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BMC Cardiovascular Disorders
Published in: BMC Cardiovascular Disorders 1/2018

Open Access 01-12-2018 | Research article

A methodology review on the incremental prognostic value of computed tomography biomarkers in addition to Framingham risk score in predicting cardiovascular disease: the use of association, discrimination and reclassification

Authors: Chun Lap Pang, Nicola Pilkington, Yinghui Wei, Jaime Peters, Carl Roobottom, Chris Hyde

Published in: BMC Cardiovascular Disorders | Issue 1/2018

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Abstract

Background

Computed tomography (CT) biomarkers claim to improve cardiovascular risk stratification. This review focuses on significant differences in incremental measures between adequate and inadequate reporting practise.

Methods

Studies included were those that used Framingham Risk Score as a baseline and described the incremental value of adding calcium score or CT coronary angiogram in predicting cardiovascular risk. Searches of MEDLINE, EMBASE, Web of Science and Cochrane Central were performed with no language restriction.

Results

Thirty five studies consisting of 206,663 patients (men = 118,114, 55.1%) were included. The baseline Framingham Risk Score included the 1998, 2002 and 2008 iterations. Selective reporting, inconsistent reference groupings and thresholds were found. Twelve studies (34.3%) had major and 23 (65.7%) had minor alterations and the respective Δ AUC were significantly different (p = 0.015). When the baseline model performed well, the Δ AUC was relatively lower with the addition of a CT biomarker (Spearman coefficient = − 0.46, p < 0.0001; n = 33; 76 pairs of data). Other factors that influenced AUC performance included exploration of data analysis, calibration, validation, multivariable and AUC documentation (all p < 0.05). Most studies (68.7%) that reported categorical NRI (n = 16; 46 pairs of data) subjectively drew strong conclusions along with other poor reporting practices. However, no significant difference in values of NRI was found between adequate and inadequate reporting.

