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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 Gram type infection-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, time to antibiotic administration. The secondary endpoint will be reduction in the administration of unnecessary antibiotics, which includes reductions in secondary antibiotics and reductions in total time on antibiotics.
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| Label | Type | Description | Intervention Names |
|---|---|---|---|
| Gram type infection-specific algorithm | Experimental | The experimental arm will involve patients monitored by the Gram type infection-customized version of InSight. |
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| Standard treatment protocol | No Intervention | The control arm will involve patients treated with the regular diagnosis and treatment protocol for gram-type infection, where fluid cultures are run to determine infection type. |
| Name | Type | Description | Arm Group Labels | Other Names |
|---|---|---|---|---|
| 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 various Gram-type infections. |
| Measure | Description | Time Frame |
|---|---|---|
| Change in time to antibiotic administration | Change in time period between diagnosis of Gram infection and administration of antibiotics to treat infection | Through study completion, an average of 8 months |
| Measure | Description | Time Frame |
|---|---|---|
| Change in administration of unnecessary antibiotics | Changes in amount of secondary antibiotics administered | Through study completion, an average of 8 months |
| Change in administration of unnecessary antibiotics |
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Inclusion Criteria:
Exclusion Criteria:
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| Name | Affiliation | Role |
|---|---|---|
| Ritankar Das, MSc | Dascena | 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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Changes in total hours spent on antibiotics
| Through study completion, an average of 8 months |
| 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 |