Inpatient Mortality Prediction Algorithm Clinical Trial (IMPACT)

NCT ID: NCT03212534

Last Updated: 2021-09-24

Study Results

Results pending

The study team has not published outcome measurements, participant flow, or safety data for this trial yet. Check back later for updates.

Basic Information

Get a concise snapshot of the trial, including recruitment status, study phase, enrollment targets, and key timeline milestones.

Recruitment Status

WITHDRAWN

Clinical Phase

NA

Study Classification

INTERVENTIONAL

Study Start Date

2017-07-31

Study Completion Date

2017-10-31

Brief Summary

Review the sponsor-provided synopsis that highlights what the study is about and why it is being conducted.

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.

Detailed Description

Dive into the extended narrative that explains the scientific background, objectives, and procedures in greater depth.

Conditions

See the medical conditions and disease areas that this research is targeting or investigating.

Decompensation, Heart Decompensation; Heart, Congestive Death

Study Design

Understand how the trial is structured, including allocation methods, masking strategies, primary purpose, and other design elements.

Allocation Method

RANDOMIZED

Intervention Model

PARALLEL

Primary Study Purpose

DIAGNOSTIC

Blinding Strategy

NONE

Study Groups

Review each arm or cohort in the study, along with the interventions and objectives associated with them.

Prediction Algorithm

Group Type EXPERIMENTAL

Patient mortality prediction

Intervention Type OTHER

Healthcare provider is notified of patient mortality prediction.

Control

Group Type NO_INTERVENTION

No interventions assigned to this group

Interventions

Learn about the drugs, procedures, or behavioral strategies being tested and how they are applied within this trial.

Patient mortality prediction

Healthcare provider is notified of patient mortality prediction.

Intervention Type OTHER

Eligibility Criteria

Check the participation requirements, including inclusion and exclusion rules, age limits, and whether healthy volunteers are accepted.

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.
Minimum Eligible Age

18 Years

Eligible Sex

ALL

Accepts Healthy Volunteers

No

Sponsors

Meet the organizations funding or collaborating on the study and learn about their roles.

University of California, San Francisco

OTHER

Sponsor Role collaborator

Dascena

INDUSTRY

Sponsor Role lead

Responsible Party

Identify the individual or organization who holds primary responsibility for the study information submitted to regulators.

Responsibility Role SPONSOR

Principal Investigators

Learn about the lead researchers overseeing the trial and their institutional affiliations.

David Shimabukuro

Role: PRINCIPAL_INVESTIGATOR

University of California, San Francisco

Locations

Explore where the study is taking place and check the recruitment status at each participating site.

UCSF Moffit-Long Hospital

San Francisco, California, United States

Site Status

Countries

Review the countries where the study has at least one active or historical site.

United States

References

Explore related publications, articles, or registry entries linked to this study.

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.

Reference Type BACKGROUND
PMID: 28638239 (View on PubMed)

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.

Reference Type BACKGROUND
PMID: 27253619 (View on PubMed)

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.

Reference Type BACKGROUND
PMID: 27026611 (View on PubMed)

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.

Reference Type BACKGROUND
PMID: 27699003 (View on PubMed)

Other Identifiers

Review additional registry numbers or institutional identifiers associated with this trial.

17-22319

Identifier Type: -

Identifier Source: org_study_id

More Related Trials

Additional clinical trials that may be relevant based on similarity analysis.

Mechanical Thrombectomy for Acute Pulmonary Embolism
NCT07032025 NOT_YET_RECRUITING EARLY_PHASE1