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
Induction of general anesthesia is often associated with significant hemodynamic changes, particularly in blood pressure (BP). These early postinduction fluctuations can vary widely among patients and contribute to perioperative complications. Current clinical approaches to managing postinduction BP changes are largely reactive and may not fully account for individual variability. This study aimed to identify distinct patterns of mean arterial pressure (MAP) response during the first 10 min following induction of general anesthesia, using a time-series clustering approach. We conducted a retrospective cohort study of 17,645 adult patients undergoing non-cardiac, non-obstetric inpatient surgery under general anesthesia at a tertiary medical center. BP was measured at 1 min intervals using either invasive arterial lines (8.3% of cases) or standard non-invasive oscillometric cuffs. An unsupervised X-means clustering algorithm with dynamic time warping was applied to identify recurring MAP trajectory patterns. Patient demographics, comorbidities, anesthetic drug doses, and other perioperative characteristics were compared across clusters. Five distinct MAP trajectories were identified: Initial Decline-Plateau (31.8%), Gradual Moderate Decline (18.4%), Initial Decline-Recovery (7.5%), Gradual Severe Decline (29.6%), and Initial Decline-Low Plateau (12.7%). These patterns differed significantly in baseline MAP, comorbidity profiles and antihypertensive use, while differences in anesthetic agent doses were statistically but not clinically meaningful. Distinct postinduction BP trajectories were identified using a time-series clustering approach. These findings provide a framework for future validation in datasets with richer clinical context.
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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