Neural Network-Based Prediction in Critical COVID-19 Patients

NCT07436572 | Completed

• 1 other identifier: 2024/07
• Study Type: observational
• Target: 113
• Locations: 1 country
• Sites: 1

Brief Summary

In the context of an emerging pandemic without an established prognostic scoring system, deep learning approaches can be used to quickly develop empirical prognostic models. This study aimed to present an artificial neural network (ANN) model to predict the duration of mechanical ventilation and mortality in COVID-19 patients at the intensive care unit. Methods: Data were collected from medical records of 113 COVID-19 patients who had followed up at the intensive care unit between February 2020 and June 2020. An ANN approach was used to predict the length of mechanical ventilation and mortality in COVID-19 patients by evaluating patients' clinical data (demographic, laboratory, and comorbidities).

Conditions

COVID-19 Pandemic

Outcome Measures

Primary Outcomes (1)

• All-cause ICU Mortality

  Prediction of in-hospital mortality (ex-status) among COVID-19 patients admitted to the intensive care unit using artificial neural network modeling based on demographic, clinical, and laboratory variables.

  From ICU admission until hospital discharge or death (up to 90 days)

Interventions

Artificial Neural Network (ANN) Analysis OTHER

Retrospective analysis of routinely collected clinical data using artificial neural network (ANN) algorithms to predict mortality and mechanical ventilation duration in ICU patients with COVID-19. No therapeutic intervention was applied to participants.

Eligibility Criteria

• Age: 18 Years - 98 Years
• Sex: all
• Healthy Volunteers: No
• Age Groups: Adult (18-64), Older Adult (65+)
• Sampling Method: Non-Probability Sample

Study Population

Adult patients diagnosed with COVID-19 and admitted to the intensive care unit (ICU) of Gaziantep University Faculty of Medicine between February 1, 2020 and June 30, 2020. The study includes patients aged 18 years and older whose demographic, clinical, laboratory, and outcome data were available for retrospective analysis.

You may qualify if:

• Age ≥ 18 years
• Confirmed diagnosis of COVID-19
• Admission to the intensive care unit (ICU) between February 1, 2020 and June 30, 2020
• Availability of complete clinical, laboratory, and outcome data in medical records

You may not qualify if:

• Age \< 18 years
• Incomplete or missing clinical data
• Transfer to another institution before outcome assessment
• Readmission to ICU during the same hospitalization (only first admission included)

Sponsors & Collaborators

• University of Gaziantep: lead

Study Sites (1)

Gaziantep University Hospital

Gaziantep, 27310, Turkey (Türkiye)

Location

MeSH Terms

Conditions

COVID-19

Condition Hierarchy (Ancestors)

Pneumonia, Viral; Pneumonia; Respiratory Tract Infections; Infections; Virus Diseases; Coronavirus Infections; Coronaviridae Infections; Nidovirales Infections; RNA Virus Infections; Lung Diseases; Respiratory Tract Diseases

Study Officials

• Elzem Sen, Assoc Prof

  University of Gaziantep

  PRINCIPAL INVESTIGATOR

Study Design

• Study Type: observational
• Observational Model: COHORT
• Time Perspective: RETROSPECTIVE
• Sponsor Type: OTHER
• Responsible Party: PRINCIPAL INVESTIGATOR
• PI Title: Assoc. Prof.

Study Record Dates

• First Submitted: February 21, 2026
• First Posted: February 27, 2026
• Study Start: February 1, 2024
• Primary Completion: February 1, 2025
• Study Completion: January 2, 2026
• Last Updated: February 27, 2026

Record last verified: 2026-02

Data Sharing

• IPD Sharing: Will not share

Locations

TU (1)