Machine Learning Sepsis Alert Notification Using Clinical Data (HindSight P2)
Using Clinical Treatment Data in a Machine Learning Approach for Sepsis Detection
Study Overview
Status
Status
Conditions
Conditions
Intervention / Treatment
Intervention / Treatment
Detailed Description
Study Type
Study Type
Enrollment (Anticipated)
Enrollment
Phase
Phase
- Phase 2
Contacts and Locations
Study Contact
Study Contact
- Name: Jana Hoffman, PhD
- Phone Number: 2158806619
- Email: jana@dascena.com
Study Contact Backup
- Name: Gina Barnes, MPH
- Phone Number: 2158806619
- Email: gbarnes@dascena.com
Study Locations
-
-
Massachusetts
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Springfield, Massachusetts, United States, 01199
- Recruiting
- Baystate Health
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Contact:
- Gregory Braden
-
-
New Jersey
-
Camden, New Jersey, United States, 08103
- Recruiting
- Cooper University Health Care
-
Contact:
- Sharad Patel
-
Contact:
- Snehal Gandhi
-
Cape May, New Jersey, United States, 08210
- Recruiting
- Cape Regional Medical Center
-
Contact:
- Andrea McCoy
-
-
Participation Criteria
Eligibility Criteria
Eligibility Criteria
Ages Eligible for Study
Accepts Healthy Volunteers
Genders Eligible for Study
Description
Inclusion Criteria:
- During the study period, all patients over the age of 18 presenting to the emergency department or admitted to an inpatient unit at the participating facilities will automatically be enrolled in the study, until the enrollment target for the study is met
Exclusion Criteria:
- Patients under the age of 18
- Prisoners
Study Plan
How is the study designed?
Design Details
- Primary Purpose: OTHER
- Allocation: RANDOMIZED
- Interventional Model: PARALLEL
- Masking: TRIPLE
Number of Arms
Arms and Interventions
Participant Group / ArmParticipant Group / Arm |
Intervention / TreatmentIntervention / Treatment |
|---|---|
|
EXPERIMENTAL: Experimental
The experimental arm will involve patients monitored by HindSight.
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HindSight will examine the dynamic trends of clinical measurements taken from a patient's EHR and analyzes correlations between vital signs to alert for the onset of sepsis.This machine learning based tool is optimized by encoder and utilizes periodic retraining to improve its performance over time.
Other Names:
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ACTIVE_COMPARATOR: Control
The control arm will involve patients monitored by InSight.
|
Compared to the ability of the InSight software's recognition of sepsis onset to HindSight's performance.
The study determines if the HindSight software has equivalent or better performance than the InSight software.
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What is the study measuring?
Primary Outcome Measures
Primary Outcome Measures
Outcome Measure |
Measure Description |
Time Frame |
|---|---|---|
|
Rate of reduction in false alerts
Time Frame: Through study completion, human subjects involvement will occur for an average of eight months
|
The primary outcome measure of interest will be false alert reduction.
Successful completion of Aim 1 will be demonstrated by a positive predictive value (PPV) in a live clinical setting for which the lower bound of the 95% confidence interval meets or exceeds the benchmark from prior retrospective studies.
Meeting the retrospective PPV benchmark indicates that prospective CDS quality reflects retrospective CDS quality, and is sufficiently high to reduce alarm fatigue and improve clinical utility.
Success of Aim 2 is contingent upon achieving a 15% relative reduction in false alerts when comparing between the two treatment arms (p < 0.05; Fisher's Exact Test).
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Through study completion, human subjects involvement will occur for an average of eight months
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Collaborators and Investigators
Sponsor
Sponsor
Collaborators
Collaborators
Investigators
Investigators
- Principal Investigator: Jana Hoffman, PhD, Dascena
Publications and helpful links
General 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.
- 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.
- 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.
- 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.
- Shimabukuro 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.
Study record dates
Study Major Dates
Study Start (ACTUAL)
Study Start
Primary Completion (ANTICIPATED)
Primary Completion
Study Completion (ANTICIPATED)
Study Completion
Study Registration Dates
First Submitted
First Submitted
First Submitted That Met QC Criteria
First Submitted That Met QC Criteria
First Posted (ACTUAL)
First Posted
Study Record Updates
Last Update Posted (ACTUAL)
Last Update Posted
Last Update Submitted That Met QC Criteria
Last Update Submitted That Met QC Criteria
Last Verified
Last Verified
More Information
Terms related to this study
Additional Relevant MeSH Terms
Other Study ID Numbers
Other Study ID Numbers
- 19-569185
- 2R44AA030000-02 (NIH)
Plan for Individual participant data (IPD)
Plan to Share Individual Participant Data (IPD)?
Drug and device information, study documents
Studies a U.S. FDA-regulated drug product
Studies a U.S. FDA-regulated device product
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