Prediction Models for Cardiac Risk Classification with Nuclear Cardiology Techniques
详细信息    查看全文
  • 作者：Mario Petretta ; Alberto Cuocolo
  • 关键词：Cardiovascular disease ; Risk stratification ; Nuclear cardiology ; Myocardial perfusion imaging ; Algorithms for risk prediction
  • 刊名：Current Cardiovascular Imaging Reports
  • 出版年：2016
  • 出版时间：January 2016
  • 年：2016
  • 卷：9
  • 期：1
  • 全文大小：319 KB
  • 参考文献：1.Lea CE. Prognosis in heart disease. Br Med J. 1915;1:1036–8.PubMedCentral CrossRef PubMed
    2.Wagner Jr HN. Cardiovascular nuclear medicine: a progress report. Hosp Pract. 1976;11:77–83.PubMed
    3.Zaret BL. Myocardial imaging with radioactive potassium and its analogs. Prog Cardiovasc Dis. 1977;20:81–94.CrossRef PubMed
    4.Iskandrian AS, Wasserman L, Segal BL. Thallium 201 myocardial scintigraphy. Advantages and limitations. Arch Intern Med. 1980;140:320–7.CrossRef PubMed
    5.•
Gibson RS, Taylor GJ, Watson DD, et al. Prognostic significance of resting anterior thallium-201 defects in patients with inferior myocardial infarction. J Nucl Med. 1980;21:1015–21. This paper demonstrates that resting Tl-201 scintigraphy is a sensitive method to detect myocardial infarction and is able to identify patients at high risk for subsequent coronary events. PubMed
6.••
Brown KA, Boucher CA, Okada RD, et al. Prognostic value of exercise thallium-201 imaging in patients presenting for evaluation of chest pain. J Am Coll Cardiol. 1983;1:994–1001. This study suggests an approach to evaluate the risk of future cardiac events in patients with possible ischemic heart disease. CrossRef PubMed
7.Ladenheim ML, Pollock BH, Rozanski A, et al. Extent and severity of myocardial hypoperfusion as predictors of prognosis in patients with suspected coronary artery disease. J Am Coll Cardiol. 1986;7:464–71.CrossRef PubMed
8.Brown KA. Prognostic value of thallium-201 myocardial perfusion imaging. A diagnostic tool comes of age. Circulation. 1991;83:363–81.CrossRef PubMed
9.Pollock SG, Abbott RD, Boucher CA, et al. Independent and incremental prognostic value of tests performed in hierarchical order to evaluate patients with suspected coronary artery disease. Validation of models based on these tests. Circulation. 1992;85:237–48.CrossRef PubMed
10.Petretta M, Bonaduce D, Cuocolo A, et al. Incremental prognostic value of thallium imaging and coronary angiography in patients with a symptom-limited ECG stress test. Coron Artery Dis. 1993;4:637–44.CrossRef PubMed
11.Petretta M, Cuocolo A, Carpinelli A, et al. Prognostic value of myocardial hypoperfusion indexes in patients with suspected or known coronary artery disease. J Nucl Cardiol. 1994;1:325–37.CrossRef PubMed
12.Petretta M, Cuocolo A, Bonaduce D, et al. Incremental prognostic value of thallium reinjection after stress-redistribution imaging in patients with previous myocardial infarction and left ventricular dysfunction. J Nucl Med. 1997;38:195–200.PubMed
13.Beller GA. New directions in myocardial perfusion imaging. Clin Cardiol. 1993;16:86–94.CrossRef PubMed
14.Shaw LJ, Iskandrian AE. Prognostic value of gated myocardial perfusion SPECT. J Nucl Cardiol. 2004;11:171–85.CrossRef PubMed
15.Travain MI, Wexler JP. Pharmacological stress testing. Semin Nucl Med. 1999;29:298–318.CrossRef PubMed
16.Currie GM, Wheat JM, Wang L, et al. Pharmacology in nuclear cardiology. Nucl Med Commun. 2011;32:617–27.CrossRef PubMed
17.Ghimire G, Hage FG, Heo J, et al. Regadenoson: a focused update. J Nucl Cardiol. 2013;20:284–8.CrossRef PubMed
18.Sorrentino AR, Acampa W, Petretta M, et al. Comparison of the prognostic value of SPECT after nitrate administration and metabolic imaging by PET in patients with ischaemic left ventricular dysfunction. Eur J Nucl Med Mol Imaging. 2007;34:558–62.CrossRef PubMed
19.Acampa W, Cuocolo A, Petretta M, et al. Tetrofosmin imaging in the detection of myocardial viability in patients with previous myocardial infarction: comparison with sestamibi and Tl-201 scintigraphy. J Nucl Cardiol. 2002;9:33–40.CrossRef PubMed
