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).

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Key information

Conditions

COVID-19 Pandemic COVID-19 Coronaviridae Infections Coronavirus Infections Infections Lung Diseases Nidovirales Infections Pneumonia Pneumonia, Viral RNA Virus Infections Respiratory Tract Diseases Respiratory Tract Infections Virus Diseases

Age range

18 year–98 year

Sex eligibility

All sexes

Study type

Observational

Primary location

Gaziantep University Hospital

Gaziantep, 27310, Turkey (Türkiye)

About this study

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.

Who can participate

Healthy volunteers accepted: No

Only the study team can determine whether someone qualifies for participation.

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)

Treatment and study plan

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.

Primary outcomes

All-cause ICU Mortality

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

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.

Sponsors and collaborators

Lead sponsor

University of Gaziantep

Other

Registry information

Important dates

Study start: 2024
Primary completion: 2025
Study completion: 2026
First posted: Feb 27, 2026
Registry last updated: Feb 27, 2026

OpenTrials presents study information sourced from ClinicalTrials.gov. The official registry record should be consulted for the latest information.

View the official ClinicalTrials.gov record (opens in a new tab)

This listing is for discovery and informational purposes only. It is not medical advice, does not guarantee that a study is recruiting, and does not determine eligibility. Contact the study team and a qualified healthcare professional when considering participation.

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