An observational study in Severe Sepsis and Severe Sepsis Without Septic Shock, sponsored by Beckman Coulter, Inc.. Enrolling by invitation at 10 sites in United States. Open to participants aged 18 Years to 89 Years. Per ClinicalTrials.gov, last updated 2024-06-21.
Sponsored by Beckman Coulter, Inc. (part of Danaher Corporation) · Observational
Among 11 studies in Sepsis by Beckman Coulter, Inc. — 1 open to participants now, 2 under way, none of them phase 3. Sepsis · Beckman Coulter, Inc..
Among the 11 studies in Sepsis by Beckman Coulter, Inc., this one has the largest planned enrollment, is one of 3 that started in 2021, is one of 2 of another kind, and was the 9th registered.
2,416 studies on the registry are indexed under Sepsis; 541 are open to participants now.
This study's planned enrollment of 40,000 is above the median of 150 across 1,162 observational studies indexed under Sepsis.
Browse Sepsis studies →1 of the 11 studies in Sepsis by Beckman Coulter, Inc. is open now, in 1 country. Each count lists the studies.
Of the 318 Sepsis studies recruiting now, 213 (67%) set no upper age limit and 53 (17%) stop at 75 or younger; 68 take children under 18; 42 take healthy volunteers.
This study: 18 Years to 89 Years · all sexes.
The full table on the hub →11 studies; the count open now beside each. This study's row is marked.
First in 2017; this study’s year is marked. 3 started in 2021.
Median enrollment 947 across the 11 observational studies. Of the 9 that completed or were terminated, 0 (0%) have results posted.
Beckman Coulter, Inc. is the lead sponsor of 31 studies on the registry; 1 is open to participants now.
None of the studies under way reaches primary completion within a year.
Among the 11 studies in Sepsis by Beckman Coulter, Inc., from their registry updates:
Nothing recorded among them since 2026-09-25.
This protocol will collect real-world data retrospectively from the electronic health record (EHR) as data obtained from the delivery of routine medical care to develop a machine learning (ML)-based Clinical Decision Support (CDS) system for severe sepsis prediction and detection.
The purpose of this study is to gather data for the clinical development of the Sepsis Onset Warning System (SOWS) Software as Medical Device (SaMD) product to support a De Novo FDA submission and commercialization in the United States. Product development of SOWS is funded in part with federal funds from the Department of Health and Human Services; Office of the Assistant Secretary for Preparedness and Response; Biomedical Advanced Research and Development Authority.
Data will be obtained from passive prospective collection of patient encounter data throughout the duration of the planned study to support the product development life cycle activities associated with developing the Sepsis Onset Warning System (SOWS) for severe sepsis risk detection. Inputs from patient health records in combination with proprietary hematology parameters developed by Beckman Coulter, such as Monocyte Distribution Width (MDW), will be used. The SOWS tool will look to use clinical measurements which are commonly and reliably available in the EHR as structured data elements, such as heart rate, temperature, blood pressure, and laboratory results and account for changes in these values over time.
This protocol has open enrollment to all genders, ages, and health statuses in patients admitted to the hospital or presenting to the ED.
Go through the criteria one by one — yes, no or not sure — and take the list to your doctor or the study team. Kept in this browser only.
Exclusion Criteria:
The primary endpoint for this study is defined as the presence of sufficient data for SOWS training and algorithm development to proceed with subsequent validation. To provide sufficient data subsets (severe sepsis EHR encounters) for training and validation of the Sepsis Onset Warning System algorithm. There will not be any interventions administered.
Severe Sepsis
Identify patients having Severe Sepsis with the use of electronic health data
Time frame: Within 6 hours from presentation to the emergency department
Mortality
Hospital mortality at hospital for Severe Sepsis patients identified by algorithm using electronic health data as potential benefits for increased early detection of risk of severe sepsis
Time frame: Within 6hours from presentation to the emergency department
Length of Stay
Determine length of stay at hospital for Severe Sepsis patients identified by algorithm using electronic health data as potential benefits for increased early detection of risk of severe sepsis
Time frame: Within 6hours from presentation to the emergency department
Re-admission Rates
Determine potential reduction of hospital readmission rates for Severe Sepsis patients identified by algorithm using electronic health data as potential benefits for increased early detection of risk of severe sepsis
Time frame: Within 6hours from presentation to the emergency department
Plan to share: No — Data elements will be retrospectively extracted from electronic health records from the respective sites and fed into the algorithm for performance testing. These data elements will not be shared with other researchers
No publications or documents are linked to this record.
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No contact was published for this record. The registry link below has the sponsor’s details.
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