Skip to main content
PMC home page
Medline Book to support NIHPA logoLink to Medline Book to support NIHPA
. 2024 Jul;28(31):1–105. doi: 10.3310/WYGC4096

A cloud-based medical device for predicting cardiac risk in suspected coronary artery disease: a rapid review and conceptual economic model.

Marie Westwood, Nigel Armstrong, Eline Krijkamp, Mark Perry, Caro Noake, Apostolos Tsiachristas, Isaac Corro-Ramos
PMCID: PMC11299050  PMID: 39023142

Abstract

BACKGROUND

The CaRi-Heart® device estimates risk of 8-year cardiac death, using a prognostic model, which includes perivascular fat attenuation index, atherosclerotic plaque burden and clinical risk factors.

OBJECTIVES

To provide an Early Value Assessment of the potential of CaRi-Heart Risk to be an effective and cost-effective adjunctive investigation for assessment of cardiac risk, in people with stable chest pain/suspected coronary artery disease, undergoing computed tomography coronary angiography. This assessment includes conceptual modelling which explores the structure and evidence about parameters required for model development, but not development of a full executable cost-effectiveness model.

DATA SOURCES

Twenty-four databases, including MEDLINE, MEDLINE In-Process and EMBASE, were searched from inception to October 2022.

METHODS

Review methods followed published guidelines. Study quality was assessed using Prediction model Risk Of Bias ASsessment Tool. Results were summarised by research question: prognostic performance; prevalence of risk categories; clinical effects; costs of CaRi-Heart. Exploratory searches were conducted to inform conceptual cost-effectiveness modelling.

RESULTS

The only included study indicated that CaRi-Heart Risk may be predictive of 8 years cardiac death. The hazard ratio, per unit increase in CaRi-Heart Risk, adjusted for smoking, hypercholesterolaemia, hypertension, diabetes mellitus, Duke index, presence of high-risk plaque features and epicardial adipose tissue volume, was 1.04 (95% confidence interval 1.03 to 1.06) in the model validation cohort. Based on Prediction model Risk Of Bias ASsessment Tool, this study was rated as having high risk of bias and high concerns regarding its applicability to the decision problem specified for this Early Value Assessment. We did not identify any studies that reported information about the clinical effects or costs of using CaRi-Heart to assess cardiac risk. Exploratory searches, conducted to inform the conceptual cost-effectiveness modelling, indicated that there is a deficiency with respect to evidence about the effects of changing existing treatments or introducing new treatments, based on assessment of cardiac risk (by any method), or on measures of vascular inflammation (e.g. fat attenuation index). A de novo conceptual decision-analytic model that could be used to inform an early assessment of the cost effectiveness of CaRi-Heart is described. A combination of a short-term diagnostic model component and a long-term model component that evaluates the downstream consequences is anticipated to capture the diagnosis and the progression of coronary artery disease.

LIMITATIONS

The rapid review methods and pragmatic additional searches used to inform this Early Value Assessment mean that, although areas of potential uncertainty have been described, we cannot definitively state where there are evidence gaps.

CONCLUSIONS

The evidence about the clinical utility of CaRi-Heart Risk is underdeveloped and has considerable limitations, both in terms of risk of bias and applicability to United Kingdom clinical practice. There is some evidence that CaRi-Heart Risk may be predictive of 8-year risk of cardiac death, for patients undergoing computed tomography coronary angiography for suspected coronary artery disease. However, whether and to what extent CaRi-Heart represents an improvement relative to current standard of care remains uncertain. The evaluation of the CaRi-Heart device is ongoing and currently available data are insufficient to fully inform the cost-effectiveness modelling.

FUTURE WORK

A large (n = 15,000) ongoing study, NCT05169333, the Oxford risk factors and non-invasive imaging study, with an estimated completion date of February 2030, may address some of the uncertainties identified in this Early Value Assessment.

STUDY REGISTRATION

This study is registered as PROSPERO CRD42022366496.

FUNDING

This award was funded by the National Institute for Health and Care Research (NIHR) Evidence Synthesis programme (NIHR award ref: NIHR135672) and is published in full in Health Technology Assessment; Vol. 28, No. 31. See the NIHR Funding and Awards website for further award information.

Plain language summary

Coronary artery disease affects around 2.3 million people in the United Kingdom. It is caused by a build-up of fatty plaques on the walls of the blood vessels that supply the heart muscle. This can reduce the flow of blood to the heart and result in people experiencing chest pain (angina), especially when they exercise. Over time, the fatty plaques can grow and block more or all of the artery and blood clots can also form, causing blockage. A heart attack happens when the supply of blood to the heart muscle is blocked. People who have episodes of chest pain, whose doctors think that they may have coronary artery disease, can have a type of imaging (computed tomography coronary angiography) which shows whether there is any narrowing of their coronary arteries. When offering treatment, specialist heart doctors are likely to consider a person’s symptoms and other risk factors (such as family history of heart disease, diabetes and smoking history), as well as how much narrowing of the arteries has happened. CaRi-Heart® is a computer programme that uses information about inflammation in a person’s coronary arteries, together with recognised risk factors, such as age, sex, smoking, high cholesterol levels, high blood pressure and diabetes, to estimate an individual’s risk of dying from a heart attack in the next 8 years. There is evidence that CaRi-Heart® is better at estimating this risk than using information recognised risk factors alone. However, there is a lack of information about how treatment could change as a result of using CaRi-Heart® and whether any changes would improve outcomes for patients. There is also a lack of information about how much CaRi-Heart® would cost the National Health Service.

Full text of this article can be found in Bookshelf.

