Unsupervised Machine Learning for Clustering of Septic Patients to Determine Optimal Treatment

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 fluid treatment-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, reductions in in-hospital mortality.

Study Overview

• Status: Not yet recruiting
• Conditions: Sepsis; Septic Shock; Severe Sepsis
• Intervention / Treatment: Diagnostic test: Treatment-specific InSight; Diagnostic test: InSight

• Study Type: Interventional
• Enrollment (Anticipated): 51645
• Phase: Phase 2

Contacts and Locations

• Study Contact: Name: Qingqing Mao, PhD; Phone Number: 5108269508; Email: qmao@dascena.com

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 clinical subpopulations studied in this trial are eligible to participate in the study.

Exclusion Criteria:

• Under age 18

Study Plan

How is the study designed?

Design Details

• Primary Purpose: Diagnostic
• Allocation: Randomized
• Interventional Model: Parallel Assignment
• Masking: Triple

Number of Arms

2

Arms and Interventions

Participant Group / Arm	Intervention / Treatment
Experimental: Fluid treatment-specific algorithm; The experimental arm will involve patients monitored by the fluid treatment-customized version of InSight.	Diagnostic test: Treatment-specific 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 clusters of patients who respond similarly to fluids treatment according to the nature of their disease progression.
Active Comparator: Standard InSight; The control arm will involve patients monitored with the standard, non-treatment specific version of InSight.	Diagnostic test: InSight; The non-customized InSight algorithm which draws information from a patient's electronic health record (EHR) to predict the onset of severe sepsis.

What is the study measuring?

Primary Outcome Measures

Outcome Measure	Measure Description	Time Frame
In-hospital SIRS-based mortality	Mortality attributed to patients meeting two or more SIRS criteria at some point during their stay	Through study completion, an average of 8 months

Collaborators and Investigators

• Sponsor: Dascena
• Investigators: Principal Investigator: Qingqing Mao, PhD, Dascena, Inc.

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): April 1, 2022
• Primary Completion (Anticipated): March 31, 2024
• Study Completion (Anticipated): March 31, 2024

Study Registration Dates

• First Submitted: November 21, 2018
• First Submitted That Met QC Criteria: November 21, 2018
• First Posted (Actual): November 26, 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; mortality; diagnostic; fluid administration; Dascena; clustering algorithm
• Additional Relevant MeSH Terms: Pathologic Processes; Infections; Systemic Inflammatory Response Syndrome; Inflammation; Sepsis; Toxemia
• Other Study ID Numbers: 19-426246

Drug and device information, study documents

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