Inpatient Mortality Prediction Algorithm Clinical Trial (IMPACT)

Death Clinical Trial

— IMPACT  

Official title:

A Randomized Clinical Trial of a Mortality Prediction Algorithm

Clinical Trial Details — Status: Withdrawn

Administrative data

• NCT number: NCT03212534
• Other study ID #: 17-22319
• Status: Withdrawn
• Phase: N/A
• Start date: July 2017
• Est. completion date: October 2017

Study information

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

Clinical Trial Summary

Through the mapping of retrospective patient data into a discrete multidimensional space, a novel algorithm for homeostatic analysis, was built to make outcome predictions. In this prospective study, the ability of the algorithm to predict patient mortality and influence clinical outcomes, will be investigated.

Recruitment information / eligibility

• Status: Withdrawn
• Enrollment: 0
• Est. completion date: October 2017
• Est. primary completion date: October 2017
• Accepts healthy volunteers: No
• Gender: All
• Age group: 18 Years and older

Eligibility

Inclusion Criteria:

• All adult patients admitted to the participating units will be eligible.

Exclusion Criteria:

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

Related Conditions & MeSH terms

• Death
• Decompensation, Heart
• Decompensation; Heart, Congestive
• Heart Failure

Intervention

Other:
• Patient mortality prediction
Healthcare provider is notified of patient mortality prediction.

Locations

Country	Name	City	State
United States	UCSF Moffit-Long Hospital	San Francisco	California

Sponsors (2)

Lead Sponsor	Collaborator
Dascena	University of California, San Francisco

Country where clinical trial is conducted

United States, 

References & Publications (4)

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

Calvert J, Mao Q, Rogers AJ, Barton C, Jay M, Desautels T, Mohamadlou H, Jan J, Das R. A computational approach to mortality prediction of alcohol use disorder inpatients. Comput Biol Med. 2016 Aug 1;75:74-9. doi: 10.1016/j.compbiomed.2016.05.015. Epub 2016 May 24. — View Citation

Calvert JS, Price DA, Barton CW, Chettipally UK, Das R. Discharge recommendation based on a novel technique of homeostatic analysis. J Am Med Inform Assoc. 2017 Jan;24(1):24-29. doi: 10.1093/jamia/ocw014. Epub 2016 Mar 28. — View Citation

Desautels T, Calvert J, Hoffman J, Mao Q, Jay M, Fletcher G, Barton C, Chettipally U, Kerem Y, Das R. Using Transfer Learning for Improved Mortality Prediction in a Data-Scarce Hospital Setting. Biomed Inform Insights. 2017 Jun 12;9:1178222617712994. doi: 10.1177/1178222617712994. eCollection 2017. — 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