Acute Kidney InjuryPhase 2NCT04200950
Trial for the Early Identification of Acute Kidney Injury
A Phase 2 study testing Previse in people with Acute Kidney Injury, run by Dascena.
Trial summary
Previse is a novel, software-based clinical decision support (CDS) system that predicts acute kidney injury (AKI). Previse uses machine learning methods and information drawn from the electronic health record (EHR) to identify the early signs of acute kidney injury; by doing so before the clinical syndrome of AKI is… Read more about this study
- Who is it for?
- Ages 18 years and olderHealthy volunteers can take part
- Conditions the study is for
- Acute Kidney Injury
- What is being tested?
- Previse
- Type of treatment
- Medical device
- Could I receive a placebo?
- No, everyone receives an active treatment
- How many people
- 0 participants
- Where
- No sites listed yet
- Study dates
- Jul 2020to Jun 2021
- 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
- Adult ≥ 18 years admitted to a participating study hospital
Who cannot join
- ﹤18 years of age
- ESRD diagnosis code
- Stage 4 or Stage 5 CKD diagnosis code
- Initial creatinine ≥4.0mg/dl
- Nephrectomy during admission
- Admission to hospice service
Show all 11Show fewer
- Admission to observation status
- Any organ transplant (including kidney transplant) within 6 months
- Dialysis order prior to AKI onset
- Dialysis order within 24 hours of admission
- Prior admission in which patient was randomized
About this study
Official title: Randomized Controlled Trial for the Early Identification of Acute Kidney Injury Using Deep Recurrent Neural Nets
Previse is a novel, software-based clinical decision support (CDS) system that predicts acute kidney injury (AKI). Previse uses machine learning methods and information drawn from the electronic health record (EHR) to identify the early signs of acute kidney injury; by doing so before the clinical syndrome of AKI is fully developed, Previse can give clinicians the time to intervene with the goals of preventing further kidney damage, and decreasing the sequelae of AKI. It has been demonstrated in retrospective work that Previse can predict AKI with high accuracy at long prediction horizons, but the tool has yet to be validated in prospective settings; therefore, in this project, the clinical utility of Previse will be assessed through an individually randomized controlled multicenter trial.
Read the detailed description
The trial is designed as an individually randomized, controlled, and non-blinded multicenter prevention trial with a baseline period and a primary endpoint of proportion of patients meeting one or more criteria for the Major Adverse Kidney Events within 30 days (MAKE30) composite of death, new renal replacement therapy, or persistent creatinine elevation ≥ 200% of baseline, all censored at the first of hospital discharge or 30 days. The trial will evaluate the efficacy of a machine learning algorithm for AKI prediction, in approximately 8,574 patients aged ≥ 18 years admitted to one of three participating study hospitals. Individual patient randomization will be performed at the time of the alert with a 1:1 allocation ratio. Patients will be evaluated for inclusion in the trial upon admission, and will be automatically enrolled upon meeting inclusion criteria. Because data collection will be conducted through noninvasive procedures that are routinely employed in clinical practice, it will require a waiver of informed consent. Trial efficacy will be assessed at regularly scheduled study visits, and safety will be monitored on an ongoing basis for all patients. Safety will be assessed through the collection of adverse events, laboratory tests, vital signs, and physical examinations throughout the study. An independent Data Monitoring Committee (DMC) will be formed to assist in the periodic monitoring of safety, data quality, and integrity of study conduct. In addition, the DMC will review the interim efficacy analysis performed to determine whether the primary endpoint has been met. Total trial duration is expected to be approximately 12 months.
- Conditions
- Acute Kidney Injury
- Being tested
- Previse
- Medical subject headings
- Acute Kidney Injury
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
PreviseDevice
Machine learning algorithm for early acute kidney injury (AKI) prediction.
| Study group | Role | What they receive |
|---|---|---|
| Intervention | Experimental | Previse alert arm Receives: Device: Previse |
| Control | No intervention | No alert |
What is being measured
The primary outcome is the main question the study is designed to answer.
Incidence of adverse kidney events as assessed by Major Adverse Kidney Event within 30 days (MAKE30) criteria
The proportion of patients meeting one or more criteria for the Major Adverse Kidney Events within 30 days (MAKE30) composite of death, new renal replacement therapy, or persistent creatinine elevation ≥ 200% of baseline, all censored at the first of hospital discharge or 30 days
Measured: Through study completion, an average of twelve months
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted16 Dec 2019
- Study startJul 2020 (estimated)
- Primary completionJun 2021 (estimated)
- Study completionJun 2021 (estimated)
Why it stopped: No longer conducting this retrospective research
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Publications (1)Papers the sponsor has linked to this study.
- Other trials for Acute Kidney InjuryStudies 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
- Prevention
- 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)
- Responsible party
- Sponsor
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT04200950
- Sponsor's protocol ID
- 07012020
- Status last verified
- Sep 2021
- First submitted
- 11 Dec 2019
- Last updated
- 24 Sep 2021
- Results posted
- No
- Data monitoring committee
- Yes
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- Undecided
Publications
Mohamadlou H, Lynn-Palevsky A, Barton C, Chettipally U, Shieh L, Calvert J, Saber NR, Das R. Prediction of Acute Kidney Injury With a Machine Learning Algorithm Using Electronic Health Record Data. Can J Kidney Health Dis. 2018 Jun 8;5:2054358118776326. doi: 10.1177/2054358118776326. eCollection 2018.
