SepsisSEW-MLNCT07734480
Comparison of NEWS2 and Machine Learning for Early Sepsis Warning
An observational study of Sepsis and News-2, run by Kocaeli Derince Education and Research Hospital at 1 site in Turkey (Türkiye).
Trial summary
This prospective observational study aims to objectively measure the lead-time (the time from the first KDS alert to sepsis diagnosis) of the NEWS2-based clinical decision support system (KDS) and compare its early warning performance with a machine learning model trained on 2000 patients and externally validated. Read more about this study
- Who is it for?
- Ages 18 years and older
- Conditions the study is for
- Sepsis, News-2, Machine Learning
- How many people
- 100 participants
- Where
- 1 siteTurkey (Türkiye)
- Study dates
- 22 Jul 2026to 13 Aug 2026
- Phase
- Observational
- Are there results?
- Not yet: the study is under way
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 years and older
- All sexes
- No healthy volunteers
- Adult, Older adult
Study population: Adult patients (≥18 years) with suspected infection admitted to the Anesthesiology and Reanimation Clinic of Kocaeli City Hospital. Patients are enrolled prospectively if they meet the inclusion criteria and provide informed consent.
Who may be able to join
- Patients aged 18 years and older
- Admitted to Kocaeli City Hospital Anesthesiology and Reanimation Clinic
- Suspected infection at the time of hospital admission
- Complete vital signs recorded
- Informed consent obtained from the patient or legal representative
Who cannot join
- Patients under 18 years of age
- Pregnant patients
- Patients with chronic kidney disease requiring dialysis
- Patients with a history of organ transplantation
- Patients with incomplete data
Trial locations and contacts
1 site in 1 country.
About this study
Official title: Comparison of NEWS2 and a Machine Learning Model for Early Sepsis Warning: A Prospective Observational Study
This prospective observational study aims to objectively measure the lead-time (the time from the first KDS alert to sepsis diagnosis) of the NEWS2-based clinical decision support system (KDS) and compare its early warning performance with a machine learning model trained on 2000 patients and externally validated. The study seeks to answer the following main questions:
How early does the NEWS2-based KDS provide an alert before sepsis diagnosis?
Read more
Does a machine learning model, developed using logistic regression and externally validated in a prospective cohort, offer superior specificity and comparable sensitivity to KDS?
Participants who are already receiving routine clinical care at Kocaeli City Hospital will have their vital signs and laboratory data monitored as part of standard practice. NEWS2 scores will be calculated automatically and the time of the first alert (T0) will be recorded. Sepsis diagnosis will be confirmed by an increase in SOFA score ≥ 2 (T1), evaluated by two independent and blinded physicians. Lead-time will be calculated as the difference between T1 (hours×60) and T0 (minutes). The machine learning model will be tested prospectively on this cohort, and its performance will be compared with KDS using sensitivity, specificity, F1 score, ROC-AUC, and accuracy.
Read the detailed description
Sepsis is a life-threatening organ dysfunction caused by a dysregulated host response to infection. Early recognition and treatment are critical for improving outcomes. The National Early Warning Score 2 (NEWS2) is widely used as an early warning system, but its lead-time (the time from alert to diagnosis) has not been objectively measured in prospective studies. This study aims to fill this gap by prospectively evaluating the lead-time of NEWS2-based KDS and comparing its performance with a machine learning model. The machine learning model was developed using 2000 patients from the PhysioNet Sepsis Prediction Challenge 2019 database and externally validated on a prospective cohort of 100 patients from Kocaeli City Hospital. The study will provide evidence on the comparative utility of traditional warning systems and machine learning approaches for early sepsis detection.
- Conditions
- Sepsis, News-2, Machine Learning
- Being tested
- Observational Study - No Intervention
- Keywords
- Sepsis, Clinical Decision Support System, Machine Learning, NEWS2, Early Warning, lead time
- Medical subject headings
- Sepsis
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
Observational Study - No InterventionOther
Observational Study - No Intervention
What is being measured
The primary outcome is the main question the study is designed to answer.
