Anticoagulants; Increased, Asthma, Atrial Fibrillation Rapid, Chronic Kidney Diseases, Chronic Obstructive Pulmonary Disease, Gout Flare, Heart Failure, Hypertensive Urgency, Infection
Conditions
Brief 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.
Interventions
OTHERTraditional 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
Sponsors
Brigham and Women's Hospital
Biofourmis Inc.
Study design
Observational model
COHORT
Time perspective
RETROSPECTIVE
Eligibility
Sex/Gender
ALL
Age
18 Years to No maximum
Healthy volunteers
No
Inclusion criteria
Cared for in the Brigham and Women's Home Hospital study
Exclusion criteria
Incomplete continuous monitoring data
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| Alarm burden | From admission to discharge, measured in hours, on average 5 days | The number of alarms fired per patient per hour |
Secondary
| Measure | Time frame | Description |
|---|---|---|
| Sensitivity for recognition of a safety composite | From admission to discharge, on average 5 days | 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). |
| Specificity for recognition of a safety composite | From admission to discharge, on average 5 days | 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). |
| Positive predictive value for recognition of a safety composite | From admission to discharge, on average 5 days | 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). |
| Negative predictive value for recognition of a safety composite | From admission to discharge, on average 5 days | 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). |
| Rate of alarms with clinical utility | From admission to discharge, on average 5 days | 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. |
Countries
United States
Contacts
PRINCIPAL_INVESTIGATORDavid Levine, MD MPH MA
Associate Physician
Outcome results
None listed