Accepted Manuscript Comparison of the Diagnostic Performance of Coronary Computed Tomography Angiography Derived Fractional Flow Reserve in Patients With Versus Without Diabetes Mellitus (From the MACHINE Consortium) Fay M.A. Nous MD , Adriaan Coenen MD , Eric Boersma MSc, PhD, FESC , Young-Hak Kim MD, PhD , Mariusz B.P. Kruk MD, PhD , Christian Tesche MD , Jakob de Geer MD, PhD , Dong Hyun Yang MD, PhD , Cezary Kepka MD, PhD , U. Joseph Schoepf MD , Anders Persson MD, PhD , Akira Kurata MD, PhD , Ricardo P.J. Budde MD, PhD , Koen Nieman MD, PhD PII: DOI: Reference: S0002-9149(18)32105-2 https://doi.org/10.1016/j.amjcard.2018.11.024 AJC 23636 To appear in: The American Journal of Cardiology Received date: Revised date: 19 July 2018 28 October 2018 Please cite this article as: Fay M.A. Nous MD , Adriaan Coenen MD , Eric Boersma MSc, PhD, FESC , Young-Hak Kim MD, PhD , Mariusz B.P. Kruk MD, PhD , Christian Tesche MD , Jakob de Geer MD, PhD , Dong Hyun Yang MD, PhD , Cezary Kepka MD, PhD , U. Joseph Schoepf MD , Anders Persson MD, PhD , Akira Kurata MD, PhD , Ricardo P.J. Budde MD, PhD , Koen Nieman MD, PhD , Comparison of the Diagnostic Performance of Coronary Computed Tomography Angiography Derived Fractional Flow Reserve in Patients With Versus Without Diabetes Mellitus (From the MACHINE Consortium), The American Journal of Cardiology (2018), doi: https://doi.org/10.1016/j.amjcard.2018.11.024 This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. ACCEPTED MANUSCRIPT Comparison of the Diagnostic Performance of Coronary Computed Tomography Angiography Derived Fractional Flow Reserve in Patients With Versus Without Diabetes Mellitus (From the MACHINE Consortium) IP T Fay M.A. Nous, MDa,b*, Adriaan Coenen, MDa,b, Eric Boersma, MSc, PhD, FESCb, Young-Hak Kim, MD, PhDc, Mariusz B.P. Kruk, MD, PhDd, Christian Tesche, MDe, Jakob de Geer, MD, PhDf, Dong US CR Hyun Yang, MD, PhDg, Cezary Kepka, MD, PhDd, U. Joseph Schoepf, MDe, Anders Persson, MD, PhDf, Akira Kurata, MD, PhDa,h, Ricardo P.J. Budde, MD, PhDa,b, Koen Nieman, MD, PhDa,b,i a Department of Radiology & Nuclear Medicine, Erasmus University Medical Center, Doctor Molewaterplein 40, 3015 GD, Rotterdam, The Netherlands. Department of Cardiology, Erasmus University Medical Center, Doctor Molewaterplein 40, 3015 GD, AN b Rotterdam, The Netherlands. Department of Cardiology, Asan Medical Center, University of Ulsan College of Medicine, 88, M c Olympic-ro 43-Gil, Songpa-gu, Seoul, 138-736, South Korea. Coronary Disease and Structural Heart Diseases Department, Institute of Cardiology, ul. Alpejska 42, PT ED d 04-628 Warsaw, Poland. e Department of Radiology and Radiological Science, Heart & Vascular Center, Medical University of f CE South Carolina, Ashley River Tower, MSC 226, 25 Courtenay Drive, Charleston, SC 29425, USA. Department of Radiology and Department of Medical and Health Sciences, Center for Medical Image AC Science and Visualization, CMIV, Linköping University, 58185 Linköping, Sweden. g Department of Radiology and Research Institute of Radiology, Asan Medical Center, University of Ulsan College of Medicine, 88, Olympic-ro 43-Gil, Songpa-gu, Seoul, 138-736, South Korea. h Department of Radiology, Ehime University Graduate School of Medicine, Shitsukawa, Toon, Ehime, 791-0295, Japan. 1 ACCEPTED MANUSCRIPT i Stanford University School of Medicine, Cardiovascular Institute, 300 Pasteur Drive, Stanford, CA 94305, USA. *Corresponding author: Tel: +31 10 7032055; E-mail: f.nous@erasmusmc.nl; Postal address: Erasmus IP T MC, Postbus 2040, 3000 CA, Rotterdam, room: Ne507k US CR Running head: Functional cardiac CT in diabetic patients Disclosures: AN The authors of this manuscript declare relationships with the following companies: U. Joseph Schoepf: Institutional research support to Medical University of South Carolina from Astellas, Bayer Healthcare, M GE Healthcare, Siemens Healthineers and speaker / consultancy fees from Bayer Healthcare, GE Healthcare, HeartFlow Inc., Guerbet, and Siemens. Koen Nieman: Institutional research support to the PT ED Erasmus MC from Siemens Healthineers, HeartFlow, GE Healthcare, Bayer Healthcare. Other authors declare that they have no competing interests. Funding: CE Fay M.A. Nous, Adriaan Coenen, Ricardo P.J. Budde and Koen Nieman were supported by grants from AC the Dutch Heart Foundation [NHS 2014T061 and NHS 2013T071]. 