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

Effect of glucagon-like peptide-1 receptor agonists on vascular risk factors among adults with type 2 diabetes and established atherosclerotic cardiovascular disease

Introduction

Limited data exist on the cardiovascular effectiveness of once-weekly (OW) glucagon-like peptide-1 receptor agonists (GLP-1 RAs) in real-world practice.

Methods

We assessed the OW GLP-1 RA effects on vascular risk factors in adults with type 2 diabetes and atherosclerotic cardiovascular disease using data from a large-scale US electronic health record database (index date = first prescription of OW GLP-1 RA). Exploratory analyses were performed on patients newly initiating OW GLP-1 RAs with semaglutide, OW GLP-1 RAs without semaglutide, and semaglutide. Changes in vascular risk factors were evaluated by comparing mean measures between the 12-month pre- and post-index periods. Analyses were conducted for all three cohorts and subpopulations including stratified by tercile of baseline vascular risk factor value.

Results

In the final cohorts ([1] OW GLP-1 RA including semaglutide: n = 20,084; [2] OW GLP-1 RA excluding semaglutide: n = 16,894; [3] semaglutide: n = 3,435), significant mean reductions (P < 0.001) were observed from baseline to post-index in hemoglobin A1c (%, [1] -1.1; [2] -1.1; [3] -1.2), low-density lipoprotein cholesterol (mg/dL, [1] -6.4; [2] -6.4; [3] -6.9), total cholesterol (mg/dL, [1] -11.0; [2] -11.1; [3] -10.7), triglycerides (mg/dL, [1] -31.8; [2] -31.4; [3] -33.1), systolic blood pressure (mmHg, [1] -1.5; [2] -1.2; [3] -3.1), body weight (kg, [1] -2.7; [2] -2.4; [3] -4.3) and body mass index (kg/m2; [1] -0.9; [2] -0.8; [3] -1.4). Largest reductions were observed in the top tercile.

Conclusion

Our data suggest GLP-1 RAs are associated with significant reductions in key vascular risk factors in real-world practice.

External validation of the accuracy of cardiovascular risk prediction tools in psoriatic disease: a UK Biobank study

INTRODUCTION

Risk prediction is important for preventing and managing cardiovascular disease (CVD). CVD risk prediction tools designed for the general population may be inaccurate in people with inflammatory diseases.

OBJECTIVES

To investigate the performance of four cardiovascular risk prediction tools (QRISK3, Framingham Risk Score, Reynolds Risk Score and SCORE) in psoriatic arthritis (PsA) and psoriasis. We also compare performance in participants with no inflammatory conditions and in people with rheumatoid arthritis (RA).

METHODS

This research utilised the UK Biobank Resource. We identified participants with PsA, psoriasis and RA and calculated their cardiovascular risk using each risk tool. We assessed model calibration by comparing observed and predicted outcomes. Discrimination of 10-year risk prediction was assessed using time-dependent area under ROC curve (AUC), sensitivity, specificity, positive and negative predictive values.

RESULTS

We included 769 individuals with PsA, 8062 with psoriasis and 4772 with RA when assessing the QRISK3 tool. Predictions for individuals with psoriasis were roughly as accurate as those with no inflammatory conditions with time-dependent AUC of 0.74 (95%CI, 0.72, 0.76) and of 0.74 (95%CI, 0.72, 0.77) respectively. In contrast, individuals with PsA obtained the least accurate predictions with an AUC of 0.70 (95%CI, 0.64, 0.76). Individuals with RA also obtained less accurate predictions with AUC of 0.72 (0.69,0.74). For the Framingham risk score, AUCs varied between 0.61 (95%CI, 0.55, 0.68) for participants with PsA and 0.71 (95%CI, 0.68, 0.74) for individuals with no inflammatory condition.

CONCLUSIONS

In general, CVD risk prediction accuracy was similar for individuals with psoriasis or no inflammatory condition, but lower for individuals with PsA or RA.

The Cardiovascular Safety of Tumour Necrosis Factor Inhibitors in Arthritic Conditions: A Structured Review with Recommendations

There is accumulating evidence that inflammation is a key driver of atherosclerosis development and thrombotic complications. This pathophysiological mechanism explains, at least in part, the increased cardiovascular risk of patients with immune-mediated arthritis. Experimental and clinical studies have shown that tumour necrosis factor (TNF) plays a pathological role in both vascular and joint diseases, suggesting that TNF inhibitors (TNFis) may limit cardiovascular events in patients with rheumatoid arthritis (RA), psoriatic arthritis (PsA) or spondyloarthritis (SpA). This review summarizes studies exploring the effects of TNFis on cardiovascular outcomes in patients with RA, PsA or SpA. Clinical studies suggest that TNFis reduce vascular inflammation and may improve (or prevent worsening of) endothelial dysfunction and arterial stiffness. There is evidence that TNFis reduce the incidence of cardiovascular events in patients with inflammatory arthritis compared with non-biological treatments, particularly in patients with rheumatoid arthritis. Fewer studies have compared the effects of different classes of biological therapy on outcomes, but found no significant difference in the risk of cardiovascular events between patients taking TNFis and other biological therapy. In contrast, patients at high cardiovascular risk may derive greater benefit from a TNFi than from a Janus kinase inhibitor (JAKi). The cardiovascular impact of JAKis is still under debate, with a recent safety warning. Targeted control of inflammation is a key strategy to reduce the risk of major adverse cardiovascular events in patients with inflammatory arthritis. Cardiovascular evaluation and risk stratification, using a multidisciplinary approach involving rheumatology and cardiology teams, are recommended to guide optimal immunomodulatory treatment.

Assessing the effects of prostate cancer therapies on cardiovascular health

Contemporary advances in prostate cancer treatments have markedly improved patient outcomes, yet concerns persist regarding the increased cardiovascular toxicity of prostate cancer treatments, which is multifaceted. Local therapies entail non-negligible cardiovascular risks. The effects of androgen deprivation therapy, which is pivotal in disease management, on cardiovascular health remains contentious, with gonadotropin-releasing hormone agonists and antagonists showing varying cardiovascular outcomes. Despite the ongoing controversy over the cardiovascular risks of gonadotropin-releasing hormone antagonists versus agonists, current evidence does not support favouring one over the other based solely on cardiovascular risk. Combination therapy with androgen receptor pathway inhibitors and androgen deprivation therapy shows additive cardiovascular risks, but robust comparative data are lacking. Chemotherapies such as docetaxel and cabazitaxel, along with emerging targeted therapies and radiopharmaceuticals, are associated with varied cardiovascular risks, necessitating personalized patient assessment. Clinicians should adhere to cardio-oncology guidelines when prescribing therapeutic agents, especially for patients with pre-existing cardiovascular conditions. Optimal monitoring and management strategies are essential to mitigate cardiovascular morbidity and mortality.

2025 Heart Disease and Stroke Statistics: A Report of US and Global Data From the American Heart Association

BACKGROUND

The American Heart Association (AHA), in conjunction with the National Institutes of Health, annually reports the most up-to-date statistics related to heart disease, stroke, and cardiovascular risk factors, including core health behaviors (smoking, physical activity, nutrition, sleep, and obesity) and health factors (cholesterol, blood pressure, glucose control, and metabolic syndrome) that contribute to cardiovascular health. The AHA Heart Disease and Stroke Statistical Update presents the latest data on a range of major clinical heart and circulatory disease conditions (including stroke, brain health, complications of pregnancy, kidney disease, congenital heart disease, rhythm disorders, sudden cardiac arrest, subclinical atherosclerosis, coronary heart disease, cardiomyopathy, heart failure, valvular disease, venous thromboembolism, and peripheral artery disease) and the associated outcomes (including quality of care, procedures, and economic costs).

METHODS

The AHA, through its Epidemiology and Prevention Statistics Committee, continuously monitors and evaluates sources of data on heart disease and stroke in the United States and globally to provide the most current information available in the annual Statistical Update with review of published literature through the year before writing. The 2025 AHA Statistical Update is the product of a full year's worth of effort in 2024 by dedicated volunteer clinicians and scientists, committed government professionals, and AHA staff members. This year's edition includes a continued focus on health equity across several key domains and enhanced global data that reflect improved methods and incorporation of ≈3000 new data sources since last year's Statistical Update.

RESULTS

Each of the chapters in the Statistical Update focuses on a different topic related to heart disease and stroke statistics.

CONCLUSIONS

The Statistical Update represents a critical resource for the lay public, policymakers, media professionals, clinicians, health care administrators, researchers, health advocates, and others seeking the best available data on these factors and conditions.

Real-world feasibility, accuracy and acceptability of automated retinal photography and AI-based cardiovascular disease risk assessment in Australian primary care settings: a pragmatic trial

We aim to assess the real-world accuracy (primary outcome), feasibility and acceptability (secondary outcomes) of an automated retinal photography and artificial intelligence (AI)-based cardiovascular disease (CVD) risk assessment system (rpCVD) in Australian primary care settings. Participants aged 45-70 years who had recently undergone all or part of a CVD risk assessment were recruited from two general practice clinics in Victoria, Australia. After consenting, participants underwent retinal imaging using an automated fundus camera, and an rpCVD risk score was generated by a deep learning algorithm. This score was compared against the World Health Organisation (WHO) CVD risk score, which incorporates age, sex, and other clinical risk factors. The predictive accuracy of the rpCVD and WHO CVD risk scores for 10-year incident CVD events was evaluated using data from the UK Biobank, with the accuracy of each system assessed through the area under the receiver operating characteristic curve (AUC). Participant satisfaction was assessed through a survey, and the imaging success rate was determined by the percentage of individuals with images of sufficient quality to produce an rpCVD risk score. Of the 361 participants, 339 received an rpCVD risk score, resulting in a 93.9% imaging success rate. The rpCVD risk scores showed a moderate correlation with the WHO CVD risk scores (Pearson correlation coefficient [PCC] = 0.526, 95% CI: 0.444-0.599). Despite this, the rpCVD system, which relies solely on retinal images, demonstrated a similar level of accuracy in predicting 10-year incident CVD (AUC = 0.672, 95% CI: 0.658-0.686) compared to the WHO CVD risk score (AUC = 0.693, 95% CI: 0.680-0.707). High satisfaction rates were reported, with 92.5% of participants and 87.5% of general practitioners (GPs) expressing satisfaction with the system. The automated rpCVD system, using only retinal photographs, demonstrated predictive accuracy comparable to the WHO CVD risk score, which incorporates multiple clinical factors including age, the most heavily weighted factor for CVD prediction. This underscores the potential of the rpCVD approach as a faster, easier, and non-invasive alternative for CVD risk assessment in primary care settings, avoiding the need for more complex clinical procedures.

Association of inflammatory risk based on the Glasgow Prognostic Score with long-term mortality in patients with cardiovascular disease

The secondary prevention strategy for cardiovascular disease (CVD) does not include anti-inflammatory treatment, which may lead to long-term inflammation in some patients. The aim of this study was to assess the association between inflammatory risk based on the Glasgow Prognostic Score (GPS) and long-term mortality risk in patients with CVD. This study included 3833 patients (≥ 20 years old) with CVD in the National Health and Nutrition Survey from 1999 to 2010 in the United States. The mortality rate was determined by correlation with the National Death Index on December 31, 2019. The GPS consists of the serum C-reactive protein and the serum albumin. The primary outcome was all-cause death, which included cardiac death and non-cardiac death. Cox proportional hazards adjusted for demographic factors and traditional cardiovascular risk factors were used to test the impact of the GPS on mortality. The sensitivity analysis was conducted on subsets within the cohort of patients with CVD, including congestive heart failure, coronary artery disease, angina, heart attack, and stroke. Among 3833 CVD patients with a median follow-up of 9.6 years, 2431 (63.4%) all-cause deaths, 822 (21.4%) cardiac deaths, and 1609 (41.9%) non-cardiac deaths were recorded. After full model adjustment, compared with those of the GPS (0) group, the hazard ratios (HRs) of all-cause death for GPS (1) and GPS (2) were 1.66 (95% confidence interval (CI), 1.48-1.86) and 2.75 (95% CI 2.01-3.75), respectively (P for trend < 0.001). Compared with those of the GPS (0) group, the HRs of cardiac death for the GPS (1) and GPS (2) groups were 1.69 (95% CI 1.39-2.05) and 2.18 (95% CI 1.22-3.91), respectively (P for trend < 0.001). Compared with those of the GPS (0) group, the HRs of non-cardiac death for the GPS (1) and GPS (2) groups were 1.65 (95% CI 1.44-1.89) and 3.05 (95% CI 2.11-4.40), respectively (P for trend < 0.001). The results of the sensitivity analysis were similar to those of the overall cohort. In our analysis of the United States National Database, we discovered that the GPS, a measure of inflammatory risk, was significantly associated with an increased risk of mortality among patients with CVD. Specifically, we observed that patients with a higher GPS had significantly higher risks of all-cause, cardiac, and non-cardiac mortality compared to those with a lower score. These findings suggest that the GPS, comprising easily obtainable biomarkers, could serve as a valuable tool for risk stratification in CVD patients and may contribute to the improvement of patient outcomes.

Treatment of Osteoporosis and Osteoarthritis in the Oldest Old

Osteoporosis and osteoarthritis are key diseases of musculoskeletal ageing and are increasing in prevalence and burden with the progressively ageing population worldwide. These conditions are thus particularly common in 'the oldest old', and there are complexities of managing them within the context of extensive multimorbidity, physical and mental disability, and polypharmacy, the rates for all of which are high in this population. In this narrative review, we explore the epidemiology of osteoporosis and osteoarthritis in the oldest old before examining trials and real-world data relating to the pharmacological treatment of these diseases in older adults, including anti-resorptives and bone-forming agents in osteoporosis and symptomatic slow-acting drugs for osteoarthritis, paracetamol, and non-steroidal anti-inflammatory drugs in osteoarthritis, recognising that the oldest old are usually excluded from clinical trials. We then review the potential benefits of nutritional interventions and exercise therapy before highlighting the health economic benefits of interventions for osteoporosis and osteoarthritis. The high prevalence of risk factors for both disease and adverse events associated with treatment in the oldest old mean that careful attention must be paid to the potential benefits of intervention (including fracture risk reduction and improvements in osteoarthritis pain and function) versus the potential harms and adverse effects. Further direct evidence relating to such interventions is urgently needed from future research.

The role of imaging in defining cardiovascular risk to help cancer patient management: a scoping review

OBJECTIVE

This scoping review explores the potential role of cancer-staging chest CT scans in assessing cardiovascular (CV) risk in cancer patients. It aims to evaluate: (1) the correlation between non-gated chest CT and the conventional Agatston score from cardiac CT; (2) the association between coronary calcium scores from non-gated chest CT and CV risk in non-oncological patients; (3) the link between coronary calcium assessed by non-gated chest CT and CV events or endothelial damage in cancer patients.

METHODS

Three different searches were performed on PubMed, according to the three steps described above. Both original articles and systematic reviews were included.

RESULTS

Many studies in the literature have found a strong correlation between coronary calcium scores from non-gated chest CTs and the conventional Agatston scores from gated cardiac CTs. Various methodologies, including Agatston scoring, ordinal scoring, and the "extent" and "length" methods, have been successfully adapted for use with non-gated chest CTs. Studies show that non-gated scans, even those using iodinated contrast, can accurately assess coronary calcification and predict CV risk, with correlations as high as r = 0.94 when compared to cardiac CTs. In oncological settings, studies demonstrated a significant link between coronary calcium levels on non-gated chest CTs and higher CV risk, including MACE and overall mortality.

CONCLUSIONS

Radiological assessment of coronary calcium on non-gated CT scans shows potential for improving CV risk prediction.

CRITICAL RELEVANCE STATEMENT

Non-gated chest CT scans can detect endothelial damage in cancer patients, highlighting the need for standardized radiological practices to assess CV risks during routine oncological follow-up, thereby enhancing radiology's role in comprehensive cancer care.

KEY POINTS

Cancer therapies improve outcomes but increase cardiovascular risk, requiring balanced management. Coronary calcification on non-gated CT correlates with Agatston scores, predicting cardiovascular risk. Routinely performed CTs predict cardiovascular risk, optimizing the management of cancer patients.

A Proteomics-Based Approach for Prediction of Different Cardiovascular Diseases and Dementia

BACKGROUND

Many studies have explored whether individual plasma protein biomarkers improve cardiovascular disease risk prediction. We sought to investigate the use of a plasma proteomics-based approach in predicting different cardiovascular outcomes.

METHODS

Among 51 859 UK Biobank participants (mean age, 56.7 years; 45.5% male) without cardiovascular disease and with proteomics measurements, we examined the primary composite outcome of fatal and nonfatal coronary heart disease, stroke, or heart failure (major adverse cardiovascular events), as well as additional secondary cardiovascular outcomes. An exposome-wide association study was conducted using relative protein concentrations, adjusted for a range of classic, demographic, and lifestyle risk factors. A prediction model using only age, sex, and protein markers (protein model) was developed using a least absolute shrinkage and selection operator-regularized approach (derivation: 80% of cohort) and validated using split-sample testing (20% of cohort). Their performance was assessed by comparing calibration, net reclassification index, and c statistic with the PREVENT (Predicting Risk of CVD Events) risk score.

RESULTS

Over a median 13.6 years of follow-up, 4857 participants experienced first major adverse cardiovascular events. After adjustment, the proteins most strongly associated with major adverse cardiovascular events included NT-proBNP (N-terminal pro B-type natriuretic peptide; hazard ratio [HR], 1.68 per SD increase), proADM (pro-adrenomedullin; HR, 1.60), GDF-15 (growth differentiation factor-15; HR, 1.47), WFDC2 (WAP four-disulfide core domain protein 2; HR, 1.46), and IGFBP4 (insulin-like growth factor-binding protein 4; HR, 1.41). In total, 222 separate proteins were predictors of all outcomes of interest in the protein model, and 86 were selected for the primary outcome specifically. In the validation cohort, compared with the PREVENT risk factor model, the protein model improved net reclassification (net reclassification index +0.09), and c statistic (+0.051) for major adverse cardiovascular events. The protein model also improved the prediction of other outcomes, including ASCVD (c statistic +0.035), myocardial infarction (+0.023), stroke (+0.024), aortic stenosis (+0.015), heart failure (+0.060), abdominal aortic aneurysm (+0.024), and dementia (+0.068).

CONCLUSIONS

Measurement of targeted protein biomarkers produced superior prediction of aggregated and disaggregated cardiovascular events. This study represents proof of concept for the application of targeted proteomics in predicting a range of cardiovascular outcomes.

Revealing the limitations of 10-year MACE observations: 20-year observed total cardiovascular burden in the EPIC-Norfolk study

BACKGROUND

Primary prevention strategies for cardiovascular disease (CVD) conventionally rely on 10-year risk estimates of major adverse cardiovascular events (MACE). However, communicating longer-term total CVD risk may better facilitate informed preventive decisions. Therefore, we aimed to quantify how well 10-year observed incidence reflects 20-year observed incidence and how MACE reflects total CVD events across demographic groups, using observations in long-term prospective data.

METHODS

In individuals aged 40-79 without CVD or diabetes from the population-based EPIC-Norfolk cohort, we compared the first occurrence of 10 and 20 years (1) 3-point MACE events (non-fatal myocardial infarction+non-fatal stroke+fatal CVD) and (2) total CVD events (all non-fatal and fatal CVD events leading to hospitalisation), stratified by sex and age.

RESULTS

Among 22 569 participants (57% women), incident 10-year and 20-year 3-point MACE was 5.3% and 15.5%, respectively, yielding 20/10 year ratios from 2.2 (in older men) to 4.5 (in younger women). Total CVD increased from 10.5% at 10 years to 26.9% at 20 years, with ratios ranging from 1.9 (older men) to 3.9 (younger women). Ratios between 10-year MACE and 20-year total CVD varied substantially, ranging from 3-fold in (older men) to 10-fold (younger women).

