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
Visão geral do estudo
Descrição detalhada
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.
Tipo de estudo
Inscrição (Real)
Contactos e Locais
Locais de estudo
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Kütahya
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Kütahya, Kütahya, Turquia (Türkiye), 43100
- Kütahya City Hospital
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Critérios de participação
Critérios de elegibilidade
Idades elegíveis para estudo
- Adulto
- Adulto mais velho
Aceita Voluntários Saudáveis
Método de amostragem
População do estudo
Descrição
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
Plano de estudo
Como o estudo é projetado?
Detalhes do projeto
Coortes e Intervenções
Grupo / Coorte |
Intervenção / Tratamento |
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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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O que o estudo está medindo?
Medidas de resultados primários
Medida de resultado |
Descrição da medida |
Prazo |
|---|---|---|
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Area Under the Receiver Operating Characteristic Curve for 30-Minute Hypotension Prediction
Prazo: 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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Medidas de resultados secundários
Medida de resultado |
Descrição da medida |
Prazo |
|---|---|---|
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Area Under the Receiver Operating Characteristic Curve for 5- and 15-Minute Hypotension Prediction
Prazo: 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
Prazo: 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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Colaboradores e Investigadores
Patrocinador
Datas de registro do estudo
Datas Principais do Estudo
Início do estudo (Real)
Conclusão Primária (Real)
Conclusão do estudo (Real)
Datas de inscrição no estudo
Enviado pela primeira vez
Enviado pela primeira vez que atendeu aos critérios de CQ
Primeira postagem (Real)
Atualizações de registro de estudo
Última Atualização Postada (Real)
Última atualização enviada que atendeu aos critérios de controle de qualidade
Última verificação
Mais Informações
Termos relacionados a este estudo
Palavras-chave
Termos MeSH relevantes adicionais
Outros números de identificação do estudo
- 2026/03-15
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