2 ACCEPTED MANUSCRIPT Abstract Coronary computed tomography angiography derived fractional flow reserve (CT-FFR) is a noninvasive application to evaluate the hemodynamic impact of coronary artery disease by simulating invasively measured FFR based on CT data. CT-FFR is based on the assumption of a normal coronary IP T microvascular response. We assessed the diagnostic performance of a machine-learning based application for on-site computation of CT-FFR in patients with and without diabetes mellitus. The population US CR included 75 diabetic and 276 non-diabetic patients, who were enrolled in the MACHINE consortium. The overall diagnostic performances of coronary CT angiography alone and in combination with CT-FFR were analyzed in 110 vessels of the diabetic group and in 415 vessels of the non-diabetic group, with direct invasive FFR comparison. Per-vessel discrimination of lesion-specific ischemia by CT-FFR was AN assessed by the area under receiver operating characteristic curves. The overall diagnostic accuracy of CT-FFR in diabetic patients was 83% and 75% in non-diabetic patients (p=0.088), improved over the M diagnostic accuracy of coronary CT angiography, which was 58% and 65% (p=0.223), respectively. Furthermore, the diagnostic accuracy of CT-FFR was similar between diabetic and non-diabetic patients PT ED per stratified CT-FFR group (CT-FFR <0.6, 0.6-0.69, 0.7-0.79, 0.8-0.89, ≥0.9). The area under curves for diabetic and non-diabetic patients were comparable, 0.88 and 0.82 (p=0.113), respectively. In conclusion, on-site machine-learning CT-FFR analysis improved the diagnostic performance of coronary CT CE angiography and was able to accurately discriminate lesion-specific ischemia in both diabetic and nondiabetic patients suspected for coronary artery disease. AC Keywords: Coronary artery disease; diabetes mellitus; computed tomography; fractional flow reserve Introduction Diabetic patients are approximately two times more likely to develop coronary artery disease (CAD) than non-diabetic patients, while the evaluation and management of CAD in these patients has proven to be more challenging1-3. Due to chronic hyperglycemia, diabetic patients are at risk of developing a more 3 ACCEPTED MANUSCRIPT diffuse atherosclerosis, a more profound microvascular dysfunction, an increased vascular resistance, and a reduced vasodilation capacity4,5. The clinical reference standard to assess CAD is invasive coronary angiography with invasively measured fractional flow reserve (FFR)6,7. To avoid unnecessary use of invasive modalities, alternatives such as coronary computed tomography (CT) angiography with IP T simulated FFR (CT-FFR) are under development8. Novel machine-learning based CT-FFR applications, based on pattern recognition and computational learning, have been recently shown to be as accurate as US CR the more established, on-site performed, computational fluid dynamics based CT-FFR, and furthermore, they have been able to calculate CT-FFR much faster9,10. All CT-FFR applications, however, are based on a computational simulation of normal coronary hyperemia, which is not the case in diabetic patients11,12. This study assessed the diagnostic performance of an on-site machine-learning based CT-FFR application AN in diabetic patients compared to non-diabetic patients using invasive FFR as the reference standard. Methods M The study population was part of the MACHINE consortium, which has been described in detail PT ED previously10. In brief, the MACHINE consortium is a collaboration between five institutions in Europe, USA, and Korea, that investigated the diagnostic performance of machine-learning based CT-FFR software (clinicaltrials.gov identifier: 02805621). Patients were identified retrospectively at four sites and prospectively in one site on the availability of coronary CT angiography and invasive FFR measurements. CE The studies were conducted in accordance with the Declaration of Helsinki and with research ethics committee approval at each of the sites. AC All coronary CT angiograms were performed on first- and second-generation dual-source CT scanners (Somatom Definition or Definition Flash; Siemens Healthineers, Forchheim, Germany) using a retrospectively ECG-gated spiral (78%), prospectively ECG-triggered axial (20%) or prospectively-ECGtriggered high-pitch