CONCLUSIONS

The observed incidence of CVD roughly triples from 10 to 20 years of follow-up, with 10-year MACE observations underestimating 20-year total CVD burden by a factor ranging from 3 (older men) to 10 (younger women). These findings highlight the limitations of communicating 10-year MACE risk assessments to facilitate informed decisions in longer-term CVD prevention-particularly in younger women.

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.

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.

Risk-directed management of chronic kidney disease

The timely and rational institution of therapy is a key step towards reducing the global burden of chronic kidney disease (CKD). CKD is a heterogeneous entity with varied aetiologies and diverse trajectories, which include risk of kidney failure but also cardiovascular events and death. Developments in the past decade include substantial progress in CKD risk prediction, driven in part by the accumulation of electronic health records data. In addition, large randomized clinical trials have demonstrated the effectiveness of sodium-glucose co-transporter 2 inhibitors, glucagon-like peptide 1 receptor agonists and mineralocorticoid receptor antagonists in reducing adverse events in CKD, greatly expanding the options for effective therapy. Alongside angiotensin-converting enzyme inhibitors and angiotensin receptor blockers, these classes of medication have been proposed to be the four pillars of CKD pharmacotherapy. However, all of these drug classes are underutilized, even in individuals at high risk. Leveraging prognostic estimates to guide therapy could help clinicians to prescribe CKD-related therapies to those who are most likely to benefit from their use. Risk-based CKD management thus aligns patient risk and care, allowing the prioritization of absolute benefit in determining therapeutic selection and timing. Here, we discuss CKD prognosis tools, evidence-based management and prognosis-guided therapies.

Risk Assessment Prior to Cardiotoxic Anticancer Therapies in 7 Steps

The burdens of cardiovascular (CV) diseases and cardiotoxic side effects of cancer treatment in oncology patients are increasing in parallel. The European Society of Cardiology (ESC) 2022 Cardio-Oncology guidelines recommend the use of standardized risk stratification tools to determine the risk of cardiotoxicity associated with different anticancer treatment modalities and the severity of their complications. The use of the Heart Failure Association-International Cardio-Oncology Society (HFA-ICOS) is essential for assessing risk prior to starting cancer treatment, and validation of these methods has been performed in patients receiving anthracyclines, human epidermal receptor 2 (HER2)-targeted therapies and breakpoint cluster region-abelson oncogene locus (BCR-ABL) inhibitors. The benefits of performing baseline CV risk assessment and stratification include early recognition of cardiotoxicities, personalisation of cancer treatment and monitoring strategies, and allocation of cardioprotection to those at the highest risk. This review summarizes the key points of risk stratification in these patients. The steps include identifying the target population, assessing nonmodifiable and modifiable CV risk factors, reviewing previous oncologic therapies and CV histories, and performing baseline investigations. In summary, this review aims to provide general physicians with a simple 7-step guide that will help steer and navigate them through cardiac risk evaluation of potentially cardiotoxic oncologic treatment strategies.

Beyond glycated haemoglobin: Modelling contemporary management of type 2 diabetes with the updated Cardiff model

AIMS

Recommendations on the use of newer type 2 diabetes (T2D) treatments (e.g., SGLT2 inhibitors and GLP-1 receptor agonists [RA]) in contemporary clinical guidelines necessitate a change in how T2D models approach therapy selection and escalation. Dynamic, person-centric clinical decision-making considers factors beyond a patient's HbA1c and glycaemic targets, including cardiovascular (CV) risk, comorbidities and bodyweight. This study aimed to update the existing Cardiff T2D health economic model to reflect modern T2D management and to remain fit-for-purpose in supporting decision-making.

MATERIALS AND METHODS

The Cardiff T2D model's therapy selection/escalation module was updated from a conventional, glucose-centric to a holistic approach. Risk factor progression equations were updated based on UKPDS90; the cardio-kidney-metabolic benefits of SGLT2i and GLP-1 RA were captured via novel risk equations derived from relevant outcomes trial data. The significance of the updates was illustrated by comparing predicted outcomes and costs for a newly diagnosed T2D population between conventional and holistic approaches to disease management, where the latter represents recent treatment guidelines.

RESULTS

A holistic approach to therapy selection/escalation enables early introduction of SGLT2i and GLP-1 RA in modelled pathways in a manner aligned to guidelines and primarily due to elevated CV risk. Compared with a conventional approach, only considering HbA1c, patients experience fewer clinical events and gain additional health benefits.

CONCLUSIONS

Predictions based on a glucose-centric approach to therapy are likely to deviate from real-world observations. A holistic approach is more able to capture the nuances of contemporary clinical practice. T2D modelling must evolve to remain robust and relevant.

Updating the Scottish national cardiovascular risk score: ASSIGN version 2.0

BACKGROUND

The Assessing cardiovascular risk using Scottish Intercollegiate Guidelines Network (ASSIGN) risk score, developed in 2006, is used in Scotland for estimating the 10-year risk of first atherosclerotic cardiovascular disease (ASCVD). Rates of ASCVD are decreasing, and an update is required. This study aimed to recalibrate ASSIGN (V.2.0) using contemporary data and to compare recalibration with other potential approaches for updating the risk score.

METHODS

Data from Scotland-resident participants from UK Biobank (2006-2010) and the Generation Scotland Scottish Family Health Study (2006-2010), aged 40-69 and without previous ASCVD, were used for the derivation of scores. External evaluation was conducted on UK Biobank participants who were not residents of Scotland. The original ASSIGN predictor variables and weights formed the basis of the new sex-specific risk equation to predict the 10-year risk of ASCVD. Different approaches for updating ASSIGN (recalibration, rederivation and regression adjustment) were tested in the evaluation cohort.

RESULTS

The original ASSIGN score overestimated ASCVD risk in the evaluation cohort, with median predicted 10-year risks of 10.6% for females and 15.1% for males, compared with observed risks of 6% and 11.4%, respectively. The derivation cohort included 44 947 (57% females and a mean age of 55) participants. The recalibrated score, ASSIGN V.2.0, improved model fit in the evaluation cohort, predicting median 10-year risk of 4% for females and 8.9% for males. Similar improvements were achieved using the regression-adjusted model. Rederivation of ASSIGN using new beta coefficients offered only modest improvements in calibration and discrimination beyond simple recalibration. At the current risk threshold of20% 10-year risk, the original ASSIGN equation yielded a positive predictive value (PPV) of 16.3% and a negative predictive value (NPV) of 94.4%. Recalibrated ASSIGN V.2.0 showed similar performance at a 10% threshold, with a PPV of 16.8% and an NPV of 94.6%.

CONCLUSIONS

The recalibrated ASSIGN V.2.0 will give a more accurate estimation of contemporary ASCVD risk in Scotland.

Cardiovascular profile of systemic lupus erythematosus patients hospitalized for COVID-19 in Spain: Analysis of the SEMI-COVID-19 Registry

BACKGROUND

Despite advancements in understanding the interplay between systemic lupus erythematosus (SLE), cardiovascular disease and COVID-19, challenges and knowledge gaps persist. This study aimed to characterize the cardiovascular profiles of SLE patients hospitalized with COVID-19 and to evaluate the influence of SLE on the development of cardiovascular complications.

METHODS

This was a multicentre, nationwide observational study in which data were sourced from the SEMI-COVID-19 Registry between March 1, 2020, and March 31, 2021, involving 150 Spanish hospitals. SLE patients were matched with non-SLE patients based on sex, age, and hospitalization date.

RESULTS

Of the 20,970 patients included in the SEMI-COVID-19 Registry, 38 were previously diagnosed with SLE. The non-SLE group was composed of 103 patients. The mean age of the SLE patients was 63 years, with 81.6% females and 21.1% non-European patients. SLE patients exhibited a significantly higher frequency of chronic kidney disease (14.4% vs 2.9%; p=0.004), stroke (23.7% vs 2.9%; p<0.001), and increased use of cardiovascular medications. SLE demonstrated an independent association with the occurrence of major cardiovascular events (MACE) (OR: 3.934; 95% CI: 1.247-12.432).

CONCLUSIONS

SLE patients hospitalized for COVID-19 are at high risk of having an unfavorable baseline cardiovascular profile and are more prone to MACEs and adverse noncardiovascular outcomes during hospitalization.

Medical history predicts phenome-wide disease onset and enables the rapid response to emerging health threats

The COVID-19 pandemic exposed a global deficiency of systematic, data-driven guidance to identify high-risk individuals. Here, we illustrate the utility of routinely recorded medical history to predict the risk for 1741 diseases across clinical specialties and support the rapid response to emerging health threats such as COVID-19. We developed a neural network to learn from health records of 502,489 UK Biobank participants. Importantly, we observed discriminative improvements over basic demographic predictors for 1546 (88.8%) endpoints. After transferring the unmodified risk models to the All of US cohort, we replicated these improvements for 1115 (78.9%) of 1414 investigated endpoints, demonstrating generalizability across healthcare systems and historically underrepresented groups. Ultimately, we showed how this approach could have been used to identify individuals vulnerable to severe COVID-19. Our study demonstrates the potential of medical history to support guidance for emerging pandemics by systematically estimating risk for thousands of diseases at once at minimal cost.

Identify the underlying true model from other models for clinical practice using model performance measures

OBJECTIVE

To assess whether the outcome generation true model could be identified from other candidate models for clinical practice with current conventional model performance measures considering various simulation scenarios and a CVD risk prediction as exemplar.

STUDY DESIGN AND SETTING

Thousands of scenarios of true models were used to simulate clinical data, various candidate models and true models were trained on training datasets and then compared on testing datasets with 25 conventional use model performance measures. This consists of univariate simulation (179.2k simulated datasets and over 1.792 million models), multivariate simulation (728k simulated datasets and over 8.736 million models) and a CVD risk prediction case analysis.

RESULTS

True models had overall C statistic and 95% range of 0.67 (0.51, 0.96) across all scenarios in univariate simulation, 0.81 (0.54, 0.98) in multivariate simulation, 0.85 (0.82, 0.88) in univariate case analysis and 0.85 (0.82, 0.88) in multivariate case analysis. Measures showed very clear differences between the true model and flip-coin model, little or none differences between the true model and candidate models with extra noises, relatively small differences between the true model and proxy models missing causal predictors.