Lead-Time Calculated from NEWS2 KDS Alert and SOFA Score Change
Lead-time is defined as the time from the first KDS alert (NEWS2 ≥ 5) to the diagnosis of sepsis, confirmed by an increase in SOFA score ≥ 2 points. It is calculated using the formula: Lead-Time (minutes) = [T1 (hours × 60)] - T0 (minutes), where T0 is the time of the first NEWS2 measurement and T1 is the time of first SOFA increase ≥ 2.
Measured: From hospital admission to sepsis diagnosis, death, or discharge, whichever occurs first, assessed up to 14 days
Secondary outcomes (1)
Predictive Performance of Machine Learning Model vs KDS
Measured: Within 14 days of hospital admission
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted29 Jul 2026
- Study start22 Jul 2026
- Primary completion11 Aug 2026
- Study completion13 Aug 2026 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for SepsisStudies 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
- Prospective
- Enrolment
- 100 participants (estimated)
Sponsor and investigators
- Lead sponsor
- Kocaeli Derince Education and Research Hospital
- Sponsor type
- University, hospital or other
- Responsible party
- Principal investigator, feyza özkan, Kocaeli Derince Education and Research Hospital
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT07734480
- Sponsor's protocol ID
- 2025-144
- Acronym
- SEW-ML
- Status last verified
- Aug 2026
- First submitted
- 21 Jul 2026
- Last updated
- 13 Aug 2026
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No: Individual participant data will not be shared due to institutional ethics committee restrictions and the absence of specific consent from participants for data sharing. Only anonymized summary data will be reported in the publication.
Study at a glance
From the registry record, last updated by the sponsor on 13 Aug 2026. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Comparison of NEWS2 and a Machine Learning Model for Early Sepsis Warning: A Prospective Observational Study
- ClinicalTrials.gov ID
- NCT07734480
- Acronym / sponsor ID
- SEW-ML / 2025-144
- Conditions
- Sepsis, News-2, Machine Learning
- Keywords
- Sepsis, Clinical Decision Support System, Machine Learning, NEWS2, Early Warning, lead time
- Study type
- Observational
- Recruitment status
- Active, not recruiting
- Enrolment
- 100 participants (estimated)
- Lead sponsor
- Kocaeli Derince Education and Research HospitalUniversity, hospital or other
- Study start
- 22 Jul 2026 (actual)
- Primary completion
- 11 Aug 2026 (actual)
- Study completion
- 13 Aug 2026 (estimated)
- First posted
- 29 Jul 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.
This prospective observational study aims to objectively measure the lead-time (the time from the first KDS alert to sepsis diagnosis) of the NEWS2-based clinical decision support system (KDS) and compare its early warning performance with a machine learning model trained on 2000 patients and externally validated. The study seeks to answer the following main questions:
How early does the NEWS2-based KDS provide an alert before sepsis diagnosis?
Does a machine learning model, developed using logistic regression and externally validated in a prospective cohort, offer superior specificity and comparable sensitivity to KDS?
Participants who are already receiving routine clinical care at Kocaeli City Hospital will have their vital signs and laboratory data monitored as part of standard practice. NEWS2 scores will be calculated automatically and the time of the first alert (T0) will be recorded. Sepsis diagnosis will be confirmed by an increase in SOFA score ≥ 2 (T1), evaluated by two independent and blinded physicians. Lead-time will be calculated as the difference between T1 (hours×60) and T0 (minutes). The machine learning model will be tested prospectively on this cohort, and its performance will be compared with KDS using sensitivity, specificity, F1 score, ROC-AUC, and accuracy.
