SepsisNCT03015454
An Algorithm Driven Sepsis Prediction Biomarker
A study testing Severe Sepsis Prediction and Severe Sepsis Detection in people with Sepsis and Septic Shock, run by Dascena at 1 site in United States.
Trial summary
A sepsis early warning predictive algorithm, InSight, has been developed and validated on a large, diverse patient cohort. In this prospective study, the ability of InSight to predict severe sepsis patients is investigated. Read more about this study
- Who is it for?
- Ages 18 years and older
- Conditions the study is for
- Sepsis, Septic Shock, Severe Sepsis
- What is being tested?
- Severe Sepsis Prediction and Severe Sepsis Detection
- Could I receive a placebo?
- No, everyone receives an active treatment
- How many people
- 142 participants
- Where
- 1 siteUnited States
- Study dates
- Dec 2016to Feb 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 Controlled Clinical Trial of an Algorithm Driven Sepsis Prediction Biomarker
A sepsis early warning predictive algorithm, InSight, has been developed and validated on a large, diverse patient cohort. In this prospective study, the ability of InSight to predict severe sepsis patients is investigated. Specifically, InSight is compared with a well established severe sepsis detector in the UCSF electronic health record (EHR).
- Conditions
- Sepsis, Septic Shock, Severe Sepsis
- Being tested
- Severe Sepsis Prediction, Severe Sepsis Detection
- Keywords
- InSight, Dascena
- Medical subject headings
- Sepsis, Shock, Septic
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
Severe Sepsis PredictionOther
Upon receiving an InSight alert, healthcare provider follows standard practices in assessing possible (severe) sepsis and intervening accordingly.
Severe Sepsis DetectionOther
Upon receiving information from the severe sepsis detector in the UCSF electronic health record, healthcare provider follows standard practices in assessing possible (severe) sepsis and intervening accordingly.
| Study group | Role | What they receive |
|---|---|---|
| With InSight | Experimental | Healthcare provider receives an alert from InSight for patients trending towards severe sepsis. Healthcare provider also receives information from the severe sepsis detector in the UCSF electronic health record. Receives: Other: Severe Sepsis Prediction; Other: Severe Sepsis Detection |
| Without InSight | Active comparator | Healthcare provider does not receive any alerts from InSight. Healthcare provider receives information from the severe sepsis detector in the UCSF electronic health record. Receives: Other: Severe Sepsis Detection |
What is being measured
The primary outcome is the main question the study is designed to answer.
Hospital length of stay
Measured: Through study completion, an average of 45 days
Secondary outcomes (1)
In-hospital mortality
Measured: Through study completion, an average of 45 days
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted10 Jan 2017 (estimated)
- Study startDec 2016
- Primary completionFeb 2017
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Publications (5)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 N/A
- Primary purpose
- Screening
- Allocation
- Randomised
- Intervention model
- Factorial
- Masking
- None (open label)
- Enrolment
- 142 participants (actual)
Sponsor and investigators
- Lead sponsor
- Dascena
- Sponsor type
- Industry (pharma, biotech, devices)
- Collaborators
- University of California, San Francisco
- Responsible party
- Sponsor
- Investigators
- Ritankar Das, Principal investigator, Dascena
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT03015454
- Sponsor's protocol ID
- 16-19647
- Status last verified
- Sep 2021
- First submitted
- 31 Dec 2016
- Last updated
- 23 Sep 2021
- Results posted
- No
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.
PubMed 27026611Calvert J, Desautels T, Chettipally U, Barton C, Hoffman J, Jay M, Mao Q, Mohamadlou H, Das R. High-performance detection and early prediction of septic shock for alcohol-use disorder patients. Ann Med Surg (Lond). 2016 May 10;8:50-5. doi: 10.1016/j.amsu.2016.04.023. eCollection 2016 Jun.
PubMed 27489621Calvert JS, Price DA, Chettipally UK, Barton CW, Feldman MD, Hoffman JL, Jay M, Das R. A computational approach to early sepsis detection. Comput Biol Med. 2016 Jul 1;74:69-73. doi: 10.1016/j.compbiomed.2016.05.003. Epub 2016 May 12.
