Gram Type Infection-Specific Sepsis Identification Using Machine Learning

NCT03734484 | Withdrawn Phase 2 DMC

• 1 other identifier: 01072019
• Study Type: interventional
• Target: N/A
• Locations: 0 countries
• Sites: N/A

Brief 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.

Conditions

Sepsis; Severe Sepsis; Septic Shock

Keywords

Dascena; Gram infection; antibiotic administration; machine learning; algorithm; diagnostic

Outcome Measures

Primary Outcomes (1)

• 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 Outcomes (2)

• Change in administration of unnecessary antibiotics

  Through study completion, an average of 8 months

• Change in administration of unnecessary antibiotics

  Through study completion, an average of 8 months

Study Arms (2)

Gram type infection-specific algorithm

EXPERIMENTAL

The experimental arm will involve patients monitored by the Gram type infection-customized version of InSight.

Diagnostic Test: InSight

Standard treatment protocol

NO INTERVENTION

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.

Interventions

InSight DIAGNOSTIC_TEST

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.

Gram type infection-specific algorithm

Eligibility Criteria

• Age: 18 Years+
• Sex: all
• Healthy Volunteers: Yes
• Age Groups: Adult (18-64), Older Adult (65+)

You may qualify if:

• 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.

You may not qualify if:

• Under age 18
• No record of Gram infection

Sponsors & Collaborators

• Dascena: lead

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

  PMID: 27699003 BACKGROUND

• 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.

  PMID: 29435343 BACKGROUND

• 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.

  PMID: 29374661 BACKGROUND

MeSH Terms

Conditions

Sepsis; Shock, Septic; Disease

Condition Hierarchy (Ancestors)

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

Study Officials

• Ritankar Das, MSc

  Dascena

  PRINCIPAL INVESTIGATOR

Study Design

• Study Type: interventional
• Phase: phase 2
• Allocation: RANDOMIZED
• Masking: TRIPLE
• Who Masked: PARTICIPANT, CARE PROVIDER, INVESTIGATOR
• Purpose: DIAGNOSTIC
• Intervention Model: PARALLEL
• Sponsor Type: INDUSTRY
• Responsible Party: SPONSOR

Study Record Dates

• First Submitted: November 1, 2018
• First Posted: November 8, 2018
• Study Start: May 1, 2022
• Primary Completion: November 30, 2022
• Study Completion: March 1, 2023
• Last Updated: September 23, 2021

Record last verified: 2021-09