risk and inflammation markers of LMU-MUD (non-psychosis group) and LMU-MAP (psychosis group) subjects and the relationship of various sociodemographic and clinical variables with these markers have not yet been examined.

METHODS

Thirty-two male subjects were included in non-psychosis group and 72 male subjects in psychosis group. Sociodemographic and clinical characteristics were recorded. Psychotic symptom severity of psychosis group subjects was measured. The ten-year CVD risk was calculated using QRISK®3 model.

RESULTS

Age, cigarettes/pack-years, alcohol use onset age, drug use onset age, methamphetamine use onset age, duration of methamphetamine use, education and marital status of the groups were similar (p > 0.05). There was a statistical difference between the non-psychosis and psychosis groups in terms of self-mutilation history (p < 0.001), suicidal attempt history (p = 0.007), homicidal attempt history (p = 0.002), psychiatric hospitalization history (p = 0.010). Ten-year QRISK®3 score was 4.90 ± 9.30 in the psychosis group, while it was 1.60 ± 1.43 in the non-psychosis group (p = 0.004). The mean heart age of the psychosis group was 14 years higher than their chronological age, while the mean heart age of the non-psychosis group was 8 years higher. Neutrophil to lymphocyte ratio (NLR) (p = 0.003) was higher in the psychosis group. A significant correlation was detected between ten-year QRISK®3 and positive psychotic symptoms in the psychosis group (r = 0.274, p = 0.020). Regression analysis showed that self-mutilation history, NLR and relative risk obtained from QRISK®3 can be used to distinguish non-psychosis group and psychosis group subjects (sensitivity = 91.7; Nagelkerke R2 0.438; p = 0.001).

CONCLUSIONS

This study is important as it demonstrates for the first time that among the subjects using marijuana and methamphetamine, those with psychotic symptoms have a higher NLR and ten-year CVD risk.

Treatment drop-in in a contemporary cohort used to derive cardiovascular risk prediction equations

BACKGROUND

No routinely recommended cardiovascular disease (CVD) risk prediction equations have adjusted for CVD preventive medications initiated during follow-up (treatment drop-in) in their derivation cohorts. This will lead to underestimation of risk when equations are applied in clinical practice if treatment drop-in is common. We aimed to quantify the treatment drop-in in a large contemporary national cohort to determine whether equations are likely to require adjustment.

METHODS

Eight de-identified individual-level national health administrative datasets in Aotearoa New Zealand were linked to establish a cohort of almost all New Zealanders without CVD and aged 30-74 years in 2006. Individuals dispensing blood-pressure-lowering and/or lipid-lowering medications between 1 July 2006 and 31 December 2006 (baseline dispensing), and in each 6-month period during 12 years' follow-up to 31 December 2018 (follow-up dispensing), were identified. Person-years of treatment drop-in were determined.

RESULTS

A total of 1 399 348 (80%) out of the 1 746 695 individuals in the cohort were not dispensed CVD medications at baseline. Blood-pressure-lowering and/or lipid-lowering treatment drop-in accounted for 14% of follow-up time in the group untreated at baseline and increased significantly with increasing predicted baseline 5-year CVD risk (12%, 31%, 34% and 37% in <5%, 5-9%, 10-14% and ≥15% risk groups, respectively) and with increasing age (8% in 30-44 year-olds to 30% in 60-74 year-olds).

CONCLUSIONS

CVD preventive treatment drop-in accounted for approximately one-third of follow-up time among participants typically eligible for preventive treatment (≥5% 5-year predicted risk). Equations derived from cohorts with long-term follow-up that do not adjust for treatment drop-in effect will underestimate CVD risk in higher risk individuals and lead to undertreatment. Future CVD risk prediction studies need to address this potential flaw.

Age and sex specific thresholds for risk stratification of cardiovascular disease and clinical decision making: prospective open cohort study

Objective

To quantify the potential advantages of using 10 year risk prediction models for cardiovascular disease, in combination with risk thresholds specific to both age and sex, to identify individuals at high risk of cardiovascular disease for allocation of statin treatment.

Design

Prospective open cohort study.

Setting

Primary care data from the UK Clinical Practice Research Datalink GOLD, linked with hospital admissions from Hospital Episode Statistics and national mortality records from the Office for National Statistics in England, 1 January 2006 to 31 May 2019.

Participants

1 046 736 individuals (aged 40-85 years) with no cardiovascular disease, diabetes, or a history of statin treatment at baseline using data from electronic health records.

Main outcome measures

10 year risk of cardiovascular disease, calculated with version 2 of the QRISK cardiovascular disease risk algorithm (QRISK2), with two main strategies to identify individuals at high risk: in strategy A, estimated risk was a fixed cut-off value of ≥10% (ie, as per the UK National Institute for Health and Care Excellence guidelines); in strategy B, estimated risk was ≥10% or ≥90th centile of age and sex specific risk distributions.

