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Evaluation of Clinical Implementation of Machine Learning Based Decision Support for ICU Discharge

NCT05497505 · Status: UNKNOWN · Type: OBSERVATIONAL · Enrollment: 1500

Last updated 2022-08-11

No results posted yet for this study

Summary

Unexpected intensive care unit (ICU) readmission is associated with longer length of stay and increased mortality. Bedside decision support may prevent readmission and mortality and may allow optimizing ICU capacity. Using a recently developed and prospectively validated machine learning model that predicts ICU readmission and mortality rate after ICU discharge and shows trends in these predictions over time, we will evaluate the implementation of the European conformity (CE)-marked software based on this model (Pacmed Critical, Pacmed, Amsterdam) by investigating whether the software improves diagnostic accuracy compared to routine clinical evaluation by the treatment team and whether availability of the information from this software leads to changes in discharge management (either postponing or advancing discharge) for patients considered eligible for discharge.

Conditions

  • Critical Illness

Interventions

DEVICE

Pacmed Critical

For patients in the On-period, Pacmed Critical will be available as decision support after initial eligibility screening for ICU discharge by treatment team

Sponsors & Collaborators

  • Leiden University Medical Center

    collaborator OTHER

  • Patrick J. Thoral

    lead OTHER

Principal Investigators

  • Patrick J Thoral, MD · Amsterdam UMC, location VUmc

Eligibility

Min Age
18 Years
Sex
ALL
Healthy Volunteers
No

Timeline & Regulatory

Start
2022-03-10
Primary Completion
2023-06-30
Completion
2023-06-30

Countries

  • Netherlands

Study Locations

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Read the full study record

This page highlights key information. For complete eligibility criteria, study locations, investigator contacts, and the full protocol, visit the original record on ClinicalTrials.gov.

View NCT05497505 on ClinicalTrials.gov