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      Prognostic value of positron emission tomography myocardial perfusion imaging beyond traditional cardiovascular risk factors: Systematic review and meta-analysis

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          Abstract

          Background

          Despite substantive growth in utilization of positron emission tomography (PET) myocardial perfusion imaging (MPI), evidence on its prognostic value is limited. We aimed to comprehensively evaluate the prognostic literature of PET perfusion measures according to the most recent American Heart Association recommendations for assessment of novel cardiovascular biomarkers.

          Methods

          We searched the literature for studies reporting associations of PET MPI measures and outcomes in patients with known or suspected coronary artery disease. We documented hazard ratios (HR) and 95% confidence intervals (CI) of association effects and quantitatively synthesized them with random-effects meta-analyses. Discrimination, calibration and risk reclassification after addition of PET MPI measures to standard prognostic models were documented.

          Results

          We identified 20 eligible studies with median n = 551 patients. In meta-analyses, the extents of ischemic and scarred myocardium were significantly associated with cardiac death. Meta-analyses of multivariate estimates for abnormal summed stress score ≥ 4 and myocardial perfusion reserve < 2 revealed significant associations with major adverse cardiovascular events [HR (95% CI) 2.30 (1.53–3.44) and 2.11 (1.33–3.36), respectively]. Changes in model discrimination, calibration or risk reclassification were reported in 5 studies (8 prognostic evaluations). There were marginal improvements in discrimination based on C index and no improvements in model calibration. Net reclassification index ranged from 9.8% to 14.2% and risk classification was significantly improved in 4/5 prognostic evaluations.

          Conclusions

          PET MPI measures were strongly associated with adverse patient outcomes. Risk classification was more consistently improved than discrimination and calibration after addition of PET MPI measures, but reporting of such metrics was overall limited.

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          Most cited references26

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          Use and misuse of the receiver operating characteristic curve in risk prediction.

          Nancy R. Cook (2007)
          The c statistic, or area under the receiver operating characteristic (ROC) curve, achieved popularity in diagnostic testing, in which the test characteristics of sensitivity and specificity are relevant to discriminating diseased versus nondiseased patients. The c statistic, however, may not be optimal in assessing models that predict future risk or stratify individuals into risk categories. In this setting, calibration is as important to the accurate assessment of risk. For example, a biomarker with an odds ratio of 3 may have little effect on the c statistic, yet an increased level could shift estimated 10-year cardiovascular risk for an individual patient from 8% to 24%, which would lead to different treatment recommendations under current Adult Treatment Panel III guidelines. Accepted risk factors such as lipids, hypertension, and smoking have only marginal impact on the c statistic individually yet lead to more accurate reclassification of large proportions of patients into higher-risk or lower-risk categories. Perfectly calibrated models for complex disease can, in fact, only achieve values for the c statistic well below the theoretical maximum of 1. Use of the c statistic for model selection could thus naively eliminate established risk factors from cardiovascular risk prediction scores. As novel risk factors are discovered, sole reliance on the c statistic to evaluate their utility as risk predictors thus seems ill-advised.
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            Criteria for evaluation of novel markers of cardiovascular risk: a scientific statement from the American Heart Association.

            Mark Hlatky, Philip Greenland, Donna K. Arnett … (2009)
            There is increasing interest in utilizing novel markers of cardiovascular disease risk, and consequently, there is a need to assess the value of their use. This scientific statement reviews current concepts of risk evaluation and proposes standards for the critical appraisal of risk assessment methods. An adequate evaluation of a novel risk marker requires a sound research design, a representative at-risk population, and an adequate number of outcome events. Studies of a novel marker should report the degree to which it adds to the prognostic information provided by standard risk markers. No single statistical measure provides all the information needed to assess a novel marker, so measures of both discrimination and accuracy should be reported. The clinical value of a marker should be assessed by its effect on patient management and outcomes. In general, a novel risk marker should be evaluated in several phases, including initial proof of concept, prospective validation in independent populations, documentation of incremental information when added to standard risk markers, assessment of effects on patient management and outcomes, and ultimately, cost-effectiveness.
            Article 0 comments Cited 231 times – based on 0 reviews     Review now
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              Advances in measuring the effect of individual predictors of cardiovascular risk: the role of reclassification measures.

              Bruce Cook, Paul Ridker (2009)
              Models for risk prediction are widely used in clinical practice to stratify risk and assign treatment strategies. The contribution of new biomarkers has largely been based on the area under the receiver-operating characteristic curve, but this measure can be insensitive to important changes in absolute risk. Methods based on risk stratification have recently been proposed to compare predictive models. Such methods include the reclassification calibration statistic, the net reclassification improvement, and the integrated discrimination improvement. This article demonstrates the use of reclassification measures and illustrates their performance for well-known cardiovascular risk predictors in a cohort of women. These measures are targeted at evaluating the potential of new models and markers to change risk strata and alter treatment decisions.
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                Author and article information

                Contributors
                Panithaya Chareonthaitawee
                Journal
                Journal ID (nlm-ta): Int J Cardiol Heart Vasc
                Journal ID (iso-abbrev): Int J Cardiol Heart Vasc
                Title: International Journal of Cardiology. Heart & Vasculature
                Publisher: Elsevier
                ISSN (Electronic): 2352-9067
                Publication date PMC-release: 09 January 2015
                Publication date Collection: 01 March 2015
                Publication date (Electronic): 09 January 2015
                Volume: 6
                Pages: 54-59
                Affiliations
                [a ]Department of Medicine, Mayo Clinic, Rochester, MN, United States
                [b ]Division of Cardiovascular Diseases, Mayo Clinic, Rochester, MN, United States
                Author notes
                [* ]Corresponding author at: Division of Cardiovascular Diseases, Mayo Clinic, 200 First St. SW, Rochester, MN 55905, United States. chareonthaitawee.panithaya@ 123456mayo.edu
                Article
                Publisher ID: S2352-9067(15)00006-8
                DOI: 10.1016/j.ijcha.2015.01.005
                PMC ID: 5497169
                SO-VID: 0c0fab46-402a-4167-b17d-b6269ccd8cef
                Copyright © © 2015 The Authors. Published by Elsevier Ireland Ltd.
                License:

                This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

                History
                Date received : 28 December 2014
                Date accepted : 2 January 2015
                Categories
                Subject: Article

                Keywords: positron emission tomography,myocardial perfusion imaging,prognosis,discrimination,calibration,risk reclassification
                Data availability:
                Keywords: positron emission tomography, myocardial perfusion imaging, prognosis, discrimination, calibration, risk reclassification

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