Sepsis Clinical Trial
Official title:
Subpopulation-Specific Sepsis Identification Using Machine Learning
NCT number | NCT03644940 |
Other study ID # | 18-347718 |
Secondary ID | |
Status | Withdrawn |
Phase | Phase 2 |
First received | |
Last updated | |
Start date | December 2020 |
Est. completion date | July 2021 |
Verified date | September 2021 |
Source | Dascena |
Contact | n/a |
Is FDA regulated | No |
Health authority | |
Study type | Interventional |
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 subpopulation-optimized 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, in-hospital SIRS-based mortality. The secondary endpoints will be in-hospital severe sepsis/shock-coded mortality, SIRS-based hospital length of stay, and severe sepsis/shock-coded hospital length of stay.
Status | Withdrawn |
Enrollment | 0 |
Est. completion date | July 2021 |
Est. primary completion date | July 2021 |
Accepts healthy volunteers | Accepts Healthy Volunteers |
Gender | All |
Age group | 18 Years and older |
Eligibility | All adults above age 18 who are a member of one of the eight subpopulations studied in this trial (Cardiology, Gastroenterology (GI), Intensive Care Unit (ICU), Medicine, Oncology, Surgery, Transplant, and Emergency Department (ED)) are eligible to participate in the study. |
Country | Name | City | State |
---|---|---|---|
n/a |
Lead Sponsor | Collaborator |
---|---|
Dascena | University of California, San Francisco |
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. eCollection 2016 Nov. — View Citation
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. — View Citation
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. — View Citation
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. — View Citation
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. — View Citation
Type | Measure | Description | Time frame | Safety issue |
---|---|---|---|---|
Primary | In-hospital SIRS-based mortality | Mortality attributed to patients meeting two or more SIRS criteria at some point during their stay | Through study completion, an average of 8 months | |
Secondary | In-hospital severe sepsis/shock-coded mortality | Mortality attributed to patients coded as severe sepsis or septic shock | Through study completion, an average of 8 months | |
Secondary | SIRS-based hospital length of stay | Hospital length of stay attributed to patients meeting two or more SIRS criteria at some point during their stay | Through study completion, an average of 8 months | |
Secondary | Severe sepsis/shock-coded hospital length of stay | Hospital length of stay attributed to patients coded as severe sepsis or septic shock | Through study completion, an average of 8 months |
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