References

  1. National Institute for Health and Care Excellence. Diagnostics Assessment Programme: Early Value Assessment: CaRi-Heart for Predicting Cardiac Risk in Suspected Coronary Artery Disease: Final Scope. London: NICE; 2022.
  2. Office for National Statistics. Heart Attack Deaths in 2019, 2020, 2021 and 2022. 2022. URL: www.ons.gov.uk/aboutus/transparencyandgovernance/freedomofinformationfoi/heartattackdeathsin201920202021and2022 (accessed 8 September 2022).
  3. Office for National Statistics. Deaths Registered in England and Wales: 2021. 2022. URL: www.ons.gov.uk/peoplepopulationandcommunity/birthsdeathsandmarriages/deaths/bulletins/deathsregistrationsummarytables/2021 (accessed 8 September 2022).
  4. National Institute for Health and Care Excellence. Recent-onset Chest Pain of Suspected Cardiac Origin: Assessment and Diagnosis. NICE Clinical Guideline 95. London: NICE; 2010. URL: www.nice.org.uk/guidance/cg95 (accessed 8 September 2022). [PubMed]
  5. Knuuti J, Wijns W, Saraste A, Capodanno D, Barbato E, Funck-Brentano C, et al.; ESC Scientific Document Group. 2019 ESC guidelines for the diagnosis and management of chronic coronary syndromes. Eur Heart J 2020;41:407–77. doi: 10.1093/eurheartj/ehz425. [DOI] [PubMed]
  6. Fishbein MC, Siegel RJ. How big are coronary atherosclerotic plaques that rupture? Circulation 1996;94:2662–6. doi: 10.1161/01.cir.94.10.2662. [DOI] [PubMed]
  7. Matter CM, Stuber M, Nahrendorf M. Imaging of the unstable plaque: how far have we got? Eur Heart J 2009;30:2566–74. doi: 10.1093/eurheartj/ehp419. [DOI] [PMC free article] [PubMed]
  8. Fleg JL, Stone GW, Fayad ZA, Granada JF, Hatsukami TS, Kolodgie FD, et al. Detection of high-risk atherosclerotic plaque: report of the NHLBI Working Group on current status and future directions. JACC Cardiovasc Imaging 2012;5:941–55. doi: 10.1016/j.jcmg.2012.07.007. [DOI] [PMC free article] [PubMed]
  9. Ross R. Atherosclerosis – an inflammatory disease. N Engl J Med 1999;340:115–26. doi: 10.1056/NEJM199901143400207. [DOI] [PubMed]
  10. Oikonomou EK, Marwan M, Desai MY, Mancio J, Alashi A, Centeno EH, et al. Non-invasive detection of coronary inflammation using computed tomography and prediction of residual cardiovascular risk (the CRISP CT study): a post-hoc analysis of prospective outcome data. Lancet 2018;392:929–39. doi: 10.1016/S0140-6736(18)31114-0. [DOI] [PMC free article] [PubMed]
  11. Oikonomou EK, Antonopoulos AS, Schottlander D, Marwan M, Mathers C, Tomlins P, et al. Standardized measurement of coronary inflammation using cardiovascular computed tomography: integration in clinical care as a prognostic medical device. Cardiovasc Res 2021;117:2677–90. doi: 10.1093/cvr/cvab286. [DOI] [PubMed]
  12. Caristo Diagnostics Ltd. CaRi-Heart Analysis. Uncover Inflammation. Predict Heart Attacks (Doc No. 1413). Oxford: Caristo Diagnostics Ltd; 2021. URL: https://caristo.com/img/pdfs/Caristo_Flyer_1413.pdf (accessed 15 September 2022).
  13. Min JK, Shaw LJ, Devereux RB, Okin PM, Weinsaft JW, Russo DJ, et al. Prognostic value of multidetector coronary computed tomographic angiography for prediction of all-cause mortality. J Am Coll Cardiol 2007;50:1161–70. doi: 10.1016/j.jacc.2007.03.067. [DOI] [PubMed]
  14. National Institute for Health and Care Excellence. Stable Angina: Management. NICE Clinical Guideline 126. London: NICE; 2011. URL: www.nice.org.uk/guidance/cg126/resources/stable-angina-management-pdf-35109453262021 (accessed 15 September 2022).
  15. National Institute for Health and Care Excellence. Cardiovascular Disease: Risk Assessment and Reduction, Including Lipid Modification. NICE Clinical Guideline 181. London: NICE; 2014. URL: www.nice.org.uk/guidance/cg181/resources/cardiovascular-disease-risk-assessment-and-reduction-including-lipid-modification-pdf-35109807660997 (accessed 21 September 22).
  16. Centre for Reviews and Dissemination. Systematic Reviews: CRD’s Guidance for Undertaking Reviews in Health Care. York: University of York; 2009. URL: www.york.ac.uk/inst/crd/SysRev/!SSL!/WebHelp/SysRev3.htm (accessed 23 March 2011).
  17. National Institute for Health and Care Excellence. NICE Health Technology Evaluations: The Manual. Process and Methods [PMG36]. London: NICE; 2022. 181p. URL: www.nice.org.uk/process/pmg36 (accessed 8 February 2022).
  18. Garritty C, Gartlehner G, Kamel C, King VJ, Nussbaumer-Streit B, Stevens A, et al. Cochrane Rapid Reviews: Interim Guidance from the Cochrane Rapid Reviews Methods Group. 2020. URL: http://methods.cochrane.org/sites/methods.cochrane.org.rapidreviews/files/uploads/cochrane_rr_-_guidance-23mar2020-v1.pdf (accessed 27 September 22).
  19. Canadian Agency for Drugs and Technologies in Health. PRESS – Peer Review of Electronic Search Strategies: 2015 Guideline Explanation and Elaboration (PRESS E&E). Ottawa: CADTH; 2016. URL: https://cadth.ca/press-peer-review-electronic-search-strategies (accessed 16 November 2020).
  20. Wolff RF, Moons KGM, Riley RD, Whiting PF, Westwood M, Collins GS, et al.; PROBAST Group. PROBAST: a tool to assess the risk of bias and applicability of prediction model studies. Ann Intern Med 2019;170:51–8. doi: 10.7326/M18-1376. [DOI] [PubMed]
  21. Abbasi J. Could a new method to detect coronary inflammation prevent heart attacks? JAMA 2017;318:1527–8. doi: 10.1001/jama.2017.13413. [DOI] [PubMed]