CONCLUSION

The study found the true model is not always identified as the "outperformed" model by current conventional measures for binary outcome, even though such true model is presented in the clinical data. New statistical approaches or measures should be established to identify the casual true model from proxy models, especially for those in proxy models with extra noises and/or missing causal predictors.

A novel algorithm-based risk classification for vascular damage in men with erectile dysfunction

BACKGROUND

Penile dynamic color doppler duplex ultrasound (CDDU) is a relevant tool in assessing men with suspected vasculogenic erectile dysfunction (V-ED).

AIM

To investigate (1) factors potentially associated with V-ED to define risk classes useful in predicting V-ED; (2) the response to phosphodiesterase type 5 inhibitors (PDE5i); and (3) the onset of incident major cardiovascular (CV) events.

METHODS

A cohort of men with ED and without known concomitant CVD was grouped into: patients undergoing CDDU (N. 301) and patients not undergoing CDDU but prospectively monitored for incident major CV events after initiating PDE5i (N. 127). Logistic regression and Chi-square Automatic Interaction Detectors (CHAID) methodology were employed to identify potential predictors and develop a novel risk classification system. Receiver operating characteristic (ROC) curves and decision curve analysis was performed to assess its accuracy.

OUTCOMES

Factors associated with V-ED useful to develop a novel risk classification system predicting incident major CV events and PDE5i response.

RESULTS

The new classification defines patients as follows: Very Low Risk [age < 53, body mass index (BMI) < 25 Kg/m2], Low Risk (age < 53, BMI > 25 Kg/m2, non-smokers), Moderate Risk (age > 53, non-smokers), High Risk (age < 53, BMI > 25 Kg/m2, smokers), and Very High Risk (age > 53, smokers). Multivariable logistic regression analysis highlighted age, BMI, and smoking as significant predictors of V-ED. CHAID methodology yielded a risk classification system with an accuracy of 0.79. Notably, "Very High Risk" class was associated with a significantly increased risk of incident major CV events [odds ratio (OR) 4.00, 95% confidence interval (CI) 1.06-15.08, P < .05]. Moreover, patients belonging to "Very High Risk" and "High Risk" classes were also associated with diminished PDE5i response. At Kaplan-Meier analysis, men belonging to "Very High Risk" class depicted a notable risk of incident major CV events (P = .03).

CLINICAL IMPLICATIONS

We propose a novel risk classification system which may have some clinical value in tailoring patients at significantly higher risk of V-ED. Although preliminary, current findings also suggest that the novel risk classification system could help tailoring men at potential increased risk of incident major CV events and those not responding to PDE5i.

STRENGTHS AND LIMITATIONS

This study introduces a novel user-friendly risk stratification tool for V-ED, emphasizing the need for CV screening and alternative therapies for higher-risk groups. A limited number of events in the cohort with follow-up for major CV events and response to PDE5is constrains the interpretation of the results. Current findings need an external validation cohort.

CONCLUSION

Patients with ED categorized as either "Very High Risk" or "High Risk" should undergo a CDDU due to an increased risk of V-ED. Additionally, despite the clinical impact of these findings need further investigation, patients classified as "Very High Risk" could face a heightened risk of major CV events and a lower response to PDE5is.

Effect of nebivolol on erectile function: a systematic review and meta-analysis of randomized controlled trials

BACKGROUND

Historically, β-blockers have been associated with erectile dysfunction (ED). Nebivolol, a third-generation β-blocker, may have had no negative effect on erectile function because of its vasodilating properties. However, the evidence level was considered either as low or very low.

AIM

A systematic review and meta-analysis of randomized controlled trials (RCTs) was conducted to determine the effect of nebivolol on erectile function.

METHODS

All published RCTs were searched through PubMed, Cochrane Library, Web of Science, and Embase until October 2023. Review Manager version 5.3.0 was used for statistical analysis. Sensitivity analyses were performed by excluding each study using Stata 17 software.

OUTCOMES

The primary outcome was the International Index of Erectile Function (IIEF)-5 score. We excluded publication types, including letters, reviews, and meta-analyses.

RESULTS

We identified four RCTs in this meta-analysis. All included studies compared the effects of nebivolol vs metoprolol on erectile function. Eight parallel groups with 397 individuals reported IIEF-5 scores. A random-effect model revealed that the IIEF-5 score was significantly higher in the nebivolol group (MD 1.81, 95%CI 0.95-2.68, P < .0001, I2 = 99%). We conducted a sensitivity analysis by removing each individual study and observed that there was no significantly different result. Furthermore, we conducted a prespecified subgroup analysis based on the dosage of metoprolol, patients with ED at the time of enrollment, and disease type. Subgroup analysis revealed that heterogeneity significantly decreased, and the result of the IIEF-5 score was stable and consistent.

CLINICAL IMPLICATIONS

Our results provides stronger evidence that nebivolol significantly reduced the risk of ED occurrence or progression.

STRENGTHS AND LIMITATIONS

Our meta-analysis included high-quality RCTs and conducted a predetermined subgroup analysis. However, the main limitations are the limited number of included studies and their heterogeneity.

CONCLUSION

Our meta-analysis provided stronger evidence that nebivolol significantly reduced the risk of ED occurrence or progression compared with metoprolol, irrespective of whether the patient had ED or not. This meta-analysis could serve as an important reference for future studies in this field.

Differences in Statin Eligibility With the Use of Predicting Risk of Cardiovascular Disease EVENTs Versus Pooled Cohort Equations in the UK Biobank

The Pooled Cohort Equations (PCEs), developed by the American Heart Association (AHA) and American College of Cardiology, have been widely used since 2013 to estimate 10-year atherosclerotic cardiovascular disease (ASCVD) risk and guide statin therapy. Recently, the AHA introduced the Predicting Risk of CVD EVENTs (PREVENT) equations to improve ASCVD risk estimation. However, the effect of using PREVENT instead of PCEs on risk classification and statin eligibility remains unclear. This retrospective cohort study analyzed 261,303 UK Biobank participants, aged 40 to 69 years, who were free from cardiovascular disease and not on statin therapy. The PCEs and the base PREVENT equations were used to estimate 10-year ASCVD risk, categorize risk levels, and determine statin eligibility based on a common risk threshold of 7.5%. The median 10-year ASCVD risk was 5.2% (2.2%, 10.6%) using the PCEs and 3.5% (1.8%, 5.8%) with the PREVENT equations. The PREVENT equations classified 14.0% of participants as high-risk (ASCVD risk >7.5%), compared to 36.9% classified by PCEs. Among participants classified as intermediate-risk by PCEs, 75.3% were reclassified as low-risk by PREVENT. The proportion of individuals eligible for statin use by the PREVENT equation was 19.9%, and by the PCEs was 40.7%. The corresponding difference was 20.8% (95% confidence intervals [CI]: 20.6% to 20.9%). More men (33.0% [95% CI: 32.7% to 33.3%]) than women (11.5% [95% CI: 11.3% to 11.7%]) and more individuals in the older age group (60 to 69 years: 34.0% [95% CI: 33.7% to34.3%]) than in the younger age group (40 to 49 years: 3.5% [95% CI: 3.3% to 3.6%]) would not be recommended for statin consideration with the PREVENT equations. In conclusion, based on the common risk threshold of 7.5%, replacing the PCEs with the base PREVENT equation would reduce statin eligibility in the UK Biobank participants by ∼20%, especially among men and older adults.

No prediction without prevention: A global qualitative study of attitudes toward using a prediction tool for risk of developing depression during adolescence

Given the rate of advancement in predictive psychiatry, there is a threat that it outpaces public and professional willingness for use in clinical care and public health. Prediction tools in psychiatry estimate the risk of future development of mental health conditions. Prediction tools used with young populations have the potential to reduce the worldwide burden of depression. However, little is known globally about adolescents' and other stakeholders' attitudes toward use of depression prediction tools. To address this, key informant interviews and focus group discussions were conducted in Brazil, Nepal, Nigeria and the United Kingdom with 23 adolescents, 45 parents, 47 teachers, 48 health-care practitioners and 78 other stakeholders (total sample = 241) to assess attitudes toward using a depression prediction risk calculator based on the Identifying Depression Early in Adolescence Risk Score. Three attributes were identified for an acceptable depression prediction tool: it should be understandable, confidential and actionable. Understandability includes depression literacy and differentiating between having a condition versus risk of a condition. Confidentiality concerns are disclosing risk and impeding educational and occupational opportunities. Prediction results must also be actionable through prevention services for high-risk adolescents. Six recommendations are provided to guide research on attitudes and preparedness for implementing prediction tools.

A novel machine learning-based cancer-specific cardiovascular disease risk score among patients with breast, colorectal, or lung cancer

BACKGROUND

Cancer patients have up to a 3-fold higher risk for cardiovascular disease (CVD) than the general population. Traditional CVD risk scores may be less accurate for them. We aimed to develop cancer-specific CVD risk scores and compare them with conventional scores in predicting 10-year CVD risk for patients with breast cancer (BC), colorectal cancer (CRC), or lung cancer (LC).

METHODS

We analyzed adults diagnosed with BC, CRC, or LC between 2005 and 2012. An machine learning (ML) Extreme Gradient Boosting algorithm ranked 40-50 covariates for predicting CVD for each cancer type using SHapley Additive exPlanations values. The top 10 ML-predictors were used to create predictive equations using logistic regression and compared with American College of Cardiology (ACC)/American Heart Association (AHA) Pooled Cohort Equations (PCE), Predicting Risk of cardiovascular disease EVENTs (PREVENT), and Systematic COronary Risk Evaluation-2 (SCORE2) using the area under the curve (AUC).

RESULTS

We included 10 339 patients: 55.5% had BC, 15.6% had CRC, and 29.7% had LC. The actual 10-year CVD rates were: BC 21%, CRC 10%, and LC 28%. The predictors derived from the ML algorithm included cancer-specific and socioeconomic factors. The cancer-specific predictive scores achieved AUCs of 0.84, 0.76, and 0.83 for BC, CRC, and LC, respectively, and outperformed PCE, PREVENT, and SCORE2, increasing the absolute AUC values by up to 0.31 points (with AUC ranging from 0 to 1). Similar results were found when excluding patients with cardiac history or advanced cancer from the analysis.

