External Validation of AI-Aided Weaning Software Using Multicenter Retrospective Data
Using Multicenter Retrospective Data to Validate the Performance of AI-Aided Weaning Software
調査の概要
詳細な説明
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.
研究の種類
入学 (実際)
連絡先と場所
研究場所
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Taichung、台湾
- Taichung Veterans General Hospital
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参加基準
適格基準
就学可能な年齢
- 大人
- 高齢者
健康ボランティアの受け入れ
サンプリング方法
調査対象母集団
説明
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.
研究計画
研究はどのように設計されていますか？
デザインの詳細
コホートと介入
グループ/コホート |
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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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この研究は何を測定していますか？
主要な結果の測定
結果測定 |
メジャーの説明 |
時間枠 |
|---|---|---|
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Model Performance (AUROC)
時間枠：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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二次結果の測定
結果測定 |
メジャーの説明 |
時間枠 |
|---|---|---|
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Sensitivity
時間枠：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
時間枠：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
時間枠：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
時間枠：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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協力者と研究者
出版物と役立つリンク
研究記録日
主要日程の研究
研究開始 (実際)
一次修了 (実際)
研究の完了 (実際)
試験登録日
最初に提出
QC基準を満たした最初の提出物
最初の投稿 (実際)
学習記録の更新
投稿された最後の更新 (実際)
QC基準を満たした最後の更新が送信されました
最終確認日
詳しくは
本研究に関する用語
その他の研究ID番号
- TCVGH-AI-WEAN-2026
- TCVGH-AI-Weaning-2026 (その他の識別子：Taichung Veterans General Hospital)
個々の参加者データ (IPD) の計画
個々の参加者データ (IPD) を共有する予定はありますか?
医薬品およびデバイス情報、研究文書
米国FDA規制医薬品の研究
米国FDA規制機器製品の研究
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