External Validation of AI-Aided Weaning Software Using Multicenter Retrospective Data
Using Multicenter Retrospective Data to Validate the Performance of AI-Aided Weaning Software
연구 개요
상세 설명
Critical care generates a large amount of digitized clinical data that may benefit from artificial intelligence-assisted decision support. The AI-Aided Weaning Software was previously developed using ICU data from Taichung Veterans General Hospital collected between 2015 and 2019.
This retrospective multicenter validation study will evaluate the external performance of the established model using independent datasets from four hospitals in Taiwan, including Taichung Veterans General Hospital, Mackay Memorial Hospital, Kaohsiung Medical University Chung-Ho Memorial Hospital, and Tungs' Taichung MetroHarbor Hospital.
The study population includes adult ICU patients with respiratory failure who received mechanical ventilation for at least 72 hours between January 2020 and December 2024. De-identified routine clinical records will be collected according to a predefined case report form and analyzed centrally.
The primary objective is to assess the external validity of the AI-Aided Weaning Software across different hospitals. Model performance will be evaluated using sensitivity, specificity, accuracy, AUROC, and F1 score.
연구 유형
등록 (실제)
연락처 및 위치
연구 장소
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Taichung, 대만
- Taichung Veterans General Hospital
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참여기준
자격 기준
공부할 수 있는 나이
- 성인
- 고령자
건강한 자원 봉사자를 받아들입니다
샘플링 방법
연구 인구
설명
Inclusion Criteria:
- Adult patients aged 20 years or older.
- Admitted to the intensive care unit (ICU) at one of the participating hospitals between January 1, 2020 and December 31, 2024.
- Received invasive mechanical ventilation for at least 72 hours.
- Availability of de-identified clinical data required for model validation.
Exclusion Criteria:
- Patients who did not receive invasive mechanical ventilation.
- Duration of mechanical ventilation less than 72 hours.
- Missing key clinical variables required for model validation.
공부 계획
연구는 어떻게 설계됩니까?
디자인 세부사항
코호트 및 개입
그룹/코호트 |
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Mechanically Ventilated ICU Patients
Adult intensive care unit patients aged 20 years or older who received invasive mechanical ventilation for at least 72 hours between January 2020 and December 2024 at four participating hospitals.
Retrospective de-identified clinical data were used to validate the performance of AI-Aided Weaning Software.
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연구는 무엇을 측정합니까?
주요 결과 측정
결과 측정 |
측정값 설명 |
기간 |
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Model Performance (AUROC)
기간: Using data collected during ICU admission
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Area under the receiver operating characteristic curve (AUROC) for predicting successful extubation.
AUROC ranges from 0.5 to 1.0, with higher values indicating better discriminative performance of the prediction model.
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Using data collected during ICU admission
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2차 결과 측정
결과 측정 |
측정값 설명 |
기간 |
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Sensitivity
기간: ICU admission
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SensitivitySensitivity of the prediction model for successful extubation.
Sensitivity ranges from 0 to 1 (or 0% to 100%), with higher values indicating better identification of patients who achieve successful extubation.
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ICU admission
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Specificity
기간: ICU admission
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Specificity of the prediction model for successful extubation.
Specificity ranges from 0 to 1 (or 0% to 100%), with higher values indicating better identification of patients who do not achieve successful extubation.
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ICU admission
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Accuracy
기간: ICU admission
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Accuracy of the prediction model for successful extubation.
Accuracy ranges from 0 to 1 (or 0% to 100%), with higher values indicating better overall prediction performance.
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ICU admission
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F1 Score
기간: ICU admission
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F1 score of the prediction model for successful extubation.
F1 score ranges from 0 to 1, with higher values indicating better balance between precision and recall.
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ICU admission
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공동 작업자 및 조사자
간행물 및 유용한 링크
연구 기록 날짜
연구 주요 날짜
연구 시작 (실제)
기본 완료 (실제)
연구 완료 (실제)
연구 등록 날짜
최초 제출
QC 기준을 충족하는 최초 제출
처음 게시됨 (실제)
연구 기록 업데이트
마지막 업데이트 게시됨 (실제)
QC 기준을 충족하는 마지막 업데이트 제출
마지막으로 확인됨
추가 정보
이 연구와 관련된 용어
기타 연구 ID 번호
- TCVGH-AI-WEAN-2026
- TCVGH-AI-Weaning-2026 (기타 식별자: Taichung Veterans General Hospital)
개별 참가자 데이터(IPD) 계획
개별 참가자 데이터(IPD)를 공유할 계획입니까?
약물 및 장치 정보, 연구 문서
미국 FDA 규제 의약품 연구
미국 FDA 규제 기기 제품 연구
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