spiral (2%) scan mode. Patients received sublingual nitroglycerin before the examination and β-blockers in case of high heart rates. Stenosis severity was classified per segment 4 ACCEPTED MANUSCRIPT following the Society of Cardiovascular Computed Tomography criteria13: normal: 0% stenosis; minimal: <25% stenosis; mild: 25-49% stenosis; moderate 50-69% stenosis; severe: 70-99%; and occluded: 100% by observers with extensive previous experience in cardiac imaging. Stenosis ≥50% was considered angiographically significant. Coronary CT angiography image quality was evaluated based on a 4-point IP T Likert scale: 1 non-diagnostic; 2 impaired image quality, differentiation of coronary arty wall possible with reduced confidence; 3 adequate, reduced image quality due to artifacts without limiting coronary US CR artery wall differentiation; 4 excellent, no artifacts present and clear differentiation of the coronary artery wall. A machine-learning based CT-FFR software prototype (cFFR version 2.1, Siemens Healthineers, Forchheim, Germany; not currently commercially available) was used, as previously described8. A 3D AN coronary artery tree was semi-automatically created and the left ventricular myocardial mass was automatically determined based on the coronary CT angiography data. Each point on the coronary artery M tree was analyzed and CT-FFR was derived based on a combination of pattern recognition and PT ED computational learning. CT-FFR ≤0.80 was considered hemodynamically significant. Invasive coronary angiography was performed following local standards and invasive FFR measurements were either performed for clinical reasons or for research purposes. An FFR pressure wire was positioned distal to the stenosis of interest and an FFR was measured during hyperemia by CE intravenous infusion of adenosine at 140μg/kg/min. FFR ≤0.80 was considered hemodynamically significant. AC Absolute variables are represented as totals and percentages and continuous variables as median and 25th-75th percentiles. Chi-square, Fisher’s exact and Mann–Whitney U-tests were used to check for differences in categorical and continuous variables between the diabetic and non-diabetic group. The correlation between CT-FFR and invasive FFR in diabetic and non-diabetic patients was assessed with a Bland-Altman plot. The diagnostic performances of coronary CT angiography and CT-FFR in diabetic 5 ACCEPTED MANUSCRIPT and non-diabetic patients were analyzed with direct comparison of invasive FFR and were reported on a per-vessel basis as sensitivity, specificity, positive predictive value, negative predictive value, and accuracy with 95% limits of agreement. A multi-variable logistic regression analysis was performed to determine whether the presence of diabetes mellitus affected the diagnostic performance after adjusting IP T for known differences in patient characteristics. Additionally, the diagnostic accuracy of CT-FFR was reported per stratified CT-FFR group (CT-FFR <0.6, 0.6-0.69, 0.7-0.79, 0.8-0.89, ≥0.9) with 95% limits of agreement. Per-vessel discrimination of lesion-specific ischemia by CT-FFR was assessed by the area US CR under curve with 95% limits of agreement using FFR ≤0.80 as the reference standard. C-statistics were calculated for CT-FFR in diabetic and non-diabetic patients, and compared by using the method of DeLong et al.14. Statistical analyses were performed using SPSS (version 21, IBM Corp, Armonk NY, AN USA). MedCalc (version 13.0; MedCalc Software, Ostend, Belgium) was used to compare the area under curves. M Results PT ED The study population consisted of 75 diabetic patients and 276 non-diabetic patients. Direct invasive FFR comparison with CT-FFR was available in 110 vessels of diabetic patients and 415 vessels of non-diabetic patients. A detailed overview of the patient characteristics is presented in Table 1. The median age of the diabetic patients was 62 years (56-71 years) and 73% were men, which was similar in CE the non-diabetic group. Diabetic patients were more likely to smoke (44% vs. 32%, p=0.043) and to have a myocardial infarction in their medical history (14% vs. 6%, p=0.035) than non-diabetic patients. Other AC patient characteristics were similar in both groups. Hemodynamically significant disease (invasive FFR ≤0.8) was present in 41 vessels (37%) of the diabetic patients and