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
To develop a model for early identification of coagulopathy in septic patients. Patients with sepsis were identified from the Medical Information Mart for Intensive Care (MIMIC)-IV database. Patients who did not meet the sepsis-induced coagulopathy (SIC) scoring criteria upon admission but developed SIC within the subsequent 7 days were considered to be in a pre-SIC state at baseline. Baseline clinical features of the patients were screened by lasso regression. Subsequently, these features underwent multivariate logistic regression for model construction, followed by testing the stability of the model in the test set. A total of 7,806 patients were included in the study from the MIMIC-IV database, comprising 7,080 without SIC and 726 with pre-SIC. Patients with pre-SIC had higher criticality scores compared to patients with Non-SIC. Pre-SIC was identified as an independent risk factor for hospitalization, 28-day, 90-day, and 1-year mortality in patients with sepsis. Patients with pre-SIC who received early heparin had lower 28-day mortality compared to those without treatment. The SIC scoring system demonstrated a sensitivity of 77.0% for identifying pre-SIC, a specificity of 53.9%, and an AUC of 0.694 (95% CI: 0.659–0.730). Based on SIC scoring system, additional clinical features were added to the pre-SIC model, ultimately yielding 70% sensitivity and 76.2% specificity with an AUC of 0.802 (95% CI: 0.773–0.830) in the validation set. The development of SIC is associated with increased mortality rate in patients with sepsis, and precise identification of this group of patients and individualized treatment may be important for improving prognosis.
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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