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Clinical Trial Details — Status: Active, not recruiting

Administrative data

NCT number NCT05045742
Other study ID # 2017P002583d
Secondary ID
Status Active, not recruiting
Phase
First received
Last updated
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
Health authority
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

Study Design


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
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