Development and validation of a cardiovascular risk score for patients in the community after acute coronary syndrome

The research progress and research trends in acute coronary syndrome nursing: A review of visual analysis based on the Web of Science database

Acute coronary syndrome (ACS) is one of the most common and severe forms of cardiovascular disease and has attracted worldwide attention with increased morbidity and mortality in recent years. There are few review studies in the field of its care in the form of bibliometric studies. We searched the Web of Science Core Collection database for articles and reviews in the area of ACS nursing for visual mapping analysis. Our objectives are to explore the hot topics and frontiers of research in the field of ACS nursing and to identify collaborative relationships between countries, institutions, and authors. This study will provide researchers with intuitive reference data for future in-depth studies of ACSs.

Development and validation of cardiovascular risk prediction equations in 76 000 people with known cardiovascular disease

AIMS

Multiple health administrative databases can be individually linked in Aotearoa New Zealand, using encrypted identifiers. These databases were used to develop cardiovascular risk prediction equations for patients with known cardiovascular disease (CVD).

METHODS AND RESULTS

Administrative health databases were linked to identify all people aged 18-84 years with known CVD, living in Auckland and Northland, Aotearoa New Zealand, on 1 January 2014. The cohort was followed until study outcome, death, or 5 years. The study outcome was death or hospitalization due to ischaemic heart disease, stroke, heart failure, or peripheral vascular disease. Sex-specific 5-year CVD risk prediction equations were developed using multivariable Fine and Gray models. A total of 43 862 men {median age: 67 years [interquartile range (IQR): 59-75]} and 32 724 women [median age: 70 years (IQR: 60-77)] had 14 252 and 9551 cardiovascular events, respectively. Equations were well calibrated with good discrimination. Increasing age and deprivation, recent cardiovascular hospitalization, Mori ethnicity, smoking history, heart failure, diabetes, chronic renal disease, atrial fibrillation, use of blood pressure lowering and anti-thrombotic drugs, haemoglobin A1c, total cholesterol/HDL cholesterol, and creatinine were statistically significant independent predictors of the study outcome. Fourteen per cent of men and 23% of women had predicted 5-year cardiovascular risk <15%, while 28 and 24% had ≥40% risk.

CONCLUSION

Robust cardiovascular risk prediction equations were developed from linked routine health databases, a currently underutilized resource worldwide. The marked heterogeneity demonstrated in predicted risk suggests that preventive therapy in people with known CVD would be better informed by risk stratification beyond a one-size-fits-all high-risk categorization.

Multivariate Sequential Analytics for Cardiovascular Disease Event Prediction

BACKGROUND

Automated clinical decision support for risk assessment is a powerful tool in combating cardiovascular disease (CVD), enabling targeted early intervention that could avoid issues of overtreatment or undertreatment. However, current CVD risk prediction models use observations at baseline without explicitly representing patient history as a time series.

OBJECTIVE

The aim of this study is to examine whether by explicitly modelling the temporal dimension of patient history event prediction may be improved.

METHODS

This study investigates methods for multivariate sequential modelling with a particular emphasis on long short-term memory (LSTM) recurrent neural networks. Data from a CVD decision support tool is linked to routinely collected national datasets including pharmaceutical dispensing, hospitalization, laboratory test results, and deaths. The study uses a 2-year observation and a 5-year prediction window. Selected methods are applied to the linked dataset. The experiments performed focus on CVD event prediction. CVD death or hospitalization in a 5-year interval was predicted for patients with history of lipid-lowering therapy.

RESULTS

The results of the experiments showed temporal models are valuable for CVD event prediction over a 5-year interval. This is especially the case for LSTM, which produced the best predictive performance among all models compared achieving AUROC of 0.801 and average precision of 0.425. The non-temporal model comparator ridge classifier (RC) trained using all quarterly data or by aggregating quarterly data (averaging time-varying features) was highly competitive achieving AUROC of 0.799 and average precision of 0.420 and AUROC of 0.800 and average precision of 0.421, respectively.

