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
<h2>Abstract</h2><h3>Objectives</h3> A candidaemia risk prediction model by Keighley <i>et al.</i> stratified patients with candidaemia into <20% or ≥20% predicted 30-day all-cause mortality based on age >65 years, intensive care unit admission, chronic organ dysfunction, no recent surgery, haematological malignancy, source of candidaemia, and prolonged antibiotic therapy. We aimed to validate this model in a contemporary patient cohort. <h3>Methods</h3> This retrospective cohort study was conducted at a 671-bed tertiary hospital comprising both abdominal solid organ and allogeneic stem cell transplant services (Melbourne, Australia). All adult inpatients with <i>Candida</i> spp. isolated from blood cultures from 2018 to 2023 were included. Model performance was evaluated using logistic regression and area under the receiver operating characteristic curve. <h3>Results</h3> A total of 121 patients with candidaemia were identified, of whom 40 (33%) died within 30 days. The median mortality risk score was 3 (interquartile range, 2.5–4.5). The risk score demonstrated good discriminative ability in predicting mortality in our cohort (area under the receiver operating characteristic curve, 0.759), with a score of 2 providing a discriminative cut off (11% mortality score ≤2, 43% mortality score >2, and negative predictive value 89.2%). Patients aged >65 years, intensive care unit admission, and a gastrointestinal or unknown source of candidaemia were independently associated with mortality. <h3>Conclusions</h3> The candidaemia mortality risk predictive model by Keighley <i>et al.</i> performed well in our contemporary and complex patient population. This model can be utilized to risk-stratify patients into low and high mortality risk to assist with candidaemia management and antifungal stewardship.
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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