Results

Compared with strategy A, strategy B stratified 20 241 (149.8%) more women aged ≤53 years and 9832 (150.2%) more men aged ≤47 years as having a high risk of cardiovascular disease; for all other ages the strategies were the same. Assuming that treatment with statins would be initiated in those identified as high risk, differences in the estimated gain in cardiovascular disease-free life years from statin treatment for strategy B versus strategy A were 0.14 and 0.16 years for women and men aged 40 years, respectively; among individuals aged 40-49 years, the numbers needed to treat to prevent one cardiovascular disease event for strategy B versus strategy A were 39 versus 21 in women and 19 versus 15 in men, respectively.

Conclusions

This study quantified the potential gains in cardiovascular disease-free life years when implementing prevention strategies based on age and sex specific risk thresholds instead of a fixed risk threshold for allocation of statin treatment. Such gains should be weighed against the costs of treating more younger people with statins for longer.

Risk of recurrent stroke and dementia following acute stroke by changes in kidney function: results from the Perindopril Protection Against Recurrent Stroke Study

BACKGROUND

Limited data exist on the relationship between declining kidney function and cardiovascular events, dementia, and mortality in patients with a history of stroke.Thus the aims of the study were to investigate functional relationships between dynamic kidney function change and cardiovascular outcomes, and clarify whether adding kidney parameters to conventional cardiovascular risk factors improves model discrimination.

METHODS

Post hoc analysis of the Perindopril Protection Against Recurrent Stroke Study (PROGRESS) clinical trial of blood pressure lowering for the secondary prevention of stroke. We examined the association between dynamic kidney function defined as percentage change (declines of >30%, and >0 to ≤30%, and increases of ≥0 to <30%, and ≥30%) in estimated glomerular filtration rate (eGFR) over 2 years and recurrent stroke, major cardiovascular events, dementia and all-cause death over the next 2 years using Cox proportional hazard models controlling for eGFR at registration and potential confounders. Restricted cubic splines were used to assess the functional relationships. C-statistics and Net Reclassification Improvement (NRI) at 2 years were used to assess model discrimination.

RESULTS

In 4591 patients followed for a mean of approximately 2 years, 254 (5.5%) developed recurrent stroke, 391 (8.5%) had a major cardiovascular event, 221 (4.8%) developed dementia, and 271 (5.9%) died. Reverse J-like or U-like relationships were observed for percent declines in eGFR and outcomes. Using declines in eGFR of >0 to ≤30% as a reference, increased risks were evident for a greater decline (>30%) in relation to recurrent stroke [adjusted hazard ratio 1.85, 95% confidence interval (CI) 1.20-2.85], major cardiovascular event (2.24, 1.62-3.10) and all-cause death (2.09, 1.39-3.15). A larger increase (≥30%) in eGFR was also associated with a greater risk of all-cause death (1.96, 1.14-3.37). Improvements in the C-statistic were found by adding baseline eGFR and percent change compared with a model with conventional cardiovascular risk factors alone, for major cardiovascular events, dementia, and all-cause mortality.

CONCLUSION

Declining kidney function following an incident cerebrovascular event is associated with additional risk of a major cardiovascular events, dementia, and 2-year mortality. However, a large increase in kidney function was also found to be associated with a higher risk of mortality.

Personalizing cardiovascular risk prediction for patients with systemic lupus erythematosus

OBJECTIVE

Cardiovascular disease (CVD) risk is increased in SLE and underestimated by general population prediction algorithms. We aimed to develop a novel SLE-specific prediction tool, SLECRISK, to provide a more accurate estimate of CVD risk in SLE.

METHODS

We studied patients in the Brigham and Women's Hospital SLE cohort. We collected one-year baseline data including the presence of traditional CVD factors and SLE-related features at cohort enrollment. Ten-year follow-up for the first major adverse cardiovascular event (MACE; myocardial infarction (MI), stroke, or cardiac death) began at day +1 following the baseline period (index date). ICD-9/10 codes identified MACE were adjudicated by board-certified cardiologists. Least absolute shrinkage and selection operator regression selected SLE-related variables to add to the American College of Cardiology/American Heart Association (ACC/AHA) Pooled Cohort Risk Equations 10-year risk Cox regression model. Model fit statistics and performance (sensitivity, specificity, positive/negative predictive value, c-statistic) for predicting moderate/high 10-year risk (≥7.5 %) of MACE were assessed and compared to ACC/AHA, Framingham risk score (FRS), and modified FRS (mFRS). Optimism adjustment internal validation was performed using bootstrapping.

RESULTS

We included 1,243 patients with 90 MACEs (46 MIs, 36 strokes, 19 cardiac deaths) over 8946.5 person-years of follow-up. SLE variables selected for the new prediction algorithm (SLECRISK) were SLE activity (remission/mild vs. moderate/severe), disease duration (years), creatinine (mg/dL), anti-dsDNA, anti-RNP, lupus anticoagulant, anti-Ro positivity, and low C4. The sensitivity for detecting moderate/high-risk (≥7.5 %) of MACE using SLECRISK was 0.74 (95 %CI: 0.65, 0.83), which was better than the sensitivity of the ACC/AHA model (0.38 (95 %CI: 0.28, 0.48)). It also identified 3.4-fold more moderate/high-risk patients than the ACC/AHA. Patients who were moderate/high-risk according to SLECRISK but not ACC/AHA, were more likely to be young women with severe SLE and few other traditional CVD risk factors. Model performance between SLECRISK, FRS, and mFRS were similar.