CONCLUSIONS

Cancer-specific CVD predictive scores outperform conventional scores and emphasize the importance of integrating cancer-related covariates for precise prediction.

Levels of sCD163 in women rheumatoid arthritis: Relationship with cardiovascular risk markers

AIM

The soluble scavenger receptor differentiation antigen 163 (sCD163), a monocyte/macrophage activation marker, is related to cardiovascular mortality in the general population. This study aimed to evaluate their relationship between serum levels of sCD163 with cardiovascular risk indicators in rheumatoid arthritis (RA).

METHODS

A cross-sectional study was performed on 80 women diagnosed with RA. The cardiovascular risks were determined using the lipid profile, metabolic syndrome, and QRISK3 calculator. For the assessment of RA activity, we evaluated the DAS28 with erythrocyte sedimentation rate (DAS28-ESR). The serum levels of sCD163 were determined by the ELISA method. Logistic regression models and receiver operating characteristics (ROC) curve were used to assess the association and predictive value of sCD163 with cardiovascular risk in RA patients.

RESULTS

Levels of sCD163 were significantly higher in RA patients with high sensitivity protein C-reactive to HDL-c ratio (CHR)≥0.121 (p=0.003), total cholesterol/HDL-c ratio>7% (p=0.004), LDL-c/HDL-c ratio>3% (p=0.035), atherogenic index of plasma>0.21 (p=0.004), cardiometabolic index (CMI)≥1.70 (p=0.005), and high DAS28-ESR (p=0.004). In multivariate analysis, levels of sCD163≥1107.3ng/mL were associated with CHR≥0.121 (OR=3.43, p=0.020), CMI≥1.70 (OR=4.25, p=0.005), total cholesterol/HDL-c ratio>7% (OR=6.63, p=0.044), as well as with DAS28-ESR>3.2 (OR=8.10, p=0.008). Moreover, levels of sCD163 predicted CHR≥0.121 (AUC=0.701), cholesterol total/HDL ratio>7% (AUC=0.764), and DAS28-ESR>3.2 (AUC=0.720).

CONCLUSION

Serum levels of sCD163 could be considered a surrogate of cardiovascular risk and clinical activity in RA.

Clinical and Genomic Prediction of Coronary Artery Disease Subtypes

BACKGROUND

Coronary artery disease (CAD) is a complex, heterogeneous disease with distinct etiological mechanisms. These different etiologies may give rise to multiple subtypes of CAD that could benefit from alternative preventions and treatments. However, so far, there have been no systematic efforts to predict CAD subtypes using clinical and genetic factors.

METHODS

Here, we trained and applied statistical models incorporating clinical and genetic factors to predict CAD subtypes in 26 036 patients with CAD in the UK Biobank. We performed external validation of the UK Biobank models in the US-based All of Us cohort (8598 patients with CAD). Subtypes were defined as high versus normal LDL (low-density lipoprotein) levels, high versus normal Lpa (lipoprotein A) levels, ST-segment-elevation myocardial infarction versus non-ST-segment-elevation myocardial infarction, occlusive versus nonocclusive CAD, and stable versus unstable CAD. Clinical predictors included levels of ApoA, ApoB, HDL (high-density lipoprotein), triglycerides, and CRP (C-reactive protein). Genetic predictors were genome-wide and pathway-based polygenic risk scores (PRSs).

RESULTS

Results showed that both clinical-only and genetic-only models can predict CAD subtypes, while combining clinical and genetic factors leads to greater predictive accuracy. Pathway-based PRSs had higher discriminatory power than genome-wide PRSs for the Lpa and LDL subtypes and provided insights into their etiologies. The 10-pathway PRS most predictive of the LDL subtype involved cholesterol metabolism. Pathway PRS models had poor generalizability to the All of Us cohort.

CONCLUSIONS

In summary, we present the first systematic demonstration that CAD subtypes can be distinguished by clinical and genomic risk factors, which could have important implications for stratified cardiovascular medicine.

Development of a clinical decision support tool for Primary care Management of lower Urinary tract Symptoms in men: the PriMUS study

Background

Lower urinary tract symptoms particularly affect older men and their quality of life. General practitioners currently have no easily available assessment tools to diagnose lower urinary tract symptom causes. Referrals to urology specialists are increasing. General practitioner access to simple, accurate tests and clinical decision tools could facilitate management of lower urinary tract symptoms in primary care.

Objectives

To determine which of several index tests in combination, best predicted three diagnoses (detrusor overactivity, bladder outlet obstruction and/or detrusor underactivity) in men presenting with lower urinary tract symptoms in primary care. To develop and validate three diagnostic prediction models, and a prototype primary care clinical decision support tool.

Design

Prospective diagnostic accuracy study. Two participant cohorts, for development and validation, underwent simple index tests and a reference standard (invasive urodynamics).

Setting

General practices in England and Wales.

Participants

Men (16 years and over) consulting their general practitioner with lower urinary tract symptoms.

Sample size

Separate calculations for model development and validation cohorts, from literature estimates of detrusor overactivity, bladder outlet obstruction and detrusor underactivity prevalences of 57%, 31% and 16%, respectively.

Predictors and index tests

Twelve potential predictors considered for three diagnostic models.

Main outcome measures

The primary outcome was diagnostic model sensitivity and specificity for detecting bladder outlet obstruction, detrusor underactivity and detrusor overactivity, with 75.0% considered minimum clinically useful performance.

Statistical analysis

Three separate logistic regression models generated with index test variables to predict the presence of bladder outlet obstruction, detrusor overactivity, detrusor underactivity conditions in men with lower urinary tract symptoms.

Results

One model each was developed and validated for bladder outlet obstruction and detrusor underactivity, two for detrusor overactivity (detrusor overactivity main, detrusor overactivity sensitivity analysis 2). Age, voiding symptoms subscore, prostate-specific antigen level, median maximum flow rate, median voided volume were predictors for bladder outlet obstruction. Median maximum flow rate and post-void residual volume were predictors for detrusor underactivity. Age, post-void residual volume and median voided volume were included in detrusor overactivity main model, while age and storage symptoms subscore predicted detrusor overactivity sensitivity analysis 2. For all four models, sensitivity of 75.0% could be achieved with a specificity of 74.2%, 47.3%, 45.6% and 46.2% for bladder outlet obstruction, detrusor underactivity, detrusor overactivity main and detrusor overactivity sensitivity analysis 2 models, respectively. Similarly, a specificity of 75.0% could be achieved with a sensitivity of 71.3%, 39.8%, 33.3% and 62.7% for bladder outlet obstruction, detrusor underactivity, detrusor overactivity main and detrusor overactivity sensitivity analysis 2 models, respectively. The prototype tool (not yet intended for use in practice) is available at Primary care Management of lower Urinary tract Symptoms decision aid for lower urinary tract symptoms (shinyapps.io). General practitioner feedback during tool development and small-scale user-testing in simulated consultation scenarios was favourable. Patients supported such management in primary care.

Strengths/limitations

This was a prospective, multicentre study in an appropriate primary care population. Most of the index tests are possible routinely in primary care or at home by patients. The diagnostic models were validated in a separate cohort from the same population. Limitations include that target condition prevalences may differ in other populations.

Conclusion

We identified sensitivities and specificities of diagnostic models for detrusor overactivity, bladder outlet obstruction and detrusor underactivity in routine United Kingdom practice and developed a prototype clinical decision support tool.

Future work

Economic modelling, a feasibility trial and powered randomised controlled trial are needed to evaluate the Primary care Management of lower Urinary tract Symptoms tool in practice.

Study registration

Current Controlled Trials ISRCTN10327305.

Funding

This award was funded by the National Institute for Health and Care Research (NIHR) Health Technology Assessment programme (NIHR award ref: 15/40/05) and is published in full in Health Technology Assessment; Vol. 29, No. 1. See the NIHR Funding and Awards website for further award information.

A Machine Learning Model Using Cardiac CT and MRI Data Predicts Cardiovascular Events in Obstructive Coronary Artery Disease

Background Multimodality imaging is essential for personalized prognostic stratification in suspected coronary artery disease (CAD). Machine learning (ML) methods can help address this complexity by incorporating a broader spectrum of variables. Purpose To investigate the performance of an ML model that uses both stress cardiac MRI and coronary CT angiography (CCTA) data to predict major adverse cardiovascular events (MACE) in patients with newly diagnosed CAD. Materials and Methods This retrospective study included consecutive symptomatic patients without known CAD referred for CCTA between December 2008 and January 2020. Patients with obstructive CAD (at least one ≥50% stenosis at CCTA) underwent stress cardiac MRI for functional assessment. Eighteen clinical, two electrocardiogram, nine CCTA, and 12 cardiac MRI parameters were evaluated as inputs for the ML model, which involved automated feature selection with the least absolute shrinkage and selection operator algorithm and model building with an XGBoost algorithm. The primary outcome was MACE, defined as a composite of cardiovascular death and nonfatal myocardial infarction. External testing was performed using two independent datasets. Performance was compared between the ML model and existing scores and other approaches using the area under the receiver operating characteristic curve (AUC). Results Of 2210 patients who completed cardiac MRI, 2038 (mean age, 70 years ± 12 [SD]; 1091 [53.5%] female participants) completed follow-up (median duration, 7 years [IQR, 6-9 years]); 281 experienced MACE (13.8%). The ML model exhibited a higher AUC (0.86) for MACE prediction than the European Society of Cardiology score (0.55), QRISK3 score (0.60), Framingham Risk Score (0.50), segment involvement score (0.71), CCTA data alone (0.76), or stress cardiac MRI data alone (0.83) (P value range, <.001 to .004). The ML model also exhibited good performance in the two external validation datasets (AUC, 0.84 and 0.92). Conclusion An ML model including both CCTA and stress cardiac MRI data demonstrated better performance in predicting MACE than traditional methods and existing scores in patients with newly diagnosed CAD. © RSNA, 2025 Supplemental material is available for this article.

