A Clinical Trial to Evaluate the Efficacy of the Morley Medical Sepsis (MMS) Software Device in Predicting Sepsis in Adult Patients Using Artificial Intelligence (AI) Machine Learning Algorithms

Sepsis Clinical Trial

Official title:

A Non-Randomized Clinical Trial to Investigate the Efficacy of the Morley Medical Sepsis (MMS) Software Device in the Prediction and Early Detection of Sepsis

Clinical Trial Details — Status: Not yet recruiting

Administrative data

• NCT number: NCT04606862
• Other study ID #: MMSEP1901
• Status: Not yet recruiting
• Phase: N/A
• Start date: October 31, 2020
• Est. completion date: December 31, 2020

Study information

• Verified date: October 2020
• Source: Morley Medical
• Contact: Kiki Diorgu, MD, MBA
• Phone: 4048911750
• Email: adiorgu[@]morleymed.com
• Is FDA regulated: No
• Study type: Interventional

Clinical Trial Summary

This is a pivotal medical device clinical trial evaluating the clinical outcomes in hospitalized patients monitored with the Morley Medical Sepsis Software Device. The device uses unique AI machine learning algorithms to analyze patient data in real time and generate clinical decision support sepsis risk predictions for clinicians.

Recruitment information / eligibility

• Status: Not yet recruiting
• Enrollment: 10000
• Est. completion date: December 31, 2020
• Est. primary completion date: December 31, 2020
• Accepts healthy volunteers: Accepts Healthy Volunteers
• Gender: All
• Age group: 18 Years and older

Eligibility

Inclusion Criteria:

• Male or female patient 18 years of age or older
• Patient is admitted or had been admitted to a participating healthcare facility

Exclusion Criteria:

• Sepsis diagnosis present on admission
• Involvement in a clinical trial of another investigational product with similar purpose

Related Conditions & MeSH terms

• Sepsis
• Toxemia

Intervention

Device:
• Morley Medical Sepsis Software Device
The Morley Medical Sepsis (MMS) Software Device is a predictive analytics, stand-alone, cloud-based software system with no hardware components. The software acquires patient data from the electronic medical record, processes the data using unique artificial intelligence (AI) powered algorithms, and generates clinical decision support outputs that aid in the proactive delivery of customized and efficient care for patients. The software output is made available to the end users (trained medical professionals) via an intuitive user interface displayed on desktop computers or mobile communication devices such as laptops, smartphones or tablets.

Locations

Country	Name	City	State
n/a

Sponsors (2)

Lead Sponsor	Collaborator
Morley Medical	Morley Research Consortium

Outcome

Type	Measure	Description	Time frame	Safety issue
Primary	In-hospital sepsis prevalence		Up to 8 weeks
Primary	In-hospital sepsis related 30-day mortality		30 days
Secondary	In-hospital all-cause 30-day mortality		30 days
Secondary	Hospital length of stay		Up to 8 weeks
Secondary	Hospital re-admission		Up to 8 weeks
Secondary	Time of initial IV fluids administration		Day 1 to Day 30, or until discharge
Secondary	Time of initial vasopressors administration		Day 1 to Day 30, or until discharge
Secondary	Time of initial antibiotics administration		Day 1 to Day 30, or until discharge
Secondary	Time of initial blood microbiology culture		Day 1 to Day 30, or until discharge
Secondary	Sepsis related adverse outcomes (septic shock)		Day 1 to Day 30, or until discharge
Secondary	Sepsis prediction to onset time		Day 1 to Day 30, or until discharge
Secondary	Sensitivity and specificity of sepsis prediction		Day 1 to Day 30, or until discharge