A cost effectiveness study into the detection of functionally significant coronary artery disease in patients with chronic coronary syndrome: a decision-analytic modelling approach. (25th November 2020)

• Record Type: Journal Article
• Authors: Nazir, M.S; Rodriguez-Guadarrama, Y; Rua, T; Chiribiri, A; Pennington, M; Plein, S
• Is Part Of: European heart journal. Volume 41:(2020)Supplement 2
• Publication Date: 2020-11-25
• Subjects: Health Economics; Cardiology -- Periodicals; Heart -- Diseases -- Periodicals; 616.12005
• DOI: 10.1093/ehjci/ehaa946.3544
• Languages: English
• ISSNs: 0195-668X

Abstract

Abstract: Background: ESC guidelines recommend functional or anatomical imaging for stable coronary artery disease (CAD) diagnosis. We investigated cost-effective diagnostic strategies for CAD detection with invasive coronary angiography (ICA) and fractional flow reserve (FFR) as reference standard [1, 2], using NHS reference costs. Methods: Deterministic and probabilistic decision-analytic models for diagnostic strategies in low (25%), intermediate (50%) and high (75%) risk CAD were devised. Strategies: standalone or combined testing with computed tomographic coronary angiography (CTCA), stress echocardiography (SE), CT-FFR, single-photon emission computed tomography (SPECT), cardiac magnetic resonance (CMR), positron emission tomography (PET), ICA, and ICA-FFR. Proportion of correct diagnosis served as measure of clinical effectiveness. Incremental cost-effectiveness ratios were calculated for dominant strategies. Cost-effectiveness acceptability curves (CEAC) tested variation of cost-effectiveness threshold (CET). Results: Base case (Table 1) consistent with probabilistic analysis (Figure 1 left). CEACs (Figure 1 right). Conclusions: Direct ICA is not cost-effective. Functional testing has significant role in low/intermediate risk. CMR is cost-effective in all risk and most likely cost-effective in CETs <£10, 000. ICA-FFR yields highest correct diagnoses in all at highest cost. Future long-term follow-up studies with quality of life measures are needed. References: 1. Knuuti et al EHJ. 2018; 39(35):3322–30 2. Danad et al. EHJ. 2016; 38(13):991–8. Funding Acknowledgement: Type of funding source: Public grant(s) – National budget only. Main funding source(s): The authors acknowledge financial support from the Department of Health through the National Institute for Health Research (NIHR) comprehensive Biomedical Research Centre award to Guy's & St Thomas' NHS Foundation Trust in partnership with King's College London and King's College Hospital NHS Foundation Trust and by the NIHR MedTech Co-operative for Cardiovascular Disease at Guy's and St Thomas' NHS Foundation Trust. This abstract presents independent research funded by the National Institute for Health Research (NIHR) under its Research for Patient Benefit (RfPB) Programme (Grant Reference Number PB-PG-0416-20008). This work was supported by the Wellcome/EPSRC Centre for Medical Engineering [WT 203148/Z/16/Z]. MSN was funded by the UK Medical Research Council under grant number MR/P01979X/1. The views expressed are those of the authors and not necessarily those of the NHS, the NIHR or the Department of Health and Social Care, EPSRC, MRC or the Wellcome Trust.