Inpatient Mortality Prediction Algorithm Clinical Trial (IMPACT)
Sponsor
Dascena (Industry)
Overall Status
Withdrawn
CT.gov ID
NCT03212534
Collaborator
University of California, San Francisco (Other)
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Study Details
Study Description
Brief 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.
| Condition or Disease | Intervention/Treatment | Phase |
|---|---|---|
|
N/A |
Study Design
Study Type:
Interventional
Actual Enrollment
:
0 participants
Allocation:
Randomized
Intervention Model:
Parallel Assignment
Masking:
None (Open Label)
Primary Purpose:
Diagnostic
Official Title:
A Randomized Clinical Trial of a Mortality Prediction Algorithm
Anticipated Study Start Date
:
Jul 1, 2017
Anticipated Primary Completion Date
:
Oct 1, 2017
Anticipated Study Completion Date
:
Oct 1, 2017
Arms and Interventions
| Arm | Intervention/Treatment |
|---|---|
| Experimental: Prediction Algorithm
|
Other: Patient mortality prediction
Healthcare provider is notified of patient mortality prediction.
|
| No Intervention: Control
|
Outcome Measures
Primary Outcome Measures
- In-hospital mortality [Through study completion, an average of 30 days]
Secondary Outcome Measures
- Hospital length of stay [Through study completion, an average of 30 days]
Other Outcome Measures
- Hospital readmission [Through study completion, an average of 30 days]
- ICU length of stay [Through study completion, an average of 30 days]
Eligibility Criteria
Criteria
Ages Eligible for Study:
18 Years
and Older
Sexes Eligible for Study:
All
Accepts Healthy Volunteers:
No
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.
Contacts and Locations
Locations
| Site | City | State | Country | Postal Code | |
|---|---|---|---|---|---|
| 1 | UCSF Moffit-Long Hospital | San Francisco | California | United States | 94143 |
Sponsors and Collaborators
- Dascena
- University of California, San Francisco
Investigators
- Principal Investigator: David Shimabukuro, University of California, San Francisco
Study Documents (Full-Text)
None provided.More Information
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. eCollection 2016 Nov.
- 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.
- 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.
- 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.
Responsible Party:
Dascena
ClinicalTrials.gov Identifier:
NCT03212534
Other Study ID Numbers:
- 17-22319
First Posted:
Jul 11, 2017
Last Update Posted:
Sep 24, 2021
Last Verified:
Sep 1, 2021
Studies a U.S. FDA-regulated Drug Product:
No
Studies a U.S. FDA-regulated Device Product:
No
Keywords provided by Dascena
Additional relevant MeSH terms: