NCT03655626

Implementation and Evaluations of Sepsis Watch

Completed NA Last updated 1 August 2019

What this trial tests

NA trial testing Sepsis Watch in Sepsis in 32,003 participants. Completed in 5 July 2019.

Timeline

5 November 2018

Primary endpoint
5 July 2019

5 July 2019

Quick facts

Lead sponsor	Duke University
Phase	NA
Status	Completed
Study type	INTERVENTIONAL
Allocation	na
Design	single group
Masking	none
Primary purpose	treatment
Enrollment	32,003
Start date	5 November 2018
Primary completion	5 July 2019
Estimated completion	5 July 2019
Sites	1 location across United States

Drugs / interventions tested

• Sepsis Watch

Conditions studied

• Sepsis — all drugs for Sepsis →
• Severe Sepsis — all drugs for Severe Sepsis →
• Septic Shock — all drugs for Septic Shock →

Sponsor

Duke University

Who can join

18 and older, any sex, with Sepsis or Severe Sepsis. Patients with the condition only — healthy volunteers not accepted.

Sponsor's own description

The purpose of this study is to study the implementation and impact of an early warning system to detect and treat sepsis in the emergency room. We are observing the implementation of a Sepsis Machine Learning Model on all Adult patients. All data (observations field notes, interview recording \& transcripts, and survey responses) will be stored on HIPAA-compliant Duke servers behind the Duke firewall, and requiring password-protected user authentication to access. The risk to patients is minimal. The two risks to interviewed clinical staff we have identified involve loss of work time and anonymity.

Publications & conference data

3 peer-reviewed publications reference this trial (live from Europe PMC):

1. Real-World Integration of a Sepsis Deep Learning Technology Into Routine Clinical Care: Implementation Study.
   Sendak MP, Ratliff W, Sarro D, Alderton E, et al · · 2020 · cited 121× · PMID 32673244 · DOI 10.2196/15182
2. Machine learning for early detection of sepsis: an internal and temporal validation study.
   Bedoya AD, Futoma J, Clement ME, Corey K, et al · · 2020 · cited 62× · PMID 32734166 · DOI 10.1093/jamiaopen/ooaa006
3. Evidence-based Clinical Decision Support Systems for the prediction and detection of three disease states in critical care: A systematic literature review.
   Medic G, Kosaner Kließ M, Atallah L, Weichert J, et al · · 2019 · cited 20× · PMID 31824670 · DOI 10.12688/f1000research.20498.2