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Use of contrast‐enhanced computed tomography to detect kidney infarction in dogs

Authors :
Somchin Sutthigran
Phasamon Saisawart
Auraiwan Klaengkaew
Kongthit Horoongruang
Nardtiwa Chaivoravitsakul
Kiatpichet Komin
Chutimon Thanaboonnipat
Nan Choisunirachon
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.

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