Roberta Green1, Valeria Cantoni1, Mario Petretta2, Wanda Acampa1, Mariarosaria Panico3, Pietro Buongiorno1, Giorgio Punzo3, Marco Salvatore4, Alberto Cuocolo5. 1. Department of Advanced Biomedical Sciences, University Federico II, Via Pansini 5, 80131, Naples, Italy. 2. Department of Translational Medical Sciences, University Federico II, Naples, Italy. 3. Institute of Biostructure and Bioimaging, National Council of Research, Naples, Italy. 4. IRCCS SDN, Naples, Italy. 5. Department of Advanced Biomedical Sciences, University Federico II, Via Pansini 5, 80131, Naples, Italy. cuocolo@unina.it.
Abstract
BACKGROUND: Comparing the prognostic value of a negative finding by stress single-photon emission computed tomography myocardial perfusion imaging (MPI) and coronary computed tomography angiography (CCTA) may be useful to evaluate how better identify low-risk patients. We performed a meta-analysis to compare the long-term negative predictive value (NPV) of normal stress MPI and normal CCTA in subjects with suspected coronary artery disease (CAD). METHODS AND RESULTS: Studies published between January 2000 and November 2016 were identified by database search. We included MPI and CCTA studies that followed-up ≥100 subjects for ≥5 years and providing data on clinical outcome for patients with negative tests. Summary risk estimates for normal perfusion at MPI or <50% coronary stenosis at CCTA were derived in random effect regression analysis, and causes of heterogeneity were determined in meta-regression analysis. We identified 12 eligible articles (6 MPI and 6 CCTA) including 33,129 patients (26,757 in MPI and 6372 in CCTA studies) with suspected CAD. The pooled annualized event rate (AER) for occurrence of hard events (death and nonfatal myocardial infarction) was 1.06 (95% confidence interval, CI 0.49-1.64) in MPI and 0.61 (95% CI 0.35-0.86) in CCTA studies. The pooled NPV was 91% (95% CI 86-96) in MPI and 96 (95% CI 95-98) in CCTA studies. The summary rates between MPI and CCTA were not statistically different. At meta-regression analysis, no significant association between AER and clinical and demographical variables considered was found for overall studies. CONCLUSIONS: Stress MPI and CCTA have a similar ability to identify low-risk patients with suspected CAD.
BACKGROUND: Comparing the prognostic value of a negative finding by stress single-photon emission computed tomography myocardial perfusion imaging (MPI) and coronary computed tomography angiography (CCTA) may be useful to evaluate how better identify low-risk patients. We performed a meta-analysis to compare the long-term negative predictive value (NPV) of normal stress MPI and normal CCTA in subjects with suspected coronary artery disease (CAD). METHODS AND RESULTS: Studies published between January 2000 and November 2016 were identified by database search. We included MPI and CCTA studies that followed-up ≥100 subjects for ≥5 years and providing data on clinical outcome for patients with negative tests. Summary risk estimates for normal perfusion at MPI or <50% coronary stenosis at CCTA were derived in random effect regression analysis, and causes of heterogeneity were determined in meta-regression analysis. We identified 12 eligible articles (6 MPI and 6 CCTA) including 33,129 patients (26,757 in MPI and 6372 in CCTA studies) with suspected CAD. The pooled annualized event rate (AER) for occurrence of hard events (death and nonfatal myocardial infarction) was 1.06 (95% confidence interval, CI 0.49-1.64) in MPI and 0.61 (95% CI 0.35-0.86) in CCTA studies. The pooled NPV was 91% (95% CI 86-96) in MPI and 96 (95% CI 95-98) in CCTA studies. The summary rates between MPI and CCTA were not statistically different. At meta-regression analysis, no significant association between AER and clinical and demographical variables considered was found for overall studies. CONCLUSIONS: Stress MPI and CCTA have a similar ability to identify low-risk patients with suspected CAD.
Authors: Jane A Simonsen; Oke Gerke; Charlotte K Rask; Mohammad Tamadoni; Anders Thomassen; Søren Hess; Allan Johansen; Hans Mickley; Lisette O Jensen; Jesper Hallas; Werner Vach; Poul F Høilund-Carlsen Journal: J Nucl Cardiol Date: 2013-03-01 Impact factor: 5.952
Authors: Matthew P Ostrom; Ambarish Gopal; Naser Ahmadi; Khurram Nasir; Eric Yang; Ioannis Kakadiaris; Ferdinand Flores; Song S Mao; Matthew J Budoff Journal: J Am Coll Cardiol Date: 2008-10-14 Impact factor: 24.094
Authors: Alan Rozanski; Heidi Gransar; James K Min; Sean W Hayes; John D Friedman; Louise E J Thomson; Daniel S Berman Journal: J Nucl Cardiol Date: 2013-12-31 Impact factor: 5.952
Authors: Daniel B Mark; Jerome J Federspiel; Patricia A Cowper; Kevin J Anstrom; Udo Hoffmann; Manesh R Patel; Linda Davidson-Ray; Melanie R Daniels; Lawton S Cooper; J David Knight; Kerry L Lee; Pamela S Douglas Journal: Ann Intern Med Date: 2016-05-24 Impact factor: 25.391
Authors: Victor Mor-Avi; Mita B Patel; Francesco Maffessanti; Amita Singh; Diego Medvedofsky; S Javed Zaidi; Anuj Mediratta; Akhil Narang; Noreen Nazir; Nadjia Kachenoura; Roberto M Lang; Amit R Patel Journal: J Am Soc Echocardiogr Date: 2018-03-22 Impact factor: 5.251
Authors: Andrea Baggiano; Gianpiero Italiano; Marco Guglielmo; Laura Fusini; Andrea Igoren Guaricci; Riccardo Maragna; Carlo Maria Giacari; Saima Mushtaq; Edoardo Conte; Andrea Daniele Annoni; Alberto Formenti; Maria Elisabetta Mancini; Daniele Andreini; Mark Rabbat; Mauro Pepi; Gianluca Pontone Journal: J Clin Med Date: 2022-01-18 Impact factor: 4.241