Estimated Life Expectancy Without Recurrent Cardiovascular Events in Patients With Vascular Disease: The SMART‐REACH Model

Kaasenbrood, Lotte; Bhatt, Deepak L.; Dorresteijn, Jannick A.N.; Wilson, Peter W.F.; D’Agostino, Ralph B.; Massaro, Joseph M.; Graaf, Yolanda van der; Cramer, Maarten J.; Kappelle, L. Jaap; Borst, Gert J. de; Steg, Ph. Gabriel; Visseren, Frank L.J.

doi:10.1161/jaha.118.009217

Cited by 64 publications

(74 citation statements)

References 42 publications

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“…SMART‐REACH uses ten predictors: age, sex, current smoking (yes/no), diabetes mellitus (yes/no), systolic BP, total cholesterol, creatinine, number of locations of cardiovascular disease (coronary artery disease, PAD), history of atrial fibrillation (yes/no) and history of congestive heart failure (yes/no). Missing variables were handled by means of imputation using predictive mean matching (aregImpute, Hmisc package, R software; R Project for Statistical Computing, Vienna, Austria) and reported population frequencies and medians from REACH Western Europe. The SMART‐REACH model consists of two complementary competing‐risk‐adjusted left‐truncated cause‐specific hazard functions: one for cardiovascular events, and one for non‐cardiovascular mortality.…”

Section: Methodsmentioning

confidence: 99%

Observational study of the medical management of patients with peripheral artery disease

Saratzis

Jaspers

Gwilym

et al. 2019

British Journal of Surgery

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Background Previous research has suggested that patients with peripheral artery disease (PAD) are not offered adequate risk factor modification, despite their high cardiovascular risk. The aim of this study was to assess the cardiovascular profiles of patients with PAD and quantify the survival benefits of target‐based risk factor modification. Methods The Vascular and Endovascular Research Network (VERN) prospectively collected cardiovascular profiles of patients with PAD from ten UK vascular centres (April to June 2018) to assess practice against UK and European goal‐directed best medical therapy guidelines. Risk and benefits of risk factor control were estimated using the SMART‐REACH model, a validated cardiovascular prediction tool for patients with PAD. Results Some 440 patients (mean(s.d.) age 70(11) years, 24·8 per cent women) were included in the study. Mean(s.d.) cholesterol (4·3(1·2) mmol/l) and LDL‐cholesterol (2·7(1·1) mmol/l) levels were above recommended targets; 319 patients (72·5 per cent) were hypertensive and 343 (78·0 per cent) were active smokers. Only 11·1 per cent of patients were prescribed high‐dose statin therapy and 39·1 per cent an antithrombotic agent. The median calculated risk of a major cardiovascular event over 10 years was 53 (i.q.r. 44–62) per cent. Controlling all modifiable cardiovascular risk factors based on UK and European guidance targets (LDL‐cholesterol less than 2 mmol/l, systolic BP under 140 mmHg, smoking cessation, antiplatelet therapy) would lead to an absolute risk reduction of the median 10‐year cardiovascular risk by 29 (20–38) per cent with 6·3 (4·0–9·3) cardiovascular disease‐free years gained. Conclusion The medical management of patients with PAD in this secondary care cohort was suboptimal. Controlling modifiable risk factors to guideline‐based targets would confer significant patient benefit.

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Section: Methodsmentioning

confidence: 99%

Observational study of the medical management of patients with peripheral artery disease

Saratzis

Jaspers

Gwilym

et al. 2019

British Journal of Surgery

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“…These include the LIFE-CVD model for apparently healthy people aged 45-80 years), 55 the DIAL-model for diabetes patients aged 30-85 years 56 and the SMART-REACH model for vascular patients aged 45-80 years. 57 All U-Prevent risk algorithms have been extensively validated in contemporary European and North American populations and geographical updates are applied when appropriate. This tool has a timely and user-friendly interface and offers infographics that can support doctor-patient communication and shared decision-making in clinical practice.…”

Section: Recommendationsmentioning

confidence: 99%

Risk prediction tools in cardiovascular disease prevention: A report from the ESC Prevention of CVD Programme led by the European Association of Preventive Cardiology (EAPC) in collaboration with the Acute Cardiovascular Care Association (ACCA) and the Association of Cardiovascular Nursing and Allied Professions (ACNAP)

