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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.
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| Label | Type | Description | Intervention Names |
|---|---|---|---|
| Fluid treatment-specific algorithm | Experimental | The experimental arm will involve patients monitored by the fluid treatment-customized version of InSight. |
|
| Standard InSight | Active Comparator | The control arm will involve patients monitored with the standard, non-treatment specific version of InSight. |
|
| Name | Type | Description | Arm Group Labels | Other Names |
|---|---|---|---|---|
| Treatment-specific InSight | Diagnostic 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. |
| Measure | Description | Time Frame |
|---|---|---|
| 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 |
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Inclusion Criteria:
Exclusion Criteria:
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| Name | Role | Phone | Extension | |
|---|---|---|---|---|
| Qingqing Mao, PhD | Contact | 5108269508 | qmao@dascena.com |
| Name | Affiliation | Role |
|---|---|---|
| Qingqing Mao, PhD | Dascena, Inc. | Principal Investigator |
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| PubMed Identifier | Type | Citation | Retractions |
|---|---|---|---|
| 27699003 | Background | 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. | |
| 29435343 | Background |
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| ID | Term |
|---|---|
| D018805 | Sepsis |
| D012772 | Shock, Septic |
| D004194 | Disease |
| ID | Term |
|---|---|
| D007239 | Infections |
| D018746 | Systemic Inflammatory Response Syndrome |
| D007249 | Inflammation |
| D010335 | Pathologic Processes |
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|
| InSight | Diagnostic Test | The non-customized InSight algorithm which draws information from a patient's electronic health record (EHR) to predict the onset of severe sepsis. |
|
| 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. |
| 29374661 | Background | Mao 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. |
| D013568 |
| Pathological Conditions, Signs and Symptoms |
| D012769 | Shock |