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A Metric for Quantification of Iodine Contrast Enhancement (Q-ICE) in Computed Tomography. J Comput Assist Tomogr 2021; 45:870-876. [PMID: 34469906 DOI: 10.1097/rct.0000000000001215] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/02/2023]
Abstract
BACKGROUND Poor contrast enhancement is related to issues with examination execution, contrast prescription, computed tomography (CT) protocols, and patient conditions. Currently, our community has no metric to monitor true enhancement on routine single-phase examinations because this requires knowledge of both pre- and postcontrast CT number. PURPOSE We propose an automatable solution to quantifying contrast enhancement without requiring a dedicated noncontrast series. METHODS The difference in CT number between a target region in an enhanced and unenhanced image defines the metric "quantification of iodine contrast enhancement" (Q-ICE). Quantification of iodine contrast enhancement uses the noncontrast bolus tracking baseline image from routine abdominal examinations, which mitigates the need for a dedicated noncontrast series. We applied this method retrospectively to 312 patient livers from 2 sites between 2017 and 2020. Each site used a weight-based contrast injection protocol for weights 60 to 113 kg and a constant volume less than 60 kg and greater than 113 kg. Hypothesis testing was performed to compare Q-ICE between sites and detect Q-ICE dependence on weight and kilovoltage (kV). RESULTS Mean Q-ICE differed between sites (P = 0.004) by 4.96 Hounsfield unit with 95% confidence interval (1.63-8.28), albeit this difference was roughly 2 times smaller than the SD in Q-ICE across patients at a single site. For patients between 60 and 113 kg, we did not observe evidence of Q-ICE varying with patient weight (P = 0.920 and 0.064 for 120 and 140 kV, respectively). The Q-ICE did vary with patient weight for patients less than 60 kg (P = 0.003) and greater than 113 kg (P = 0.04). We observed a roughly 10 Hounsfield unit reduction in Q-ICE liver for patients scanned with 140 versus 120 kV. We observed several underenhancing examinations with an arterial phase appearance motivating our CT protocol optimization team to consider increasing the delay for slowly enhancing patients. CONCLUSIONS A quality metric for quantifying CT contrast enhancement was developed and suggested tangible opportunities for quality improvement and potential financial savings.
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Martinez G, Katz JM, Pandya A, Wang JJ, Boltyenkov A, Malhotra A, Mushlin AI, Sanelli PC. Cost-Effectiveness Study of Initial Imaging Selection in Acute Ischemic Stroke Care. J Am Coll Radiol 2021; 18:820-833. [PMID: 33387454 PMCID: PMC8186007 DOI: 10.1016/j.jacr.2020.12.013] [Citation(s) in RCA: 24] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2020] [Revised: 12/04/2020] [Accepted: 12/06/2020] [Indexed: 12/18/2022]
Abstract
PURPOSE National guidelines recommend prompt identification of candidates for acute ischemic stroke (AIS) treatment, requiring timely neuroimaging with CT and/or MRI. CT is often preferred because of its widespread availability and rapid acquisition. Despite higher diagnostic accuracy of MRI, it commonly involves complex workflows that could potentially cause treatment time delays. The purpose of this study was to analyze the impact on outcomes of imaging utilization before treatment decisions at comprehensive stroke centers for patients presenting with suspected AIS in the anterior circulation with last-known-well-to-arrival time 0 to 24 hours. METHODS A decision simulation model based on the American Heart Association's recommendations for AIS care pathways was developed from a health care perspective to compare initial imaging strategies: (1) stepwise-CT: noncontrast CT (NCCT) at the time of presentation, with CT angiography (CTA) ± CT perfusion (CTP) only in select patients (initial imaging to exclude hemorrhage and extensive ischemia) for mechanical thrombectomy (MT) evaluation; (2) stepwise-hybrid: NCCT at the time of presentation, with MR angiography (MRA) ± MR perfusion (MRP) only for MT evaluation; (3) stepwise-advanced: NCCT + CTA at presentation, with MR diffusion-weighted imaging (MR DWI) + MRP only for MT evaluation; (4) comprehensive-CT: NCCT + CTA + CTP at the time of presentation; and (5) comprehensive-MR: MR DWI + MRA + MRP at the time of presentation. Model parameters were defined using evidence-based data. Cost-effectiveness and sensitivity analyses were performed. RESULTS The cost-effectiveness analyses revealed that comprehensive-CT and comprehensive-MR yield the highest lifetime quality-adjusted life-years (QALYs) (4.81 and 4.82, respectively). However, the incremental cost-effectiveness ratio of comprehensive-MR is $233,000/QALY compared with comprehensive-CT. Stepwise-CT, stepwise-hybrid, and stepwise-advanced strategies are dominated, yielding lower QALYs and higher costs compared with comprehensive-CT. CONCLUSIONS Performing comprehensive-CT at presentation is the most cost-effective initial imaging strategy at comprehensive stroke centers.
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Affiliation(s)
- Gabriela Martinez
- Siemens Healthineers, Malvern, Pennsylvania; Department of Radiology, Northwell Health, Manhasset, New York; Feinstein Institutes for Medical Research, Manhasset, New York.
| | - Jeffrey M Katz
- Chief, Neurovascular Services and Director Comprehensive Stroke Center at North Shore University Hospital, Department of Neurology, North Shore University Hospital, Manhasset, New York; Director of Neuroendovascular surgery, Neurology Service Line, Northwell Health, Manhasset, New York; Donald and Barbara Zucker School of Medicine at Hofstra/Northwell, Hempstead, New York
| | - Ankur Pandya
- T. H. Trustee (unpaid), Society for Medical Decision Making, T.H Chan School of Public Health, Harvard University, Boston, Massachusetts
| | - Jason J Wang
- Feinstein Institutes for Medical Research, Manhasset, New York; Donald and Barbara Zucker School of Medicine at Hofstra/Northwell, Hempstead, New York
| | - Artem Boltyenkov
- Siemens Healthineers, Malvern, Pennsylvania; Department of Radiology, Northwell Health, Manhasset, New York; Feinstein Institutes for Medical Research, Manhasset, New York
| | - Ajay Malhotra
- Radiology & Biomedical Imaging, Yale School of Medicine, New Haven, Connecticut
| | - Alvin I Mushlin
- Healthcare Policy and Research, Weill Cornell Medical College, New York, New York
| | - Pina C Sanelli
- Department of Radiology, Northwell Health, Manhasset, New York; Feinstein Institutes for Medical Research, Manhasset, New York; Radiology & Biomedical Imaging, Yale School of Medicine, New Haven, Connecticut; Healthcare Policy and Research, Weill Cornell Medical College, New York, New York; Vice Chair of Research, Department of Radiology, Donald and Barbara Zucker School of Medicine at Hofstra/Northwell, Hempstead, New York
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