Detailed description
Sepsis is a life-threatening organ dysfunction caused by a dysregulated host response to infection. Early recognition and treatment are critical for improving outcomes. The National Early Warning Score 2 (NEWS2) is widely used as an early warning system, but its lead-time (the time from alert to diagnosis) has not been objectively measured in prospective studies. This study aims to fill this gap by prospectively evaluating the lead-time of NEWS2-based KDS and comparing its performance with a machine learning model. The machine learning model was developed using 2000 patients from the PhysioNet Sepsis Prediction Challenge 2019 database and externally validated on a prospective cohort of 100 patients from Kocaeli City Hospital. The study will provide evidence on the comparative utility of traditional warning systems and machine learning approaches for early sepsis detection.
Study design
- Observational model
- Cohort
- Time perspective
- Prospective
Who may qualify
- Ages
- 18 years and older
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
- Study population
- Adult patients (≥18 years) with suspected infection admitted to the Anesthesiology and Reanimation Clinic of Kocaeli City Hospital. Patients are enrolled prospectively if they meet the inclusion criteria and provide informed consent.
Key inclusion criteria (5 listed)
- Patients aged 18 years and older
- Admitted to Kocaeli City Hospital Anesthesiology and Reanimation Clinic
- Suspected infection at the time of hospital admission
- Complete vital signs recorded
- Informed consent obtained from the patient or legal representative
Key exclusion criteria (5 listed)
- Patients under 18 years of age
- Pregnant patients
- Patients with chronic kidney disease requiring dialysis
- Patients with a history of organ transplantation
- Patients with incomplete data
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 |
|---|---|---|
| Observational Study - No InterventionObservational Study - No Intervention | Other | – |
Primary outcome
Lead-Time Calculated from NEWS2 KDS Alert and SOFA Score Change
Time frame: From hospital admission to sepsis diagnosis, death, or discharge, whichever occurs first, assessed up to 14 days
Lead-time is defined as the time from the first KDS alert (NEWS2 ≥ 5) to the diagnosis of sepsis, confirmed by an increase in SOFA score ≥ 2 points. It is calculated using the formula: Lead-Time (minutes) = [T1 (hours × 60)] - T0 (minutes), where T0 is the time of the first NEWS2 measurement and T1 is the time of first SOFA increase ≥ 2.
1 secondary outcome
Predictive Performance of Machine Learning Model vs KDS
Time frame: Within 14 days of hospital admission
Results and safety
This study is still under way. Results are not available in the source record yet.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 13 Aug 2026.
- Individual participant dataSharing planned: No. Individual participant data will not be shared due to institutional ethics committee restrictions and the absence of specific consent from participants for data sharing. Only anonymized summary data will be reported in the publication.
Related trials for Sepsis
Not yet recruitingPhase 2/3
A Pivotal Safety and Immunogenicity GBS-NN/NN2 Vaccine Maternal Immunisation Trial
Streptococcus Agalactiae Infection, Neonatal Sepsis
Active, not recruiting
CT for Personalized Mechanical Ventilation
Ventilator-Induced Lung Injury, Sepsis Syndrome
RecruitingPhase 4
A Study to Compare the Efficacy and Safety of Extended and Intermittent Infusion of Beta-lactams in Critically Ill Paediatric Patients
Infection, Sepsis
Not yet recruiting
Double Plasma Separation and Adsorption in Acute-on-Chronic Liver Failure (DPMAS-ACLF Trial)
Acute-on-Chronic Liver Failure (ACLF)
Active, not recruitingPhase 4
Thymosin Alpha 1 and IgM Levels in Sepsis
Sepsis, Septic Shock
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.
The record lists no contact. Use the official record to reach the sponsor.
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 Sepsis and News-2, run by Kocaeli Derince Education and Research Hospital 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 5 inclusion and 5 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: under way, not looking for participants, as of the sponsor's last update on 13 Aug 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?
This study is still under way. Results are not available in the source record yet.
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?
Lead-Time Calculated from NEWS2 KDS Alert and SOFA Score Change (time frame: From hospital admission to sepsis diagnosis, death, or discharge, whichever occurs first, assessed up to 14 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 13 Aug 2026.