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

7. juli 2026 oppdatert 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.

Studieoversikt

Status

Fullført

Forhold

Intervensjon / Behandling

Detaljert beskrivelse

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.

Studietype

Observasjonsmessig

Registrering (Faktiske)

107

Kontakter og plasseringer

Denne delen inneholder kontaktinformasjon for de som utfører studien, og informasjon om hvor denne studien blir utført.

Studiesteder

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

Deltakelseskriterier

Forskere ser etter personer som passer til en bestemt beskrivelse, kalt kvalifikasjonskriterier. Noen eksempler på disse kriteriene er en persons generelle helsetilstand eller tidligere behandlinger.

Kvalifikasjonskriterier

Alder som er kvalifisert for studier

  • Voksen
  • Eldre voksen

Tar imot friske frivillige

Nei

Prøvetakingsmetode

Ikke-sannsynlighetsprøve

Studiepopulasjon

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.

Beskrivelse

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

Denne delen gir detaljer om studieplanen, inkludert hvordan studien er utformet og hva studien måler.

Hvordan er studiet utformet?

Designdetaljer

Kohorter og intervensjoner

Gruppe / Kohort
Intervensjon / 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.
Annen: 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.

Hva måler studien?

Primære resultatmål

Resultatmål
Tiltaksbeskrivelse
Tidsramme
Area Under the Receiver Operating Characteristic Curve for 30-Minute Hypotension Prediction
Tidsramme: 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

Sekundære resultatmål

Resultatmål
Tiltaksbeskrivelse
Tidsramme
Area Under the Receiver Operating Characteristic Curve for 5- and 15-Minute Hypotension Prediction
Tidsramme: 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
Tidsramme: 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

Samarbeidspartnere og etterforskere

Det er her du vil finne personer og organisasjoner som er involvert i denne studien.

Sponsor

Kutahya Health Sciences University

Studierekorddatoer

Disse datoene sporer fremdriften for innsending av studieposter og sammendragsresultater til ClinicalTrials.gov. Studieposter og rapporterte resultater gjennomgås av National Library of Medicine (NLM) for å sikre at de oppfyller spesifikke kvalitetskontrollstandarder før de legges ut på det offentlige nettstedet.

Studer hoveddatoer

Studiestart (Faktiske)

15. mars 2026

Primær fullføring (Faktiske)

30. juni 2026

Studiet fullført (Faktiske)

30. juni 2026

Datoer for studieregistrering

Først innsendt

31. mai 2026

Først innsendt som oppfylte QC-kriteriene

31. mai 2026

Først lagt ut (Faktiske)

4. juni 2026

Oppdateringer av studieposter

Sist oppdatering lagt ut (Faktiske)

9. juli 2026

Siste oppdatering sendt inn som oppfylte QC-kriteriene

7. juli 2026

Sist bekreftet

1. juli 2026

Mer informasjon

Begreper knyttet til denne studien

Nøkkelord

Ytterligere relevante MeSH-vilkår

Andre studie-ID-numre

  • 2026/03-15

Plan for individuelle deltakerdata (IPD)

Planlegger du å dele individuelle deltakerdata (IPD)?

NEI

Legemiddel- og utstyrsinformasjon, studiedokumenter

Studerer et amerikansk FDA-regulert medikamentprodukt

Nei

Studerer et amerikansk FDA-regulert enhetsprodukt

Nei

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