  22. Antoniades C, Kotanidis CP, Berman DS. State-of-the-art review article. Atherosclerosis affecting fat: what can we learn by imaging perivascular adipose tissue? J Cardiovasc Comput Tomogr 2019;13:288–96. doi: 10.1016/j.jcct.2019.03.006. [DOI] [PMC free article] [PubMed]
  23. Antoniades C, Shirodaria C. Detecting coronary inflammation with perivascular fat attenuation imaging: making sense from perivascular attenuation maps. JACC Cardiovasc Imaging 2019;12:2011–4. doi: 10.1016/j.jcmg.2018.12.024. [DOI] [PubMed]
  24. Antoniades C, Antonopoulos AS, Deanfield J. Imaging residual inflammatory cardiovascular risk. Eur Heart J 2020;41:748–58. doi: 10.1093/eurheartj/ehz474. [DOI] [PubMed]
  25. Antonopoulos AS, Angelopoulos A, Papanikolaou P, Simantiris S, Oikonomou EK, Vamvakaris K, et al. Biomarkers of vascular inflammation for cardiovascular risk prognostication: a meta-analysis. JACC Cardiovasc Imaging 2022;15:460–71. doi: 10.1016/j.jcmg.2021.09.014. [DOI] [PubMed]
  26. Antonopoulos AS, Angelopoulos A, Tsioufis K, Antoniades C, Tousoulis D. Cardiovascular risk stratification by coronary computed tomography angiography imaging: current state-of-the-art. Eur J Prev Cardiol 2022;29:608–24. doi: 10.1093/eurjpc/zwab067. [DOI] [PubMed]
  27. Antonopoulos AS, Angelopoulos A, Papanikolaou P, Simantiris S, Oikonomou EK, Vamvakaris K, et al. Biomarkers of vascular inflammation for cardiovascular risk prognostication: a meta-analysis. JACC Cardiovasc Imaging 2022;15:460–71. doi: 10.1016/j.jcmg.2021.09.014. [DOI] [PubMed]
  28. Antonopoulos AS, Antoniades C. Perivascular fat attenuation index by computed tomography as a metric of coronary inflammation. J Am Coll Cardiol 2018;71:2708–9. doi: 10.1016/j.jacc.2018.03.511. [DOI] [PubMed]
  29. Antonopoulos AS, Sanna F, Sabharwal N, Thomas S, Oikonomou EK, Herdman L, et al. Detecting human coronary inflammation by imaging perivascular fat. Sci Transl Med 2017;9:12. doi: 10.1126/scitranslmed.aal2658. [DOI] [PubMed]
  30. Bao W, Yang M, Xu Z, Yan F, Yang Q, Li X, Yang W. Coronary inflammation assessed by perivascular fat attenuation index in patients with psoriasis: a propensity score-matched study. Dermatology 2022;238:562–70. doi: 10.1159/000518771. [DOI] [PubMed]
  31. Bengs S, Haider A, Warnock GI, Fiechter M, Pargaetzi Y, Rampidis G, et al. Quantification of perivascular inflammation does not provide incremental prognostic value over myocardial perfusion imaging and calcium scoring. Eur J Nucl Med Mol Imaging 2021;48:1806–12. doi: 10.1007/s00259-020-05106-0. [DOI] [PMC free article] [PubMed]
  32. Berman DS, Kwiecinski J. Imaging coronary inflammatory risk. JACC Cardiovasc Imaging 2022;15:472–5. doi: 10.1016/j.jcmg.2021.11.007. [DOI] [PubMed]
  33. Bittner DO, Goeller M, Zopf Y, Achenbach S, Marwan M. High level of EPA is associated with lower perivascular coronary attenuation as measured by coronary CTA. Presented at European Society of Cardiology Congress (ESC 2019); 31 Aug–4 Sept 2019; Paris, France. Eur Heart J 2019;40(Suppl. 1):3776.
  34. Cabrelle G, Pergola V, Cattarin S, Dellino C, Continisio S, Montonati C, et al. 514 Usefulness and clinical implications of plaque analysis and Pfai for the evaluation of cardiovascular risk. Presented at Society of Cardiovascular Computed Tomography 17th Annual Scientific Meeting; 15–17 July 2022; Las Vegas, US. J Cardiovasc Comput Tomogr 2022;16(4 Suppl.):S53.
  35. Cecere A, De Kreutzenberg S, Motta R, Benvenuti F, Iliceto S, Avogaro A, Tona F. Coronary perivascular inflammation in type 2 diabetes mellitus patients: the missing piece in the puzzle of their increased cardiovascular risk? Presented at European Society of Cardiology Congress (ESC 2021); 27–30 Aug 2021; Virtual. Eur Heart J 2021;42(Suppl. 1):157.
  36. Chatterjee D, Shou BL, Matheson MB, Ostovaneh MR, Rochitte C, Chen MY, et al. Perivascular fat attenuation for predicting adverse cardiac events in stable patients undergoing invasive coronary angiography. J Cardiovasc Comput Tomogr 2022;16:483–90. doi: 10.1016/j.jcct.2022.05.004. [DOI] [PMC free article] [PubMed]
  37. Chatterjee D, Shou B, Ostovaneh M, Matheson M, Ortman J, Cox C, et al. Perivascular fat attenuation index for predicting outcome in patients referred for invasive coronary angiography. Presented at 16th Annual Scientific Meeting of the Society of Cardiovascular Computed Tomography; 16–17 July 2021; Virtual. J Cardiovasc Comput Tomogr 2021;15(4 Suppl.):S53–4.
  38. Chen X, Dang Y, Hu H, Ma S, Ma Y, Wang K, et al. Pericoronary adipose tissue attenuation assessed by dual-layer spectral detector computed tomography is a sensitive imaging marker of high-risk plaques. Quant Imaging Med Surg 2021;11:2093–103. doi: 10.21037/qims-20-860. [DOI] [PMC free article] [PubMed]
  39. Dai X, Hou Y, Tang C, Lu Z, Shen C, Zhang L, Zhang J. Long-term prognostic value of the serial changes of CT-derived fractional flow reserve and perivascular fat attenuation index. Quant Imaging Med Surg 2022;12:752–65. doi: 10.21037/qims-21-424. [DOI] [PMC free article] [PubMed]
  40. Dai X, Yu L, Lu Z, Shen C, Tao X, Zhang J. Serial change of perivascular fat attenuation index after statin treatment: insights from a coronary CT angiography follow-up study. Int J Cardiol 2020;319:144–9. doi: 10.1016/j.ijcard.2020.06.008. [DOI] [PubMed]