in 171 vessels (41%) of the non-diabetic patients (p=0.455). On coronary CT angiography a ≥50% stenosis was present in 74 vessels (67%) of the diabetic patients and in 308 vessels (74%) of the non-diabetic patients (p=0.146). CT-FFR classified 48 vessels (44%) as functionally 6 ACCEPTED MANUSCRIPT obstructed in diabetic patients and 197 vessels (47%) of the non-diabetic patients (p=0.474) (Figure 1). The Bland-Altman plot revealed a mean difference between CT-FFR and invasive FFR of -0.034±0.126 in diabetic patients and -0.034±0.116 in non-diabetic patients (p=0.95) (Figure 2). Because of differences in patient characteristics between the diabetic patients and non-diabetic IP T patients, a multivariable logistic regression model was used to evaluate the effect of diabetes mellitus on the diagnostic performance of coronary CT angiography and CT-FFR adjusted for smoking and prior US CR myocardial infarction. The diagnostic performance of CT-FFR was similar in diabetic and non-diabetic patients and improved the diagnostic performance of coronary CT angiography (≥50% stenosis) in both groups (Table 2). No differences were found in accuracy of CT-FFR per stratified group (CT-FFR 0.6, 0.6-0.69, 0.7-0.79, 0.8-0.89, ≥0.9) between diabetic and non-diabetic patients (Figure 3). Additionally, the AN area under curves of the diagnostic performance of CT-FFR were similar (p=0.113) (Figure 4). M Discussion We demonstrated that CT-FFR improved the diagnostic performance of coronary CT suspected for CAD. PT ED angiography and discriminated ischemia with good accuracy in both diabetic and non-diabetic patients The high prevalence of diabetes mellitus in the general population and its significant risk for CE developing cardiovascular disease emphasizes the importance of accurate anatomical and functional assessment in patients suspected for CAD3. Coronary CT angiography has emerged as a valuable AC approach in the diagnosis of CAD15,16. However, the interpretation of coronary CT angiography results differs in diabetic patients due to a more diffuse presentation of CAD and faster progression of the disease5,17. An Agatston coronary calcium score of zero has a much shorter warranty period for all-cause death in diabetic patients compared to a general asymptomatic population18. These findings might be explained by a higher prevalence of non-calcified plaques in this population19. Moreover, diabetic patients have greater plaque progression of especially the non-calcified plaques, assumed to increase their risk for 7 ACCEPTED MANUSCRIPT plaque rupture17. Therefore, plaque morphology assessment by coronary CT angiography has an additional value in cardiovascular risk stratification and could lead to a more focused medical therapy especially in diabetic patients20,21. Nevertheless, no studies have been conducted that show if this will lead to a reduction in cardiovascular events. IP T CT-FFR has incremental value over coronary CT angiography by ruling out hemodynamic significance of angiographic lesions, which is especially relevant in diabetic patients due to their less US CR typical presentation of CAD22. However, CT-FFR algorithms are based on the assumption that the hyperemic response to adenosine is predictable12. This could potentially make CT-FFR less reliable in diabetic patients due to a higher prevalence of microvascular dysfunction and higher vascular resistance4,5. Few studies are available on the interpretation of on-site CT-FFR analysis in diabetic AN patients. Coenen et al. suggested that the presence of diabetes mellitus resulted in an increased discrepancy between computational fluid dynamics based CT-FFR and invasive FFR8. However, these M results were not sufficiently powered. A study in which the diagnostic performance of the CT-FFR application commercialized by HeartFlow (HeartFlow Inc., Redwood City, California, USA) was PT ED analyzed, showed no differences in diagnostic accuracy of CT-FFR between diabetic patients and a control group23. In the present study, we confirm a similar performance of CT-FFR in diabetic and nondiabetic patients. Additionally, the diagnostic performance of CT-FFR per stratified group was CE comparable between diabetic and non-diabetic patients. The overall accuracy of CT-FFR did vary depending on the CT-FFR outcome, in line with the systemic review of published studies by Cook et al.24. AC Overall, these findings suggest that the presence of diabetic mellitus does not negatively affect the accuracy of CT-FFR. Validation studies of invasively measured FFR have shown contradictive results on the diagnostic accuracy in diabetes patients25-27. This might be explained by differences in study population, since limited data was available on the coronary vasodilation capacity and blood glucose levels of these patients. Clinical outcome studies of invasive FFR guided revascularization have shown higher event 8 ACCEPTED MANUSCRIPT rates in diabetic patients than in non-diabetic patients28,29. Therefore, the diagnostic accuracy and interpretation of invasively measured FFR in diabetic patients remains ambiguous. This study has several limitations that should be acknowledged. Given the retrospective nature of the study and the small sample size, the potential impact of other variables on the diagnostic performance IP T cannot be excluded. Moreover, we assumed that the presence of microvascular dysfunction is based on the presence of diabetes mellitus. Our study is limited by the unavailability of directly measured US CR microvascular circulation and vascular resistance and therefore we do not know the impact of microvascular dysfunction in our population. Vasodilatory dysfunction could also have been reversed when patients were optimally treated30. Therefore, further studies are needed with extensive information on long-term glucose control of diabetic patients and the extent of microvascular dysfunction to fully AN evaluate the diagnostic performance of CT-FFR and its effect on clinical outcome of diabetic patient with CAD. 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World J Cardiol 2014;6:802-813. 23. Eftekhari A, Min J, Achenbach S, Marwan M, Budoff M, Leipsic J, Gaur S, Jensen JM, Ko BS, AN Christiansen EH, Kaltoft A, Botker HE, Jensen JF, Norgaard BL. Fractional flow reserve derived from coronary computed tomography angiography: diagnostic performance in hypertensive and diabetic M patients. Eur Heart J Cardiovasc Imaging 2016. 24. Cook CM, Petraco R, Shun-Shin MJ, Ahmad Y, Nijjer S, Al-Lamee R, Kikuta Y, Shiono Y, Mayet J, PT ED Francis DP, Sen S, Davies JE. Diagnostic Accuracy of Computed Tomography-Derived Fractional Flow Reserve : A Systematic Review. JAMA Cardiol 2017;2:803-810. 25. Sahinarslan A, Kocaman SA, Olgun H, Kunak T, Kiziltunc E, Ozdemir M, Timurkaynak T. The reliability of fractional flow reserve measurement in patients with diabetes mellitus. Coron Artery Dis CE 2009;20:317-321. 26. Yanagisawa H, Chikamori T, Tanaka N, Usui Y, Takazawa K, Yamashina A. Application of pressure- AC derived myocardial fractional flow reserve in assessing the functional severity of coronary artery stenosis in patients with diabetes mellitus. Circ J 2004;68:993-998. 27. Reith S, Battermann S, Hellmich M, Marx N, Burgmaier M. Impact of type 2 diabetes mellitus and glucose control on fractional flow reserve measurements in intermediate grade coronary lesions. Clin Res Cardiol 2014;103:191-201. 13 ACCEPTED MANUSCRIPT 28. Kennedy MW, Kaplan E, Hermanides RS, Fabris E, Hemradj V, Koopmans PC, Dambrink JH, Marcel Gosselink AT, Van't Hof AW, Ottervanger JP, Roolvink V, Remkes WS, van der Sluis A, Suryapranata H, Kedhi E. Clinical outcomes of deferred revascularisation using fractional flow reserve in patients with and without diabetes mellitus. Cardiovasc Diabetol 2016;15:100. IP T 29. Liu Z, Matsuzawa Y, Herrmann J, Li J, Lennon RJ, Crusan DJ, Kwon TG, Zhang M, Sun T, Yang S, Gulati R, Bell MR, Lerman LO, Lerman A. Relation between fractional flow reserve value of coronary US CR lesions with deferred revascularization and cardiovascular outcomes in non-diabetic and diabetic patients. Int J Cardiol 2016;219:56-62. 30. Takiuchi S, Rakugi H, Masuyama T, Ikegami H, Nishikage T, Shintani M, Komai N, Nagai M, Kamide K, Higaki J, Ogihara T. Hypertension attenuates the efficacy of hypoglycemic therapy for AC CE PT ED M AN preserving coronary flow reserve in patients with type 2 diabetes. Hypertens Res 2002;25:893-900. 