SepsisPhase 2NCT03752489
Unsupervised Machine Learning for Clustering of Septic Patients to Determine Optimal Treatment
A Phase 2 study testing Treatment-specific InSight and InSight in people with Sepsis and Severe Sepsis, run by Dascena.
Trial summary
The focus of this study will be to conduct a prospective, randomized controlled trial (RCT) at Cape Regional Medical Center (CRMC), Oroville Hospital (OH), and UCSF Medical Center (UCSF) in which a fluid treatment-specific algorithm will be applied to EHR data for the detection of severe sepsis. Read more about this study
- Who is it for?
- Ages 18 years and olderHealthy volunteers can take part
- Conditions the study is for
- Sepsis, Severe Sepsis, Septic Shock
- What is being tested?
- Treatment-specific InSight and InSight
- Could I receive a placebo?
- No, everyone receives an active treatment
- How many people
- 51,645 participants
- Where
- No sites listed yet
- Study dates
- 1 Apr 2022to 31 Mar 2024
- Phase
- Phase 2
- 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
- Healthy volunteers accepted
- Adult, Older adult
Who may be able to join
- All adults above age 18 who are a member of one of the clinical subpopulations studied in this trial are eligible to participate in the study.
Who cannot join
- Under age 18
Trial locations and contacts
No sites are listed yet.
About this study
The focus of this study will be to conduct a prospective, randomized controlled trial (RCT) at Cape Regional Medical Center (CRMC), Oroville Hospital (OH), and UCSF Medical Center (UCSF) in which a fluid treatment-specific algorithm will be applied to EHR data for the detection of severe sepsis. For patients determined to have a high risk of severe sepsis, the algorithm will generate automated voice, telephone notification to nursing staff at CRMC, OH, and UCSF. The algorithm's performance will be measured by analysis of the primary endpoint, reductions in in-hospital mortality.
- Conditions
- Sepsis, Severe Sepsis, Septic Shock
- Being tested
- Treatment-specific InSight, InSight
- Keywords
- Dascena, machine learning, fluid administration, clustering algorithm, mortality, diagnostic
- Medical subject headings
- Sepsis, Shock, Septic, Disease
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
Treatment-specific InSightDiagnostic test
The InSight algorithm which draws information from a patient's electronic health record (EHR) to predict the onset of severe sepsis, and in this study will be customized to differentiate between clusters of patients who respond similarly to fluids treatment according to the nature of their disease progression.
InSightDiagnostic test
The non-customized InSight algorithm which draws information from a patient's electronic health record (EHR) to predict the onset of severe sepsis.
| Study group | Role | What they receive |
|---|---|---|
| Fluid treatment-specific algorithm | Experimental | The experimental arm will involve patients monitored by the fluid treatment-customized version of InSight. Receives: Diagnostic Test: Treatment-specific InSight |
| Standard InSight | Active comparator | The control arm will involve patients monitored with the standard, non-treatment specific version of InSight. Receives: Diagnostic Test: InSight |
What is being measured
The primary outcome is the main question the study is designed to answer.
In-hospital SIRS-based mortality
Mortality attributed to patients meeting two or more SIRS criteria at some point during their stay
Measured: Through study completion, an average of 8 months
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted26 Nov 2018
- Study start1 Apr 2022 (estimated)
- Primary completion31 Mar 2024 (estimated)
- Study completion31 Mar 2024 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Publications (3)Papers the sponsor has linked to this study.
- Other trials for SepsisStudies 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 2
- Primary purpose
- Diagnosis
- Allocation
- Randomised
- Intervention model
- Parallel groups
- Masking
- Triple blind
- Who is masked
- Participant, Care provider, Investigator
- Enrolment
- 51,645 participants (estimated)
Sponsor and investigators
- Lead sponsor
- Dascena
- Sponsor type
- Industry (pharma, biotech, devices)
- Responsible party
- Sponsor
- Investigators
- Qingqing Mao, PhD, Principal investigator, Dascena, Inc.
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT03752489
- Sponsor's protocol ID
- 19-426246
- Status last verified
- Sep 2021
- First submitted
- 21 Nov 2018
- Last updated
- 23 Sep 2021
- Results posted
- No
- Data monitoring committee
- Yes
- FDA-regulated drug
- No
- FDA-regulated device
- No
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. doi: 10.1016/j.amsu.2016.09.002. eCollection 2016 Nov.
PubMed 27699003Shimabukuro DW, Barton CW, Feldman MD, Mataraso SJ, Das R. Effect of a machine learning-based severe sepsis prediction algorithm on patient survival and hospital length of stay: a randomised clinical trial. BMJ Open Respir Res. 2017 Nov 9;4(1):e000234. doi: 10.1136/bmjresp-2017-000234. eCollection 2017.
PubMed 29435343Mao Q, Jay M, Hoffman JL, Calvert J, Barton C, Shimabukuro D, Shieh L, Chettipally U, Fletcher G, Kerem Y, Zhou Y, Das R. Multicentre validation of a sepsis prediction algorithm using only vital sign data in the emergency department, general ward and ICU. BMJ Open. 2018 Jan 26;8(1):e017833. doi: 10.1136/bmjopen-2017-017833.
