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Predicting major adverse cardiovascular events for secondary prevention: protocol for a systematic review and meta-analysis of risk prediction models.

ABSTRACT: INTRODUCTION:Cardiovascular disease (CVD) is the leading cause of morbidity and mortality globally. With advances in early diagnosis and treatment of CVD and increasing life expectancy, more people are surviving initial CVD events. However, models for stratifying disease severity risk in patients with established CVD for effective secondary prevention strategies are inadequate. Multivariable prognostic models to stratify CVD risk may allow personalised treatment interventions. This review aims to systematically review the existing multivariable prognostic models for the recurrence of CVD or major adverse cardiovascular events in adults with established CVD diagnosis. METHODS AND ANALYSIS:Bibliographic databases (Ovid MEDLINE, EMBASE, PsycINFO and Web of Science) will be searched, from database inception to April 2020, using terms relating to the clinical area and prognosis. A hand search of the reference lists of included studies will also be done to identify additional published studies. No restrictions on language of publications will be applied. Eligible studies present multivariable models (derived or validated) of adults (aged 16 years and over) with an established diagnosis of CVD, reporting at least one of the components of the primary outcome of major adverse cardiovascular events (defined as either coronary heart disease, stroke, peripheral artery disease, heart failure or CVD-related mortality). Reviewing will be done by two reviewers independently using the pre-defined criteria. Data will be extracted for included full-text articles. Risk of bias will be assessed using the Prediction model study Risk Of Bias ASsessment Tool (PROBAST). Prognostic models will be summarised narratively. If a model is tested in multiple validation studies, the predictive performance will be summarised using a random-effects meta-analysis model to account for any between-study heterogeneity. ETHICS AND DISSEMINATION:Ethics approval is not required. The results of this study will be submitted to relevant conferences for presentation and a peer-reviewed journal for publication. PROSPERO REGISTRATION NUMBER:CRD42019149111.

SUBMITTER: Akyea RK

PROVIDER: S-EPMC7389481 | biostudies-literature | 2020 Jul

REPOSITORIES: biostudies-literature

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Predicting major adverse cardiovascular events for secondary prevention: protocol for a systematic review and meta-analysis of risk prediction models.

Akyea Ralph K RK Leonardi-Bee Jo J Asselbergs Folkert W FW Patel Riyaz S RS Durrington Paul P Wierzbicki Anthony S AS Ibiwoye Oluwaseun H OH Kai Joe J Qureshi Nadeem N Weng Stephen F SF

BMJ open 20200727 7

<h4>Introduction</h4>Cardiovascular disease (CVD) is the leading cause of morbidity and mortality globally. With advances in early diagnosis and treatment of CVD and increasing life expectancy, more people are surviving initial CVD events. However, models for stratifying disease severity risk in patients with established CVD for effective secondary prevention strategies are inadequate. Multivariable prognostic models to stratify CVD risk may allow personalised treatment interventions. This revie ...[more]

PMID: 32718921

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Project description:Background Carotid endarterectomy (CEA) is a major vascular operation for stroke prevention that carries significant perioperative risks; however, outcome prediction tools remain limited. The authors developed machine learning algorithms to predict outcomes following CEA. Methods and Results The National Surgical Quality Improvement Program targeted vascular database was used to identify patients who underwent CEA between 2011 and 2021. Input features included 36 preoperative demographic/clinical variables. The primary outcome was 30-day major adverse cardiovascular events (composite of stroke, myocardial infarction, or death). The data were split into training (70%) and test (30%) sets. Using 10-fold cross-validation, 6 machine learning models were trained using preoperative features. The primary metric for evaluating model performance was area under the receiver operating characteristic curve. Model robustness was evaluated with calibration plot and Brier score. Overall, 38 853 patients underwent CEA during the study period. Thirty-day major adverse cardiovascular events occurred in 1683 (4.3%) patients. The best performing prediction model was XGBoost, achieving an area under the receiver operating characteristic curve of 0.91 (95% CI, 0.90-0.92). In comparison, logistic regression had an area under the receiver operating characteristic curve of 0.62 (95% CI, 0.60-0.64), and existing tools in the literature demonstrate area under the receiver operating characteristic curve values ranging from 0.58 to 0.74. The calibration plot showed good agreement between predicted and observed event probabilities with a Brier score of 0.02. The strongest predictive feature in our algorithm was carotid symptom status. Conclusions The machine learning models accurately predicted 30-day outcomes following CEA using preoperative data and performed better than existing tools. They have potential for important utility in guiding risk-mitigation strategies to improve outcomes for patients being considered for CEA.

Project description:Soluble TNF-like weak inducer of apoptosis (sTWEAK) is a proinflammatory cytokine belonging to the TNF superfamily. sTWEAK concentrations have been associated with the presence of CKD and cardiovascular disease (CVD). We hypothesized that sTWEAK levels may relate to a higher prevalence of atherosclerotic plaques, vascular calcification, and cardiovascular outcomes observed in patients with CKD.A 4-year prospective, multicenter, longitudinal study was conducted in 1058 patients with CKD stages 3-5D (mean age =58±13 years old; 665 men) but without any history of CVD from the NEFRONA Study (a study design on the prevalence of surrogate markers of CVD). Ankle-brachial index and B-mode ultrasound were performed to detect the presence of carotid and/or femoral atherosclerotic plaques together with biochemical measurements and sTWEAK assessment. Patients were followed for cardiovascular outcomes (follow-up of 3.13±1.15 years).Patients with more advanced CKD had lower sTWEAK levels. sTWEAK concentrations were independently and negatively associated with carotid intima-media thickness. sTWEAK levels were lower in patients with carotid atherosclerotic plaques but not in those with femoral plaques. After adjustment by confounders, the odds ratio (OR) for presenting carotid atherosclerotic plaques in patients in the lowest versus highest tertile of sTWEAK was 4.18 (95% confidence interval [95% CI], 2.89 to 6.08; P<0.001). Furthermore, sTWEAK levels were lower in patients with calcified carotid atherosclerotic plaques. The OR for presenting calcified carotid plaques was 1.77 (95% CI, 1.06 to 2.93; P=0.02) after multivariable adjustment. After the follow-up, 41 fatal and 68 nonfatal cardiovascular events occurred. In a Cox model, after controlling for potential confounding factors, patients in the lowest tertile of sTWEAK concentrations had a higher risk of fatal and nonfatal cardiovascular events (hazard ratio [HR], 2.40; 95% CI, 1.33 to 4.33; P=0.004) and cardiovascular mortality (HR, 2.67; 95% CI, 1.05 to 6.76; P=0.04).Low sTWEAK levels were associated with the presence of carotid atherosclerotic plaques in patients with CKD. Additionally, lower sTWEAK levels were associated with a higher risk of cardiovascular morbidity and mortality.

Dataset Information

Predicting major adverse cardiovascular events for secondary prevention: protocol for a systematic review and meta-analysis of risk prediction models.

Publications

Predicting major adverse cardiovascular events for secondary prevention: protocol for a systematic review and meta-analysis of risk prediction models.

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