CONCLUSION

This study provides evidence that the use of deep temporal models particularly LSTM in clinical decision support for chronic disease would be advantageous with LSTM significantly improving on commonly used regression models such as logistic regression and Cox proportional hazards on the task of CVD event prediction.

PCSK9 inhibitors and ezetimibe with or without statin therapy for cardiovascular risk reduction: a systematic review and network meta-analysis

OBJECTIVE

To compare the impact of ezetimibe and proprotein convertase subtilisin/kexin type 9 (PCSK9) inhibitors on cardiovascular outcomes in adults taking maximally tolerated statin therapy or who are statin intolerant.

DESIGN

Network meta-analysis.

DATA SOURCES

Medline, EMBASE, and Cochrane Library up to 31 December 2020.

ELIGIBILITY CRITERIA FOR SELECTING STUDIES

Randomised controlled trials of ezetimibe and PCSK9 inhibitors with ≥500 patients and follow-up of ≥6 months.

MAIN OUTCOME MEASURES

We performed frequentist fixed-effects network meta-analysis and GRADE (grading of recommendations, assessment, development, and evaluation) to assess certainty of evidence. Results included relative risks (RR) and absolute risks per 1000 patients treated for five years for non-fatal myocardial infarction (MI), non-fatal stroke, all-cause mortality, and cardiovascular mortality. We estimated absolute risk differences assuming constant RR (estimated from network meta-analysis) across different baseline therapies and cardiovascular risk thresholds; the PREDICT risk calculator estimated cardiovascular risk in primary and secondary prevention. Patients were categorised at low to very high cardiovascular risk. A guideline panel and systematic review authors established the minimal important differences (MID) of 12 per 1000 for MI and 10 per 1000 for stroke.

RESULTS

We identified 14 trials assessing ezetimibe and PCSK9 inhibitors among 83 660 adults using statins. Adding ezetimibe to statins reduced MI (RR 0.87 (95% confidence interval 0.80 to 0.94)) and stroke (RR 0.82 (0.71 to 0.96)) but not all-cause mortality (RR 0.99 (0.92 to 1.06)) or cardiovascular mortality (RR 0.97 (0.87 to 1.09)). Similarly, adding PCSK9 inhibitor to statins reduced MI (0.81 (0.76 to 0.87)) and stroke (0.74 (0.64 to 0.85)) but not all-cause (0.95 (0.87 to 1.03)) or cardiovascular mortality (0.95 (0.87 to 1.03)). Among adults with very high cardiovascular risk, adding PCSK9 inhibitor was likely to reduce MI (16 per 1000) and stroke (21 per 1000) (moderate to high certainty); whereas adding ezetimibe was likely to reduce stroke (14 per 1000), but the reduction of MI (11 per 1000) (moderate certainty) did not reach MID. Adding ezetimibe to PCSK9 inhibitor and statin may reduce stroke (11 per 1000), but the reduction of MI (9 per 1000) (low certainty) did not reach MID. Adding PCSK9 inhibitors to statins and ezetimibe may reduce MI (14 per 1000) and stroke (17 per 1000) (low certainty). Among adults with high cardiovascular risk, adding PCSK9 inhibitor probably reduced MI (12 per 1000) and stroke (16 per 1000) (moderate certainty); adding ezetimibe probably reduced stroke (11 per 1000), but the reduction in MI did not achieve MID (8 per 1000) (moderate certainty). Adding ezetimibe to PCSK9 inhibitor and statins did not reduce outcomes beyond MID, while adding PCSK9 inhibitor to ezetimibe and statins may reduce stroke (13 per 1000). These effects were consistent in statin-intolerant patients. Among moderate and low cardiovascular risk groups, adding PCSK9 inhibitor or ezetimibe to statins yielded little or no benefit for MI and stroke.