PubMed 30094049
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
- Randomized Controlled Trial for the Early Identification of Acute Kidney Injury Using Deep Recurrent Neural Nets
- ClinicalTrials.gov ID
- NCT04200950
- Acronym / sponsor ID
- 07012020
- Conditions
- Acute Kidney Injury
- Phase
- Phase 2
- Study type
- Interventional
- Recruitment status
- WithdrawnReason stopped: No longer conducting this retrospective research
- Enrolment
- 0 participants (actual)
- Lead sponsor
- DascenaIndustry (pharma, biotech, devices)
- Study start
- Jul 2020 (estimated)
- Primary completion
- Jun 2021 (estimated)
- Study completion
- Jun 2021 (estimated)
- First posted
- 16 Dec 2019 (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.
Previse is a novel, software-based clinical decision support (CDS) system that predicts acute kidney injury (AKI). Previse uses machine learning methods and information drawn from the electronic health record (EHR) to identify the early signs of acute kidney injury; by doing so before the clinical syndrome of AKI is fully developed, Previse can give clinicians the time to intervene with the goals of preventing further kidney damage, and decreasing the sequelae of AKI. It has been demonstrated in retrospective work that Previse can predict AKI with high accuracy at long prediction horizons, but the tool has yet to be validated in prospective settings; therefore, in this project, the clinical utility of Previse will be assessed through an individually randomized controlled multicenter trial.
Detailed description
The trial is designed as an individually randomized, controlled, and non-blinded multicenter prevention trial with a baseline period and a primary endpoint of proportion of patients meeting one or more criteria for the Major Adverse Kidney Events within 30 days (MAKE30) composite of death, new renal replacement therapy, or persistent creatinine elevation ≥ 200% of baseline, all censored at the first of hospital discharge or 30 days. The trial will evaluate the efficacy of a machine learning algorithm for AKI prediction, in approximately 8,574 patients aged ≥ 18 years admitted to one of three participating study hospitals. Individual patient randomization will be performed at the time of the alert with a 1:1 allocation ratio. Patients will be evaluated for inclusion in the trial upon admission, and will be automatically enrolled upon meeting inclusion criteria. Because data collection will be conducted through noninvasive procedures that are routinely employed in clinical practice, it will require a waiver of informed consent. Trial efficacy will be assessed at regularly scheduled study visits, and safety will be monitored on an ongoing basis for all patients. Safety will be assessed through the collection of adverse events, laboratory tests, vital signs, and physical examinations throughout the study. An independent Data Monitoring Committee (DMC) will be formed to assist in the periodic monitoring of safety, data quality, and integrity of study conduct. In addition, the DMC will review the interim efficacy analysis performed to determine whether the primary endpoint has been met. Total trial duration is expected to be approximately 12 months.
Study design
- Primary purpose
- Prevention
- Allocation
- Randomised
- Intervention model
- Parallel groups
- Masking
- None (open label)
- Number of arms
- 2
| Arm | Type | Receives |
|---|---|---|
| Intervention | Experimental | Device: PrevisePrevise alert arm |
| Control | No intervention | –No alert |
Who may qualify
- Ages
- 18 years and older
- Sex
- All
- Healthy volunteers
- Accepted
- Age groups
- Adult, Older adult
Key inclusion criteria (1 listed)
- Adult ≥ 18 years admitted to a participating study hospital
Key exclusion criteria (11 listed)
- ﹤18 years of age
- ESRD diagnosis code
- Stage 4 or Stage 5 CKD diagnosis code
- Initial creatinine ≥4.0mg/dl
- Nephrectomy during admission
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 |
|---|---|---|
| PreviseMachine learning algorithm for early acute kidney injury (AKI) prediction. | Device | Intervention |
Primary outcome
Incidence of adverse kidney events as assessed by Major Adverse Kidney Event within 30 days (MAKE30) criteria
Time frame: Through study completion, an average of twelve months
The proportion of patients meeting one or more criteria for the Major Adverse Kidney Events within 30 days (MAKE30) composite of death, new renal replacement therapy, or persistent creatinine elevation ≥ 200% of baseline, all censored at the first of hospital discharge or 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 30094049Mohamadlou H, Lynn-Palevsky A, Barton C, Chettipally U, Shieh L, Calvert J, Saber NR, Das R. Prediction of Acute Kidney Injury With a Machine Learning Algorithm Using Electronic Health Record Data. Can J Kidney Health Dis. 2018 Jun 8;5:2054358118776326. doi: 10.1177/2054358118776326. eCollection 2018.
- Individual participant dataSharing planned: Undecided
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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.
The record lists no contact. Use the official record to reach the sponsor.
Questions about this trial
What is this clinical trial studying?
A Phase 2 study testing Previse in people with Acute Kidney Injury, 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 11 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.
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?
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?
Incidence of adverse kidney events as assessed by Major Adverse Kidney Event within 30 days (MAKE30) criteria (time frame: Through study completion, an average of twelve 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 NCT04200950, which also lists 1 linked publication.
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.