Development of a Scoring and Prediction Model for Weaning Success in ARDS Patients Using Ventilation Parameters Combined with Artificial Intelligence and Deep Learning Techniques

NCT ID: NCT06751693

Last Updated: 2024-12-30

Study Results

Results pending

The study team has not published outcome measurements, participant flow, or safety data for this trial yet. Check back later for updates.

Basic Information

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Recruitment Status

COMPLETED

Total Enrollment

25000 participants

Study Classification

OBSERVATIONAL

Study Start Date

2024-12-10

Study Completion Date

2024-12-24

Brief Summary

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This study aims to develop an AI-supported scoring model to optimize the weaning processes of ARDS patients from mechanical ventilation. Retrospective analysis will be conducted on the data of 25,000 patients, focusing on ventilator parameters and hemodynamic variables. The model will be designed to contribute to clinical decision support systems.

Detailed Description

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The aim of this study is to develop an artificial intelligence and deep learning-supported scoring system using ventilator parameters obtained during the mechanical ventilation process in patients diagnosed with ARDS. This system seeks to predict and optimize the weaning process, facilitating successful liberation from mechanical ventilation.

In this context, our study will analyze data from 25,000 patients obtained from the Metavision system. From this data pool, ARDS patients will be filtered and divided into two groups: those successfully weaned from mechanical ventilation (weaned) and those who were not (non-weaned). The ventilator parameters of both groups, including oxygenation indices, driving pressure, and total mechanical power, will be examined in detail.

The collected data will be analyzed using artificial intelligence and deep learning algorithms to develop a scoring system capable of predicting patients' weaning processes. This system is designed to guide clinicians in patient management and enhance the success of weaning procedures.

The results of this study aim to contribute to more efficient and safer management of the weaning process for ARDS patients. Furthermore, the implementation of AI-supported scoring systems in intensive care units is expected to promote widespread adoption and improve the quality of patient care.

Conditions

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Deep Learning Artificial Intelegence ARDS (Acute Respiratory Distress Syndrome)

Keywords

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Deep Learning

Study Design

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Observational Model Type

COHORT

Study Time Perspective

RETROSPECTIVE

Study Groups

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Weaned

Those successfully weaned from mechanical ventilation

No interventions assigned to this group

Non-weaned

Those who not weaned from mechanical ventilation

No interventions assigned to this group

Eligibility Criteria

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Inclusion Criteria

* ARDS diagnosis
* Aged 18 years and older
* Intubated and followed by Mechanical ventilation
* Admission on Intensive care unit
* Complete data on clinical support and desicion system

Exclusion Criteria

* Missing data
* Under 18 years of age
* Followed by non-ARDS conditions
* Terminal status
Minimum Eligible Age

18 Years

Eligible Sex

ALL

Accepts Healthy Volunteers

No

Sponsors

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Bakirkoy Dr. Sadi Konuk Research and Training Hospital

OTHER_GOV

Sponsor Role lead

Responsible Party

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Responsibility Role SPONSOR

Principal Investigators

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Zafer Cukurova, M.D

Role: STUDY_CHAIR

Bakırkoy Dr. Sadi Konuk Training and Research Hospital

Locations

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Bakirkoy Dr Sadi Konuk Research and Training Hospital

Istanbul, , Turkey (Türkiye)

Site Status

Countries

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Turkey (Türkiye)

Other Identifiers

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2024-12-07

Identifier Type: -

Identifier Source: org_study_id