Conclusions

The widespread practice of poor reporting particularly association, discrimination, reclassification, calibration and validation undermines the claimed incremental value of CT biomarkers over the Framingham Risk Score alone. Inadequate reporting of discrimination inflates effect estimate, however, that is not necessarily the case for reclassification.
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Literature
1.
Damen JAAG, Hooft L, Schuit E, Debray TPA, Collins GS, Tzoulaki I, et al. Prediction models for cardiovascular disease risk in the general population: systematic review. BMJ. 2016;353:i2416.
2.
Bouwmeester W, Zuithoff NP, Mallett S, Geerlings MI, Vergouwe Y, Steyerberg EW, et al. Reporting and methods in clinical prediction research: a systematic review. PLoS Med. 2012;9(5):1–12.CrossRefPubMed
3.
Wilson PW, D'Agostino RB, Levy D, Belanger AM, Silbershatz H, Kannel WB. Prediction of coronary heart disease using risk factor categories. Circulation. 1998;97(18):1837–47.CrossRefPubMed
4.
Erbel R, Mohlenkamp S, Moebus S, Schmermund A, Lehmann N, Stang A, et al. Coronary risk stratification, discrimination, and reclassification improvement based on quantification of subclinical coronary atherosclerosis: the Heinz Nixdorf recall study. J Am Coll Cardiol. 2010;56(17):1397–406.CrossRefPubMed
5.
Kavousi M, Elias-Smale S, Rutten JH, Leening MJ, Vliegenthart R, Verwoert GC, et al. Evaluation of newer risk markers for coronary heart disease risk classification: a cohort study. Ann Intern Med. 2012;156(6):438–44.CrossRefPubMed
6.
Moons KGM, Royston P, Vergouwe Y, Grobbee DE, Altman DG. Prognosis and prognostic research: what, why, and how? BMJ. 2009;338
7.
Hlatky MA, Greenland P, Arnett DK, Ballantyne CM, Criqui MH, Elkind MS, et al. Criteria for evaluation of novel markers of cardiovascular risk: a scientific statement from the American Heart Association. Circulation. 2009;119(17):2408–16.CrossRefPubMedPubMedCentral
8.
O'Connor JP, Aboagye EO, Adams JE, Aerts HJ, Barrington SF, Beer AJ, et al. Imaging biomarker roadmap for cancer studies. Nat Rev Clin Oncol. 2017;14(3):169–86.CrossRefPubMed
9.
Valenti V, B OH, Heo R, Cho I, Schulman-Marcus J, Gransar H, et al. A 15-year warranty period for asymptomatic individuals without coronary artery calcium: a prospective follow-up of 9,715 individuals. JACC Cardiovasc Imaging 2015;8(8):900–909.
10.
Oudkerk M, Stillman AE, Halliburton SS, Kalender WA, Möhlenkamp S, McCollough CH, et al. Coronary artery calcium screening: current status and recommendations from the European Society of Cardiac Radiology and North American Society for cardiovascular imaging. Int J Cardiovasc Imaging. 2008;24(6):645–71.CrossRefPubMedPubMedCentral
11.
Ioannidis JP, Tzoulaki I. What makes a good predictor?: the evidence applied to coronary artery calcium score. JAMA. 2010;303(16):1646–7.CrossRefPubMed
12.
Agatston AS, Janowitz WR, Hildner FJ, Zusmer NR, Viamonte M Jr, Detrano R. Quantification of coronary artery calcium using ultrafast computed tomography. J Am Coll Cardiol. 1990;15(4):827–32.CrossRefPubMed
13.
Wong ND, Gransar H, Shaw L, Polk D, Moon JH, Miranda-Peats R, et al. Thoracic aortic calcium versus coronary artery calcium for the prediction of coronary heart disease and cardiovascular disease events. JACC-Cardiovasc Imag. 2009;2(3):319–26.CrossRef
14.
Hadamitzky M, Achenbach S, Al-Mallah M, Berman D, Budoff M, Cademartiri F, et al. Optimized prognostic score for coronary computed tomographic angiography: results from the CONFIRM registry (COronary CT angiography EvaluatioN for clinical outcomes: an InteRnational multicenter registry). J Am Coll Cardiol. 2013;62(5):468–76.CrossRefPubMed
15.
Tzoulaki I, Liberopoulos G, Ioannidis JP. Assessment of claims of improved prediction beyond the Framingham risk score. JAMA. 2009;302(21):2345–52.CrossRefPubMed
16.
Kyzas PA, Loizou KT, Ioannidis JP. Selective reporting biases in cancer prognostic factor studies. J Natl Cancer Inst. 2005;97(14):1043–55.CrossRefPubMed
