Prediction of Patient Deterioration Using Machine Learning

Asthma Clinical Trial

Official title:

Prediction of Patient Deterioration Using Machine Learning

Clinical Trial Details — Status: Active, not recruiting

Administrative data

• NCT number: NCT05045742
• Other study ID #: 2017P002583d
• Status: Active, not recruiting
• Start date: March 20, 2021
• Est. completion date: December 1, 2021

Study information

• Verified date: September 2021
• Source: Brigham and Women's Hospital
• Contact: n/a
• Is FDA regulated: No
• Study type: Observational

Clinical Trial Summary

This is a retrospective observational study drawing on data from the Brigham and Women's Home Hospital database. Sociodemographic and clinic data from a training cohort were used to train a machine learning algorithm to predict patient deterioration throughout a patient's admission. This algorithm was then validated in a validation cohort.

Recruitment information / eligibility

• Status: Active, not recruiting
• Enrollment: 500
• Est. completion date: December 1, 2021
• Est. primary completion date: December 1, 2021
• Accepts healthy volunteers: No
• Gender: All
• Age group: 18 Years and older

Eligibility

Inclusion Criteria:

Cared for in the Brigham and Women's Home Hospital study Exclusion Criteria: Incomplete continuous monitoring data

Related Conditions & MeSH terms

• Anticoagulants; Increased
• Asthma
• Atrial Fibrillation
• Atrial Fibrillation Rapid
• Chronic Kidney Diseases
• Chronic Obstructive Pulmonary Disease
• Gout Flare
• Heart Failure
• Hypertensive Urgency
• Infection
• Kidney Diseases
• Lung Diseases, Obstructive
• Pulmonary Disease, Chronic Obstructive
• Renal Insufficiency, Chronic

Intervention

Other:
• Traditional vital sign alarms versus the BioVitals Index vs the National Early Warning Score 2
We will retrospectively compare the alarms produced from traditional vital sign alarms (thresholds set by clinicians) versus the BioVitals Index vs the National Early Warning Score 2

Locations

Country	Name	City	State
United States	Brigham and Women's Faulkner Hospital	Boston	Massachusetts
United States	Brigham and Women's Hospital	Boston	Massachusetts

Sponsors (2)

Lead Sponsor	Collaborator
Brigham and Women's Hospital	Biofourmis Inc.

Country where clinical trial is conducted

United States, 

Outcome

Type	Measure	Description	Time frame	Safety issue
Primary	Alarm burden	The number of alarms fired per patient per hour	From admission to discharge, measured in hours, on average 5 days
Secondary	Sensitivity for recognition of a safety composite	The sensitivity (true positives divided by condition positives) for detection of a safety composite (overnight visit, extra unplanned visit, transfer back to the hospital, death during admission, delirium, loss of consciousness, or other major event).	From admission to discharge, on average 5 days
Secondary	Specificity for recognition of a safety composite	The specificity (true negatives divided by condition negatives) for detection of a safety composite (overnight visit, extra unplanned visit, transfer back to the hospital, death during admission, delirium, loss of consciousness, or other major event).	From admission to discharge, on average 5 days
Secondary	Positive predictive value for recognition of a safety composite	The positive predictive value (true positives divided by the sum of true positives plus false positives) for detection of a safety composite (overnight visit, extra unplanned visit, transfer back to the hospital, death during admission, delirium, loss of consciousness, or other major event).	From admission to discharge, on average 5 days
Secondary	Negative predictive value for recognition of a safety composite	The negative predictive value (true negatives divided by the sum of true negatives plus false negatives) for detection of a safety composite (overnight visit, extra unplanned visit, transfer back to the hospital, death during admission, delirium, loss of consciousness, or other major event).	From admission to discharge, on average 5 days
Secondary	Rate of alarms with clinical utility	We will use general estimating equations (GEE) with three outcomes per patient (the number of clinically important alarms for BioVitals, NEWS2, and traditional vital signs); the GEE will account for the clustering between the three outcomes on a patient. The GEE will use a negative binomial marginal model with a log-link for the number of alarms with clinical utility and an offset for log length-of stay (in hours); with this model, we model the rate per hour of number of alarms with clinical utility with BI, NEWS2, and traditional vital signs. The main covariate in the negative binomial model will be a three-level covariate for method: BI vs NEWS2 vs traditional vital signs, and the exponential of the effect of this covariate will be a pair-wise rate ratio for BI vs NEWS2 vs traditional vital signs.	From admission to discharge, on average 5 days