Machine learning based prediction models for cardiovascular disease risk using electronic health records data: systematic review and meta-analysis

Cardiovascular disease (CVD) remains a major cause of mortality in the UK, prompting the need for improved risk predictive models for primary prevention. Machine learning (ML) models utilizing electronic health records (EHRs) offer potential enhancements over traditional risk scores like QRISK3 and ASCVD. To systematically evaluate and compare the efficacy of ML models against conventional CVD risk prediction algorithms using EHR data for medium to long-term (5-10 years) CVD risk prediction. A systematic review and random-effect meta-analysis were conducted according to preferred reporting items for systematic reviews and meta-analyses guidelines, assessing studies from 2010 to 2024. We retrieved 32 ML models and 26 conventional statistical models from 20 selected studies, focusing on performance metrics such as area under the curve (AUC) and heterogeneity across models. ML models, particularly random forest and deep learning, demonstrated superior performance, with the highest recorded pooled AUCs of 0.865 (95% CI: 0.812-0.917) and 0.847 (95% CI: 0.766-0.927), respectively. These significantly outperformed the conventional risk score of 0.765 (95% CI: 0.734-0.796). However, significant heterogeneity (I² > 99%) and potential publication bias were noted across the studies. While ML models show enhanced calibration for CVD risk, substantial variability and methodological concerns limit their current clinical applicability. Future research should address these issues by enhancing methodological transparency and standardization to improve the reliability and utility of these models in clinical settings. This study highlights the advanced capabilities of ML models in CVD risk prediction and emphasizes the need for rigorous validation to facilitate their integration into clinical practice.

Antipsychotic management in general practice: serial cross-sectional study (2011-2020)

BACKGROUND

Long-term use of antipsychotics confers increased risk of cardiometabolic disease. Ongoing need should be reviewed regularly by psychiatrists.

AIM

To explore trends in antipsychotic management in general practice, and the proportions of patients prescribed antipsychotics receiving psychiatrist review.

DESIGN AND SETTING

Serial cross-sectional study using linked general practice and hospital data in Wales (2011-2020).

METHOD

Participants were adults (aged ≥18 years) registered with general practices in Wales. Outcome measures were prevalence of patients receiving ≥6 antipsychotic prescriptions annually, the proportion of patients prescribed antipsychotics receiving annual psychiatrist review, and the proportion of patients prescribed antipsychotics who were registered on the UK serious mental illness, depression, and/or dementia registers, or not on any of these registers.

RESULTS

Prevalence of adults prescribed long-term antipsychotics increased from 1.055% (95% confidence interval [CI] = 1.041 to 1.069) in 2011 to 1.448% (95% CI = 1.432 to 1.464) in 2020. The proportion receiving annual psychiatrist review decreased from 59.6% (95% CI = 58.9 to 60.4) in 2011 to 52.0% (95% CI = 51.4 to 52.7) in 2020. The proportion of overall antipsychotic use prescribed to patients on the serious mental illness register decreased from 50.0% (95% CI = 49.4 to 50.7) in 2011 to 43.6% (95% CI = 43.0 to 44.1) by 2020.

CONCLUSION

Prevalence of long-term antipsychotic use is increasing. More patients are managed by GPs without psychiatrist review and are not on monitored disease registers; they thus may be less likely to undergo cardiometabolic monitoring and miss opportunities to optimise or deprescribe antipsychotics. These trends pose risks for patients and need to be addressed urgently.

Chronic inorganic nitrate supplementation does not improve metabolic health and worsens disease progression in mice with diet-induced obesity

Inorganic nitrate (NO3-) has been proposed to be of therapeutic use as a dietary supplement in obesity and related conditions including the metabolic syndrome (MetS), type II diabetes, and metabolic dysfunction-associated steatotic liver disease (MASLD). Administration of NO3- to endothelial nitric oxide synthase-deficient mice reversed aspects of MetS; however, the impact of NO3- supplementation in diet-induced obesity is not well understood. Here we investigated the whole body metabolic phenotype and cardiac and hepatic metabolism in mice fed a high-fat, high-sucrose (HFHS) diet for up to 12 mo of age, supplemented with 1 mM NaNO3 (or NaCl) in their drinking water. HFHS feeding was associated with a progressive obesogenic and diabetogenic phenotype, which was not ameliorated by NO3-. Furthermore, HFHS-fed mice supplemented with NO3- showed elevated levels of cardiac fibrosis and accelerated progression of MASLD including development of hepatocellular carcinoma in comparison with NaCl-supplemented mice. NO3- did not enhance mitochondrial β-oxidation capacity in any tissue assayed and did not suppress hepatic lipid accumulation, suggesting it does not prevent lipotoxicity. We conclude that NO3- is ineffective in preventing the metabolic consequences of an obesogenic diet and may instead be detrimental to metabolic health against the background of HFHS feeding. This is the first report of an unfavorable effect of long-term nitrate supplementation in the context of the metabolic challenges of overfeeding, warranting urgent further investigation into the mechanism of this interaction.NEW & NOTEWORTHY Inorganic nitrate has been suggested to be of therapeutic benefit in obesity-related conditions, as it increases nitric oxide bioavailability, enhances mitochondrial β-oxidation, and reverses metabolic syndrome in eNOS-/- mice. However, we here show that over 12 months nitrate was ineffective in preventing metabolic consequences in high fat, high sucrose-fed mice and worsened aspects of metabolic health, impairing cholesterol handling, increasing cardiac fibrosis, and exacerbating steatotic liver disease progression, with acceleration to hepatocellular carcinoma.

Moving Beyond the Bladder Diary: Does New Technology Now Allow Us to Take Investigation of LUTS Into the Community? ICI-RS 2024

CONTEXT

Lower Urinary Tract Symptoms (LUTS) are defined by their distressing effect on patients' day-to-day life. Given the pressures on secondary care resources, LUTS may be overlooked or inadequately assessed and therefore patients may be burdened for an extended period before treatment.

METHODS

In a debate held at the International Consultation on Incontinence Research Society (ICI-RS) meeting in Bristol in June 2024, we considered how new technologies might contribute to an expedited, dignified and effective investigation of LUTS.

RESULTS

We describe three broad areas where technology has a role to play: streamlining of existing investigations through mobile and miniaturized technology; entirely new investigations made possible by the technology; and advanced analytics to provide better insights from the data available.

CONCLUSION

We describe key research questions that will signpost us toward answering the question raised in the title.

Cost-effectiveness of catheter-based radiofrequency renal denervation for the treatment of uncontrolled hypertension: an analysis for the UK based on recent clinical evidence

AIMS

Catheter-based radiofrequency renal denervation (RF RDN) has recently been approved for clinical use in the European Society of Hypertension guidelines and by the US Food and Drug Administration. This study evaluated the lifetime cost-effectiveness of RF RDN using contemporary evidence.

METHODS AND RESULTS

A decision-analytic model based on multivariate risk equations projected clinical events, quality-adjusted life years (QALYs), and costs. The model consisted of seven health states: hypertension alone, myocardial infarction (MI), other symptomatic coronary artery disease, stroke, heart failure (HF), end-stage renal disease, and death. Risk reduction associated with changes in office systolic blood pressure (oSBP) was estimated based on a published meta-regression of hypertension trials. The base case effect size of -4.9 mmHg oSBP (observed vs. sham control) was taken from the SPYRAL HTN-ON MED trial of 337 patients. Costs were based on National Health Service England data. The incremental cost-effectiveness ratio (ICER) was evaluated against the UK National Institute for Health and Care Excellence (NICE) cost-effectiveness threshold of £20 000-30 000 per QALY gained. Extensive scenario and sensitivity analyses were conducted, including the ON-MED subgroup on three medications and pooled effect sizes. RF RDN resulted in a relative risk reduction in clinical events over 10 years (0.80 for stroke, 0.88 for MI, 0.72 for HF), with an increase in health benefit over a patient's lifetime, adding 0.35 QALYs at a cost of £4763, giving an ICER of £13 482 per QALY gained. Findings were robust across tested scenarios.

CONCLUSION

Catheter-based radiofrequency RDN can be a cost-effective strategy for uncontrolled hypertension in the UK, with an ICER substantially below the NICE cost-effectiveness threshold.

Underlying disease risk among patients with fatigue: a population-based cohort study in primary care

BACKGROUND

Presenting to primary care with fatigue is associated with a wide range of conditions, including cancer, although their relative likelihood is unknown.

AIM

To quantify associations between new-onset fatigue presentation and subsequent diagnosis of various diseases, including cancer.

DESIGN AND SETTING

A cohort study of patients presenting in English primary care with new-onset fatigue during 2007-2017 (the fatigue group) compared with patients who presented without fatigue (the non-fatigue group), using Clinical Practice Research Datalink data linked to hospital episodes and national cancer registration data.

METHOD

The excess short-term incidence of 237 diseases in patients who presented with fatigue compared with those who did not present with fatigue is described. Disease-specific 12-month risk by sex was modelled and the age-adjusted risk calculated.

RESULTS

The study included 304 914 people in the fatigue group and 423 671 in the non-fatigue group. In total, 127 of 237 diseases studied were more common in men who presented with fatigue than in men who did not, and 151 were more common in women who presented with fatigue. Diseases that were most strongly associated with fatigue included: depression; respiratory tract infections; insomnia and sleep disturbances; and hypo/hyperthyroidism (women only). By age 80 years, cancer was the third most common disease and had the fourth highest absolute excess risk in men who presented with fatigue (fatigue group: 7.01%, 95% confidence interval [CI] = 6.54 to 7.51; non-fatigue group: 3.36%, 95% CI = 3.08 to 3.67; absolute excess risk 3.65%). In women, cancer remained relatively infrequent; by age 80 years it had the thirteenth highest excess risk in patients who presented with fatigue.

CONCLUSION

This study ranked the likelihood of possible diagnoses in patients who presented with fatigue, to inform diagnostic guidelines and doctors' decisions. Age-specific findings support recommendations to prioritise cancer investigation in older men (aged ≥70 years) with fatigue, but not in women at any age, based solely on the presence of fatigue.

