Inpatient Mortality Prediction Algorithm Clinical Trial (IMPACT)

NCT03212534 | Withdrawn

• 1 other identifier: 17-22319
• Study Type: interventional
• Target: N/A
• Locations: 1 country
• Sites: 1

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.

Conditions

Decompensation, Heart; Decompensation; Heart, Congestive; Death

Keywords

Dascena; patient mortality; machine learning; algorithm; diagnostic

Outcome Measures

Primary Outcomes (1)

• In-hospital mortality

  Through study completion, an average of 30 days

Secondary Outcomes (1)

• Hospital length of stay

  Through study completion, an average of 30 days

Other Outcomes (2)

• Hospital readmission

  Through study completion, an average of 30 days

• ICU length of stay

  Through study completion, an average of 30 days

Study Arms (2)

Prediction Algorithm

EXPERIMENTAL

Other: Patient mortality prediction

Control

NO INTERVENTION

Interventions

Patient mortality prediction OTHER

Healthcare provider is notified of patient mortality prediction.

Prediction Algorithm

Eligibility Criteria

• Age: 18 Years+
• Sex: all
• Healthy Volunteers: No
• Age Groups: Adult (18-64), Older Adult (65+)

You may qualify if:

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

You may not qualify if:

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

Sponsors & Collaborators

• Dascena: lead
• University of California, San Francisco: collaborator

Study Sites (1)

UCSF Moffit-Long Hospital

San Francisco, California, 94143, United States

Location

Related Publications (4)

• 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.

  PMID: 28638239 BACKGROUND

• 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.

  PMID: 27253619 BACKGROUND

• 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.

  PMID: 27026611 BACKGROUND

• 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.

  PMID: 27699003 BACKGROUND

MeSH Terms

Conditions

Heart Failure; Death; Disease

Condition Hierarchy (Ancestors)

Heart Diseases; Cardiovascular Diseases; Pathologic Processes; Pathological Conditions, Signs and Symptoms

Study Officials

• David Shimabukuro

  University of California, San Francisco

  PRINCIPAL INVESTIGATOR

Study Design

• Study Type: interventional
• Phase: not applicable
• Allocation: RANDOMIZED
• Masking: NONE
• Purpose: DIAGNOSTIC
• Intervention Model: PARALLEL
• Sponsor Type: INDUSTRY
• Responsible Party: SPONSOR

Study Record Dates

• First Submitted: July 6, 2017
• First Posted: July 11, 2017
• Study Start: July 1, 2017
• Primary Completion: October 1, 2017
• Study Completion: October 1, 2017
• Last Updated: September 24, 2021

Record last verified: 2021-09

Locations

US (1)