인문학
사회과학
자연과학
공학
의약학
농수해양학
예술체육학
복합학
DBpia AI
AI 에이전트
AI 아이디어
AI 리더
AI 뷰어
Citeasy
크롬 서지관리 다운로드
지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
초록·키워드
Information on the clinical outcomes of feline azotemia using ultrasound examinations is limited. This study aimed to understand the correlation between cortical anisotropy backscattering artifact (CABA) and serum creatinine (sCr) changes in feline azotemia after hospitalization and to investigate whether CABA is useful for predicting the clinical outcome of feline azotemia. Sixty-five hospitalized cats with azotemia, including 49 cats with moderate or severe azotemia (severe group) and 16 cats with mild azotemia (mild group). This retrospective study reviewed the CABA using ultrasound images of cats hospitalized with azotemia between 2016 and 2021. The correlation between CABA and the clinical outcomes of cats with azotemia was investigated using the chi-squared or Fisher's exact test, and the intra- and inter-observer agreements in CABA were assessed using McNemar's and Cohen's kappa tests. The presence of CABA was significantly positively correlated with the clinical outcomes of cats with azotemia only in the severe group (<i>p</i> = 0.0034, odds ratio = 8.57). There was no association between CABA and clinical outcomes in cats with mild azotemia (<i>p</i> = 0.75). CABA can be used for clinical outcome prediction in moderate and severe feline azotemia, with a sensitivity of 80.8% and a specificity of 73.9%. Also, satisfactory intra- and inter-observer agreements were revealed in the detection of CABA during ultrasound image review. Our study demonstrated that cats with moderate and severe azotemia with CABA observed during ultrasonography might have better clinical outcomes. These findings provide additional information on the prognosis and treatment of feline azotemia.
인공지능 문자 인식 모델을 통해 추출된 텍스트로, 일부 오타나 오류가 포함될 수 있으나 지속적으로 개선 중입니다.
오류를 발견하셨다면 해당 부분을 드래그한 후 ' 를 통해 신고해주세요.
오류를 발견하셨다면 해당 부분을 드래그한 후 ' 를 통해 신고해주세요.
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