Predicting control of cardiovascular disease risk factors in South Asia using machine learning

A substantial share of patients at risk of developing cardiovascular disease (CVD) fail to achieve control of CVD risk factors, but clinicians lack a structured approach to identify these patients. We applied machine learning to longitudinal data from two completed randomized controlled trials among 1502 individuals with diabetes in urban India and Pakistan. Using commonly available clinical data, we predict each individual's risk of failing to achieve CVD risk factor control goals or meaningful improvements in risk factors at one year after baseline. When classifying those in the top quartile of predicted risk scores as at risk of failing to achieve goals or meaningful improvements, the precision for not achieving goals was 73% for HbA1c, 30% for SBP, and 24% for LDL, and for not achieving meaningful improvements 88% for HbA1c, 87% for SBP, and 85% for LDL. Such models could be integrated into routine care and enable efficient and targeted delivery of health resources in resource-constrained settings.

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.

Incremental Prognostic Value of a Coronary Heart Disease Polygenic Risk Score in Type 2 Diabetes

OBJECTIVE

The recent availability of cardiovascular risk-reducing type 2 diabetes (T2D) therapies makes it imperative to optimally identify individuals who could derive benefit. Current clinical risk prediction may misclassify individuals as low risk and could be improved. Our aim was to determine the incremental prognostic value of a coronary heart disease (CHD) genome-wide polygenic risk score (PRS) to a clinical risk score in prediction of major adverse cardiovascular events (MACE) in patients with T2D.

RESEARCH DESIGN AND METHODS

We evaluated 10,556 individuals with T2D aged 40-79 years without a prior cardiovascular hospitalization. We calculated 10-year clinical cardiovascular risk at the date of recruitment using the Pooled Cohort Equation (PCE Risk) and constructed a CHD PRS. The primary outcome was time to first MACE incidence, and we assessed the additional incremental predictive value of the CHD PRS to the PCE risk.

RESULTS

At 10 years, there were 1,477 MACE. After adjustment for clinical risk, the CHD PRS was significantly associated with MACE (hazard ratio [HR] 1.69 per SD increase, 95% CI 1.60-1.79). Individuals with PCE Risk <7.5% but in the top quintile of CHD PRS had a significantly increased likelihood of MACE (HR 10.69, 95% CI 5.07-22.55) compared with those in the lowest. The addition of the PRS to the clinical risk score led to significant improvements in risk prediction, particularly in those at low clinical risk.

CONCLUSIONS

The addition of a CHD PRS to clinical assessment improved MACE prediction in T2D individuals without prior cardiovascular disease, particularly in those at low clinical risk.

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.

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.

The predictive accuracy of cardiovascular disease risk prediction tools in inflammatory arthritis and psoriasis: an observational validation study using the Clinical Practice Research Datalink

OBJECTIVES

Cardiovascular risk prediction tools developed for the general population often underperform for individuals with RA, and their predictive accuracy are unclear for other inflammatory conditions that also have increased cardiovascular risk. We investigated the performance of QRISK-3, the Framingham Risk Score (FRS) and the Reynolds Risk Score (RRS) in RA, psoriatic disease (PsA and psoriasis) and AS. We considered OA as a non-inflammatory comparator.

METHODS

We utilized primary care records from the Clinical Practice Research Datalink (CPRD) Aurum database to identify individuals with each condition and calculated 10-year cardiovascular risk using each prediction tool. The discrimination and calibration of each tool was assessed for each disease.

RESULTS

The time-dependent area under the curve (AUC) for QRISK3 was 0.752 for RA (95% CI 0.734-0.777), 0.794 for AS (95% CI 0.764-0.812), 0.764 for PsA (95% CI 0.741-0.791), 0.815 for psoriasis (95% CI 0.789-0.835) and 0.698 for OA (95% CI 0.670-0.717), indicating reasonably good predictive performance. The AUCs for the FRS were similar, and slightly lower for the RRS. The FRS was reasonably well calibrated for each condition but underpredicted risk for patients with RA. The RRS tended to underpredict CVD risk, while QRISK3 overpredicted CVD risk, especially for the most high-risk individuals.

CONCLUSION

CVD risk for individuals with RA, AS and psoriatic disease was generally less accurately predicted using each of the three CVD risk prediction tools than the reported accuracies in the original publications. Individuals with OA also had less accurate predictions, suggesting inflammation is not the sole reason for underperformance. Disease-specific risk prediction tools may be required.

Assessing long-term effectiveness and cost-effectiveness of statin therapy in the UK: a modelling study using individual participant data sets

Background

Cardiovascular disease has declined but remains a major disease burden across developed countries.

Objective

To assess the effectiveness and cost-effectiveness of statin therapy across United Kingdom population categories.

Design

The cardiovascular disease microsimulation model, developed using Cholesterol Treatment Trialists' Collaboration data and the United Kingdom Biobank cohort, projected cardiovascular events, mortality, quality of life and healthcare costs using participant characteristics.

Setting

United Kingdom primary health care.

Participants

A total of 117,896 participants in 16 statin trials in the Cholesterol Treatment Trialists' Collaboration; 501,854 United Kingdom Biobank participants by previous cardiovascular disease status, sex, age (40-49, 50-59 and 60-70 years), 10-year cardiovascular disease risk [QRISK®3 (%): < 5, 5-10, 10-15, 15-20 and ≥ 20] and low-density lipoprotein cholesterol level (< 3.4, 3.4-4.1 and ≥ 4.1 mmol/l); 20,122 United Kingdom Biobank and Whitehall II participants aged ≥ 70 years by previous cardiovascular disease status, sex and low-density lipoprotein cholesterol (< 3.4, 3.4-4.1 and ≥ 4.1 mmol/l).

Interventions

Lifetime standard (35-45% low-density lipoprotein cholesterol reduction) or higher-intensity (≥ 45% reduction) statin.

Main outcome measures

Quality-adjusted life-years and incremental cost per quality-adjusted life-year gained from the United Kingdom healthcare perspective.

Data sources

Cholesterol Treatment Trialists' Collaboration and United Kingdom Biobank data informed risk equations. United Kingdom primary and hospital care data informed healthcare costs (2020-1 Great British pounds); £1.10 standard or £1.68 higher-intensity generic statin therapy per 28 tablets; and Health Survey for England data informed health-related quality of life. Meta-analyses of trials and cohort studies informed the effects of statin therapies on cardiovascular events, incident diabetes, myopathy and rhabdomyolysis.

Results

Across categories of participants 40-70 years old, lifetime use of standard statin therapy resulted in undiscounted 0.20-1.09 quality-adjusted life-years gained per person, and higher-intensity statin therapy added a further 0.03-0.20 quality-adjusted life-years per person. Among participants aged ≥ 70 years, lifetime standard statin was estimated to increase quality-adjusted life-years by 0.24-0.70 and higher-intensity statin by a further 0.04-0.13 quality-adjusted life-years per person. Benefits were larger among participants at higher cardiovascular disease risk or with higher low-density lipoprotein cholesterol. Standard statin therapy was cost-effective across all categories of people 40-70 years old, with incremental costs per quality-adjusted life-year gained from £280 to £8530. Higher-intensity statin therapy was cost-effective at higher cardiovascular disease risk or higher low-density lipoprotein cholesterol. Both standard and higher-intensity statin therapies appeared to be cost-effective for people aged ≥ 70 years, with an incremental cost per quality-adjusted life-year gained of under £3500 for standard and under £11,780 for higher-intensity statin. Standard or higher-intensity statin therapy was certain to be cost effective in the base-case analysis at a threshold of £20,000 per quality-adjusted life-year. Statins remained cost-effective in sensitivity analyses.

Limitations

The randomised evidence for effects of statin therapy is for about 5 years of treatment. There is limited randomised evidence of the effects of statin therapy in older people without previous cardiovascular disease.

Conclusions

Based on the current evidence of the effects of statin therapy and modelled contemporary disease risks, low-cost statin therapy is cost-effective across all categories of men and women aged ≥ 40 years in the United Kingdom, with higher-intensity statin therapy cost-effective at higher cardiovascular disease risk or higher low-density lipoprotein cholesterol.

Future work

Cholesterol Treatment Trialists' Collaboration has ongoing studies of effects of statin therapy using individual participant data from randomised statin trials. Ongoing large randomised controlled trials are studying the effects of statin therapy in people ≥ 70 years old. Future economic analyses should integrate the emerging new evidence.

Funding

This award was funded by the National Institute for Health and Care Research (NIHR) Health Technology Assessment programme (NIHR award ref: 17/140/02) and is published in full in Health Technology Assessment; Vol. 28, No. 79. See the NIHR Funding and Awards website for further award information.

Opportunities and challenges for identifying undiagnosed Rare Disease patients through analysis of primary care records: long QT syndrome as a test case

BACKGROUND

Patients with rare genetic diseases frequently experience significant diagnostic delays. Routinely collected data in the electronic health record (EHR) may be used to help identify patients at risk of undiagnosed conditions. Long QT syndrome (LQTS) is a rare inherited cardiac condition associated with significant morbidity and premature mortality. In this study, we examine LQTS as an exemplar disease to assess if clinical features recorded in the primary care EHR can be used to develop and validate a predictive model to aid earlier detection.

METHODS

1495 patients with an LQTS diagnostic code and 7475 propensity-score matched controls were identified from 10.5 million patients' electronic primary care records in the UK's Clinical Practice Research Datalink (CPRD). Associated clinical features recorded before diagnosis (with p < 0.05) were incorporated into a multivariable logistic regression model, the final model was determined by backwards regression and validated by bootstrapping to determine model optimism.

RESULTS

The mean age at LQTS diagnosis was 58.4 (SD 19.41). 18 features were included in the final model. Discriminative accuracy, assessed by area under the curve (AUC), was 0.74, (95% CI 0.73, 0.75) (optimism 6%). Features occurring at significantly greater frequency before diagnosis included: epilepsy, palpitations, syncope, collapse, mitral valve disease and irritable bowel syndrome.

CONCLUSION

This study demonstrates the potential to develop primary care prediction models for rare conditions, like LQTS, in routine primary care records and highlights key considerations including disease suitability, finding an appropriate linked dataset, the need for accurate case ascertainment and utilising an approach to modelling suitable for rare events.

