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Early Prediction of ICU Hypotension Using Machine Learning (ICU-HypoAI)

7 juli 2026 uppdaterad av: Serkan TELLİ, Kutahya Health Sciences University

A Prospective Observational Machine Learning Study for the Early Prediction of Hypotension in Adult Intensive Care Unit Patients

This prospective observational study aims to develop and internally validate a machine learning model for the early prediction of hypotension in adult intensive care unit patients. The model will use routinely collected non-invasive vital signs, heart rate, medication-dose records, and fluid-balance data recorded during standard ICU care. No intervention will be assigned by the study, and patient management will not be changed according to the model output. The primary aim is to predict hypotension 30 minutes before its occurrence; shorter 5- and 15-minute prediction horizons will also be evaluated.

Studieöversikt

Status

Avslutad

Betingelser

Intervention / Behandling

Detaljerad beskrivning

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.

Studietyp

Observationell

Inskrivning (Faktisk)

107

Kontakter och platser

Det här avsnittet innehåller kontaktuppgifter för dem som genomför studien och information om var denna studie genomförs.

Studieorter

  • Turkiet (Türkiye)
    • Kütahya
      • Kütahya, Kütahya, Turkiet (Türkiye), 43100
        • Kütahya City Hospital

Deltagandekriterier

Forskare letar efter personer som passar en viss beskrivning, så kallade behörighetskriterier. Några exempel på dessa kriterier är en persons allmänna hälsotillstånd eller tidigare behandlingar.

Urvalskriterier

Åldrar som är berättigade till studier

  • Vuxen
  • Äldre vuxen

Tar emot friska volontärer

Nej

Testmetod

Icke-sannolikhetsprov

Studera befolkning

The study population will consist of consecutive adult patients monitored in the adult intensive care unit of Kutahya Health Sciences University during the study period. Routine clinical monitoring data, including non-invasive blood pressure, heart rate, medication-dose records, and fluid-balance data, will be used for machine learning model development and internal validation. No treatment or intervention will be assigned by the study protocol.

Beskrivning

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

Studieplan

Det här avsnittet ger detaljer om studieplanen, inklusive hur studien är utformad och vad studien mäter.

Hur är studien utformad?

Designdetaljer

Kohorter och interventioner

Grupp / Kohort
Intervention / Behandling
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.
Övrig: Routine ICU Data Collection
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.

Vad mäter studien?

Primära resultatmått

Resultatmått
Åtgärdsbeskrivning
Tidsram
Area Under the Receiver Operating Characteristic Curve for 30-Minute Hypotension Prediction
Tidsram: From enrollment through the end of ICU monitoring, up to 4 months
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.
From enrollment through the end of ICU monitoring, up to 4 months

Sekundära resultatmått

Resultatmått
Åtgärdsbeskrivning
Tidsram
Area Under the Receiver Operating Characteristic Curve for 5- and 15-Minute Hypotension Prediction
Tidsram: From enrollment through the end of ICU monitoring, up to 4 months
Discriminative performance of separate machine learning models for predicting hypotension at 5-minute and 15-minute prediction horizons.
From enrollment through the end of ICU monitoring, up to 4 months
Classification Performance of the Hypotension Prediction Model
Tidsram: From enrollment through the end of ICU monitoring, up to 4 months
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.
From enrollment through the end of ICU monitoring, up to 4 months

Samarbetspartners och utredare

Det är här du hittar personer och organisationer som är involverade i denna studie.

Sponsor

Kutahya Health Sciences University

Studieavstämningsdatum

Dessa datum spårar framstegen för inlämningar av studieposter och sammanfattande resultat till ClinicalTrials.gov. Studieposter och rapporterade resultat granskas av National Library of Medicine (NLM) för att säkerställa att de uppfyller specifika kvalitetskontrollstandarder innan de publiceras på den offentliga webbplatsen.

Studera stora datum

Studiestart (Faktisk)

15 mars 2026

Primärt slutförande (Faktisk)

30 juni 2026

Avslutad studie (Faktisk)

30 juni 2026

Studieregistreringsdatum

Först inskickad

31 maj 2026

Först inskickad som uppfyllde QC-kriterierna

31 maj 2026

Första postat (Faktisk)

4 juni 2026

Uppdateringar av studier

Senaste uppdatering publicerad (Faktisk)

9 juli 2026

Senaste inskickade uppdateringen som uppfyllde QC-kriterierna

7 juli 2026

Senast verifierad

1 juli 2026

Mer information

Termer relaterade till denna studie

Nyckelord

Ytterligare relevanta MeSH-villkor

Andra studie-ID-nummer

  • 2026/03-15

Plan för individuella deltagardata (IPD)

Planerar du att dela individuella deltagardata (IPD)?

NEJ

Läkemedels- och apparatinformation, studiedokument

Studerar en amerikansk FDA-reglerad läkemedelsprodukt

Nej

Studerar en amerikansk FDA-reglerad produktprodukt

Nej

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