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

Sepsis Clinical Trial

Official title:

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

Clinical Trial Details — Status: Not yet recruiting

Administrative data

• NCT number: NCT03752489
• Other study ID #: 19-426246
• Status: Not yet recruiting
• Phase: Phase 2
• Start date: April 1, 2022
• Est. completion date: March 31, 2024

Study information

• Verified date: September 2021
• Source: Dascena
• Contact: Qingqing Mao, PhD
• Phone: 5108269508
• Email: qmao[@]dascena.com
• 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 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.

Recruitment information / eligibility

• Status: Not yet recruiting
• Enrollment: 51645
• Est. completion date: March 31, 2024
• Est. primary completion date: March 31, 2024
• 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 clinical subpopulations studied in this trial are eligible to participate in the study.

Exclusion Criteria:

• Under age 18

Related Conditions & MeSH terms

• Sepsis
• Septic Shock
• Severe Sepsis
• Toxemia

Intervention

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.
• InSight
The non-customized InSight algorithm which draws information from a patient's electronic health record (EHR) to predict the onset of severe sepsis.

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	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