PubMed 27208704Desautels T, Calvert J, Hoffman J, Jay M, Kerem Y, Shieh L, Shimabukuro D, Chettipally U, Feldman MD, Barton C, Wales DJ, Das R. Prediction of Sepsis in the Intensive Care Unit With Minimal Electronic Health Record Data: A Machine Learning Approach. JMIR Med Inform. 2016 Sep 30;4(3):e28. doi: 10.2196/medinform.5909.
PubMed 27694098Shimabukuro 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 29435343
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
- A Randomized Controlled Clinical Trial of an Algorithm Driven Sepsis Prediction Biomarker
- ClinicalTrials.gov ID
- NCT03015454
- Acronym / sponsor ID
- 16-19647
- Conditions
- Sepsis, Septic Shock, Severe Sepsis
- Keywords
- InSight, Dascena
- Study type
- Interventional
- Recruitment status
- Completed
- Enrolment
- 142 participants (actual)
- Lead sponsor
- DascenaIndustry (pharma, biotech, devices)
- Collaborators
- University of California, San Francisco
- Study start
- Dec 2016
- Primary completion
- Feb 2017 (actual)
- First posted
- 10 Jan 2017 (estimated)
- 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.
A sepsis early warning predictive algorithm, InSight, has been developed and validated on a large, diverse patient cohort. In this prospective study, the ability of InSight to predict severe sepsis patients is investigated. Specifically, InSight is compared with a well established severe sepsis detector in the UCSF electronic health record (EHR).
Study design
- Primary purpose
- Screening
- Allocation
- Randomised
- Intervention model
- Factorial
- Masking
- None (open label)
- Number of arms
- 2
| Arm | Type | Receives |
|---|---|---|
| With InSight | Experimental | Other: Severe Sepsis Prediction, Other: Severe Sepsis DetectionHealthcare provider receives an alert from InSight for patients trending towards severe sepsis. Healthcare provider also receives information from the severe sepsis detector in the UCSF electronic health record. |
| Without InSight | Active comparator | Other: Severe Sepsis DetectionHealthcare provider does not receive any alerts from InSight. Healthcare provider receives information from the severe sepsis detector in the UCSF electronic health record. |
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 |
|---|---|---|
| Severe Sepsis PredictionUpon receiving an InSight alert, healthcare provider follows standard practices in assessing possible (severe) sepsis and intervening accordingly. | Other | With InSight |
| Severe Sepsis DetectionUpon receiving information from the severe sepsis detector in the UCSF electronic health record, healthcare provider follows standard practices in assessing possible (severe) sepsis and intervening accordingly. | Other | With InSight, Without InSight |
Primary outcome
Hospital length of stay
Time frame: Through study completion, an average of 45 days
1 secondary outcome
In-hospital mortality
Time frame: Through study completion, an average of 45 days
Results and safety
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 23 Sep 2021.
- 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 27489621Calvert J, Desautels T, Chettipally U, Barton C, Hoffman J, Jay M, Mao Q, Mohamadlou H, Das R. High-performance detection and early prediction of septic shock for alcohol-use disorder patients. Ann Med Surg (Lond). 2016 May 10;8:50-5. doi: 10.1016/j.amsu.2016.04.023. eCollection 2016 Jun.
- Background: PubMed 27208704Calvert JS, Price DA, Chettipally UK, Barton CW, Feldman MD, Hoffman JL, Jay M, Das R. A computational approach to early sepsis detection. Comput Biol Med. 2016 Jul 1;74:69-73. doi: 10.1016/j.compbiomed.2016.05.003. Epub 2016 May 12.
- Background: PubMed 27694098Desautels T, Calvert J, Hoffman J, Jay M, Kerem Y, Shieh L, Shimabukuro D, Chettipally U, Feldman MD, Barton C, Wales DJ, Das R. Prediction of Sepsis in the Intensive Care Unit With Minimal Electronic Health Record Data: A Machine Learning Approach. JMIR Med Inform. 2016 Sep 30;4(3):e28. doi: 10.2196/medinform.5909.
- Derived from this study: 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.
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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.
Ritankar Das principal investigator
Dascena
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
A study testing Severe Sepsis Prediction and Severe Sepsis Detection in people with Sepsis and Septic Shock, 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: finished, 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.
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 is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
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?
Hospital length of stay (time frame: Through study completion, an average of 45 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 NCT03015454, which also lists 5 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.