Early Prediction of Sepsis (ExPRESS)

• ClinicalTrials.gov Identifier: NCT04570618
• Recruitment Status: Recruiting
• First Posted: September 30, 2020
• Last Update Posted: December 7, 2020
• Sponsor: AlgoDx
• Information provided by (Responsible Party): AlgoDx

Study Description

Brief Summary:

In this clinical trial a novel Medical Device Software will be validated prospectively. The software incorporates a machine learning algorithm capable of predicting sepsis by using routine clinical variables in adult patients at Intensive Care Units.

Condition or disease	Intervention/treatment	Phase
Sepsis	Device: Unblinded AlgoDx Sepsis Prediction Algorithm; Other: Blinded AlgoDx Sepsis Prediction Algorithm	Not Applicable

Study Design

• Study Type: Interventional (Clinical Trial)
• Estimated Enrollment: 300 participants
• Allocation: Randomized
• Intervention Model: Parallel Assignment
• Masking: Triple (Participant, Care Provider, Investigator)
• Primary Purpose: Diagnostic
• Official Title: Early Prediction of Sepsis in Hospitalized Patients Using a Machine Learning Algorithm, a Randomized Clinical Validation Trial.
• Actual Study Start Date: December 1, 2020
• Estimated Primary Completion Date: March 1, 2021
• Estimated Study Completion Date: March 1, 2021

Arms and Interventions

Arm	Intervention/treatment
Sham Comparator: Standard of Care; Subjects are monitored for potential development of sepsis according to the local established clinical management guidelines.	Other: Blinded AlgoDx Sepsis Prediction Algorithm; Standard of Care, i.e. no sepsis prediction alert.
Experimental: Standard of Care + AlgoDx Sepsis Prediction Algorithm; Subjects are monitored for potential development of sepsis according to the local established clinical management guidelines, and sepsis prediction algorithm alerts are unblinded to clinical staff.	Device: Unblinded AlgoDx Sepsis Prediction Algorithm; When applicable, a sepsis prediction alert is displayed in the AlgoDx Medical Device Software.

Outcome Measures

Primary Outcome Measures :

1. Validate the prognostic accuracy of the algorithm at predicting sepsis. [ Time Frame: Up to 30 days (ICU hospitalization period) ]

   In order to clinically validate the sepsis prediction performance the following endpoints have been selected:

   • accuracy,
   • specificity, and
   • sensitivity of the AlgoDx Sepsis Prediction Algorithm in the SoC group (not possible to assess these in the SoC + Algorithm cohort).

Eligibility Criteria

• Ages Eligible for Study: 18 Years and older (Adult, Older Adult)
• Sexes Eligible for Study: All
• Accepts Healthy Volunteers: No

Criteria

Inclusion Criteria:

1. Adult patient (age ≥18 years).
2. Patient is admitted to the ICU during the recruitment period of the trial.

Exclusion Criteria:

1. Patient is participating in another interventional clinical trial which, as judged by the investigator, could potentially impact variables used by the sepsis prediction algorithm or has participated in such interventional clinical trial within the last 30 days.
2. Patient is known to be pregnant.
3. Death is deemed imminent and inevitable, at the investigator's discretion.
4. Patient has, due to chronic reduced mental capacity, been assessed by the investigator as incapable of making an informed decision
5. Patient has previously been enrolled in this trial.

Contacts and Locations

Locations

Sweden

Intensiv- och perioperativ vård; Recruiting

Malmö, Sweden, 20502

Contact: Fredrik Sjövall, MD PhD +4640337606 fredrik.sjovall@med.lu.se

Sponsors and Collaborators

AlgoDx

More Information

• Responsible Party: AlgoDx
• ClinicalTrials.gov Identifier: NCT04570618
• Other Study ID Numbers: SEP-SE-02
• First Posted: September 30, 2020
• Last Update Posted: December 7, 2020
• Last Verified: December 2020

Individual Participant Data (IPD) Sharing Statement:

• Plan to Share IPD: No

• Studies a U.S. FDA-regulated Drug Product: No
• Studies a U.S. FDA-regulated Device Product: No

Keywords provided by AlgoDx:

Prediction

Additional relevant MeSH terms:

Sepsis; Toxemia; Infection; Systemic Inflammatory Response Syndrome; Inflammation; Pathologic Processes