Gram Type Infection-Specific Sepsis Identification Using Machine Learning

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.

Study Overview

• Status: Withdrawn
• Conditions: Sepsis; Septic Shock; Severe Sepsis
• Intervention / Treatment: Diagnostic test: InSight

• Study Type: Interventional
• Phase: Phase 2

Participation Criteria

Eligibility Criteria

• Ages Eligible for Study: 18 years and older (ADULT, OLDER_ADULT)
• Accepts Healthy Volunteers: Yes
• Genders Eligible for Study: All

Description

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

Study Plan

How is the study designed?

Design Details

• Primary Purpose: DIAGNOSTIC
• Allocation: RANDOMIZED
• Interventional Model: PARALLEL
• Masking: TRIPLE

Number of Arms

2

Arms and Interventions

Participant Group / Arm	Intervention / Treatment
EXPERIMENTAL: Gram type infection-specific algorithm; The experimental arm will involve patients monitored by the Gram type infection-customized version of InSight.	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.
NO_INTERVENTION: Standard treatment protocol; The control arm will involve patients treated with the regular diagnosis and treatment protocol for gram-type infection, where fluid cultures are run to determine infection type.

What is the study measuring?

Primary Outcome Measures

Outcome Measure	Measure Description	Time Frame
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 Outcome Measures

Outcome Measure	Measure Description	Time Frame
Change in administration of unnecessary antibiotics	Changes in amount of secondary antibiotics administered	Through study completion, an average of 8 months
Change in administration of unnecessary antibiotics	Changes in total hours spent on antibiotics	Through study completion, an average of 8 months

Collaborators and Investigators

• Sponsor: Dascena

Publications and helpful links

General Publications

• 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. doi: 10.1016/j.amsu.2016.09.002. eCollection 2016 Nov.
• 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.
• 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.

Study record dates

Study Major Dates

• Study Start (ANTICIPATED): May 1, 2022
• Primary Completion (ANTICIPATED): November 30, 2022
• Study Completion (ANTICIPATED): March 1, 2023

Study Registration Dates

• First Submitted: November 1, 2018
• First Submitted That Met QC Criteria: November 6, 2018
• First Posted (ACTUAL): November 8, 2018

Study Record Updates

• Last Update Posted (ACTUAL): September 23, 2021
• Last Update Submitted That Met QC Criteria: September 17, 2021
• Last Verified: September 1, 2021

More Information

Terms related to this study

• Keywords: machine learning; algorithm; diagnostic; Dascena; Gram infection; antibiotic administration
• Additional Relevant MeSH Terms: Pathologic Processes; Systemic Inflammatory Response Syndrome; Inflammation; Sepsis; Toxemia; Infections
• Other Study ID Numbers: 01072019

Drug and device information, study documents

• Studies a U.S. FDA-regulated drug product: No
• Studies a U.S. FDA-regulated device product: No