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
연구 개요
상세 설명
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.
연구 유형
등록 (실제)
연락처 및 위치
연구 장소
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Kütahya
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Kütahya, Kütahya, 터키 (Türkiye), 43100
- Kütahya City 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:
- 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
공부 계획
연구는 어떻게 설계됩니까?
디자인 세부사항
코호트 및 개입
그룹/코호트 |
개입 / 치료 |
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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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연구는 무엇을 측정합니까?
주요 결과 측정
결과 측정 |
측정값 설명 |
기간 |
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Area Under the Receiver Operating Characteristic Curve for 30-Minute Hypotension Prediction
기간: 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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2차 결과 측정
결과 측정 |
측정값 설명 |
기간 |
|---|---|---|
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Area Under the Receiver Operating Characteristic Curve for 5- and 15-Minute Hypotension Prediction
기간: 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
기간: 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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공동 작업자 및 조사자
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 정보는 변경 없이 clinicaltrials.gov 웹사이트에서 직접 가져온 것입니다. 귀하의 연구 세부 정보를 변경, 제거 또는 업데이트하도록 요청하는 경우 register@clinicaltrials.gov. 문의하십시오. 변경 사항이 clinicaltrials.gov에 구현되는 즉시 저희 웹사이트에도 자동으로 업데이트됩니다. .