Decompensation, HeartIMPACTNCT03212534
Inpatient Mortality Prediction Algorithm Clinical Trial (IMPACT)
A study testing Patient mortality prediction in people with Decompensation, Heart and Decompensation; Heart, Congestive, run by Dascena at 1 site in United States.
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. Read more about this study
- Who is it for?
- Ages 18 years and older
- Conditions the study is for
- Decompensation, Heart, Decompensation; Heart, Congestive, Death
- What is being tested?
- Patient mortality prediction
- Could I receive a placebo?
- No, everyone receives an active treatment
- How many people
- 0 participants
- Where
- 1 siteUnited States
- Study dates
- Jul 2017to Oct 2017
- Phase
- Interventional
- Are there results?
- None posted in the registry
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 18 years and older
- All sexes
- No healthy volunteers
- Adult, Older adult
Who may be able to join
- All adult patients admitted to the participating units will be eligible.
Who cannot join
- All patients younger than 18 years of age will be excluded.
Trial locations and contacts
1 site in 1 country.
United States1 site
UCSF Moffit-Long Hospital
San Francisco, California, 94143
About this study
Official title: 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.
- Being tested
- Patient mortality prediction
- Keywords
- Dascena, patient mortality, machine learning, algorithm, diagnostic
- Medical subject headings
- Heart Failure, Death, Disease
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
Patient mortality predictionOther
Healthcare provider is notified of patient mortality prediction.
| Study group | Role | What they receive |
|---|---|---|
| Prediction Algorithm | Experimental | Not described Receives: Other: Patient mortality prediction |
| Control | No intervention | Not described |
What is being measured
The primary outcome is the main question the study is designed to answer.
In-hospital mortality
Measured: Through study completion, an average of 30 days
Secondary outcomes (1)
Hospital length of stay
Measured: Through study completion, an average of 30 days
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted11 Jul 2017
- Study startJul 2017 (estimated)
- Primary completionOct 2017 (estimated)
- Study completionOct 2017 (estimated)
Why it stopped: This trial did not start. No participants enrolled
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Publications (4)Papers the sponsor has linked to this study.
- Other trials for Decompensation, HeartStudies of the same condition, open and under way.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Interventional
- Phase
- Phase N/A
- Primary purpose
- Diagnosis
- Allocation
- Randomised
- Intervention model
- Parallel groups
- Masking
- None (open label)
- Enrolment
- 0 participants (actual)
Sponsor and investigators
- Lead sponsor
- Dascena
- Sponsor type
- Industry (pharma, biotech, devices)
- Collaborators
- University of California, San Francisco
- Responsible party
- Sponsor
- Investigators
- David Shimabukuro, Principal investigator, University of California, San Francisco
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT03212534
- Sponsor's protocol ID
- 17-22319
- Acronym
- IMPACT
- Status last verified
- Sep 2021
- First submitted
- 6 Jul 2017
- Last updated
- 24 Sep 2021
- Results posted
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
Publications
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.
PubMed 28638239Calvert 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.
PubMed 27253619Calvert 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.
PubMed 27026611Calvert 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.
PubMed 27699003
Study at a glance
From the registry record, last updated by the sponsor on 24 Sep 2021. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- A Randomized Clinical Trial of a Mortality Prediction Algorithm
- ClinicalTrials.gov ID
- NCT03212534
- Acronym / sponsor ID
- IMPACT / 17-22319
- Keywords
- Dascena, patient mortality, machine learning, algorithm, diagnostic
- Study type
- Interventional
- Recruitment status
- WithdrawnReason stopped: This trial did not start. No participants enrolled
- Enrolment
- 0 participants (actual)
- Lead sponsor
- DascenaIndustry (pharma, biotech, devices)
- Collaborators
- University of California, San Francisco
- Study start
- Jul 2017 (estimated)
- Primary completion
- Oct 2017 (estimated)
- Study completion
- Oct 2017 (estimated)
- First posted
- 11 Jul 2017 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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 design
- Primary purpose
- Diagnosis
- Allocation
- Randomised
- Intervention model
- Parallel groups
- Masking
- None (open label)
- Number of arms
- 2
| Arm | Type | Receives |
|---|---|---|
| Prediction Algorithm | Experimental | Other: Patient mortality prediction |
| Control | No intervention | – |
Who may qualify
- Ages
- 18 years and older
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
Key inclusion criteria (1 listed)
- All adult patients admitted to the participating units will be eligible.
Key exclusion criteria (1 listed)
- All patients younger than 18 years of age will be excluded.
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| Patient mortality predictionHealthcare provider is notified of patient mortality prediction. | Other | Prediction Algorithm |
Primary outcome
In-hospital mortality
Time frame: Through study completion, an average of 30 days
1 secondary outcome
Hospital length of stay
Time frame: Through study completion, an average of 30 days
Results and safety
The study was withdrawn before enrolling participants, so there are no results.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 24 Sep 2021.
- Background: PubMed 28638239Desautels 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.
- Background: PubMed 27253619Calvert 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.
- Background: PubMed 27026611Calvert 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.
- Background: PubMed 27699003Calvert 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.
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part. This study is not recruiting at the moment.
David Shimabukuro principal investigator
University of California, San Francisco
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
A study testing Patient mortality prediction in people with Decompensation, Heart and Decompensation; Heart, Congestive, run by Dascena at 1 site in United States.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 1 inclusion and 1 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: withdrawn, as of the sponsor's last update on 24 Sep 2021. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in United States. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
The study was withdrawn before enrolling participants, so there are no results.
How can a doctor refer a patient or contact the research team?
The record lists no contact. The official record names the sponsor, who can be asked directly. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
In-hospital mortality (time frame: Through study completion, an average of 30 days). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
On the ClinicalTrials.gov record for NCT03212534, which also lists 4 linked publications.
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 24 Sep 2021.