Den här översättningen är inte klar ännu. Den här sidan är just nu på engelska.

Gå till den engelska sidan

Cure My Disease

Don't miss your cure!

 Log in / Sign up

Can a machine learning model beat standard tools in spotting sepsis early?

NCT ID NCT07734480

Knowledge-focused Sponsor: Kocaeli Derince Education and Research Hospital Source: ClinicalTrials.gov ↗

What the study statuses mean

This study's is highlighted.

Recruitment status, easiest to join first

Recruiting now
This trial is taking on new participants right now.
Not yet recruiting
Registered, but not yet taking participants.
By invitation only
Not open to general applications. Only people the study team invites can take part.
Paused
Paused for now. It may or may not start again.
Ongoing This study
Running, but no longer taking on new participants.
Completed
The trial has finished. Results may not be published yet.
Stopped early
Stopped early, before it reached the end. That can be for many reasons, including safety.
Cancelled
Cancelled before anyone took part.

Expanded access (not trials)

Expanded access
Not a trial. This treatment can be requested outside a study, case by case, for people who qualify.
Expanded access (paused)
Not a trial. The treatment can normally be requested outside a study, but is unavailable right now.
Expanded access (ended)
Not a trial. The treatment could once be requested outside a study, but no longer can.
Approved
The treatment has been approved, so it is available normally rather than through this programme.

When the status isn't known

Details not published
The full record has not been published yet, so there is little to show here.
Status unknown
This status has not been confirmed recently, so it may be out of date.

First seen Jul 29, 2026 · Last updated Aug 14, 2026 · Updated 3 times

Summary

This study compares a standard early-warning system called NEWS2 with a machine-learning model to see which can detect sepsis earlier in hospital patients. Researchers will monitor 100 adults admitted with suspected infection, recording when each system first raises an alert and how that relates to the actual diagnosis. The goal is to determine whether the machine-learning approach can provide faster and more accurate warnings than the current standard.

What this could mean

Our plain-language read of the trial. This is informational only, not medical advice or a prediction.

What this could lead to
If the machine-learning model proves more accurate, it could lead to faster sepsis detection and better patient outcomes.
What could go wrong
This is an observational study, not a treatment trial. The machine-learning model may not perform better than the existing system in real-world conditions.

This is an AI summary of the original study and may miss details. Read our disclaimer.

Study facts

What this study's own registry entry says, in plain language.

Participants

About 100 people

The number the study aims to enrol. It can still change while the study runs.

Started

Jul 2026

Expected to finish

Aug 2026

An estimate. End dates often move.

Lead sponsor

Other sponsor

The registry's catch-all category, for sponsors it does not file as a company, a government agency, or a research network.

Who can take part

This study's own entry requirements. Only the study team can say for certain whether you qualify.

Who is studied

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.

Ages

18 years and older

Sex

Anyone

Healthy volunteers

Not accepted

This study is not open to healthy volunteers. The entry requirements below say who it is open to.

Show the full entry requirements

Copied word for word from the study's registry entry, so the wording is the study team's rather than ours.

Inclusion Criteria: * 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 Exclusion Criteria: * 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

Get updates

Get notified about this study

Sign up to get updates when this study changes or when new studies for Machine learning are added.

Vår säkerhetsrekommendation!

Genom att skicka in godkänner du våra Användarvillkor

Conditions

The condition(s) this trial relates to.

infectious disease with sepsis Sepsis

As listed by the trial registrant

The condition terms exactly as the trial's registrant entered them.

Machine learning News-2 Sepsis

Contacts and locations

Locations

  • Kocaeli City Hospital

    Kocaeli, Izmit, Turkey (Türkiye)

More trials for these conditions

Other studies related to the condition(s) this trial covers.