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| Name | Class |
|---|---|
| Cabell Huntington Hospital | OTHER |
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In this prospective study, the ability of a machine learning algorithm to predict sepsis and influence clinical outcomes, will be investigated at Cabell Huntington Hospital (CHH).
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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 CHH 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 CHH 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 |
|---|---|---|
| In-hospital mortality | Through study completion, an average of 30 days |
| Measure | Description | Time Frame |
|---|---|---|
| Hospital length of stay | Through study completion, an average of 30 days |
| Measure | Description | Time Frame |
|---|---|---|
| Hospital readmission | Through study completion, an average of 30 days | |
| ICU length of stay | Through study completion, an average of 30 days |
Inclusion Criteria:
Exclusion Criteria:
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| Name | Affiliation | Role |
|---|---|---|
| Hoyt Burdick | Cabell Huntington Hospital | Principal Investigator |
| Facility | Status | City | State | ZIP | Country | Contacts |
|---|---|---|---|---|---|---|
| Cabell Huntington Hospital | Huntington | West Virginia | 25701 | United States |
| PubMed Identifier | Type | Citation | Retractions |
|---|---|---|---|
| 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. | |
| 27208704 | Background |
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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 CHH electronic health record, healthcare provider follows standard practices in assessing possible (severe) sepsis and intervening accordingly. |
|
| 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. |
| D013568 |
| Pathological Conditions, Signs and Symptoms |
| D012769 | Shock |