Comparison of Recommendations and Use of Cardiovascular Risk Equations by Health Technology Assessment Agencies and Clinical Guidelines

Abstract

To identify risk equations for cardiovascular diseases (CVDs) in primary and secondary prevention settings that are used or recommended by health technology assessment (HTA) organizations and in clinical guidelines (CGs). Methods: A targeted literature review was conducted using a two-stage search strategy. First, HTA reviews of manufacturers' drug submissions, reports from established HTA organizations (Europe, Canada, and Australia), and CGs from countries with and without HTA organizations, including the United States, were identified. Documents published between September 30, 2006 and September 30, 2016, were examined for cardiovascular risk equations, recommendations, and commentaries. Next, publications associated with risk equations and cited by HTA and CG documents were retrieved. This literature was examined to extract commentaries and risk equation study characteristics. Results: The review identified 47 risk equations, 25 in the primary CVD prevention setting (i.e., patients with no CVD history), including 5 for CVD prevention in diabetes and 22 solely in secondary prevention settings; 11 were identified for heart failure, 3 for stroke or transient ischemic attack, 2 for stable angina, and 11 for acute coronary syndrome or related conditions. A small set of primary prevention equations was found to be commonly used by HTAs, whereas secondary prevention equations were less common in HTA documents. CGs provided more risk equations as options than HTA documents. Conclusions: Although there is an abundance of risk equations developed for primary and secondary prevention, there remains a need for additional research to provide sufficient clinical and HTA guidance for risk estimation, particularly in high-risk or secondary prevention settings.

Evaluating risk prediction models for adults with heart failure: A systematic literature review

“…Publications on risk prediction models have become more common in recent years, but distinct prediction models frequently exist for the same outcome or target population. As such, healthcare professionals, policy makers, or guideline committees have competing information regarding which prediction models should be used or recommended [61,62]. To aid these decisions, SLRs of risk prediction models are increasingly demanded and performed [11][12][13][14][15].…”

“…Introduction of bias could lead to the wrong patients being identified and treated, and ultimately costly mistakes within a healthcare system, if the model was widely used [16,17]. The risk that patients will be inappropriately treated could partly explain why models are not being confidently used as an aid to HF patient management [61,62], alongside other concerns discussed in more detail below.…”

“…If risk assessments are to be useful at the bedside, providers need pragmatic models that rely upon easily accessible variables to stratify patients. Given these diverse needs and conflicting evidence of value, further research is required to develop tools, or moreover automated techniques that can provide clinical guidance for risk estimation, in primary care and high-risk or secondary prevention settings [62]. Beyond these concerns, our study highlights the wide variation in statistical approach, the complexity of certain models, and lack of clear external validation, other important considerations for decision makers when recommending any model for predicting risk or stratifying patients according to future risk.…”

New risk prediction models in England may lead to targeted PCSK9 inhibitor treatment, for patients with established cardiovascular disease

“…Three recent health technology assessments conducted in Scotland, Sweden and The Netherlands have cited the REACH model, when evaluating the potential applications of evolocumab, a new PCSK9 inhibitor developed by Amgen. 8 In this issue of the European Journal of Preventative Cardiology, Danese et al 9 looked at 60,838 patients in England with existing CVD, to predict recurrent cardiovascular events over 5 years of follow-up. They demonstrate convincingly that the 11% of patients with severe atherosclerotic disease (as denoted by recurrent myocardial infarcts or strokes, and/or multimorbidity with more than one concurrent CVD) had an elevated risk of recurrent events, when compared with the 89% patients who lacked such features.…”

Clinical Predictors of Mortality in Prehospital Distress Calls by Emergency Medical Service Subscribers

“…Studies investigating prehospital patient data, including early predictors for mortality, are becoming more common in recent years, and prior publications have reported multiple risk predictors for use in the prehospital occurrence for numerous conditions, including HF, diabetes mellitus (DM), and hypertension (HTN) [1]. Currently, there is considerable competing information regarding which prediction models should be used or recommended [2,3].…”