  41. Dang Y, Chen X, Ma S, Ma Y, Ma Q, Zhou K, et al. Association of pericoronary adipose tissue quality determined by dual-layer spectral detector CT with severity of coronary artery disease: a preliminary study. Front Cardiovasc Med 2021;8:720127. doi: 10.3389/fcvm.2021.720127. [DOI] [PMC free article] [PubMed]
  42. Elnabawi YA, Oikonomou EK, Dey AK, Mancio J, Rodante JA, Aksentijevich M, et al. Association of biologic therapy with coronary inflammation in patients with psoriasis as assessed by perivascular fat attenuation index. JAMA Cardiol 2019;4:885–91. doi: 10.1001/jamacardio.2019.2589. [DOI] [PMC free article] [PubMed]
  43. Gaibazzi N, Tuttolomondo D, Nicolini F, Tafuni A, Sartorio D, Martini C, et al. The histopathological correlate of peri-vascular adipose tissue attenuation on computed tomography in surgical ascending aorta aneurysms: is this a measure of tissue inflammation? Diagnostics (Basel) 2021;11:1799. doi: 10.3390/diagnostics11101799. [DOI] [PMC free article] [PubMed]
  44. Hoshino M, Yang S, Sugiyama T, Zhang J, Kanaji Y, Hamaya R, et al. Characteristic findings of microvascular dysfunction on coronary computed tomography angiography in patients with intermediate coronary stenosis. Eur Radiol 2021;31:9198–210. doi: 10.1007/s00330-021-07909-7. [DOI] [PubMed]
  45. Hoshino M, Zhang J, Sugiyama T, Yang S, Kanaji Y, Hamaya R, et al. Prognostic value of pericoronary inflammation and unsupervised machine-learning-defined phenotypic clustering of CT angiographic findings. Int J Cardiol 2021;333:226–32. doi: 10.1016/j.ijcard.2021.03.019. [DOI] [PubMed]
  46. Hoshino M, Yang S, Sugiyama T, Zhang J, Kanaji Y, Yamaguchi M, et al. Prognostic value of peri-coronary adipose tissue attenuation and whole vessel and lesion plaque quantification on Coronary Computed Tomography Angiography. Presented at European Society of Cardiology Congress (ESC 2020); 29 Aug–1 Sep 2020; Virtual. Eur Heart J 2020;41(Suppl. 2):155.
  47. Kanaji Y, Sugiyama T, Hoshino M, Hirano H, Horie T, Kanno Y, et al. The association between global coronary flow reserve and coronary inflammation assessed by fat attenuation index on computed tomography in patients with acute coronary syndrome. Presented at European Society of Cardiology Congress (ESC 2019); 31 Aug–4 Sep 2019; Paris, France. Eur Heart J 2019;40(Suppl. 1):1306.
  48. Kato S, Utsunomiya D, Horita N, Hoshino M, Kakuta T. Prognostic significance of the perivascular fat attenuation index derived by coronary computed tomography: a meta-analysis. Hellenic J Cardiol 2022;67:73–5. doi: 10.1016/j.hjc.2022.07.004. [DOI] [PubMed]
  49. Li JH, Tang CX, Liu TY, Chen YC, Zhou CS, Lu GM, et al. [Association of coronary perivascular fat attenuation index, the parameters of plaque and fractional flow reserve]. Zhonghua Yi Xue Za Zhi 2021;101:3214–20. doi: 10.3760/cma.j.cn112137-20210414-00889. [DOI] [PubMed]
  50. Liu Y, Sun Y, Hu C, Liu J, Gao A, Han H, et al. Perivascular adipose tissue as an indication, contributor to, and therapeutic target for atherosclerosis. Front Physiol 2020;11:615503. doi: 10.3389/fphys.2020.615503. [DOI] [PMC free article] [PubMed]
  51. Montonati C, Pergola V, Dellino C, Continisio S, Mattesi G, Zolin A, et al. Coro-CT plaque analysis in assessment of cardiovascular risk. Presented at 53rd Congress of the Italian Association of Hospital Cardiologists (ANMCO 2022); 19–21 May 2022; Rimini, Italy. Eur Heart J Suppl 2022;24(Suppl. C):C23–4.
  52. Oikonomou EK, Schottlander D, Antonopoulos AS, Marwan M, Kotanidis CP, Kluner LV, et al. Standardised quantification of coronary inflammation using cardiac computed tomography: the Fat Attenuation Index Score (FAI-Score). Presented at ESC Preventive Cardiology 2021; 15–17 April 2021; Online. Eur J Prev Cardiol 2021;28(Suppl. 1):i440–1.
  53. Oikonomou EK, Desai MY, Marwan M, Kotanidis CP, Antonopoulos AS, Schottlander D, et al. Perivascular fat attenuation index stratifies cardiac risk associated with high-risk plaques in the CRISP-CT study. J Am Coll Cardiol 2020;76:755–7. doi: 10.1016/j.jacc.2020.05.078. [DOI] [PubMed]
  54. Oikonomou EK, Marwan M, Mancio J, Kotanidis CK, Thomas KE, Alashi A, et al. Perivascular fat attenuation index stratifies the cardiac risk associated with high-risk plaque features on coronary computed tomography angiography. Presented at European Society of Cardiology Congress (ESC 2019); 31 Aug–4 Sep 2019; Paris, France. Eur Heart J 2019;40(Suppl. 1):1924.
  55. Oikonomou EK, Williams MC, Kotanidis CP, Desai MY, Marwan M, Antonopoulos AS, et al. A novel machine learning-derived radiotranscriptomic signature of perivascular fat improves cardiac risk prediction using coronary CT angiography. Eur Heart J 2019;40:3529–43. doi: 10.1093/eurheartj/ehz592. [DOI] [PMC free article] [PubMed]
  56. Oikonomou EK, Thomas S, Mancio J, Antonopoulos AS, Sabharwal N, Kelion A, et al. Computed tomography-based perivascular fat phenotyping identifies unstable coronary lesions and active vascular calcification. Presented at European Society of Cardiology Congress (ESC 2018); 25–29 Aug 2018; Munich, Germany. Eur Heart J 2018;39(Suppl. 1):233.