Medical Ethics and Informed Consent to Treatment: Past, Present and Future

It has been asserted that there was an erosion of medical ethics during the Covid-19 pandemic and a departure from the principle of obtaining fully informed consent from patients before treatment. In light of these assertions, this article reviews the historical development of medical ethics and the approach to obtaining informed consent and critiques the consent practices before and during the pandemic. It then describes a new tool for displaying key statistics on the benefits and risks of interventions to help explain them to patients and suggests a more rigorous process for seeking fully informed consent in the future.

Assessing the Accuracy of Cardiovascular Disease Prediction Using Female-Specific Risk Factors in Women Aged 45 to 69 Years in the UK Biobank Study

BACKGROUND

Cardiovascular disease (CVD) is the leading cause of mortality in women. We aimed to assess whether adding female-specific risk factors to traditional factors could improve CVD risk prediction.

METHODS

We used a cohort of women from the UK Biobank Study aged 45 to 69 years, free of CVD at baseline (2006-2010) followed until the end of 2019. We developed Cox proportional hazards models using the risk factors included in 3 contemporary CVD risk calculators: Pooled Cohort Equation - Atherosclerotic Cardiovascular Disease, Qrisk2, and PREDICT. We added each of the following female-specific risk factors, individually and all together, to determine if these improved measures of discrimination and calibration for predicting CVD: early menarche (<11 years), endometriosis, excessive, frequent or irregular menstruation, miscarriage, number of miscarriages, number of stillbirths, infertility, preeclampsia or eclampsia, gestational diabetes (without subsequent type 2 diabetes), premature menopause (<40 years), early menopause (<45 years), and natural or surgical early menopause (menopause <45 years or timing of menopause reported as unknown and oophorectomy reported at age <45).

RESULTS

In the model of 135 142 women (mean age, 57.5 years; SD, 6.8) using risk factors from Pooled Cohort Equation - Atherosclerotic Cardiovascular Disease, CVD incidence was 5.3 per 1000 person-years. The c-indices for the Pooled Cohort Equation - Atherosclerotic Cardiovascular Disease, Qrisk2, and PREDICT models were 0.710, 0.713, and 0.718, respectively. Adding each of the female-specific risk factors did not improve the c-index, the net reclassification index, the integrated discrimination index, the slope of the regression line for predicted versus observed events, and the Brier score or plots of calibration. Adding all female-specific risk factors simultaneously increased the c-index for the Pooled Cohort Equation - Atherosclerotic Cardiovascular Disease, Qrisk2, and PREDICT models to 0.712, 0.715, and 0.720, respectively.

CONCLUSIONS

Although several female-specific factors have been shown to be early indicators of CVD risk, these factors should not be used to reclassify risk in women aged 45 to 69 years when considering whether to commence a blood pressure or lipid-lowering medication.

Sex-Specific Association Between Genetic Risk of Psychiatric Disorders and Cardiovascular Diseases

BACKGROUND

Though epidemiological studies show increased cardiovascular disease (CVD) risks among individuals with psychiatric disorders, findings on sex differences in comorbidity have been inconsistent.

METHODS

This genetic epidemiology study examined the sex-specific association between the genetic risk of 3 psychiatric disorders (major depression [MD], schizophrenia, and bipolar disorder), estimated using polygenic scores (PGSs), and risks of 3 CVDs (atrial fibrillation [AF], coronary artery disease [CAD], and heart failure [HF]) in 345 169 European-ancestry individuals (UK Biobank), with analyses replicated in an independent BioVU cohort (n=49 057). Mediation analysis was conducted to determine whether traditional CVD risk factors could explain any observed sex difference.

RESULTS

In the UK Biobank, a 1-SD increase in PGSMD was significantly associated with the incident risks of all 3 CVDs in females after multiple testing corrections (hazard ratio [HR]AF-female=1.04 [95% CI, 1.02-1.06]; P=1.5×10-4; HRCAD-female=1.07 [95% CI, 1.04-1.11]; P=2.6×10-6; and HRHF-female=1.09 [95% CI, 1.06-1.13]; P=9.7×10-10), but not in males. These female-specific associations remained even in the absence of any psychiatric disorder diagnosis or psychiatric medication use. Although mediation analysis demonstrated that the association between PGSMD and CVDs in females was partly mediated by baseline body mass index, hypercholesterolemia, hypertension, and smoking, these risk factors did not explain the higher risk compared with males. The association between PGSMD and CAD was consistent between females who were premenopausal and postmenopausal at baseline, while the association with AF and HF was only observed in the baseline postmenopausal cohort. No significant association with CVD risks was observed for the PGS of schizophrenia or bipolar disorder. The female-specific positive association of PGSMD with CAD risk was replicated in BioVU.

CONCLUSIONS

Genetic predisposition to MD confers a greater risk of CVDs in females versus males, even in the absence of any depression diagnosis. This study warrants further investigation into whether genetic predisposition to depression could be useful for improving cardiovascular risk prediction, especially in women.

Metabolomic and genomic prediction of common diseases in 700,217 participants in three national biobanks

Identifying individuals at high risk of chronic diseases via easily measured biomarkers could enhance efforts to prevent avoidable illness and death. Using 'omic data can stratify risk for many diseases simultaneously from a single measurement that captures multiple molecular predictors of risk. Here we present nuclear magnetic resonance metabolomics in blood samples from 700,217 participants in three national biobanks. We built metabolomic scores that identify high-risk groups for diseases that cause the most morbidity in high-income countries and show consistent cross-biobank replication of the relative risk of disease for these groups. We show that these metabolomic scores are more strongly associated with disease onset than polygenic scores for most of these diseases. In a subset of 18,709 individuals with metabolomic biomarkers measured at two time points we show that people whose scores change have different risk of disease, suggesting that repeat measurements capture changes both to health status and disease risk possibly due to treatment, lifestyle changes or other factors. Lastly, we assessed the incremental predictive value of metabolomic scores over existing clinical risk scores for multiple diseases and found modest improvements in discrimination for several diseases whose clinical utility, while promising, remains to be determined.

Sex-specific prediction of cardiogenic shock after acute coronary syndromes: the SEX-SHOCK score

BACKGROUND AND AIMS

Cardiogenic shock (CS) remains the primary cause of in-hospital death after acute coronary syndromes (ACS), with its plateauing mortality rates approaching 50%. To test novel interventions, personalized risk prediction is essential. The ORBI (Observatoire Régional Breton sur l'Infarctus) score represents the first-of-its-kind risk score to predict in-hospital CS in ACS patients undergoing percutaneous coronary intervention (PCI). However, its sex-specific performance remains unknown, and refined risk prediction strategies are warranted.

METHODS

This multinational study included a total of 53 537 ACS patients without CS on admission undergoing PCI. Following sex-specific evaluation of ORBI, regression and machine-learning models were used for variable selection and risk prediction. By combining best-performing models with highest-ranked predictors, SEX-SHOCK was developed, and internally and externally validated.

RESULTS

The ORBI score showed lower discriminative performance for the prediction of CS in females than males in Swiss (area under the receiver operating characteristic curve [95% confidence interval]: 0.78 [0.76-0.81] vs. 0.81 [0.79-0.83]; P =.048) and French ACS patients (0.77 [0.74-0.81] vs. 0.84 [0.81-0.86]; P = .002). The newly developed SEX-SHOCK score, now incorporating ST-segment elevation, creatinine, C-reactive protein, and left ventricular ejection fraction, outperformed ORBI in both sexes (females: 0.81 [0.78-0.83]; males: 0.83 [0.82-0.85]; P < .001), which prevailed following internal and external validation in RICO (females: 0.82 [0.79-0.85]; males: 0.88 [0.86-0.89]; P < .001) and SPUM-ACS (females: 0.83 [0.77-0.90], P = .004; males: 0.83 [0.80-0.87], P = .001).

CONCLUSIONS

The ORBI score showed modest sex-specific performance. The novel SEX-SHOCK score provides superior performance in females and males across the entire spectrum of ACS, thus providing a basis for future interventional trials and contemporary ACS management.

A service evaluation of the uptake and effectiveness of a digital delivery of the NHS health check service

OBJECTIVES

To compare the uptake, effectiveness and costs of a digital version of the National Health Service (NHS) Health Check (DHC) to the standard face-to-face NHS Health Check (F2F).

PARTICIPANTS AND SETTING

A random sample of 9000 patients aged 40-74 eligible for an NHS Health Check in Southwark, England, between January and April 2023.

INTERVENTION AND DESIGN

The DHC was an online tool with a health assessment section, an advice and support section, and a section on how to obtain and update follow-up physical measures (blood pressure, cholesterol, glycated haemoglobin (HbA1c)). 6000 patients from GP records were randomly allocated to receive a DHC invitation and 3000 to receive an F2F invitation. Those invited to DHC were able to choose F2F if they preferred.

OUTCOMES

The primary outcome was the uptake of any type of health check, either a completed F2F appointment or completion of the DHC health assessment section, along with demographics and data on appointments, medications and referrals within the study period. QRISK3 and QDiabetes risk scores were calculated. Management and operation costs were estimated for F2F and DHC pathways.

RESULTS

Excluding participants who moved away or died, the DHC uptake to the health assessment section was 21% (1189/5705), with a further 3% (198/5705) choosing F2F, compared with 11% (305/2900) for F2F completion (p<0.001). The DHC uptake was lower among those from Black (14%) and Mixed (13%) compared with White (29%) ethnicities (p<0.001), and there was no evidence of higher DHC uptake among groups less likely to engage in NHS Health Checks. Of those who completed the health assessment, 60% (714) completed the support section, and 7% (84) completed the provision and updating of physical measures. Appointments, medications and referrals were lower among DHC service users than among F2F users (p<0.001). The estimated total management and operation costs for F2F were £154.80 per user, compared with total management and operation costs for DHC of £68.48 per user for health assessment only, £134.46 including the support section and £1479.01 per user with completed physical measures.

CONCLUSIONS

The study suggests that a choice of Health Check pathways may potentially reduce pressures on the NHS. Cholesterol and HbA1c were not generally known, and the options to obtain and update these measures require further development for the DHC to be considered a viable comparable alternative to the F2F service for estimating cardiovascular disease and diabetes risk. Strategies are still needed to reach those groups not currently engaging with NHS Health Checks.

REGISTRATION

This study was registered on the Open Science Framework: https://osf.io/y87zt.