CONCLUSIONS

Ezetimibe or PCSK9 inhibitors may reduce non-fatal MI and stroke in adults at very high or high cardiovascular risk who are receiving maximally tolerated statin therapy or are statin-intolerant, but not in those with moderate and low cardiovascular risk.

The Multi-Ethnic New Zealand Study of Acute Coronary Syndromes (MENZACS): Design and Methodology

Background. Each year, approximately 5000 New Zealanders are admitted to hospital with first-time acute coronary syndrome (ACS). The Multi-Ethnic New Zealand Study of Acute Coronary Syndromes (MENZACS) is a prospective longitudinal cohort study embedded within the All New Zealand Acute Coronary Syndrome Quality Improvement (ANZACS-QI) registry in six hospitals. The objective of MENZACS is to examine the relationship between clinical, genomic, and cardiometabolic markers in relation to presentation and outcomes post-ACS. Methods. Patients with first-time ACS are enrolled and study-specific research data is collected alongside the ANZACS-QI registry. The research blood samples are stored for future genetic/biomarker assays. Dietary information is collected with a food frequency questionnaire and information about physical activity, smoking, and stress is also collected via questionnaire. Detailed family history, ancestry, and ethnicity data are recorded on all participants. Results. During the period between 2015 and 2019, there were 2015 patients enrolled. The mean age was 61 years, with 60% of patients aged <65 years and 21% were female. Ethnicity and cardiovascular (CV) risk factor distribution was similar to ANZACS-QI: 13% Māori, 5% Pacific, 5% Indian, and 74% NZ European. In terms of CV risk factors, 56% were ex-/current smokers, 42% had hypertension, and 19% had diabetes. ACS subtype was ST elevation myocardial infarction (STEMI) in 41%, non-ST elevation myocardial infarction (NSTEM) in 54%, and unstable angina in 5%. Ninety-nine percent of MENZACS participants underwent coronary angiography and 90% had revascularization; there were high rates of prescription of secondary prevention medications upon discharge from hospital. Conclusion. MENZACS represents a cohort with optimal contemporary management and will be a significant epidemiological bioresource for the study of environmental and genetic factors contributing to ACS in New Zealand’s multi-ethnic environment. The study will utilise clinical, nutritional, lifestyle, genomic, and biomarker analyses to explore factors influencing the progression of coronary disease and develop risk prediction models for health outcomes.

Predicting cardiovascular disease risk across the atherosclerotic disease continuum

AIMS

Cardiovascular disease (CVD) guidelines dichotomize populations into primary and secondary prevention. We sought to develop a risk equation for secondary prevention of CVD that complements existing equations for primary prevention of CVD, and to describe the distributions of CVD risk across the population.

METHODS AND RESULTS

Adults aged 30-79 years who had routine CVD risk assessment in 2007-16 were identified from a large primary care cohort (PREDICT) with linkage to national and regional datasets. The 5-year risk of developing CVD among people without atherosclerotic CVD (ASCVD) was calculated using published equations (PREDICT-1°). A new risk equation (PREDICT-2°) was developed from Cox regression models to estimate the 5-year risk of CVD event recurrence among patients with known ASCVD. The outcome for both equations was hospitalization for a CVD event or cardiovascular death. Of the 475 161 patients, 12% (57 061) had ASCVD. For those without ASCVD, median (interquartile range) 5-year risks with the PREDICT-1° score were women 2.2% (1.2-4.2%), men 3.5% (2.0-6.6%), and whole group 2.9% (1.6-5.5%). For those with ASCVD, the 5-year risks with the new PREDICT-2° equation were women 21% (15-33%), men 23% (16-35%), and whole group 22% (16-34%).

CONCLUSION

We developed CVD risk scores for people with ASCVD (PREDICT-2°) to complement the PREDICT-1° scores. Median CVD risk is eight-fold higher among those with ASCVD than those without; however, there was overlap and the widest distribution of CVD risk was among people with ASCVD. This study describes a CVD risk continuum and the limitations of a 'one size fits all' approach to assessing risk in people with ASCVD.