  57. Pandey NN, Sharma S, Jagia P, Kumar S. Epicardial fat attenuation, not volume, predicts obstructive coronary artery disease and high risk plaque features in patients with atypical chest pain. Br J Radiol 2020;93:20200540. doi: 10.1259/bjr.20200540. [DOI] [PMC free article] [PubMed]
  58. Pergola V, Cabrelle G, Mattesi G, Cattarin S, Furlan A, Dellino CM, et al. Added value of CCTA-derived features to predict MACEs in stable patients undergoing coronary computed tomography. Diagnostics (Basel) 2022;12:1446. doi: 10.3390/diagnostics12061446. [DOI] [PMC free article] [PubMed]
  59. Pergola V, Previtero M, Cecere A, Storer V, Castiello T, Baritussio A, et al. Clinical value and time course of pericoronary fat inflammation in patients with angiographically nonobstructive coronaries: a preliminary report. J Clin Med 2021;10:1786. doi: 10.3390/jcm10081786. [DOI] [PMC free article] [PubMed]
  60. Plackett B. Caristo Diagnostics: taking a fresh look at CT scans. Nature 2020;588:S70–2. doi: 10.1038/d41586-020-01798-4. [DOI] [PubMed]
  61. Sagris MS, Antonopoulos AA, Simantiris SS, Panagiotopoulos GP, Papanikolaou PP, Oikonomou EO, et al. Pericoronary adipose tissue attenuation index (FAI) – a newimaging biomarker and its diagnostic and prognostic utility: a systematic review and meta-analysis. Presented at ESC Congress 2021; 27–30 Aug 2021; Virtual. Eur Heart J 2021;42(Suppl. 1):197.
  62. Sen N, Tanwar S. Correlation of coronary inflammatory markers like high sensitive C reactive protein, tumor necrosis factor, interleukin 6 with perivascular fat attenuation index to stratify the cardiac risk associated with the presence of adverse plaque morphology. Presented at ACC20 – World Congress of Cardiology; 28–30 Mar 2020; Chicago, US. J Am Coll Cardiol 2020;75(11 Suppl. 1):42.
  63. Shan D, Wang G, Wang X, Ding Y, Chen Y, Chen J. [Value of maximum area stenosis combined with perivascular fat attenuation index in predicting hemodynamically significant coronary artery disease]. Nan Fang Yi Ke Da Xue Xue Bao 2021;41:988–94. doi: 10.12122/j.issn.1673-4254.2021.07.04. [DOI] [PMC free article] [PubMed]
  64. Simantiris S, Antonopoulos AS, Angelopoulos A, Papanikolaou P, Oikonomou EK, Vamvakaris K, et al. Prognostic value of vascular inflammation biomarkers over clinical risk factors for cardiovascular risk: a meta-analysis. Presented at ESC Preventive Cardiology 2021; 15–17 Apr 2021; Online. Eur J Prev Cardiol 2021;28(Suppl. 1):i223.
  65. Sugiyama T, Kanaji Y, Hoshino M, Yamaguchi M, Hada M, Misawa T, et al. Prognostic value of fat attenuation index of pericoronary adipose tissue surrounding left anterior descending artery on coronary computed tomography angiography. presented at European Society of Cardiology Congress, ESC 2020; 29 Aug–1 Sep 2020; Virtual. Eur Heart J 2020;41(Suppl. 2):1346.
  66. Sutanto SA, James NE, Loy RHY. Using perivascular fat attenuation index to monitor coronary inflammation in patients with psoriasis. JAMA Cardiol 2020;5:359. doi: 10.1001/jamacardio.2019.5236. [DOI] [PubMed]
  67. Yan H, Zhao N, Geng W, Hou Z, Gao Y, Lu B. The perivascular fat attenuation index improves the diagnostic performance for functional coronary stenosis. J Cardiovasc Dev Dis 2022;9:128. doi: 10.3390/jcdd9050128. [DOI] [PMC free article] [PubMed]
  68. Zhu X, Chen X, Ma S, Zhou K, Hou Y. Dual-layer spectral detector CT to study the correlation between pericoronary adipose tissue and coronary artery stenosis. J Cardiothorac Surg 2021;16:325. doi: 10.1186/s13019-021-01709-2. [DOI] [PMC free article] [PubMed]
  69. University of Oxford, Caristo Diagnostics Limited, National Consortium of Intelligent Medical Imaging, Oxford University Hospitals NHS Trust, British Heart Foundation, Innovate UK, et al. The Oxford Risk Factors and Non-invasive Imaging Study (ORFAN). NCT05169333. Bethesda, MD: National Library of Medicine (US); 2021. URL: https://clinicaltrials.gov/show/ NCT05169333 (accessed 23 November 22).
  70. Hicks KA, Tcheng JE, Bozkurt B, Chaitman BR, Cutlip DE, Farb A, et al. 2014 ACC/AHA key data elements and definitions for cardiovascular endpoint events in clinical trials: a report of the American College of Cardiology/American Heart Association Task Force on Clinical Data Standards (Writing Committee to Develop Cardiovascular Endpoints Data Standards). J Am Coll Cardiol 2015;66:403–69. doi: 10.1016/j.jacc.2014.12.018. [DOI] [PubMed]
  71. James PA, Oparil S, Carter BL, Cushman WC, Dennison-Himmelfarb C, Handler J, et al. 2014 evidence-based guideline for the management of high blood pressure in adults: report from the panel members appointed to the Eighth Joint National Committee (JNC 8). JAMA 2014;311:507–20. doi: 10.1001/jama.2013.284427. [DOI] [PubMed]
  72. American Diabetes Association. Diagnosis and classification of diabetes mellitus. Diabetes Care 2014;37(Suppl. 1):S81–90. doi: 10.2337/dc14-S081. [DOI] [PubMed]
  73. Stone NJ, Robinson JG, Lichtenstein AH, Bairey Merz CN, Blum CB, Eckel RH, et al.; American College of Cardiology/American Heart Association Task Force on Practice Guidelines. 2013 ACC/AHA guideline on the treatment of blood cholesterol to reduce atherosclerotic cardiovascular risk in adults: a report of the American College of Cardiology/American Heart Association Task Force on Practice Guidelines. J Am Coll Cardiol 2014;63(25 Pt B):2889–934. doi: 10.1016/j.jacc.2013.11.002. [DOI] [PubMed]
