SEPSIS EARLY WARNING: NEWS2 VS MACHINE LEARNING

NCT07734480 | Recruiting

• 1 other identifier: 2025-144
• Study Type: observational
• Target: 100
• Locations: 1 country
• Sites: 1

Brief 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. 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.

Conditions

Sepsis; News-2; Machine Learning

Keywords

Sepsis; Clinical Decision Support System; Machine Learning; NEWS2; Early Warning; lead time

Outcome Measures

Primary Outcomes (1)

• Lead-Time of NEWS2 KDS in Sepsis

  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

  Within 14 days of hospital admission

Interventions

Observational Study - No Intervention OTHER

Observational Study - No Intervention

Eligibility Criteria

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

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.

You may qualify if:

• 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

You may not qualify if:

• 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

Sponsors & Collaborators

• Kocaeli Derince Education and Research Hospital: lead

Study Sites (1)

Kocaeli City Hospital

Kocaeli, Izmit, Turkey (Türkiye)

RECRUITING

MeSH Terms

Conditions

Sepsis

Condition Hierarchy (Ancestors)

Infections; Systemic Inflammatory Response Syndrome; Inflammation; Pathologic Processes; Pathological Conditions, Signs and Symptoms

Central Study Contacts

feyza özkan, specialist doctor

CONTACT

+905456010099; feyza__ozkan@hotmail.com

ahmet yüksek, Assoc. Prof.

CONTACT

+905326580351; ahmetyuksek@yandex.com

Study Design

• Study Type: observational
• Observational Model: COHORT
• Time Perspective: PROSPECTIVE
• Sponsor Type: OTHER
• Responsible Party: PRINCIPAL INVESTIGATOR
• PI Title: Anestesiology and reanimation Doctor Principal Investigator

Study Record Dates

• First Submitted: July 21, 2026
• First Posted: July 29, 2026
• Study Start: July 22, 2026
• Primary Completion (Estimated): August 2, 2026
• Study Completion (Estimated): August 9, 2026
• Last Updated: July 29, 2026

Record last verified: 2026-07

Data Sharing

• IPD Sharing: Will not share

Locations

TU (1)