CONCLUSION

The novel SLECRISK tool is more sensitive than the ACC/AHA for predicting moderate/high 10-year risk for MACE and may be particularly useful in predicting risk for young females with severe SLE. Future external validation studies utilizing cohorts with more severe SLE are needed.

Preventing and managing cardiovascular events in patients with inflammatory bowel diseases treated with small-molecule drugs, an international Delphi consensus

Janus kinase (JAK) inhibitors and sphingosine 1 phosphate (S1P) receptor modulators are small molecule drugs (SMDs) approved for IBD treatment. Their use in clinical practice might be limited due to cardiovascular concerns. We aimed to provide guidance on risk assessment, monitoring, and management strategies, aiming to minimize potential cardiovascular risks of SMDs and to facilitate an adequate shared decision-making. A systematic literature search was conducted, and proposed statements were prepared. A virtual consensus meeting was held, in which eleven IBD physicians and two cardiovascular specialists from ten countries attended. Proposed statements were voted upon in an anonymous manner. Agreement was defined as at least 75 % of participants voting as 'agree' with each statement. Consensus was reached for eighteen statements. Available evidence does not show a higher risk of cardiovascular events with JAK inhibitors in the overall IBD population, although it might be increased in patients with an unfavorable cardiovascular profile. S1P receptor modulators may be associated with a risk of bradycardia, atrioventricular blocks, and hypertension. Cardiovascular risk stratification should be done before initiation of SMDs. Although the risk of cardiovascular events in patients with IBD on SMDs appears to be low overall, caution should still be taken in certain scenarios.

Pitfalls in Developing Machine Learning Models for Predicting Cardiovascular Diseases: Challenge and Solutions

In recent years, there has been explosive development in artificial intelligence (AI), which has been widely applied in the health care field. As a typical AI technology, machine learning models have emerged with great potential in predicting cardiovascular diseases by leveraging large amounts of medical data for training and optimization, which are expected to play a crucial role in reducing the incidence and mortality rates of cardiovascular diseases. Although the field has become a research hot spot, there are still many pitfalls that researchers need to pay close attention to. These pitfalls may affect the predictive performance, credibility, reliability, and reproducibility of the studied models, ultimately reducing the value of the research and affecting the prospects for clinical application. Therefore, identifying and avoiding these pitfalls is a crucial task before implementing the research. However, there is currently a lack of a comprehensive summary on this topic. This viewpoint aims to analyze the existing problems in terms of data quality, data set characteristics, model design, and statistical methods, as well as clinical implications, and provide possible solutions to these problems, such as gathering objective data, improving training, repeating measurements, increasing sample size, preventing overfitting using statistical methods, using specific AI algorithms to address targeted issues, standardizing outcomes and evaluation criteria, and enhancing fairness and replicability, with the goal of offering reference and assistance to researchers, algorithm developers, policy makers, and clinical practitioners.

Proposal of a novel cardiovascular risk prediction score in lupus nephritis

Introduction

Patients with systemic lupus erythematosus are prone to develop cardiovascular disease (CVD), and have increased morbidity and mortality.

Methods

We conducted a retrospective analysis on lupus nephritis patients to assess the occurrence and predictors of major adverse cardiovascular events (MACE). Data were collected from patients who underwent kidney biopsy between 2005 and 2020. Statistical analysis was performed to unveil correlations.

Results

91 patients were analyzed in this period, with a mean age of 37.3 ± 12.3 years and 86% being female. The mean follow-up time was 62 ± 48 months. 15.38% of the patients underwent at least one MACE. Two patients deceased of CVD. Increased age (35.81 ± 11.14 vs 45.5 ± 15.11 years, p=0.012) entailed a higher occurrence of MACEs. Neutrophil count (5.15 ± 2.83 vs 7.3 ± 2.99 Giga/L, p=0.001) was higher, whereas diastolic blood pressure (DBP) was lower (89.51 ± 10.96 vs 78.43 ± 6.9 mmHg, p<0.001) at the time of the biopsy in patients with MACE. Age, neutrophil count, and DBP proved to be independent predictors of MACEs. We propose a new model (CANDE - Cardiovascular risk based on Age, Neutrophil count, and Diastolic blood pressure Estimation score) calculated from these variables, which predicts the probability of MACE occurrence.

Conclusion

This study underscores the importance of actively screening for cardiovascular risks in this vulnerable patient population. Age, neutrophil count, and diastolic blood pressure have been established as independent risk factors for MACE in lupus nephritis. The CANDE score derived from these parameters may serve as a prompt, cost-effective, and easily accessible estimation tool for assessing the likelihood of major adverse cardiovascular risk. These findings emphasize the necessity for comprehensive management strategies addressing both immune dysregulation and cardiovascular risk factors in systemic lupus erythematosus to mitigate adverse outcomes.