  74. Cutlip DE, Windecker S, Mehran R, Boam A, Cohen DJ, van Es GA, et al.; Academic Research Consortium. Clinical end points in coronary stent trials: a case for standardized definitions. Circulation 2007;115:2344–51. doi: 10.1161/CIRCULATIONAHA.106.685313. [DOI] [PubMed]
  75. Riley RD, Snell KI, Ensor J, Burke DL, Harrell FE Jr, Moons KG, Collins GS. Minimum sample size for developing a multivariable prediction model: PART II – binary and time-to-event outcomes. Stat Med 2019;38:1276–96. doi: 10.1002/sim.7992. [DOI] [PMC free article] [PubMed]
  76. National Institute for Health and Care Excellence. Diagnostics Assessment Programme: Early Value Assessment: CaRi-Heart® for predicting CTCA Risk in Suspected Coronary Artery Disease (CAD): Request for Information. London: NICE; 2022.
  77. Westwood M, Armstrong N, Krijkamp E, Perry M, Noake C, Corro-Ramos I. CaRi-Heart® for Predicting Cardiac Risk in Suspected Coronary Heart Disease (CAD): A Rapid Review and Conceptual Economic Model to Inform Early Value Assessment (EVA). Final Protocol. York: Kleijnen Systematic Reviews Ltd; 2022.
  78. Ma Z, Chen J, Jin K, Chen X. Colchicine and coronary heart disease risks: a meta-analysis of randomized controlled clinical trials. Front Cardiovasc Med 2022;9:947959. doi: 10.3389/fcvm.2022.947959. [DOI] [PMC free article] [PubMed]
  79. Bytyci I, Bajraktari G, Penson PE, Henein MY, Banach M, Lipid and Blood Pressure Meta-Analysis Collaboration (LBPMC) Group; International Lipid Expert Panel (ILEP). Efficacy and safety of colchicine in patients with coronary artery disease: a systematic review and meta-analysis of randomized controlled trials. Br J Clin Pharmacol 2022;88:1520–8.
  80. Xu H, Mao L, Liu H, Lin Z, Zhang Y, Yang J. Colchicine for secondary prevention of coronary artery disease: a meta-analysis of randomised controlled trials. Heart Lung Circ 2022;31:685–95. doi: 10.1016/j.hlc.2021.09.017. [DOI] [PubMed]
  81. Niu Y, Bai N, Ma Y, Zhong PY, Shang YS, Wang ZL. Safety and efficacy of anti-inflammatory therapy in patients with coronary artery disease: a systematic review and meta-analysis. BMC Cardiovasc Disord 2022;22:84. doi: 10.1186/s12872-022-02525-9. [DOI] [PMC free article] [PubMed]
  82. Fiolet ATL, Opstal TSJ, Mosterd A, Eikelboom JW, Jolly SS, Keech AC, et al. Efficacy and safety of low-dose colchicine in patients with coronary disease: a systematic review and meta-analysis of randomized trials. Eur Heart J 2021;42:2765–75. doi: 10.1093/eurheartj/ehab115. [DOI] [PubMed]
  83. Sen S, Karahan E, Buyukulas C, Polat YO, Uresin AY. Colchicine for cardiovascular therapy: a drug interaction perspective and a safety meta-analysis. Anatol J Cardiol 2021;25:753–61. doi: 10.5152/AnatolJCardiol.2021.707. [DOI] [PMC free article] [PubMed]
  84. Samuel M, Tardif JC, Bouabdallaoui N, Khairy P, Dube MP, Blondeau L, Guertin M-C. Colchicine for secondary prevention of cardiovascular disease: a systematic review and meta-analysis of randomized controlled trials. Can J Cardiol 2021;37:776–85. doi: 10.1016/j.cjca.2020.10.006. [DOI] [PubMed]
  85. Masson W, Lobo M, Lavalle-Cobo A, Molinero G. Can colchicine prevent acute myocardial infarction? Systematic review and meta-analysis. Rev Argent Cardiol 2021;89:42–9.
  86. Aimo A, Pascual Figal DA, Bayes-Genis A, Emdin M, Georgiopoulos G. Effect of low-dose colchicine in acute and chronic coronary syndromes: a systematic review and meta-analysis. Eur J Clin Invest 2021;51:e13464. doi: 10.1111/eci.13464. [DOI] [PubMed]
  87. Ullah W, Haq S, Zahid S, Gowda SN, Ottman P, Saleem S, et al. Safety and efficacy of colchicine in patients with stable CAD and ACS: a systematic review and meta-analysis. Am J Cardiovasc Drugs 2021;21:659–68. doi: 10.1007/s40256-021-00485-7. [DOI] [PubMed]
  88. Xia M, Yang X, Qian C. Meta-analysis evaluating the utility of colchicine in secondary prevention of coronary artery disease. Am J Cardiol 2021;140:33–8. doi: 10.1016/j.amjcard.2020.10.043. [DOI] [PubMed]
  89. Tien YY, Huang HK, Shih MC, Tu YK. Drug repurposing? Cardiovascular effect of colchicine on patients with coronary artery disease: a systematic review and meta-analysis. J Cardiol 2021;77:576–82. doi: 10.1016/j.jjcc.2020.11.010. [DOI] [PubMed]
  90. Xiang Z, Yang J, Yang J, Zhang J, Fan Z, Yang C, et al. Efficacy and safety of colchicine for secondary prevention of coronary heart disease: a systematic review and meta-analysis. Intern Emerg Med 2021;16:487–96. doi: 10.1007/s11739-020-02606-7. [DOI] [PubMed]
  91. Kofler T, Kurmann R, Lehnick D, Cioffi GM, Chandran S, Attinger-Toller A, et al. Colchicine in patients with coronary artery disease: a systematic review and meta-analysis of randomized trials. J Am Heart Assoc 2021;10:e021198. doi: 10.1161/JAHA.121.021198. [DOI] [PMC free article] [PubMed]
  92. Roman YM, Hernandez AV, White CM. The role of suppressing inflammation in the treatment of atherosclerotic cardiovascular disease. Ann Pharmacother 2020;54:1021–9. doi: 10.1177/1060028020922994. [DOI] [PubMed]
  93. Webb CA, Barry AR. Colchicine for secondary cardiovascular prevention: a systematic review. Pharmacotherapy 2020;40:575–83. doi: 10.1002/phar.2401. [DOI] [PubMed]
  94. Verma S, Eikelboom JW, Nidorf SM, Al-Omran M, Gupta N, Teoh H, Friedrich JO. Colchicine in cardiac disease: a systematic review and meta-analysis of randomized controlled trials. BMC Cardiovasc Disord 2015;15:96. doi: 10.1186/s12872-015-0068-3. [DOI] [PMC free article] [PubMed]
