COVID-19 PandemicNCT07436572
Neural Network-Based Prediction in Critical COVID-19 Patients
An observational study of COVID-19 Pandemic, run by University of Gaziantep at 1 site in Turkey (Türkiye).
Trial 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… Read more about this study
- Who is it for?
- Ages 18 to 98 years
- Conditions the study is for
- COVID-19 Pandemic
- What is being tested?
- Artificial Neural Network (ANN) Analysis
- How many people
- 113 participants
- Where
- 1 siteTurkey (Türkiye)
- Study dates
- 1 Feb 2024to 2 Jan 2026
- Phase
- Observational
- Are there results?
- None posted in the registry
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 18 to 98 years
- All sexes
- No healthy volunteers
- Adult, Older adult
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.
Who may be able to join
- 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
Who cannot join
- 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)
Trial locations and contacts
1 site in 1 country.
Turkey (Türkiye)1 site
Gaziantep University Hospital
Gaziantep, 27310
About this study
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).
Read the 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.
- Conditions
- COVID-19 Pandemic
- Being tested
- Artificial Neural Network (ANN) Analysis
- Medical subject headings
- COVID-19
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
Artificial Neural Network (ANN) AnalysisOther
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.
What is being measured
The primary outcome is the main question the study is designed to answer.
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.
Measured: From ICU admission until hospital discharge or death (up to 90 days)
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted27 Feb 2026
- Study start1 Feb 2024
- Primary completion1 Feb 2025
- Study completion2 Jan 2026
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for COVID-19 PandemicStudies of the same condition, open and under way.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Cohort
- Time perspective
- Retrospective
- Enrolment
- 113 participants (actual)
Sponsor and investigators
- Lead sponsor
- University of Gaziantep
- Sponsor type
- University, hospital or other
- Responsible party
- Principal investigator, Elzem SEN, University of Gaziantep
- Investigators
- Elzem Sen, Assoc Prof, Principal investigator, University of Gaziantep
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT07436572
- Sponsor's protocol ID
- 2024/07
- Status last verified
- Feb 2026
- First submitted
- 21 Feb 2026
- Last updated
- 27 Feb 2026
- Results posted
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No
Study at a glance
From the registry record, last updated by the sponsor on 27 Feb 2026. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Neural Network-Based Prediction in Critical COVID-19 Patients
- ClinicalTrials.gov ID
- NCT07436572
- Acronym / sponsor ID
- 2024/07
- Conditions
- COVID-19 Pandemic
- Study type
- Observational
- Recruitment status
- Completed
- Enrolment
- 113 participants (actual)
- Lead sponsor
- University of GaziantepUniversity, hospital or other
- Study start
- 1 Feb 2024 (actual)
- Primary completion
- 1 Feb 2025 (actual)
- Study completion
- 2 Jan 2026 (actual)
- First posted
- 27 Feb 2026 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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).
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 design
- Observational model
- Cohort
- Time perspective
- Retrospective
Who may qualify
- Ages
- 18 to 98 years
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
- 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.
Key inclusion criteria (4 listed)
- 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
Key exclusion criteria (4 listed)
- 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)
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| Artificial Neural Network (ANN) AnalysisRetrospective 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. | Other | – |
Primary outcome
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.
Results and safety
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 27 Feb 2026.
- Individual participant dataSharing planned: No
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part. This study is not recruiting at the moment.
Elzem Sen, Assoc Prof principal investigator
University of Gaziantep
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of COVID-19 Pandemic, run by University of Gaziantep at 1 site in Turkey (Türkiye).
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 4 inclusion and 4 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: finished, as of the sponsor's last update on 27 Feb 2026. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in Turkey (Türkiye). Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
How can a doctor refer a patient or contact the research team?
The record lists no contact. The official record names the sponsor, who can be asked directly. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
All-cause ICU Mortality (time frame: From ICU admission until hospital discharge or death (up to 90 days)). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 27 Feb 2026.