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Use of contrast‐enhanced computed tomography to detect kidney infarction in dogs
- Source :
- Journal of Veterinary Internal Medicine, Vol 36, Iss 1, Pp 164-170 (2022)
- Publication Year :
- 2022
- Publisher :
- Wiley, 2022.
-
Abstract
- Abstract Background Kidney infarction is a renovascular disease diagnosed by contrast‐enhanced computed tomography (CECT) in humans. Objectives To describe the frequency of kidney infarction and to determine the detection of kidney infarction with CECT in dogs. Animals Eight hundred and twenty‐six abdominal CECT studies of 826 dogs. Methods A cross‐sectional retrospective study. Dogs with abdominal CT scans including CECT were retrospectively retrieved. Kidney infarction was classified into 3 grades based on the extent of infarction relative to total kidney area. The location and number of kidney infarctions in each kidney were expressed as number and percentage. The ability of visualization of kidney infarction in each multiplanar reconstruction (MPR) image plane was evaluated by agreement of 2 observers. Results The frequency of kidney infarction in dogs was 3.15% (26/826 dogs; 95% CI = 2.05‐4.61). Most kidney infarctions were classified as grade 1, or the lesions were less than 25% of the kidney (47/56, 83.93%) and most were detected at the caudal pole of the kidney (31/56, 55.35%) on the sagittal plane. On MPR image planes, the sagittal plane had the highest proportion (34/56, 60.71%) of excellent visual category to detect kidney infarction. Conclusions and Clinical Importance The CECT, especially the sagittal plane, is a useful diagnostic tool for the detection of kidney infarction in dogs.
- Subjects :
- canine
contrast medium
CT
ischemia
kidney
Veterinary medicine
SF600-1100
Subjects
Details
- Language :
- English
- ISSN :
- 19391676 and 08916640
- Volume :
- 36
- Issue :
- 1
- Database :
- Directory of Open Access Journals
- Journal :
- Journal of Veterinary Internal Medicine
- Publication Type :
- Academic Journal
- Accession number :
- edsdoj.5e27ccdf02ee427e8e90ea475fb0ca4f
- Document Type :
- article
- Full Text :
- https://doi.org/10.1111/jvim.16343