Tillmann, T

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Invited editorialNew risk prediction models in Englandmay lead to targeted PCSK9 inhibitortreatment, for patients with establishedcardiovascular diseaseTaavi TillmannMore than 40 years ago, Kannel et al. published per-haps the world’s first cardiovascular disease (CVD) riskprediction model, using data from the Framinghamcohort study.1 Since then the quantity, complexityand utility of risk prediction models has grown expo-nentially, particularly during the past decade when clin-ical guidelines have begun to recommend their use forcertain clinical situations. Just like the initial model byKannel et al., the most common application has beenfor healthy people to estimate their risk of developing afirst-ever ischaemic or coronary event in the next 10years. For such situations, the European Society ofCardiology (ESC) recommended in 2003 to use theSCORE2 risk model (unless country-specific modelsare available, such as QRISK,3 which was publishedfor the English population in 2007). By now, suchrisk prediction models are becoming routine practicein primary care opportunistic work, as well as amongpublic health specialists implementing population-based CVD screening programmes. One of its functionsis to help target preventive interventions such as statinsto those with the most favourable ratio of risks to bene-fits. A key component in the success of this transla-tional work in countries with high implementation(such as England) has been the widespread adoptionof the risk prediction model into the routine IT systemsthat clinicians use, thereby not taking too much clinicaltime. This story, of moving from basic science to pre-diction science, to updated clinical guidelines, alteredIT systems and better clinical practice (in some coun-tries), serves as an illuminating example for other clin-ical questions in which prediction science is evolving,but has yet to become an integral part of routine care.Another example might be the development of risk pre-diction models for people with atrial fibrillation, to esti-mate their risk of stroke and likely utility from targetedanticoagulation. Here too, various prediction modelssuch as CHA2DS2-VASC have been proposed, exter-nally validated, and are making their way into routinepractice of primary and acute medical care.4This optimism should also be counterbalanced bysome realism. A recent report by the ESC Preventionof CVD Programme has rightly pointed out how imple-mentation remains far from optimal across many if notmost European countries.5 More needs to be done todisseminate, standardise and normalise good multidis-ciplinary practice like this.In contrast to general practitioners who often seepatients without overt CVD, cardiologists tend to seepatients later in their life course, who have often had atleast one cardiovascular event. Risk prediction sciencefor such patients remains a step or two behind, but iscatching up fast. In 2012 the REACH prediction modelwas derived (on 33,419 persons from 44 countries withexisting CVD) to predict the risk of recurrent CVDevents (with 2394 events detected over 2 years offollow-up).6 In 2013 a similar model was published byRapsomaniki et al.,7 using data from a larger cohort of102,023 patients in England (with 22,999 events ordeaths in the next 5 years). The larger size of the der-ivation dataset of Rapsomaniki et al. allowed theauthors to model the hazard ratios associated withdetailed cardiovascular event histories, arising fromconcurrent comorbidities such as stable angina,unstable angina, myocardial infarction, ST-segmentelevation myocardial infarction, non-ST-segment eleva-tion myocardial infarction, stoke, peripheral arterialdisease and non-CVD. However, as this model wasderived exclusively on patients from England, it is lessclear to what degree their model might be generalisableto other countries. The REACH model might be bet-ter suited to settings that are dissimilar to England.Centre for Global Non-Communicable Disease, University CollegeLondon, UKCorresponding author:Taavi Tillmann, Centre for Global Non-Communicable Disease, Institutefor Global Health, University College London, 30 Guilford St, London,WC1N 1DP, UK.Email: t.tillmann@ucl.ac.ukEuropean Journal of PreventiveCardiology0(00) 1–3! The European Society ofCardiology 2020Article reuse guidelines:sagepub.com/journals-permissionsDOI: 10.1177/2047487320904513journals.sagepub.com/home/cprBoth models have been well cited by other academicstudies; however, neither has been endorsed by leadingclinical guidelines (such as those of the ESC). Threerecent health technology assessments conducted inScotland, Sweden and The Netherlands have cited theREACH model, when evaluating the potential applica-tions of evolocumab, a new PCSK9 inhibitor developedby Amgen.8In this issue of the European Journal of PreventativeCardiology, Danese et al.9 looked at 60,838 patients inEngland with existing CVD, to predict recurrent car-diovascular events over 5 years of follow-up. They dem-onstrate convincingly that the 11% of patients withsevere atherosclerotic disease (as denoted by recurrentmyocardial infarcts or strokes, and/or multimorbiditywith more than one concurrent CVD) had an elevatedrisk of recurrent events, when compared with the 89%patients who lacked such features. It is reassuring thatthese results align with those reported by Rapsomanikiet al., who also reported that