Clinical trial NCT03734484: Gram Type Infection-Specific Sepsis Identification Using Machine Learning
Official title: Gram Type Infection-Specific Sepsis Identification Using Machine Learning
Study type: INTERVENTIONAL
Overall status: WITHDRAWN
Phase: PHASE2
Conditions: Sepsis
Severe Sepsis
Septic Shock
Keywords: Dascena
Gram infection
antibiotic administration
machine learning
algorithm
diagnostic
Lead sponsor: Dascena
Sponsor class: INDUSTRY
First posted: 2018-11-08
Enrollment: 0
Sex: ALL
Minimum age (years): 18
Standard ages: ADULT
OLDER_ADULT
Accepts healthy volunteers: yes
Allocation: RANDOMIZED
Intervention model: PARALLEL
Primary purpose: DIAGNOSTIC

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.


Eligibility criteria: 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

Primary outcomes:
measure: Change in time to antibiotic administration
timeFrame: Through study completion, an average of 8 months
Secondary outcomes:
measure: Change in administration of unnecessary antibiotics
timeFrame: Through study completion, an average of 8 months
measure: Change in administration of unnecessary antibiotics
timeFrame: Through study completion, an average of 8 months
Other outcomes:
Locations:
Study officials:
affiliation: Dascena
name: Ritankar Das, MSc
role: PRINCIPAL_INVESTIGATOR
References:
citation: 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.
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.
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.