14 AN US CR IP T ACCEPTED MANUSCRIPT Figure 1 Coronary CT angiography, invasive coronary angiography, and CT-FFR in a single patient. The coronary CT angiography showed several moderate stenoses in the right (A) and left coronary artery (B), M but the evaluation was hampered by the amount of calcium in the coronary arteries. Invasive coronary PT ED angiography showed no significant stenoses in right coronary artery (C) and multiple lesions in the left anterior descending artery with a FFR of 0.90 (D). The CT-FFR analysis showed comparable results with AC CE the invasively measured FFR (E). 15 PT ED M AN US CR IP T ACCEPTED MANUSCRIPT Figure 2 A Bland-Altman plot of CT-FFR and invasive FFR in vessels of diabetic (red) and non-diabetic CE patients (blue). A horizontal line is placed at the mean difference between CT-FFR and invasive FFR. The dotted lines are placed at the limits of agreement: mean ± 1.96 standard deviation. CT-FFR: computed AC tomography based fractional flow reserve. 16 AN US CR IP T ACCEPTED MANUSCRIPT M Figure 3 PT ED Per-vessel diagnostic accuracy of CT-FFR per stratified group in diabetic (red) and non-diabetic patients (blue). Error bars represent the standard error in each group. On the right y-axis the number of vessels with an invasive FFR value corresponding to each interval of diabetic (red) and non-diabetic patients AC CE (blue). CT-FFR: computed tomography based fractional flow reserve. 17 M AN US CR IP T ACCEPTED MANUSCRIPT PT ED Figure 4 Per vessel receiver operating characteristics curves displaying the diagnostic performance of CT-FFR in diabetic (red) and non-diabetic patients (blue). CT-FFR: computed tomography based fractional flow AC CE reserve; AUC: area under curve; CI: confidence interval. 18 ACCEPTED MANUSCRIPT Table 1: Patients characteristics Diabetes Mellitus a Yes (n=75) p-value Age (years) 63 (56-69) 62 (56-71) 0.374 Male gender (%) 203 (74%) 55 (73%) 0.970 Body mass index (kg/m2) * 27 (25-29) 26 (24-30) 0.988 181 (66%) 51 (68%) 0.695 163 (59%) 47 (63%) 0.572 90 (33%) 29 (39%) 0.336 Smoking within the last year 87 (32%) 33 (44%) 0.043 Body mass index ≥25 (kg/m ) * 194 (72%) 45 (63%) 0.124 Prior myocardial infarction (%) † 13 (6%) 9 (14%) 0.035 Prior percutaneous coronary intervention (%) † 43 (19%) 10 (15%) 0.526 Left ventricular mass (gram) † 161 (139-179) 165 (150-184) 0.061 Agatston coronary calcium score** 227 (39-661) 259 (32-712) 0.965 Coronary CT angiography image quality e 3 (3-4) 3 (3-4) 0.585 Dyslipidemia c Family history of coronary artery disease d 2 M Hypertension b US CR Cardiovascular risk factors (%) IP T No (n=276) AN Variables PT ED Values are reported as median and 25th-75th percentile or an absolute number n and percentage (%). Differences were tested by Mann-Whitney-U-, Chi-square and Fisher’s t-tests. Data was not available * in 9 patients, ** in 37 patients and † in 53 patients. a defined as a fasting glucose level of 126 mg/dL (6.99 mmol/L) and higher, HbA1c >= 6.5% or use of antidiabetic medication. b defined as blood pressure >140 mmHg systolic, >90 mmHg diastolic, or use of antihypertensive medication. c defined as a total cholesterol of >200mg/dl or use of lipid-lowering therapy. d included known coronary artery disease, former CE myocardial infarction or any revascularizations in any first degree relative. e based on a 4-point Likert scale (1 non-diagnostic; 2 impaired image quality, differentiation of the coronary artery wall possible with reduced confidence; 3 adequate reduced image quality due to artifacts without limiting coronary artery wall differentiation; 4 excellent, no artifacts present an clear AC differentiation of the coronary artery wall. CT: computed tomography 19 ACCEPTED MANUSCRIPT Table 2: Per-vessel diagnostic performance Sensitivity Specificity PPV NPV Accuracy Non-diabetes 87% (82-93%) 37% (30-43%) 50% (44-56%) 80% (72-88%) 58% (53-63%) Diabetes 90% (80-100%) 48% (35-61%) 54% (41-66%) 88% (76-100%) 65% (55-75%) p-value 0.532 0.271 0.687 0.271 0.223 Non-diabetes 79% (73-86%) 72% (66-78%) 67% (60-74%) 83% (77-88%) 75% (71-80%) Diabetes 88% (77-98%) 80% (70-90%) 74% (62-87%) 91% (82-99%) 83% (76-90%) p-value 0.234 0.190 0.350 0.151 0.088 CT-FFR ≤0.80 US CR ≥50% IP T Coronary CT angiography Values are reported as percentage with 95% confidence interval. Differences are conducted by a multivariable logistic regression model including diabetes, smoking and prior myocardial infarction. CT: computed tomography; CT-FFR: computed tomography based fractional flow reserve; AC CE PT ED M AN NPV: negative predictive value; PPV: positive predictive value. 20