Early Prediction of ICU Hypotension Using Machine Learning (ICU-HypoAI)
A Prospective Observational Machine Learning Study for the Early Prediction of Hypotension in Adult Intensive Care Unit Patients
調査の概要
詳細な説明
Hypotension is a frequent hemodynamic event in critically ill patients and may occur before clear clinical deterioration is recognized. Earlier identification of patients at risk may support closer clinical attention and more timely evaluation. This study is designed as a prospective, observational machine learning study in adult intensive care unit patients.
Routinely available ICU data will be collected at five-minute intervals, including systolic, mean, and diastolic non-invasive blood pressure, heart rate, medication-dose entries, and fluid-balance records. These data will be used to construct time-dependent features reflecting recent values, short-term changes, and rolling trends. Hypotension will be defined at each five-minute time point as systolic blood pressure below 90 mmHg, mean arterial pressure below 65 mmHg, or diastolic blood pressure below 60 mmHg.
The primary prediction horizon will be 30 minutes. Separate secondary analyses will evaluate 5- and 15-minute prediction horizons. A gradient-boosted decision-tree model will be developed and internally validated using patient-level data partitioning to avoid assigning observations from the same patient to both training and validation sets. Model performance will be assessed using discrimination, classification performance, and calibration measures. Feature-importance analyses will be used to describe the variables contributing to model predictions.
The study is observational. No treatment, medication, device, alarm, or clinical decision will be assigned by the study protocol. The prediction model will be developed and evaluated using collected data and will not be used to guide real-time patient management during the study period.
研究の種類
入学 (実際)
連絡先と場所
研究場所
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Kütahya
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Kütahya、Kütahya、トルコ（Türkiye）、43100
- Kutahya City 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:
- Age 18 years or older
- Admission to the adult intensive care unit during the study period
- Length of stay in the intensive care unit of at least 24 hours
- Availability of routine intensive care unit monitoring data
- Availability of non-invasive blood pressure and heart rate measurements recorded during ICU monitoring
- Availability of medication-dose and/or fluid-balance records during ICU monitoring
Exclusion Criteria:
- Age younger than 18 years
- Length of stay in the intensive care unit of less than 24 hours
- Absence of usable blood pressure monitoring data
- Records with irrecoverable timestamp inconsistencies
- Insufficient monitoring duration for feature construction and future outcome labeling
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 |
介入・治療 |
|---|---|
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Adult Intensive Care Unit Patients
Adult patients admitted to the intensive care unit who are monitored during routine clinical care.
Routinely collected non-invasive blood pressure, heart rate, medication-dose, and fluid-balance data will be used for machine learning model development and internal validation.
No treatment or clinical intervention will be assigned by the study protocol.
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Routinely collected intensive care unit data, including non-invasive blood pressure, heart rate, medication-dose records, and fluid-balance data, will be recorded and analyzed for development and internal validation of a machine learning model.
The study does not assign any treatment, medication, device, alarm, or clinical decision.
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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 |
|---|---|---|
|
Area Under the Receiver Operating Characteristic Curve for 30-Minute Hypotension Prediction
時間枠：From enrollment through the end of ICU monitoring, up to 4 months
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Discriminative performance of the machine learning model for predicting hypotension 30 minutes before its occurrence.
Hypotension will be defined as systolic blood pressure below 90 mmHg, mean arterial pressure below 65 mmHg, or diastolic blood pressure below 60 mmHg at a five-minute observation point.
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From enrollment through the end of ICU monitoring, up to 4 months
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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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Area Under the Receiver Operating Characteristic Curve for 5- and 15-Minute Hypotension Prediction
時間枠：From enrollment through the end of ICU monitoring, up to 4 months
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Discriminative performance of separate machine learning models for predicting hypotension at 5-minute and 15-minute prediction horizons.
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From enrollment through the end of ICU monitoring, up to 4 months
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Classification Performance of the Hypotension Prediction Model
時間枠：From enrollment through the end of ICU monitoring, up to 4 months
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Classification performance of the machine learning model will be assessed using sensitivity, specificity, positive predictive value, negative predictive value, and F1 score at predefined classification thresholds.
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From enrollment through the end of ICU monitoring, up to 4 months
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協力者と研究者
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
この情報は、Web サイト clinicaltrials.gov から変更なしで直接取得したものです。研究の詳細を変更、削除、または更新するリクエストがある場合は、register@clinicaltrials.gov。 までご連絡ください。 clinicaltrials.gov に変更が加えられるとすぐに、ウェブサイトでも自動的に更新されます。