17.
Tzoulaki I, Liberopoulos G, Ioannidis JP. Use of reclassification for assessment of improved prediction: an empirical evaluation. Int J Epidemiol. 2011;40(4):1094–105.CrossRefPubMed
18.
Liberati A, Altman DG, Tetzlaff J, Mulrow C, Gotzsche PC, Ioannidis JP, et al. The PRISMA statement for reporting systematic reviews and meta-analyses of studies that evaluate healthcare interventions: explanation and elaboration. BMJ. 2009;339:b2700.CrossRefPubMedPubMedCentral
19.
Pang CL, Peters J, Hyde C, Roobottom C. The added value of computed tomography coronary angiogram in predicting future cardiovascular events in a low risk population: comparison with Framingham Risk Score. PROSPERO: International prospective register for systematic reviews. 2015:CRD42015023795.
20.
21.
22.
DeLong ER, DeLong DM, Clarke-Pearson DL. Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. Biometrics. 1988;44(3):837–45.CrossRefPubMed
23.
Kerr KF, Wang Z, Janes H, McClelland RL, Psaty BM, Pepe MS. Net Reclassification indices for evaluating risk-prediction instruments: a critical review. Epidemiology (Cambridge, Mass). 2014;25(1):114–121.
24.
Pencina MJ, D'Agostino RB Sr, Demler OV. Novel metrics for evaluating improvement in discrimination: net reclassification and integrated discrimination improvement for normal variables and nested models. Stat Med. 2012;31(2):101–13.CrossRefPubMed
25.
Pencina MJ, D'Agostino RB Sr, Steyerberg EW. Extensions of net reclassification improvement calculations to measure usefulness of new biomarkers. Stat Med. 2011;30(1):11–21.CrossRefPubMed
26.
Pencina MJ, D'Agostino RB Sr, D'Agostino RB Jr, Vasan RS. Evaluating the added predictive ability of a new marker: from area under the ROC curve to reclassification and beyond. Stat Med. 2008;27(2):157–72. discussion 207-12CrossRefPubMed
27.
Pencina MJ, D'Agostino RB, D'Agostino RB, Vasan RS. Comments on ‘integrated discrimination and net reclassification improvements—practical advice’. Stat Med. 2008;27(2):207–12.CrossRef
28.
Ferreira-González I, Permanyer-Miralda G, Domingo-Salvany A, Busse JW, Heels-Ansdell D, Montori VM, et al. Problems with use of composite end points in cardiovascular trials: systematic review of randomised controlled trials. BMJ. 2007;334(7597):786.CrossRefPubMedPubMedCentral
29.
Ciani O, Buyse M, Garside R, Pavey T, Stein K, Sterne JAC, et al. Comparison of treatment effect sizes associated with surrogate and final patient relevant outcomes in randomised controlled trials: meta-epidemiological study. BMJ. 2013;346:f457.
30.
Third Report of the National Cholesterol Education Program (NCEP) Expert Panel on Detection, Evaluation, and Treatment of High Blood Cholesterol in Adults (Adult Treatment Panel III) Final Report. Circulation. 2002;106(25):3143.
31.
D’Agostino RB, Vasan RS, Pencina MJ, Wolf PA, Cobain M, Massaro JM, et al. General cardiovascular risk profile for use in primary care: the Framingham heart study. Circulation. 2008;117(6):743–53.CrossRefPubMed
32.
Hanley JA, McNeil BJ. A method of comparing the areas under receiver operating characteristic curves derived from the same cases. Radiology. 1983;148(3):839–43.CrossRefPubMed
33.
Altman DG, Bland JM. How to obtain the P value from a confidence interval. BMJ. 2011;343:d2304.
34.
Guyot P, Ades AE, Ouwens MJNM, Welton NJ. Enhanced secondary analysis of survival data: reconstructing the data from published Kaplan-Meier survival curves. BMC Med Res Methodol. 2012;12(1):9.CrossRefPubMedPubMedCentral
35.
Leeflang MM, Moons KG, Reitsma JB, Zwinderman AH. Bias in sensitivity and specificity caused by data-driven selection of optimal cutoff values: mechanisms, magnitude, and solutions. Clin Chem. 2008;54(4):729–37.CrossRefPubMed
36.
Pencina MJ, D'Agostino RB, Pencina KM, Janssens AC, Greenland P. Interpreting incremental value of markers added to risk prediction models. Am J Epidemiol. 2012;176(6):473–81.CrossRefPubMedPubMedCentral
37.