  95. Varotto L, Bonanno C, Caprioglio F. Network meta-analysis evaluating the utility of non-corticosteroid anti-inflammatory therapy in secondary prevention of coronaryartery disease. Eur Heart J Suppl 2021;23(Suppl. C):C114–5.
  96. Iskandarani ME, Shatla I, Khalid M, Paul T. Colchicine in stable coronary artery disease, a systematic review and metanalysis of randomized clinical trials. J Am Coll Cardiol 2021;77(18 Suppl. 1):179.
  97. Kassab K, Chuy KL, Vij A. Use of colchicine for secondary prevention of cardiovascular events: systematic review and meta-analysis. J Am Coll Cardiol 2021;77(18 Suppl. 1):24.
  98. Galli M, Princi G, Crea F, D’Amario D. Colchicine significantly increases the risk of non-cardiovascular death in patients with coronary artery disease. Eur Heart J Suppl 2020;22(Suppl. N):N112.
  99. Fong HK, Tan JL, Eniezat M, Yap J, Low KM, Desai R, et al. Outcomes of anti-inflammatory agents in coronary artery disease. Catheter Cardiovasc Interv 2020;95(Suppl. 2):S204–5.
  100. Wudexi I, Shokri E, Abo-Aly M, Shindo K, Abdel-Latif A. Comparative effectiveness of anti-inflammatory drug treatments in coronary heart disease patients: a systematic review and network meta-analysis. Mediators Inflamm 2021;2021:5160728. doi: 10.1155/2021/5160728. [DOI] [PMC free article] [PubMed]
  101. Abrantes AM, Nogueira-Garcia B, Alves M, Teixeira Passos D, Brito D, Pinto FJ, Caldeira D. Low-dose colchicine in coronary artery disease – systematic review and meta-analysis. Circ Rep 2021;3:457–64. doi: 10.1253/circrep.CR-21-0065. [DOI] [PMC free article] [PubMed]
  102. Chen Y, Zhang H, Chen Y, Li M, Luo W, Liu Y, et al. Colchicine may become a new cornerstone therapy for coronary artery disease: a meta-analysis of randomized controlled trials. Clin Rheumatol 2022;41:1873–87. doi: 10.1007/s10067-022-06050-0. [DOI] [PubMed]
  103. Grajek S, Michalak M, Urbanowicz T, Olasinska-Wisniewska A. A meta-analysis evaluating the colchicine therapy in patients with coronary artery disease. Front Cardiovasc Med 2021;8:740896. doi: 10.3389/fcvm.2021.740896. [DOI] [PMC free article] [PubMed]
  104. Al-Atta A, Kuzemczak M, Alkhalil M. Colchicine for the prevention of ischemic stroke: an updated meta-analysis of randomized clinical trials. Brain Circ 2021;7:187–93. doi: 10.4103/bc.bc_24_21. [DOI] [PMC free article] [PubMed]
  105. University of Sao Paulo General Hospital. Colchicine Effect on Perivascular Inflammation Index on Coronary CTA (COPIX). NCT05347316. Bethesda, MD: National Library of Medicine (US); 2023. URL: https://clinicaltrials.gov/show/ NCT05347316 (accessed 9 November 2022).
  106. Fulcher J, O’Connell R, Voysey M, Emberson J, Blackwell L, Mihaylova B, et al.; Cholesterol Treatment Trialists’ (CTT) Collaboration. Efficacy and safety of LDL-lowering therapy among men and women: meta-analysis of individual data from 174,000 participants in 27 randomised trials. Lancet 2015;385:1397–405. doi: 10.1016/S0140-6736(14)61368-4. [DOI] [PubMed]
  107. Mihaylova B, Emberson J, Blackwell L, Keech A, Simes J, Barnes EH, et al.; Cholesterol Treatment Trialists’ (CTT) Collaborators. The effects of lowering LDL cholesterol with statin therapy in people at low risk of vascular disease: meta-analysis of individual data from 27 randomised trials. Lancet 2012;380:581–90. doi: 10.1016/S0140-6736(12)60367-5. [DOI] [PMC free article] [PubMed]
  108. British National Formulary (BNF). British National Formulary. London: BNF Publications; 2022. URL: https://bnf.nice.org.uk/ (accessed 23 November 2022).
  109. Jones K, Burns A. Unit Costs of Health and Social Care 2021. Canterbury: Personal Social Services Research Unit, University of Kent; 2021. URL: www.pssru.ac.uk/project-pages/unit-costs/unit-costs-of-health-and-social-care-2021/ (accessed 23 November 22).
  110. NHS (England). 2019/20 National Cost Collection Data Publication. 2021. URL: www.england.nhs.uk/publication/2019-20-national-cost-collection-data-publication/ (accessed 23 November 22).
  111. Vemer P, Corro Ramos I, van Voorn GAK, Al MJ, Feenstra TL. AdViSHE: a validation-assessment tool of health-economic models for decision makers and model users. PharmacoEconomics 2016;34:349–61. doi: 10.1007/s40273-015-0327-2. [DOI] [PMC free article] [PubMed]
  112. Büyükkaramikli NC, Rutten-van Mölken MPMH, Severens JL, Al M. TECH-VER: a verification checklist to reduce errors in models and improve their credibility. PharmacoEconomics 2019;37:1391–408. doi: 10.1007/s40273-019-00844-y. [DOI] [PMC free article] [PubMed]
  113. National Institute for Health and Care Excellence. Early Value Assessment for Medtech: Actively Drawing in Medical Devices, Diagnostics and Digital Products that Address National Unmet Needs. Providing Quicker Assessments of Early Value to Identify the Most Promising Technologies, Conditional on Further Evidence Generation. [PowerPoint slides provided by NICE]. London: NICE; 2022. URL: (accessed 14 September 2022).
  114. Cury RC, Leipsic J, Abbara S, Achenbach S, Berman D, Bittencourt M, et al. CAD-RADS™ 20 – 2022 coronary artery disease – reporting and data system an expert consensus document of the Society of Cardiovascular Computed Tomography (SCCT), the American College of Cardiology (ACC), the American College of Radiology (ACR) and the North America Society of Cardiovascular Imaging (NASCI). Radiol Cardiothorac Imag 2022;4:e220183. doi: 10.1148/ryct.220183. [DOI] [PMC free article] [PubMed]
