Predictive algoRithm for EValuation and Intervention in SEpsis

Sepsis Clinical Trial

— PREVISE  

Official title:

Prediction of Severe Sepsis Using a Machine Learning Algorithm

Clinical Trial Details — Status: Completed

Administrative data

• NCT number: NCT03235193
• Other study ID #: 1097090-1
• Status: Completed
• Phase: N/A
• Start date: July 1, 2017
• Est. completion date: August 30, 2017

Study information

• Verified date: September 2021
• Source: Dascena
• Contact: n/a
• Is FDA regulated: No
• Study type: Interventional

Clinical Trial Summary

In this prospective study, the ability of a machine learning algorithm to predict sepsis and influence clinical outcomes, will be investigated at Cabell Huntington Hospital (CHH).

Recruitment information / eligibility

• Status: Completed
• Enrollment: 2296
• Est. completion date: August 30, 2017
• Est. primary completion date: August 30, 2017
• Accepts healthy volunteers: No
• Gender: All
• Age group: 18 Years and older

Eligibility

Inclusion Criteria:

• All adult patients visiting the emergency department, or admitted to the participating intensive care unit (ICU) wards of Cabell Huntington Hospital will be eligible.

Exclusion Criteria:

• All patients younger than 18 years of age will be excluded.

Related Conditions & MeSH terms

• Sepsis
• Septic Shock
• Severe Sepsis
• Toxemia

Intervention

Other:
• Severe Sepsis Prediction
Upon receiving an InSight alert, healthcare provider follows standard practices in assessing possible (severe) sepsis and intervening accordingly.
• Severe Sepsis Detection
Upon receiving information from the severe sepsis detector in the CHH electronic health record, healthcare provider follows standard practices in assessing possible (severe) sepsis and intervening accordingly.

Locations

Country	Name	City	State
United States	Cabell Huntington Hospital	Huntington	West Virginia

Sponsors (2)

Lead Sponsor	Collaborator
Dascena	Cabell Huntington Hospital

Country where clinical trial is conducted

United States, 

References & Publications (3)

Calvert J, Desautels T, Chettipally U, Barton C, Hoffman J, Jay M, Mao Q, Mohamadlou H, Das R. High-performance detection and early prediction of septic shock for alcohol-use disorder patients. Ann Med Surg (Lond). 2016 May 10;8:50-5. doi: 10.1016/j.amsu.2016.04.023. eCollection 2016 Jun. — View Citation

Calvert JS, Price DA, Chettipally UK, Barton CW, Feldman MD, Hoffman JL, Jay M, Das R. A computational approach to early sepsis detection. Comput Biol Med. 2016 Jul 1;74:69-73. doi: 10.1016/j.compbiomed.2016.05.003. Epub 2016 May 12. — View Citation

Desautels T, Calvert J, Hoffman J, Jay M, Kerem Y, Shieh L, Shimabukuro D, Chettipally U, Feldman MD, Barton C, Wales DJ, Das R. Prediction of Sepsis in the Intensive Care Unit With Minimal Electronic Health Record Data: A Machine Learning Approach. JMIR Med Inform. 2016 Sep 30;4(3):e28. — View Citation

Outcome

Type	Measure	Description	Time frame	Safety issue
Other	Hospital readmission		Through study completion, an average of 30 days
Other	ICU length of stay		Through study completion, an average of 30 days
Primary	In-hospital mortality		Through study completion, an average of 30 days
Secondary	Hospital length of stay		Through study completion, an average of 30 days