Gram Type Infection-Specific Sepsis Identification Using Machine Learning

Sepsis Clinical Trial

Official title:

Gram Type Infection-Specific Sepsis Identification Using Machine Learning

Clinical Trial Details — Status: Withdrawn

Administrative data

• NCT number: NCT03734484
• Other study ID #: 01072019
• Status: Withdrawn
• Phase: Phase 2
• Start date: May 1, 2022
• Est. completion date: March 1, 2023

Study information

• Verified date: September 2021
• Source: Dascena
• Contact: n/a
• Is FDA regulated: No
• Study type: Interventional

Clinical Trial Summary

The focus of this study will be to conduct a prospective, randomized controlled trial (RCT) at Cape Regional Medical Center (CRMC), Oroville Hospital (OH), and UCSF Medical Center (UCSF) in which a Gram type infection-specific algorithm will be applied to EHR data for the detection of severe sepsis. For patients determined to have a high risk of severe sepsis, the algorithm will generate automated voice, telephone notification to nursing staff at CRMC, OH, and UCSF. The algorithm's performance will be measured by analysis of the primary endpoint, time to antibiotic administration. The secondary endpoint will be reduction in the administration of unnecessary antibiotics, which includes reductions in secondary antibiotics and reductions in total time on antibiotics.

Recruitment information / eligibility

• Status: Withdrawn
• Enrollment: 0
• Est. completion date: March 1, 2023
• Est. primary completion date: November 30, 2022
• Accepts healthy volunteers: Accepts Healthy Volunteers
• Gender: All
• Age group: 18 Years and older

Eligibility

Inclusion Criteria:

• All adults above age 18 who are a member of one of the three subpopulations studied in this trial (patients with Gram-positive infection, patients with Gram-negative infection, and patients with mixed Gram-positive and Gram-negative infection) are eligible to participate in the study.

Exclusion Criteria:

• Under age 18

• No record of Gram infection

Related Conditions & MeSH terms

• Infections
• Sepsis
• Septic Shock
• Severe Sepsis
• Toxemia

Intervention

Diagnostic Test:
• InSight
The InSight algorithm which draws information from a patient's electronic health record (EHR) to predict the onset of severe sepsis, and in this study will be customized to differentiate between various Gram-type infections.

Locations

Country	Name	City	State
n/a

Sponsors (1)

Lead Sponsor	Collaborator
Dascena

References & Publications (3)

Calvert J, Mao Q, Hoffman JL, Jay M, Desautels T, Mohamadlou H, Chettipally U, Das R. Using electronic health record collected clinical variables to predict medical intensive care unit mortality. Ann Med Surg (Lond). 2016 Sep 6;11:52-57. eCollection 2016 Nov. — View Citation

Mao Q, Jay M, Hoffman JL, Calvert J, Barton C, Shimabukuro D, Shieh L, Chettipally U, Fletcher G, Kerem Y, Zhou Y, Das R. Multicentre validation of a sepsis prediction algorithm using only vital sign data in the emergency department, general ward and ICU. BMJ Open. 2018 Jan 26;8(1):e017833. doi: 10.1136/bmjopen-2017-017833. — View Citation

Shimabukuro DW, Barton CW, Feldman MD, Mataraso SJ, Das R. Effect of a machine learning-based severe sepsis prediction algorithm on patient survival and hospital length of stay: a randomised clinical trial. BMJ Open Respir Res. 2017 Nov 9;4(1):e000234. doi: 10.1136/bmjresp-2017-000234. eCollection 2017. — View Citation

Outcome

Type	Measure	Description	Time frame	Safety issue
Primary	Change in time to antibiotic administration	Change in time period between diagnosis of Gram infection and administration of antibiotics to treat infection	Through study completion, an average of 8 months
Secondary	Change in administration of unnecessary antibiotics	Changes in amount of secondary antibiotics administered	Through study completion, an average of 8 months
Secondary	Change in administration of unnecessary antibiotics	Changes in total hours spent on antibiotics	Through study completion, an average of 8 months