Ben R Saleem1, Paul Berger2, Ilonca Vaartjes3, Bart de Keizer4, Evert-Jan P A Vonken5, Riemer H J A Slart6, Gert Jan de Borst2, Clark J Zeebregts7. 1. Division of Vascular Surgery, Department of Surgery, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands. 2. Division of Vascular Surgery, Department of Surgery, University Medical Center Utrecht, Utrecht, The Netherlands. 3. Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht, The Netherlands. 4. Department of Nuclear Medicine, University Medical Center Utrecht, Utrecht, The Netherlands. 5. Department of Radiology, University Medical Center Utrecht, Utrecht, The Netherlands. 6. Department of Nuclear Medicine and Molecular Imaging, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands. 7. Division of Vascular Surgery, Department of Surgery, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands. Electronic address: czeebregts@hotmail.com.
Abstract
BACKGROUND: The clinical dilemma in suspected aortic graft infection (AGI) is how to noninvasively obtain a reliable proof of infection. In addition to confirming the presence of infection, obtaining information regarding the extent of infection to select a proper strategy for reoperation is also necessary. Therefore, developing a more reliable noninvasive physiologic approach to detect infected prostheses is required. (18)F-fluorodeoxyglucose positron emission tomography scanning ((18)F-FDG PET) has been suggested to have a pivotal role in the detection of AGI. In this study, we assessed the contribution of two (semi) quantitative parameters-maximal standardized uptake value (SUVmax) and tissue-to-background ratio (TBR)-and of two visual parameters-fluorodeoxyglucose (FDG) distribution patterns and visual grading scale-in the final confirmation of the diagnosis of AGI. METHODS: Patients with a central aortic prosthetic graft and symptoms clinically suggestive of AGI were gathered from a prospectively maintained database. Included were those who underwent (18)F-FDG PET scanning combined with computed tomography angiography and in whom periprosthetic samples were taken at some stage in the diagnostic process. AGI was considered proven in case of a positive culture and compared with a group with negative cultures. Positive predictive values (PPVs) and negative predictive values (NPVs) were calculated. Receiver operating characteristics curves were used to assess the ability of SUVmax and TBR to identify the presence and absence of AGI (ie, accuracy). RESULTS: In 37 of 77 patients with suspected AGI, (18)FDG-PET and perigraft material for culturing was obtained. The tissue culture was positive in 21 of these 37 patients (56.7%). Mean ± standard deviation SUVmax for proven infection was 8.1 ± 3.7 (range, 3.6-18.5) and TBR was 5.9 ± 2.7 (range, 1.7-13.0). The area under the curve for SUVmax was 0.78 (95% confidence interval, 0.63-0.93). A cutoff value of 8 yielded a PPV of 80% and a NPV of 54%. The area under the curve for TBR was 0.70 (95% confidence interval, 0.52-0.87). A cutoff value of 6 yielded a PPV of 73% and NPV of 52%. The PPVs for the visual grading scale and (18)F-FDG distribution patterns were 75% and 61%, respectively; the NPVs were 77% and 67%, respectively. CONCLUSIONS: Our study, performed in a small sample of patients suspected of AGI, showed that the diagnostic abilities of quantitative and visual (18)F-FDG PET parameters are modest.
BACKGROUND: The clinical dilemma in suspected aortic graft infection (AGI) is how to noninvasively obtain a reliable proof of infection. In addition to confirming the presence of infection, obtaining information regarding the extent of infection to select a proper strategy for reoperation is also necessary. Therefore, developing a more reliable noninvasive physiologic approach to detect infected prostheses is required. (18)F-fluorodeoxyglucose positron emission tomography scanning ((18)F-FDG PET) has been suggested to have a pivotal role in the detection of AGI. In this study, we assessed the contribution of two (semi) quantitative parameters-maximal standardized uptake value (SUVmax) and tissue-to-background ratio (TBR)-and of two visual parameters-fluorodeoxyglucose (FDG) distribution patterns and visual grading scale-in the final confirmation of the diagnosis of AGI. METHODS:Patients with a central aortic prosthetic graft and symptoms clinically suggestive of AGI were gathered from a prospectively maintained database. Included were those who underwent (18)F-FDG PET scanning combined with computed tomography angiography and in whom periprosthetic samples were taken at some stage in the diagnostic process. AGI was considered proven in case of a positive culture and compared with a group with negative cultures. Positive predictive values (PPVs) and negative predictive values (NPVs) were calculated. Receiver operating characteristics curves were used to assess the ability of SUVmax and TBR to identify the presence and absence of AGI (ie, accuracy). RESULTS: In 37 of 77 patients with suspected AGI, (18)FDG-PET and perigraft material for culturing was obtained. The tissue culture was positive in 21 of these 37 patients (56.7%). Mean ± standard deviation SUVmax for proven infection was 8.1 ± 3.7 (range, 3.6-18.5) and TBR was 5.9 ± 2.7 (range, 1.7-13.0). The area under the curve for SUVmax was 0.78 (95% confidence interval, 0.63-0.93). A cutoff value of 8 yielded a PPV of 80% and a NPV of 54%. The area under the curve for TBR was 0.70 (95% confidence interval, 0.52-0.87). A cutoff value of 6 yielded a PPV of 73% and NPV of 52%. The PPVs for the visual grading scale and (18)F-FDG distribution patterns were 75% and 61%, respectively; the NPVs were 77% and 67%, respectively. CONCLUSIONS: Our study, performed in a small sample of patients suspected of AGI, showed that the diagnostic abilities of quantitative and visual (18)F-FDG PET parameters are modest.
Authors: Lars Husmann; Martin W Huellner; Bruno Ledergerber; Alexia Anagnostopoulos; Paul Stolzmann; Bert-Ram Sah; Irene A Burger; Zoran Rancic; Barbara Hasse Journal: Eur J Nucl Med Mol Imaging Date: 2018-11-13 Impact factor: 9.236
Authors: Ben R Saleem; Roelof J Beukinga; Ronald Boellaard; Andor W J M Glaudemans; Michel M P J Reijnen; Clark J Zeebregts; Riemer H J A Slart Journal: Eur J Nucl Med Mol Imaging Date: 2016-12-24 Impact factor: 9.236
Authors: Neval E Wareham; J D Lundgren; C Da Cunha-Bang; F Gustafsson; M Iversen; H H Johannesen; A Kjær; A Rasmussen; H Sengeløv; S S Sørensen; B M Fischer Journal: Eur J Nucl Med Mol Imaging Date: 2016-11-12 Impact factor: 9.236
Authors: Riemer H J A Slart; Andor W J M Glaudemans; Olivier Gheysens; Mark Lubberink; Tanja Kero; Marc R Dweck; Gilbert Habib; Oliver Gaemperli; Antti Saraste; Alessia Gimelli; Panagiotis Georgoulias; Hein J Verberne; Jan Bucerius; Christoph Rischpler; Fabien Hyafil; Paola A Erba Journal: Eur Heart J Cardiovasc Imaging Date: 2020-12-01 Impact factor: 6.875