PubMed 29374661
Study at a glance
From the registry record, last updated by the sponsor on 23 Sep 2021. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Unsupervised Machine Learning for Clustering of Septic Patients to Determine Optimal Treatment
- ClinicalTrials.gov ID
- NCT03752489
- Acronym / sponsor ID
- 19-426246
- Conditions
- Sepsis, Severe Sepsis, Septic Shock
- Keywords
- Dascena, machine learning, fluid administration, clustering algorithm, mortality, diagnostic
- Phase
- Phase 2
- Study type
- Interventional
- Recruitment status
- Unknown status
- Enrolment
- 51,645 participants (estimated)
- Lead sponsor
- DascenaIndustry (pharma, biotech, devices)
- Study start
- 1 Apr 2022 (estimated)
- Primary completion
- 31 Mar 2024 (estimated)
- Study completion
- 31 Mar 2024 (estimated)
- First posted
- 26 Nov 2018 (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.
The focus of this study will be to conduct a prospective, randomized controlled trial (RCT) at Cape Regional Medical Center (CRMC), Oroville Hospital (OH), and UCSF Medical Center (UCSF) in which a fluid treatment-specific algorithm will be applied to EHR data for the detection of severe sepsis. For patients determined to have a high risk of severe sepsis, the algorithm will generate automated voice, telephone notification to nursing staff at CRMC, OH, and UCSF. The algorithm's performance will be measured by analysis of the primary endpoint, reductions in in-hospital mortality.
Study design
- Primary purpose
- Diagnosis
- Allocation
- Randomised
- Intervention model
- Parallel groups
- Masking
- Triple blind (participant, care provider, investigator)
- Number of arms
- 2
| Arm | Type | Receives |
|---|---|---|
| Fluid treatment-specific algorithm | Experimental | Diagnostic Test: Treatment-specific InSightThe experimental arm will involve patients monitored by the fluid treatment-customized version of InSight. |
| Standard InSight | Active comparator | Diagnostic Test: InSightThe control arm will involve patients monitored with the standard, non-treatment specific version of InSight. |
Who may qualify
- Ages
- 18 years and older
- Sex
- All
- Healthy volunteers
- Accepted
- Age groups
- Adult, Older adult
Key inclusion criteria (1 listed)
- All adults above age 18 who are a member of one of the clinical subpopulations studied in this trial are eligible to participate in the study.
Key exclusion criteria (1 listed)
- Under age 18
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 |
|---|---|---|
| Treatment-specific InSightThe InSight algorithm which draws information from a patient's electronic health record (EHR) to predict the onset of severe sepsis, and in this study will be customized to differentiate between clusters of patients who respond similarly to fluids treatment according to the nature of their disease progression. | Diagnostic test | Fluid treatment-specific algorithm |
| InSightThe non-customized InSight algorithm which draws information from a patient's electronic health record (EHR) to predict the onset of severe sepsis. | Diagnostic test | Standard InSight |
Primary outcome
In-hospital SIRS-based mortality
Time frame: Through study completion, an average of 8 months
Mortality attributed to patients meeting two or more SIRS criteria at some point during their stay
Results and safety
Results are not available in the source record.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 23 Sep 2021.
- 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.
- Background: PubMed 29435343Shimabukuro DW, Barton CW, Feldman MD, Mataraso SJ, Das R. Effect of a machine learning-based severe sepsis prediction algorithm on patient survival and hospital length of stay: a randomised clinical trial. BMJ Open Respir Res. 2017 Nov 9;4(1):e000234. doi: 10.1136/bmjresp-2017-000234. eCollection 2017.
- Background: PubMed 29374661Mao Q, Jay M, Hoffman JL, Calvert J, Barton C, Shimabukuro D, Shieh L, Chettipally U, Fletcher G, Kerem Y, Zhou Y, Das R. Multicentre validation of a sepsis prediction algorithm using only vital sign data in the emergency department, general ward and ICU. BMJ Open. 2018 Jan 26;8(1):e017833. doi: 10.1136/bmjopen-2017-017833.
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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.
Qingqing Mao, PhD principal investigator
Dascena, Inc.
Questions about this trial
What is this clinical trial studying?
A Phase 2 study testing Treatment-specific InSight and InSight in people with Sepsis and Severe Sepsis, run by Dascena.
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: status not confirmed, as of the sponsor's last update on 23 Sep 2021. Status can change between updates, so confirm with the study contact before making plans.
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.
What does this phase mean?
Phase 2 studies give the treatment to a larger group to see whether it has the intended effect and to keep checking safety.
Not every study follows the same path, and some studies (observational studies, device studies) have no phase.
Are results available for this study?
Results are not available in the source record.
How can a doctor refer a patient or contact the research team?
Contact Qingqing Mao, PhD (5108269508, qmao@dascena.com), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
In-hospital SIRS-based mortality (time frame: Through study completion, an average of 8 months). 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 NCT03752489, which also lists 3 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 23 Sep 2021.