Leening MJG, Vedder MM, Witteman JCM, Pencina MJ, Steyerberg EW. Net reclassification improvement: computation, interpretation, and ControversiesA literature review and Clinician's guide. Ann Intern Med. 2014;160(2):122–31.CrossRefPubMed
38.
Hayden JA, van der Windt DA, Cartwright JL, Cote P, Bombardier C. Assessing bias in studies of prognostic factors. Ann Intern Med. 2013;158(4):280–6.CrossRefPubMed
39.
Lau KK, Wong YK, Chan YH, Yiu KH, Teo KC, Li LS, et al. Prognostic implications of surrogate markers of atherosclerosis in low to intermediate risk patients with Type 2 Diabetes. Cardiovasc Diabetol. 2012;11(101).
40.
Ahmadi N, Hajsadeghi F, Blumenthal RS, Budoff MJ, Stone GW, Ebrahimi R. Mortality in individuals without known coronary artery disease but with discordance between the Framingham risk score and coronary artery calcium. Am J Cardiol. 2011;107(6):799–804.CrossRefPubMed
41.
Budoff MJ, Shaw LJ, Liu ST, Weinstein SR, Mosler TP, Tseng PH, et al. Long-term prognosis associated with coronary calcification: observations from a registry of 25,253 patients. J Am Coll Cardiol. 2007;49(18):1860–70.CrossRefPubMed
42.
Raggi P, Shaw LJ, Berman DS, Callister TQ. Gender-based differences in the prognostic value of coronary calcification. J Women's Health. 2004;13(3):273–83.CrossRef
43.
Chang SM, Nabi F, Xu J, Pratt CM, Mahmarian AC, Frias ME, et al. Value of CACS compared with ETT and myocardial perfusion imaging for predicting long-term cardiac outcome in asymptomatic and symptomatic patients at low risk for coronary disease clinical implications in a multimodality imaging world. JACC-Cardiovasc Imag. 2015;8(2):134–44.CrossRef
44.
Elias-Smale SE, Proenca RV, Koller MT, Kavousi M, van Rooij FJ, Hunink MG, et al. Coronary calcium score improves classification of coronary heart disease risk in the elderly: the Rotterdam study. J Am Coll Cardiol. 2010;56(17):1407–14.CrossRefPubMed
45.
Forouzandeh F, Chang SM, Muhyieddeen K, Zaid RR, Trevino AR, Xu J, et al. Does quantifying epicardial and intrathoracic fat with noncontrast computed tomography improve risk stratification beyond calcium scoring alone? Circ Cardiovasc Imag. 2013;6(1):58–66.CrossRef
46.
Hadamitzky M, Meyer T, Hein F, Bischoff B, Martinoff S, Schomig A, et al. Prognostic value of coronary computed tomographic angiography in asymptomatic patients. Am J Cardiol. 2010;105(12):1746–51.CrossRefPubMed
47.
Elias-Smale SE, Wieberdink RG, Odink AE, Hofman A, Hunink MG, Koudstaal PJ, et al. Burden of atherosclerosis improves the prediction of coronary heart disease but not cerebrovascular events: the Rotterdam study. Eur Heart J. 2011;32(16):2050–8.CrossRefPubMed
48.
Yeboah J, Carr JJ, Terry JG, Ding J, Zeb I, Liu S, et al. Computed tomography-derived cardiovascular risk markers, incident cardiovascular events, and all-cause mortality in nondiabetics: the multi-ethnic study of atherosclerosis. Eur J Prev Cardiol. 2014;21(10):1233–41.CrossRefPubMed
49.
Greenland P, LaBree L, Azen SP, Doherty TM, Detrano RC. Coronary artery calcium score combined with Framingham score for risk prediction in asymptomatic individuals.[erratum appears in JAMA. 2004 Feb 4;291(5):563]. JAMA. 2004;291(2):210–5.CrossRefPubMed
50.
Yeboah J, McClelland RL, Polonsky TS, Burke GL, Sibley CT, O'Leary D, et al. Comparison of novel risk markers for improvement in cardiovascular risk assessment in intermediate-risk individuals. JAMA. 2012;308(8):788–95.CrossRefPubMedPubMedCentral
51.
Matsushita K, Sang YY, Ballew SH, Shlipak M, Katz R, Rosas SE, et al. Subclinical atherosclerosis measures for cardiovascular prediction in CKD. J Am Soc Nephrol. 2015;26(2):439–47.CrossRefPubMed
52.
Mohlenkamp S, Lehmann N, Greenland P, Moebus S, Kalsch H, Schmermund A, et al. Coronary artery calcium score improves cardiovascular risk prediction in persons without indication for statin therapy. Atherosclerosis. 2011;215(1):229–36.CrossRefPubMed
53.