  115. Craig P, Cooper C, Gunnell D, Haw S, Lawson K, Macintyre S, et al. Using natural experiments to evaluate population health interventions: new Medical Research Council guidance. J Epidemiol Community Health 2012;66:1182–6. doi: 10.1136/jech-2011-200375. [DOI] [PMC free article] [PubMed]
  116. Blieden Betts M, Gandra SR, Cheng LI, Szatkowski A, Toth PP. Differences in utility elicitation methods in cardiovascular disease: a systematic review. J Med Econ 2018;21:74–84. doi: 10.1080/13696998.2017.1379410. [DOI] [PubMed]
  117. Betts MB, Rane P, Bergrath E, Chitnis M, Bhutani MK, Gulea C, et al. Utility value estimates in cardiovascular disease and the effect of changing elicitation methods: a systematic literature review. Health Qual Life Outcomes 2020;18:251. doi: 10.1186/s12955-020-01407-y. [DOI] [PMC free article] [PubMed]
  118. Creber RM, Dimagli A, Spadaccio C, Myers A, Moscarelli M, Demetres M, et al. Effect of coronary artery bypass grafting on quality of life: a meta-analysis of randomized trials. Eur Heart J Qual Care Clin Outcomes 2022;8:259–68. doi: 10.1093/ehjqcco/qcab075. [DOI] [PMC free article] [PubMed]
  119. De la Puente C, Vallejos C, Bustos L, Zaror C, Velasquez M, Lanas F. Latin American Clinical Epidemiology Network Series – Paper 8: Ticagrelor was cost-effective vs. clopidogrel in acute coronary syndrome in Chile. J Clin Epidemiol 2017;86:117–24. doi: 10.1016/j.jclinepi.2016.04.019. [DOI] [PubMed]
  120. Di Tanna GL, Urbich M, Wirtz HS, Potrata B, Heisen M, Bennison C, et al. Health state utilities of patients with heart failure: a systematic literature review. PharmacoEconomics 2021;39:211–29. doi: 10.1007/s40273-020-00984-6. [DOI] [PMC free article] [PubMed]
  121. Duarte A, Llewellyn A, Walker R, Schmitt L, Wright K, Walker S, et al. Non-invasive imaging software to assess the functional significance of coronary stenoses: a systematic review and economic evaluation. Health Technol Assess 2021;25:1–230. doi: 10.3310/hta25560. [DOI] [PubMed]
  122. Gao L, Nguyen P, Dunstan D, Moodie M. Are office-based workplace interventions designed to reduce sitting time cost-effective primary prevention measures for cardiovascular disease? A systematic review and modelled economic evaluation. Int J Environ Res Public Health 2019;16:834. doi: 10.3390/ijerph16050834. [DOI] [PMC free article] [PubMed]
  123. Health Quality Ontario. Mechanical thrombectomy in patients with acute ischemic stroke: a health technology assessment. Ont Health Technol Assess Ser 2016;16:1–79. [PMC free article] [PubMed]
  124. Health Quality Ontario. Percutaneous ventricular assist devices: a Health Technology Assessment. Ont Health Technol Assess Ser 2017;17:1–97. [PMC free article] [PubMed]
  125. Ontario Health (Quality). Transcatheter aortic valve implantation in patients with severe, symptomatic aortic valve stenosis at intermediate surgical risk: a Health Technology Assessment. Ont Health Technol Assess Ser 2020;20:1–121. [PMC free article] [PubMed]
  126. Joundi RA, Adekanye J, Leung AA, Ronksley P, Smith EE, Rebchuk AD, et al. Health state utility values in people with stroke: a systematic review and meta-analysis. J Am Heart Assoc 2022;11:e024296. doi: 10.1161/JAHA.121.024296. [DOI] [PMC free article] [PubMed]
  127. Perera R, McFadden E, McLellan J, Lung T, Clarke P, Pérez T, et al. Optimal strategies for monitoring lipid levels in patients at risk or with cardiovascular disease: a systematic review with statistical and cost-effectiveness modelling. Health Technol Assess 2015;19:1–401, vii. doi: 10.3310/hta191000. [DOI] [PMC free article] [PubMed]
  128. Shan L, Shan J, Saxena A, Robinson D. Quality of life and functional status after carotid revascularisation: a systematic review and meta-analysis. Eur J Vasc Endovasc Surg 2015;49:634–45. doi: 10.1016/j.ejvs.2015.03.020. [DOI] [PubMed]
  129. Health Quality Ontario. Left atrial appendage closure device with delivery system: a Health Technology Assessment. Ont Health Technol Assess Ser 2017;17:1–106. [PMC free article] [PubMed]
  130. Sterne JA, Bodalia PN, Bryden PA, Davies PA, López-López JA, Okoli GN, et al. Oral anticoagulants for primary prevention, treatment and secondary prevention of venous thromboembolic disease, and for prevention of stroke in atrial fibrillation: systematic review, network meta-analysis and cost-effectiveness analysis. Health Technol Assess 2017;21:1–386. doi: 10.3310/hta21090. [DOI] [PMC free article] [PubMed]
  131. Stevanovic J, Pechlivanoglou P, Kampinga MA, Krabbe PFM, Postma MJ. Multivariate meta-analysis of preference-based quality of life values in coronary heart disease. PLOS ONE 2016;11:e0152030. doi: 10.1371/journal.pone.0152030. [DOI] [PMC free article] [PubMed]
  132. Westwood M, Ramaekers B, Grimm S, Worthy G, Fayter D, Armstrong N, et al. High-sensitivity troponin assays for early rule-out of acute myocardial infarction in people with acute chest pain: a systematic review and economic evaluation. Health Technol Assess 2021;25:1–276. doi: 10.3310/hta25330. [DOI] [PMC free article] [PubMed]

RESOURCES