- ICH GCP
- US Clinical Trials Registry
- Clinical Trial NCT03212534
Inpatient Mortality Prediction Algorithm Clinical Trial (IMPACT) (IMPACT)
September 20, 2021 updated by: Dascena
A Randomized Clinical Trial of a Mortality Prediction Algorithm
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.
Study Overview
Status
Withdrawn
Intervention / Treatment
Study Type
Interventional
Phase
- Not Applicable
Contacts and Locations
This section provides the contact details for those conducting the study, and information on where this study is being conducted.
Study Locations
-
-
California
-
San Francisco, California, United States, 94143
- UCSF Moffit-Long Hospital
-
-
Participation Criteria
Researchers look for people who fit a certain description, called eligibility criteria. Some examples of these criteria are a person's general health condition or prior treatments.
Eligibility Criteria
Ages Eligible for Study
18 years and older (Adult, Older Adult)
Accepts Healthy Volunteers
No
Genders Eligible for Study
All
Description
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.
Study Plan
This section provides details of the study plan, including how the study is designed and what the study is measuring.
How is the study designed?
Design Details
- Primary Purpose: Diagnostic
- Allocation: Randomized
- Interventional Model: Parallel Assignment
- Masking: None (Open Label)
Arms and Interventions
Participant Group / Arm |
Intervention / Treatment |
|---|---|
|
No Intervention: Control
|
|
|
Experimental: Prediction Algorithm
|
Healthcare provider is notified of patient mortality prediction.
|
What is the study measuring?
Primary Outcome Measures
Outcome Measure |
Time Frame |
|---|---|
|
In-hospital mortality
Time Frame: Through study completion, an average of 30 days
|
Through study completion, an average of 30 days
|
Secondary Outcome Measures
Outcome Measure |
Time Frame |
|---|---|
|
Hospital length of stay
Time Frame: Through study completion, an average of 30 days
|
Through study completion, an average of 30 days
|
Other Outcome Measures
Outcome Measure |
Time Frame |
|---|---|
|
Hospital readmission
Time Frame: Through study completion, an average of 30 days
|
Through study completion, an average of 30 days
|
|
ICU length of stay
Time Frame: Through study completion, an average of 30 days
|
Through study completion, an average of 30 days
|
Collaborators and Investigators
This is where you will find people and organizations involved with this study.
Sponsor
Collaborators
Investigators
- Principal Investigator: David Shimabukuro, University of California, San Francisco
Publications and helpful links
The person responsible for entering information about the study voluntarily provides these publications. These may be about anything related to the study.
General Publications
- 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.
- 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 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.
Study record dates
These dates track the progress of study record and summary results submissions to ClinicalTrials.gov. Study records and reported results are reviewed by the National Library of Medicine (NLM) to make sure they meet specific quality control standards before being posted on the public website.
Study Major Dates
Study Start (Anticipated)
July 1, 2017
Primary Completion (Anticipated)
October 1, 2017
Study Completion (Anticipated)
October 1, 2017
Study Registration Dates
First Submitted
July 6, 2017
First Submitted That Met QC Criteria
July 6, 2017
First Posted (Actual)
July 11, 2017
Study Record Updates
Last Update Posted (Actual)
September 24, 2021
Last Update Submitted That Met QC Criteria
September 20, 2021
Last Verified
September 1, 2021
More Information
Terms related to this study
Additional Relevant MeSH Terms
Other Study ID Numbers
- 17-22319
Drug and device information, study documents
Studies a U.S. FDA-regulated drug product
No
Studies a U.S. FDA-regulated device product
No
This information was retrieved directly from the website clinicaltrials.gov without any changes. If you have any requests to change, remove or update your study details, please contact register@clinicaltrials.gov. As soon as a change is implemented on clinicaltrials.gov, this will be updated automatically on our website as well.
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