20.Dilsizian V, Taillefer R. Journey in evolution of nuclear cardiology: will there be another quantum leap with the F-18-labeled myocardial perfusion tracers? JACC Cardiovasc Imaging. 2012;5:1269–84.CrossRef PubMed
21.Murthy VL, Lee BC, Sitek A, et al. Comparison and prognostic validation of multiple methods of quantification of myocardial blood flow with 82Rb PET. J Nucl Med. 2014;55:1952–8.CrossRef PubMed
22.Schindler TH, Quercioli A, Valenta I, et al. Quantitative assessment of myocardial blood flow--clinical and research applications. Semin Nucl Med. 2014;44:274–93.CrossRef PubMed
23.Slomka PJ, Pan T, Berman DS, et al. Advances in SPECT and PET Hardware. Prog Cardiovasc Dis. 2015;57:566–78.CrossRef PubMed
24.Gaemperli O, Kaufmann PA, Alkadhi H. Cardiac hybrid imaging. Eur J Nucl Med Mol Imaging. 2014;41 Suppl 1:S91–103.CrossRef PubMed
25.Nappi C, Acampa W, Pellegrino T, et al. Beyond ultrasound: advances in multimodality cardiac imaging. Intern Emerg Med. 2015;10:9–20.CrossRef PubMed
26.Bourque JM, Beller GA. Stress myocardial perfusion imaging for assessing prognosis: an update. JACC Cardiovasc Imaging. 2011;4:1305–19.CrossRef PubMed
27.Shaw LJ, Hage FG, Berman DS, et al. Prognosis in the era of comparative effectiveness research: where is nuclear cardiology now and where should it be? J Nucl Cardiol. 2012;19:1026–43.CrossRef PubMed
28.•Acampa W, Cantoni V, Green R, et al. Prognostic value of normal stress myocardial perfusion imaging in diabetic patients: a meta-analysis. J Nucl Cardiol. 2014;21:893–902. A large meta-analysis showing that stress myocardial perfusion single-photon emission computed tomography has a high negative predictive value for adverse cardiac events in diabetic patients leading to define a “relatively low-risk” patients category. CrossRef PubMed
29.Hansen CL. The prognosis for prognosis remains excellent. J Nucl Cardiol. 2013;20:501–3.CrossRef PubMed
30.Moons KG, Royston P, Vergouwe Y, et al. Prognosis and prognostic research: what, why, and how? BMJ. 2009;338:b375.CrossRef PubMed
31.George B, Seals S, Aban I. Survival analysis and regression models. J Nucl Cardiol. 2014;21:686–94.PubMedCentral CrossRef PubMed
32.Akaike H. A new look at the statistical model identification. IEEE Trans Autom Control. 1974;19:716–23.CrossRef
33.Schwarz G. Estimating the dimension of a model. Ann Stat. 1978;6:461–4.CrossRef
34.Acampa W, Evangelista L, Petretta M, et al. Usefulness of stress cardiac single-photon emission computed tomographic imaging late after percutaneous coronary intervention for assessing cardiac events and time to such events. Am J Cardiol. 2007;100:436–41.CrossRef PubMed
35.Petretta M, Acampa W, Evangelista L, et al. Impact of inducible ischemia by stress SPECT in cardiac risk assessment in diabetic patients: rationale and design of a prospective multicenter trial. J Nucl Cardiol. 2008;15:100–4.CrossRef PubMed
36.Daniele S, Nappi C, Acampa W, et al. Incremental prognostic value of coronary flow reserve assessed with single-photon emission computed tomography. J Nucl Cardiol. 2011;18:612–9.CrossRef PubMed
37.Acampa W, Petretta M, Cuocolo R, et al. Warranty period of normal stress myocardial perfusion imaging in diabetic patients: a propensity score analysis. J Nucl Cardiol. 2014;21:50–6.CrossRef PubMed
38.Crowther MJ, Lambert PC. A general framework for parametric survival analysis. Stat Med. 2014;33:5280–97.CrossRef PubMed
39.Royston P, Parmar MK. Flexible parametric proportional-hazards and proportional-odds models for censored survival data, with application to prognostic modelling and estimation of treatment effects. Stat Med. 2002;21:2175–97.CrossRef PubMed
40.•Steyerberg EW, Vergouwe Y. Towards better clinical prediction models: seven steps for development and an ABCD for validation. Eur Heart J. 2014;35:1925–31. This article illustrates the usefulness of a framework to strengthen the methodological rigour and quality for prediction models in cardiovascular research. PubMedCentral CrossRef PubMed
41.Hayden JA, van der Windt DA, Cartwright JL, et al. Assessing bias in studies of prognostic factors. Ann Intern Med. 2013;158:280–6.CrossRef PubMed
42.Collins GS, Reitsma JB, Altman DG, et al. TRIPOD Group. Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD): the TRIPOD statement. The TRIPOD Group. Circulation. 2015;131:211–9.PubMedCentral CrossRef PubMed
43.Hachamovitch R. Assessing the prognostic value of cardiovascular imaging: a statistical exercise or a guide to clinical value and application? Circulation. 2009;120:1342–4.CrossRef PubMed
44.Delgado-Rodríguez M, Llorca J. Bias. J Epidemiol Community Health. 2004;58:635–41.PubMedCentral CrossRef PubMed
45.Shaw LJ, Min JK, Hachamovitch R, et al. Cardiovascular imaging research at the crossroads. JACC Cardiovasc Imaging. 2010;3:316–24.CrossRef PubMed
46.Metz LD, Beattie M, Hom R, et al. The prognostic value of normal exercise myocardial perfusion imaging and exercise echocardiography: a meta-analysis. J Am Coll Cardiol. 2007;49:227–37.CrossRef PubMed
47.Shaw LJ, Narula J. Cardiovascular imaging quality—more than a pretty picture! JACC Cardiovasc Imaging. 2008;1:266–9.CrossRef PubMed
48.Hadamitzky M, Freissmuth B, Meyer T, et al. Prognostic value of coronary computed tomographic angiography for prediction of cardiac events in patients with suspected coronary artery disease. JACC Cardiovasc Imaging. 2009;2:404–11.CrossRef PubMed
49.Hachamovitch R, Berman DS, Kiat H, et al. Exercise myocardial perfusion SPECT in patients without known coronary artery disease: incremental prognostic value and use in risk stratification. Circulation. 1996;93:905–14.CrossRef PubMed
50.Kim HL, Kim YJ, Lee SP, et al. Incremental prognostic value of sequential imaging of single-photon emission computed tomography and coronary computed tomography angiography in patients with suspected coronary artery disease. Eur Heart J Cardiovasc Imaging. 2014;15:878–85.CrossRef PubMed
51.Steel K, Broderick R, Gandla V, et al. Complementary prognostic values of stress myocardial perfusion and late gadolinium enhancement imaging by cardiac magnetic resonance in patients with known or suspected coronary artery disease. Circulation. 2009;120:1390–400.PubMedCentral CrossRef PubMed
52.Georgoulias P, Demakopoulos N, Tzavara C, et al. Long-term prognostic value of Tc-99m tetrofosmin myocardial gated-SPECT imaging in asymptomatic patients after percutaneous coronary intervention. Clin Nucl Med. 2008;33:743–7.CrossRef PubMed
53.Galassi AR, Azzarelli S, Tomaselli A, et al. Incremental prognostic value of technetium-99m-tetrofosmin exercise myocardial perfusion imaging for predicting outcomes in patients with suspected or known coronary artery disease. Am J Cardiol. 2001;88:101–6.CrossRef PubMed
54.Kip KE, Hollabaugh K, Marroquin OC, et al. The problem with composite end points in cardiovascular studies: the story of major adverse cardiac events and percutaneous coronary intervention. J Am Coll Cardiol. 2008;51:701–7.CrossRef PubMed
55.Hachamovitch R, Di Carli MF. Methods and limitations of assessing new noninvasive tests: part II: outcomes-based validation and reliability assessment of noninvasive testing. Circulation. 2008;117:2793–801.CrossRef PubMed
56.Goldberg R, Gore JM, Barton B, et al. Individual and composite study endpoints: separating the wheat from the chaff. Am J Med. 2014;127:379–84.PubMedCentral CrossRef PubMed
57.Asmar R, Hosseini H. Endpoints in clinical trials: does evidence only originate from ‘hard’ or mortality endpoints? J Hypertens Suppl. 2009;27:S45–50.CrossRef PubMed
58.Jacobson AF, Senior R, Cerqueira MD, et al. ADMIRE-HF Investigators. Myocardial iodine-123 meta-iodobenzylguanidine imaging and cardiac events in heart failure. Results of the prospective ADMIRE-HF (AdreView Myocardial Imaging for Risk Evaluation in Heart Failure) study. J Am Coll Cardiol. 2010;55:2212–21.CrossRef PubMed
