COVID-19 Clinical Trial
Official title:
A Rapid Diagnostic of Risk in Hospitalized Patients With COVID-19, Sepsis, and Other High-Risk Conditions to Improve Outcomes and Critical Resource Allocation Using Machine Learning
NCT number | NCT05893420 |
Other study ID # | 1.0 |
Secondary ID | |
Status | Recruiting |
Phase | N/A |
First received | |
Last updated | |
Start date | December 1, 2022 |
Est. completion date | June 30, 2025 |
In this study, the investigators will deploy a software-based clinical decision support tool (eCARTv5) into the electronic health record (EHR) workflow of multiple hospital wards. eCART's algorithm is designed to analyze real-time EHR data, such as vitals and laboratory results, to identify which patients are at increased risk for clinical deterioration. The algorithm specifically predicts imminent death or the need for intensive care unit (ICU) transfer. Within the eCART interface, clinical teams are then directed toward standardized guidance to determine next steps in care for elevated-risk patients. The investigators hypothesize that implementing such a tool will be associated with a decrease in ventilator utilization, length of stay, and mortality for high-risk hospitalized adults.
Status | Recruiting |
Enrollment | 30000 |
Est. completion date | June 30, 2025 |
Est. primary completion date | December 31, 2023 |
Accepts healthy volunteers | No |
Gender | All |
Age group | 18 Years and older |
Eligibility | Inclusion Criteria: - 18 years old - Admitted to an eCART-monitored medical-surgical unit (scoring location) Exclusion Criteria: - Younger than 18 years old - Not admitted to an eCART-monitored medical surgical unit (scoring location) |
Country | Name | City | State |
---|---|---|---|
United States | BayCare Health System | Clearwater | Florida |
United States | University of Wisconsin Health | Madison | Wisconsin |
Lead Sponsor | Collaborator |
---|---|
AgileMD, Inc. | BayCare Health System, Department of Health and Human Services, University of Chicago, University of Wisconsin, Madison |
United States,
Churpek MM, Yuen TC, Park SY, Meltzer DO, Hall JB, Edelson DP. Derivation of a cardiac arrest prediction model using ward vital signs*. Crit Care Med. 2012 Jul;40(7):2102-8. doi: 10.1097/CCM.0b013e318250aa5a. — View Citation
Churpek MM, Yuen TC, Winslow C, Robicsek AA, Meltzer DO, Gibbons RD, Edelson DP. Multicenter development and validation of a risk stratification tool for ward patients. Am J Respir Crit Care Med. 2014 Sep 15;190(6):649-55. doi: 10.1164/rccm.201406-1022OC. — View Citation
Kang MA, Churpek MM, Zadravecz FJ, Adhikari R, Twu NM, Edelson DP. Real-Time Risk Prediction on the Wards: A Feasibility Study. Crit Care Med. 2016 Aug;44(8):1468-73. doi: 10.1097/CCM.0000000000001716. — View Citation
Winslow CJ, Edelson DP, Churpek MM, Taneja M, Shah NS, Datta A, Wang CH, Ravichandran U, McNulty P, Kharasch M, Halasyamani LK. The Impact of a Machine Learning Early Warning Score on Hospital Mortality: A Multicenter Clinical Intervention Trial. Crit Care Med. 2022 Sep 1;50(9):1339-1347. doi: 10.1097/CCM.0000000000005492. Epub 2022 Aug 15. — View Citation
Type | Measure | Description | Time frame | Safety issue |
---|---|---|---|---|
Other | Sepsis Mortality | Hospital mortality, a measure of how many patients died in the hospital, will come from administrative data, specifically from the discharge disposition of each eCART elevated risk patient meeting Sep-1 criteria for sepsis. | The outcome of sepsis mortality will be tracked across 12 months | |
Other | Sepsis Length of Stay (LOS) | Total hospital length of stay (LOS) for patients with any elevated eCART score during hospitalization that met Sep-1 criteria for sepsis. | The outcome of sepsis length of stay (LOS) will be tracked across 12 months | |
Other | COVID-19 Mortality | Hospital mortality, a measure of how many patients died in the hospital, will come from administrative data, specifically from the discharge disposition of each eCART elevated risk patient with a COVID-19 diagnosis or positive COVID-19 test result. | The outcome of COVID-19 mortality will be tracked across 12 months | |
Other | COVID-19 Length of Stay (LOS) | Total hospital length of stay (LOS) for patients with any elevated eCART score during hospitalization with a COVID-19 diagnosis or positive COVID-19 test result. | The outcomes of COVID-19 length of stay (LOS) will be tracked across 12 months | |
Primary | Hospital mortality for elevated risk patients | Hospital mortality, a measure of how many patients died in the hospital, will come from administrative data, specifically from the discharge disposition of each eCART elevated risk patient. This data will be taken from the complete hospitalization, from admission to discharge. | The outcome of hospital mortality for elevated risk patients will be tracked across 12 months | |
Secondary | Total hospital length of stay (LOS) for elevated risk patients | Total hospital length of stay (LOS) for patients with any elevated eCART score during hospitalization, defined as the time period between hospital admission and discharge. LOS is defined as the time (hours or fraction of a day) from first vital sign to last vital sign within a patient encounter. | Total hospital length of stay (LOS) for elevated risk patients will be tracked across 12 months | |
Secondary | ICU-free days following an eCART elevation | 30-day ICU-free days, defined as the number of days patients were both alive and not being cared for in an ICU in the first 30 days following hospital admission with any elevated eCART score. Because death is biased toward fewer ICU days and is a competing outcome, patients who die prior to day 30 are assigned with 0 ICU-free days. | The outcome of 30-day ICU-free days will be tracked across 12 months | |
Secondary | Ventilator-free days following an eCART elevation | 30-day ventilator-free days, defined as the number of days patients were both alive and not mechanically ventilated in the first 30 days following hospital admission with any elevated eCART score. Because death is biased toward fewer ventilator days and is a competing outcome, patients who die prior to day 30 are assigned with 0 ventilator-free days. | The outcome of 30-day ventilator-free days will be tracked across 12 months |
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