Rosselló

Dorresteijn

Janssen

et al. 2019

Eur J Prev Cardiolog

107

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Risk assessment have become essential in the prevention of cardiovascular disease. Even though risk prediction tools are recommended in the European guidelines, they are not adequately implemented in clinical practice. Risk prediction tools are meant to estimate prognosis in an unbiased and reliable way and to provide objective information on outcome probabilities. They support informed treatment decisions about the initiation or adjustment of preventive medication. Risk prediction tools facilitate risk communication to the patient and their family, and this may increase commitment and motivation to improve their health. Over the years many risk algorithms have been developed to predict 10-year cardiovascular mortality or lifetime risk in different populations, such as in healthy individuals, patients with established cardiovascular disease and patients with diabetes mellitus. Each risk algorithm has its own limitations, so different algorithms should be used in different patient populations. Risk algorithms are made available for use in clinical practice by means of – usually interactive and online available – tools. To help the clinician to choose the right tool for the right patient, a summary of available tools is provided. When choosing a tool, physicians should consider medical history, geographical region, clinical guidelines and additional risk measures among other things. Currently, the U-prevent.com website is the only risk prediction tool providing prediction algorithms for all patient categories, and its implementation in clinical practice is suggested/advised by the European Association of Preventive Cardiology.

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“…Individualized 10-year risk reduction and lifetime benefit treatment effects of treatment with a PCSK9-mAb versus no treatment were estimated using the SMART-REACH model [7]. The SMART-REACH model is a model to estimate life expectancy free of a recurrent CVD in patients with a history of CVD.…”

Section: Individual Treatment Effect Estimationsmentioning

confidence: 99%

“…A recently developed prediction model for secondary prevention, the SMART-REACH model, is able to estimate individual benefit of medication for prevention of CVD in patients with a history of stable CVD as 10-year risk reduction or as months gained from a lifetime perspective, the lifetime benefit (supplemental methods) [7]. Estimation of treatment effects expressed by a lifetime benefit could overcome some disadvantages of the 10year risk-based strategies.…”

Section: Introductionmentioning

confidence: 99%

Would treatment decisions about secondary prevention of CVD based on estimated lifetime benefit rather than 10-year risk reduction be cost-effective?

et al. 2020

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Objective: To test the hypothesis that treatment decisions (treatment with a PCSK9-mAb versus no treatment) are both more effective and more cost-effective when based on estimated lifetime benefit than when based on estimated risk reduction over 10 years.Methods: A microsimulation model was constructed for 10,000 patients with stable cardiovascular disease (CVD). Costs and quality-adjusted life years (QALYs) due to recurrent cardiovascular events and (non)vascular death were estimated for lifetime benefit-based compared to 10-year risk-based treatment, with PCSK9 inhibition as an illustration example. Lifetime benefit in months gained and 10-year absolute risk reduction were estimated using the SMART-REACH model, including an individualized treatment effect of PCSK9 inhibitors based on baseline lowdensity lipoprotein cholesterol. For the different numbers of patients treated (i.e. the 5%, 10%, and 20% of patients with the highest estimated benefit of both strategies), cost-effectiveness was assessed using the incremental costeffectiveness ratio (ICER), indicating additional costs per QALY gain. Results: Lifetime benefit-based treatment of 5%, 10%, and 20% of patients with the highest estimated benefit resulted in an ICER of €36,440/QALY, €39,650/QALY, or €41,426/QALY. Ten-year risk-based treatment decisions of 5%, 10%, and 20% of patients with the highest estimated risk reduction resulted in an ICER of €48,187/QALY, €53,368/ QALY, or €52,390/QALY. Conclusion: Treatment decisions (treatment with a PCSK9-mAb versus no treatment) are both more effective and more cost-effective when based on estimated lifetime benefit than when based on estimated risk reduction over 10 years

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Estimated Life Expectancy Without Recurrent Cardiovascular Events in Patients With Vascular Disease: The SMART‐REACH Model

Cited by 64 publications

References 42 publications

Observational study of the medical management of patients with peripheral artery disease

Observational study of the medical management of patients with peripheral artery disease

Would treatment decisions about secondary prevention of CVD based on estimated lifetime benefit rather than 10-year risk reduction be cost-effective?

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