59.Diamond GA, Kaul S. Forbidden fruit: on the analysis of recurrent events in randomized clinical trials. Am J Cardiol. 2013;111:1530–6.CrossRef PubMed
60.•Wolbers M, Koller MT, Stel VS, et al. Competing risks analyses: objectives and approaches. Eur Heart J. 2014;35:2936–41. This paper stresses the importance of choosing statistical methods that are appropriate if competing risks are present and also clarifies the role of competing risks for the analysis of composite endpoints. PubMedCentral CrossRef PubMed
61.Petretta M, Pellegrino T, Cuocolo A. Cardiac neuronal imaging with 123I-meta-iodobenzylguanidine in heart failure: implications of endpoint selection and quantitative analysis on clinical decisions. Eur J Nucl Med Mol Imaging. 2014;41:1663–5.CrossRef PubMed
62.Aban I. Time to event analysis in the presence of competing risks. J Nucl Cardiol. 2015;22:466–7.CrossRef PubMed
63.Perin EC, Willerson JT, Pepine CJ, et al. Cardiovascular Cell Therapy Research Network (CCTRN). Effect of transendocardial delivery of autologous bone marrow mononuclear cells on functional capacity, left ventricular function, and perfusion in chronic heart failure: the FOCUS-CCTRN trial. JAMA. 2012;307:1717–26.PubMedCentral CrossRef PubMed
64.Phillips LM, Hachamovitch R, Berman DS, et al. Lessons learned from MPI and physiologic testing in randomized trials of stable ischemic heart disease: COURAGE, BARI 2D, FAME, and ISCHEMIA. J Nucl Cardiol. 2013;20:969–75.PubMedCentral CrossRef PubMed
65.Sadat K, Ather S, Aljaroudi W, et al. The effect of bone marrow mononuclear stem cell therapy on left ventricular function and myocardial perfusion. J Nucl Cardiol. 2014;21:351–67.CrossRef PubMed
66.Iskandrian AE, Hage FG, Shaw LJ, et al. Serial myocardial perfusion imaging: defining a significant change and targeting management decisions. JACC Cardiovasc Imaging. 2014;7:79–96.CrossRef PubMed
67.El-Hajj S, AlJaroudi WA, Farag A, Bleich S, Manaoragada P, Iskandrian AE, et al. Effect of changes in perfusion defect size during serial regadenoson myocardial perfusion imaging on cardiovascular outcomes in high-risk patients. J Nucl Cardiol. 2016. doi:10.​1007/​s12350-015-0174-8 .
68.Sherwood M, Hage FG, Heo J, et al. SPECT myocardial perfusion imaging as an endpoint. J Nucl Cardiol. 2012;19:891–4.CrossRef PubMed
69.Petretta M, Salvatore M, Cuocolo A. Immortality time and serial myocardial perfusion imaging: only those who do not die may repeat the exam. J Nucl Cardiol. 2016. doi:10.​1007/​s12350-015-0171-y .
70.Alpert JS, Thygesen K, Antman E, et al. Myocardial infarction redefined—a consensus document of The Joint European Society of Cardiology/American College of Cardiology Committee for the redefinition of myocardial infarction. J Am Coll Cardiol. 2000;36:959–69.CrossRef PubMed
71.Thygesen K, Alpert JS, White HD. Joint ESC/ACCF/AHA/WHF Task Force for the Redefinition of Myocardial Infarction. Universal definition of myocardial infarction. J Am Coll Cardiol. 2007;50:2173–95.CrossRef PubMed
72.Thygesen K, Alpert JS, Jaffe AS, et al. Joint ESC/ACCF/AHA/WHF Task Force for the Universal Definition of Myocardial Infarction. Third universal definition of myocardial infarction. Circulation. 2012;126:2020–35.CrossRef PubMed
73.Luepker RV, Duval S, Jacobs Jr DR, et al. The effect of changing diagnostic algorithms on acute myocardial infarction rates. Ann Epidemiol. 2011;21:824–9.PubMedCentral CrossRef PubMed
74.••Hicks KA, Tcheng JE, Bozkurt B, et al. 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). Circulation. 2015;132:302–61. This paper aims to identify and harmonize the common data elements involved in key cardiovascular endpoint definitions. CrossRef PubMed
75.Andersson C. Incorrect ICD-10 Code and MACE Endpoint. JAMA Intern Med. 2016. doi:10.​1001/​jamainternmed.​2015.​3219 .
