Clinical trial NCT06751693: Development of a Scoring and Prediction Model for Weaning Success in ARDS Patients Using Ventilation Parameters Combined with Artificial Intelligence and Deep Learning Techniques
Official title: Development of a Scoring and Prediction Model for Weaning Success in ARDS Patients Using Ventilation Parameters Combined with Artificial Intelligence and Deep Learning Techniques
Study type: OBSERVATIONAL
Overall status: COMPLETED
Conditions: Deep Learning
Artificial Intelegence
ARDS (Acute Respiratory Distress Syndrome)
Keywords: Deep Learning
Lead sponsor: Bakirkoy Dr. Sadi Konuk Research and Training Hospital
Sponsor class: OTHER_GOV
First posted: 2024-12-30
Enrollment: 25000
Sex: ALL
Minimum age (years): 18
Standard ages: ADULT
OLDER_ADULT
Accepts healthy volunteers: no
Observational model: COHORT
Time perspective: RETROSPECTIVE
Sampling method: NON_PROBABILITY_SAMPLE
Study population: Intubated patients which diagnosed with ARDS and followed up in the ICU for more than 48 hours.

Brief summary: 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: 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.

Eligibility criteria: 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

Primary outcomes:
measure: Successful Weaning
timeFrame: 48 hours
Secondary outcomes:
measure: Mechanical Ventilatory Parameters
timeFrame: 48 hours
Other outcomes:
Locations:
city: Istanbul
country: Turkey
facility: Bakirkoy Dr Sadi Konuk Research and Training Hospital
Study officials:
affiliation: Bakırkoy Dr. Sadi Konuk Training and Research Hospital
name: Zafer Cukurova, M.D
role: STUDY_CHAIR
References: