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| Name | Class |
|---|---|
| University of California, San Francisco | OTHER |
A sepsis early warning predictive algorithm, InSight, has been developed and validated on a large, diverse patient cohort. In this prospective study, the ability of InSight to predict severe sepsis patients is investigated. Specifically, InSight is compared with a well established severe sepsis detector in the UCSF electronic health record (EHR).
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| Label | Type | Description | Intervention Names |
|---|---|---|---|
| With InSight | Experimental | Healthcare provider receives an alert from InSight for patients trending towards severe sepsis. Healthcare provider also receives information from the severe sepsis detector in the UCSF electronic health record. |
|
| Without InSight | Active Comparator | Healthcare provider does not receive any alerts from InSight. Healthcare provider receives information from the severe sepsis detector in the UCSF electronic health record. |
|
| Name | Type | Description | Arm Group Labels | Other Names |
|---|---|---|---|---|
| Severe Sepsis Prediction | Other | Upon receiving an InSight alert, healthcare provider follows standard practices in assessing possible (severe) sepsis and intervening accordingly. |
| Measure | Description | Time Frame |
|---|---|---|
| Hospital length of stay | Through study completion, an average of 45 days |
| Measure | Description | Time Frame |
|---|---|---|
| In-hospital mortality | Through study completion, an average of 45 days |
| Measure | Description | Time Frame |
|---|---|---|
| ICU length of stay | Through study completion, an average of 45 days |
Inclusion Criteria:
Exclusion Criteria:
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| Name | Affiliation | Role |
|---|---|---|
| Ritankar Das | Dascena | Principal Investigator |
| Facility | Status | City | State | ZIP | Country | Contacts |
|---|---|---|---|---|---|---|
| UCSF Moffit-Long Hospital | San Francisco | California | 94143 | United States |
| PubMed Identifier | Type | Citation | Retractions |
|---|---|---|---|
| 27026611 | Background | 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. | |
| 27489621 | Background | Calvert J, Desautels T, Chettipally U, Barton C, Hoffman J, Jay M, Mao Q, Mohamadlou H, Das R. High-performance detection and early prediction of septic shock for alcohol-use disorder patients. Ann Med Surg (Lond). 2016 May 10;8:50-5. doi: 10.1016/j.amsu.2016.04.023. eCollection 2016 Jun. |
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| ID | Term |
|---|---|
| D018805 | Sepsis |
| D012772 | Shock, Septic |
| ID | Term |
|---|---|
| D007239 | Infections |
| D018746 | Systemic Inflammatory Response Syndrome |
| D007249 | Inflammation |
| D010335 | Pathologic Processes |
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| Severe Sepsis Detection | Other | Upon receiving information from the severe sepsis detector in the UCSF electronic health record, healthcare provider follows standard practices in assessing possible (severe) sepsis and intervening accordingly. |
|
| 27208704 | Background | Calvert JS, Price DA, Chettipally UK, Barton CW, Feldman MD, Hoffman JL, Jay M, Das R. A computational approach to early sepsis detection. Comput Biol Med. 2016 Jul 1;74:69-73. doi: 10.1016/j.compbiomed.2016.05.003. Epub 2016 May 12. |
| 27694098 | Background | Desautels T, Calvert J, Hoffman J, Jay M, Kerem Y, Shieh L, Shimabukuro D, Chettipally U, Feldman MD, Barton C, Wales DJ, Das R. Prediction of Sepsis in the Intensive Care Unit With Minimal Electronic Health Record Data: A Machine Learning Approach. JMIR Med Inform. 2016 Sep 30;4(3):e28. doi: 10.2196/medinform.5909. |
| 29435343 | Derived | 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. |
| D013568 |
| Pathological Conditions, Signs and Symptoms |
| D012769 | Shock |