76.McEvoy JW, Diamond GA, Detrano RC, et al. Risk and the physics of clinical prediction. Am J Cardiol. 2014;113:1429–35.CrossRef PubMed
77.Hubbard AE, Ahern J, Fleischer NL, et al. To GEE or not to GEE: comparing population average and mixed models for estimating the associations between neighborhood risk factors and health. Epidemiology. 2010;21:467–74.CrossRef PubMed
78.Gu W, Pepe M. Measures to summarize and compare the predictive capacity of markers. Int J Biostat. 2009;5:27.
79.Gerds TA, Cai T, Schumacher M. The performance of risk prediction models. Biom J. 2008;50:457–79.CrossRef PubMed
80.Pepe MS, Kerr KF, Longton G, et al. Testing for improvement in prediction model performance. Stat Med. 2013;32:1467–82.PubMedCentral CrossRef PubMed
81.Steyerberg EW, Vickers AJ, Cook NR, et al. Assessing the performance of prediction models: a framework for traditional and novel measures. Epidemiology. 2010;21:128–38.PubMedCentral CrossRef PubMed
82.••Pencina MJ, D'Agostino Sr RB, D’Agostino Jr RB, et al. Evaluating the added predictive ability of a new marker: from area under the ROC curve to reclassification and beyond. Stat Med. 2008;27:157–72. The authors discuss the properties of new predictive measures and develop simple asymptotic tests of significance. CrossRef PubMed
83.Steyerberg EW, Van Calster B, Pencina MJ. Performance measures for prediction models and markers: evaluation of predictions and classifications. Rev Esp Cardiol. 2011;64:788–94.CrossRef PubMed
84.Pickering JW, Endre ZH. New metrics for assessing diagnostic potential of candidate biomarkers. Clin J Am Soc Nephrol. 2012;7:1355–64.CrossRef PubMed
85.Shaw LJ, Giambrone AE, Blaha MJ, et al. Long-term prognosis after coronary artery calcification testing in asymptomatic patients: a cohort study. Ann Intern Med. 2015;163:14–21.CrossRef PubMed
86.•Pencina MJ, D’Agostino Sr RB, Steyerberg EW. Extensions of net reclassification improvement calculations to measure usefulness of new biomarkers. Stat Med. 2011;30:11–21. In this paper the authors develop a general form for the net reclassification improvement that presents it as a prospective measure, which quantifies the correctness of upward and downward reclassification or movement of predicted probabilities as a result of adding a new marker. PubMedCentral CrossRef PubMed
87.Petretta M, Cuocolo A. Prognosis in the era of comparative effectiveness research. J Nucl Cardiol. 2013;20:313.CrossRef PubMed
88.Kerr KF, Wang Z, Janes H, et al. Net reclassification indices for evaluating risk prediction instruments: a critical review. Epidemiology. 2014;25:114–21.PubMedCentral CrossRef PubMed
89.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;33:3405–14.CrossRef PubMed
90.Pepe MS, Janes H, Li CI. Net risk reclassification p values: valid or misleading? J Natl Cancer Inst. 2014;106:dju041.
91.Pencina KM, Pencina MJ, D’Agostino Sr RB. What to expect from net reclassification improvement with three categories. Stat Med. 2014;33:4975–87.CrossRef PubMed
  • 作者单位：Mario Petretta (1)
    Alberto Cuocolo (2)

    1. Department of Translational Medical Sciences, University Federico II, Naples, Italy
    2. Department of Advanced Biomedical Sciences, University Federico II, Naples, Italy
  • 刊物主题：Cardiology; Imaging / Radiology; Diagnostic Radiology; Interventional Radiology; Ultrasound; Nuclear Medicine;
  • 出版者：Springer US
  • ISSN：1941-9074
    • 文摘
    Regression modeling strategies are increasingly used for the management of subjects with cardiovascular diseases as well as for decision-making of subjects without known disease but who are at risk of disease in the short- or long-term or during life span. Accurate individual risk assessment, taking in account clinical, laboratory, and imaging data is useful for choosing among prevention strategies and/or treatments. The value of nuclear cardiology techniques for risk stratification has been well documented. Many models have been proposed and are available for diagnostic and prognostic purposes and several statistical techniques are available for risk stratification. However, current approaches for prognostic modeling are not perfect and present limitations. This review analyzes some specific aspects related to prediction model development and validation. Keywords Cardiovascular disease Risk stratification Nuclear cardiology Myocardial perfusion imaging Algorithms for risk prediction
    NGLC 2004-2010.National Geological Library of China All Rights Reserved.
    Add:29 Xueyuan Rd,Haidian District,Beijing,PRC. Mail Add: 8324 mailbox 100083
    For exchange or info please contact us via email.