Mohlenkamp S, Lehmann N, Moebus S, Schmermund A, Dragano N, Stang A, et al. Quantification of coronary atherosclerosis and inflammation to predict coronary events and all-cause mortality. J Am Coll Cardiol. 2011;57(13):1455–64.CrossRefPubMed
54.
Polonsky TS, McClelland RL, Jorgensen NW, Bild DE, Burke GL, Guerci AD, et al. Coronary artery calcium score and risk classification for coronary heart disease prediction. JAMA. 2010;303(16):1610–6.CrossRefPubMedPubMedCentral
55.
Raggi P, Cooil B, Callister TQ. Use of electron beam tomography data to develop models for prediction of hard coronary events. Am Heart J. 2001;141(3):375–82.CrossRefPubMed
56.
Rana JS, Gransar H, Wong ND, Shaw L, Pencina M, Nasir K, et al. Comparative value of coronary artery calcium and multiple blood biomarkers for prognostication of cardiovascular events. Am J Cardiol. 2012;109(10):1449–53.CrossRefPubMed
57.
Agarwal S, Cox AJ, Herrington DM, Jorgensen NW, Xu J, Freedman BI, et al. Coronary calcium score predicts cardiovascular mortality in diabetes: diabetes heart study. Diabetes Care. 2013;36(4):972–7.CrossRefPubMedPubMedCentral
58.
Arad Y, Goodman KJ, Roth M, Newstein D, Guerci AD. Coronary calcification, coronary disease risk factors, C-reactive protein, and atherosclerotic cardiovascular disease events: the St. Francis heart study. J Am Coll Cardiol. 2005;46(1):158–65.CrossRefPubMed
59.
Cho I, Chang HJ, Sung JM, Pencina MJ, Lin FY, Dunning AM, et al. Coronary computed tomographic angiography and risk of all-cause mortality and nonfatal myocardial infarction in subjects without chest pain syndrome from the CONFIRM registry (coronary CT angiography evaluation for clinical outcomes: an international multicenter registry). Circulation. 2012;126(3):304–13.CrossRefPubMed
60.
Versteylen MO, Kietselaer BL, Dagnelie PC, Joosen IA, Dedic A, Raaijmakers RH, et al. Additive value of Semiautomated quantification of coronary artery disease using cardiac computed tomographic angiography to predict future acute coronary syndrome. J Am Coll Cardiol. 2013;61(22):2296–305.CrossRefPubMed
61.
Gibson AO, Blaha MJ, Arnan MK, Sacco RL, Szklo M, Herrington DM, et al. Coronary artery calcium and incident cerebrovascular events in an asymptomatic cohort the MESA study. JACC-Cardiovasc Imag. 2014;7(11):1108–15.CrossRef
62.
Hermann DM, Gronewold J, Lehmann N, Moebus S, Jockel KH, Bauer M, et al. Coronary artery calcification is an independent stroke predictor in the general population. Stroke. 2013;44(4):1008–13.CrossRefPubMed
63.
Chow BJ, Small G, Yam Y, Chen L, Achenbach S, Al-Mallah M, et al. Incremental prognostic value of cardiac computed tomography in coronary artery disease using CONFIRM: COroNary computed tomography angiography evaluation for clinical outcomes: an InteRnational multicenter registry. Circ Cardiovasc Imag. 2011;4(5):463–72.CrossRef
64.
Lin FY, Shaw LJ, Dunning AM, LaBounty TM, Choi JH, Weinsaft JW, et al. Mortality risk in symptomatic patients with nonobstructive coronary artery disease a prospective 2-center study of 2,583 patients undergoing 64-detector row coronary computed tomographic angiography. J Am Coll Cardiol. 2011;58(5):510–9.CrossRefPubMed
65.
Chow BJ, Wells GA, Chen L, Yam Y, Galiwango P, Abraham A, et al. Prognostic value of 64-slice cardiac computed tomography severity of coronary artery disease, coronary atherosclerosis, and left ventricular ejection fraction. J Am Coll Cardiol. 2010;55(10):1017–28.CrossRefPubMed
66.
Park HE, Chun EJ, Choi SI, Lee SP, Yoon CH, Kim HK, et al. Clinical and imaging parameters to predict cardiovascular outcome in asymptomatic subjects. Int J Cardiovasc Imag. 2013;29(7):1595–602.CrossRef
67.
Cho I, Chang HJ, Hartaigh BO, Shin S, Sung JM, Lin FY, et al. Incremental prognostic utility of coronary CT angiography for asymptomatic patients based upon extent and severity of coronary artery calcium: results from the COronary CT angiography EvaluatioN for clinical outcomes InteRnational multicenter (CONFIRM) study. Eur Heart J. 2015;36(8):501–8.CrossRefPubMed
68.
Han D, B OH, Gransar H, Yoon JH, Kim KJ, Kim MK, et al. Incremental benefit of coronary artery calcium score above traditional risk factors for all-cause mortality in asymptomatic Korean adults. Circulation J 2015;79(11):2445–2451.
69.
Raggi P, Gongora MC, Gopal A, Callister TQ, Budoff M, Shaw LJ. Coronary artery calcium to predict all-cause mortality in elderly men and women. J Am Coll Cardiol. 2008;52(1):17–23.CrossRefPubMed
70.
Muhlenbruch K, Heraclides A, Steyerberg EW, Joost HG, Boeing H, Schulze MB. Assessing improvement in disease prediction using net reclassification improvement: impact of risk cut-offs and number of risk categories. Eur J Epidemiol. 2013;28(1):25–33.CrossRefPubMed
71.
Pepe MS, Feng Z, Gu JW. Comments on 'Evaluating the added predictive ability of a new marker: from area under the ROC curve to reclassification and beyond' by M. J. Pencina et al., statistics in medicine (DOI: 10.1002/sim.2929). Stat Med. 2008;27(2):173–81.CrossRefPubMed
72.
Hilden J, Gerds TA. A note on the evaluation of novel biomarkers: do not rely on integrated discrimination improvement and net reclassification index. Stat Med. 2014;33(19):3405–14.CrossRefPubMed
73.
Cox DR. Two further applications of a model for binary regression. Biometrika. 1958;45(3/4):562–5.CrossRef
74.
75.
Steyerberg EW, Pencina MJ. Reclassification calculations for persons with incomplete follow-up. Ann Intern Med. 2010;152(3):195–6. author reply 6-7CrossRefPubMed
76.
Janssens ACJW, Khoury MJ. Assessment of improved prediction beyond traditional risk factors. Circ Cardiovasc Genet. 2010;3(1):3.CrossRefPubMed
77.
Bero L, Oostvogel F, Bacchetti P, Lee K. Factors associated with findings of published trials of drug–drug comparisons: why some statins appear more efficacious than others. PLoS Med. 2007;4(6):e184.CrossRefPubMedPubMedCentral
78.
Pepe MS, Janes H, Longton G, Leisenring W, Newcomb P. Limitations of the odds ratio in gauging the performance of a diagnostic, prognostic, or screening marker. Am J Epidemiol. 2004;159(9):882–90.CrossRefPubMed
79.
Moons KGM. Criteria for scientific evaluation of novel markers: a perspective. Clin Chem. 2010;56(4):537.CrossRefPubMed
80.
Pepe MS, Fan J, Feng Z, Gerds T, Hilden J. The net reclassification index (NRI): a misleading measure of prediction improvement even with independent test data sets. Stat Biosci. 2015;7(2):282–95.CrossRefPubMed
81.
Mihaescu R, van Zitteren M, van Hoek M, Sijbrands EJ, Uitterlinden AG, Witteman JC, et al. Improvement of risk prediction by genomic profiling: reclassification measures versus the area under the receiver operating characteristic curve. Am J Epidemiol. 2010;172(3):353–61.CrossRefPubMed
82.
Hemingway H, Croft P, Perel P, Hayden JA, Abrams K, Timmis A, et al. Prognosis research strategy (PROGRESS) 1: a framework for researching clinical outcomes. BMJ. 2013;346
83.
Riley RD, Hayden JA, Steyerberg EW, Moons KGM, Abrams K, Kyzas PA, et al. Prognosis research strategy (PROGRESS) 2: prognostic factor research. PLoS Med. 2013;10(2):e1001380.CrossRefPubMedPubMedCentral
84.
Steyerberg EW, Moons KGM, van der Windt DA, Hayden JA, Perel P, Schroter S, et al. Prognosis research strategy (PROGRESS) 3: prognostic model research. PLoS Med. 2013;10(2):e1001381.CrossRefPubMedPubMedCentral
85.
Hingorani AD, Windt DAvd, Riley RD, Abrams K, Moons KGM, Steyerberg EW, et al. Prognosis research strategy (PROGRESS) 4: Stratified medicine research. BMJ. 2013;346:e5793.
Metadata
Title
A methodology review on the incremental prognostic value of computed tomography biomarkers in addition to Framingham risk score in predicting cardiovascular disease: the use of association, discrimination and reclassification
Authors
Chun Lap Pang
Nicola Pilkington
Yinghui Wei
Jaime Peters
Carl Roobottom
Chris Hyde
Publication date
01-12-2018
Publisher
BioMed Central
Published in
BMC Cardiovascular Disorders / Issue 1/2018
Electronic ISSN: 1471-2261
DOI
https://doi.org/10.1186/s12872-018-0777-5

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