cardiovascular past med-ical history predictors were more useful than otherswhen guiding treatment decisions.Altogether, this body of risk prediction scienceappears ripe for transitioning to the next stage, inexploring more targeted application of therapies suchas PCSK9 inhibitors. These new drugs are effective inlowering cholesterol and preventing cardiovascularevents; however, at current on-patent prices they arenot cost-effective when used in an untargetedmanner.10 It is plausible that if their use could berationed or targeted for higher risk patients it maybecome cost-effective for some health insurance sys-tems. The authors rightly point out the need now formore sophisticated simulations and cost-effectivenessanalyses to help inform these decisions. One way ofmaking this applied work even more robust againsttype I error would be to conduct sensitivity analyses,in which the hazard ratios derived by Danese et al. arereplaced by those derived by Rapsomaniki et al., tocheck if the subsequent simulations arrive at similarrecommendations and treatment thresholds. Anotherstrength of both current models is that they werederived from administrative data, and hence in real-life application need not create any additional dataentry burdens for clinicians. If a subgroup of patientscould be identified in which PCSK9 inhibitor therapyis cost-effective, then IT companies could create soft-ware solutions that help identify such patients rela-tively automatically, with as little clinical time aspossible.Returning to academic risk prediction science,future work could seek to make these models evenmore accurate, by testing the added value of clinicalmarkers of disease severity that are captured in rou-tine cardiological care, but rarely make it to billingdetails and discharge summaries that comprised thedatasets used to date. For example, specific ECG par-ameters (e.g. QRS width), echocardiogram findings(e.g. ejection fraction) or blood markers (e.g. peaktroponin) are just some examples of the trove of clin-ical data available to cardiologists. The challenge is toaggregate and standardise these into sizeable cohorts,ideally with over 50,000 patients. Once done, this mayalso be ripe territory to experiment with machinelearning and artificial intelligence approaches, in pick-ing up increased signal from noise about who is athighest risk of recurrent events, potentially benefittingfrom enhanced care.Declaration of conflicting interestsThe author(s) declared no potential conflicts of interest withrespect to the research, authorship, and/or publication of thisarticle.FundingThe author(s) disclosed receipt of the following financial sup-port for the research, authorship, and/or publication of thisarticle: The author(s) are funded by an NIHR AcademicClinical Lectureship.References1. Kannel WB, McGee D and Gordon T. A general cardio-vascular risk profile: the Framingham Study. Am J Cardiol1976; 38: 46–51.2. Conroy R, Pyörälä K, Fitzgerald Ae, et al. Estimation often-year risk of fatal cardiovascular disease in Europe: theSCORE project. Eur Heart J 2003; 24: 987–1003.3. Hippisley-Cox J, Coupland C, Vinogradova Y, et al.Derivation and validation of QRISK, a new cardiovascu-lar disease risk score for the United Kingdom: prospectiveopen cohort study. BMJ 2007; 335: 136.4. van den Ham HA, Klungel OH, Singer DE, et al.Comparative performance of ATRIA, CHADS2, andCHA2DS2-VASc risk scores predicting stroke in patientswith atrial fibrillation: results from a national primary caredatabase. J Am Coll Cardiol 2015; 66: 1851–1859.5. Rossello X, Dorresteijn JA, Janssen A, et al. Risk predictiontools in cardiovascular disease prevention: a report from theESC Prevention of CVD Programme led by the EuropeanAssociation of Preventive Cardiology (EAPC) in collabor-ation with the Acute Cardiovascular Care Association(ACCA) and the Association of Cardiovascular Nursingand Allied Professions (ACNAP). Eur J Prev Cardiol2019; 26: 1534–1544.6. Wilson PW, D’Agostino Sr R, Bhatt DL, et al. An inter-national model to predict recurrent cardiovascular disease.Am J Med 2012; 125: 695–703.7. Rapsomaniki E, Shah A, Perel P, et al. Prognostic modelsfor stable coronary artery disease based on electronichealth record cohort of 102,023 patients. Eur Heart J2013; 35: 844–852.2 European Journal of Preventive Cardiology 0(00)8. Betts MB, Milev S, Hoog M, et al. Comparison of rec-ommendations and use of cardiovascular risk equationsby health technology assessment agencies and clinicalguidelines. Value Health 2019; 22: 210–219.9. Danese M, Pemberton-Ross P and Catterick D.Estimation of the increased risk associated with recurrentevents or poly-vascular atherosclerotic cardiovasculardisease in the United Kingdom. Eur J Prev Cardiol,Epub ahead of print 21 January 2020. DOI: 10.1177/2047487319899212.10. Kazi DS, Moran AE, Coxson PG, et al. Cost-effective-ness of PCSK9 inhibitor therapy in patients with hetero-zygous familial hypercholesterolemia or atheroscleroticcardiovascular disease. J Am Med Assoc 2016; 316:743–753.Tillmann 3

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

https://discovery.ucl.ac.uk/10092612/9/Tillmann_New%20risk%20prediction%20models%20in%20England%20may%20lead%20to%20targeted%20PCSK9%20inhibitor%20treatment%2C%20for%20patients%20with%20established%20cardiovascular%20disease_AOP.pdf

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

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