Neural Network-Based Prediction in Critical COVID-19 Patients

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).

Study Overview

• Status: Completed
• Conditions: COVID-19 Pandemic
• Intervention / Treatment: Other: Artificial Neural Network (ANN) Analysis

Detailed Description

Coronavirus disease 2019 (COVID-19) has led to an unprecedented burden on intensive care units (ICUs), particularly due to high rates of respiratory failure requiring invasive mechanical ventilation. Early identification of patients at risk for prolonged mechanical ventilation and mortality is crucial for optimizing resource allocation and clinical decision-making.

This retrospective cohort study aimed to develop and evaluate an artificial neural network (ANN) model to predict mechanical ventilation duration and in-hospital mortality among COVID-19 patients admitted to the ICU.

After approval by the Gaziantep University Clinical Research Ethics Committee (Decision No: 2024/07, Date: 17.01.2024), data from 113 adult patients admitted to the ICU between February 1, 2020 and June 30, 2020 were retrospectively analyzed. Demographic characteristics, comorbidities, vital signs, laboratory parameters, severity scores (e.g., APACHE, SOFA), treatment modalities, and clinical outcomes were extracted from medical records.

Artificial neural network models were developed using commercially available software (Alyuda NeuroIntelligence, Alyuda Research Inc., Los Altos, CA, USA). Multiple training algorithms, including Quick Propagation, Conjugate Gradient Descent, Limited Memory Quasi-Newton, Online Backpropagation, and Batch Backpropagation, were tested. Model performance was evaluated using 10-fold cross-validation. Predictive accuracy for mortality and correlation performance for mechanical ventilation duration were calculated. Classical statistical methods, including multiple linear regression and binary logistic regression, were also performed for comparison.

The primary objective was to assess the predictive performance of ANN models for ICU mortality. A secondary objective was to evaluate ANN performance in estimating mechanical ventilation duration. This study was conducted in accordance with the Declaration of Helsinki.

• Study Type: Observational
• Enrollment (Actual): 113

Contacts and Locations

Study Locations

• Turkey (Türkiye)
  • Gaziantep, Turkey (Türkiye), 27310
    • Gaziantep University Hospital

Participation Criteria

Eligibility Criteria

• Ages Eligible for Study: Adult; Older Adult
• Accepts Healthy Volunteers: No

• 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.

Description

Inclusion Criteria:

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

Exclusion Criteria:

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)

Study Plan

How is the study designed?

What is the study measuring?

Primary Outcome Measures

Outcome Measure	Measure Description	Time Frame
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)

Collaborators and Investigators

• Sponsor: University of Gaziantep
• Investigators: Principal Investigator: Elzem Sen, Assoc Prof, University of Gaziantep

Study record dates

Study Major Dates

• Study Start (Actual): February 1, 2024
• Primary Completion (Actual): February 1, 2025
• Study Completion (Actual): January 2, 2026

Study Registration Dates

• First Submitted: February 21, 2026
• First Submitted That Met QC Criteria: February 21, 2026
• First Posted (Actual): February 27, 2026

Study Record Updates

• Last Update Posted (Actual): February 27, 2026
• Last Update Submitted That Met QC Criteria: February 21, 2026
• Last Verified: February 1, 2026

More Information

Terms related to this study

• Additional Relevant MeSH Terms: Respiratory Tract Infections; Infections; RNA Virus Infections; Virus Diseases; Respiratory Tract Diseases; Lung Diseases; Pneumonia, Viral; Pneumonia; Coronavirus Infections; Coronaviridae Infections; Nidovirales Infections; COVID-19
• Other Study ID Numbers: 2024/07

Plan for Individual participant data (IPD)

• Plan to Share Individual Participant Data (IPD)?: NO

Drug and device information, study documents

• Studies a U.S. FDA-regulated drug product: No
• Studies a U.S. FDA-regulated device product: No