Prediction of Patient Deterioration Using Machine Learning

• Conditions: Chronic Obstructive Pulmonary Disease; Asthma; Gout Flare; Chronic Kidney Diseases; Anticoagulants; Increased; Infection; Atrial Fibrillation Rapid; Heart Failure; Hypertensive Urgency
• Interventions: Other: Traditional vital sign alarms versus the BioVitals Index vs the National Early Warning Score 2

• Registration Number: NCT05045742
• Lead Sponsor: Brigham and Women's Hospital

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.

Detailed Description

Not available

Study Timeline

• Study Start: 2021-03-20
• Study First Submitted: 2021-04-14
• Status Verified: 2021-09
• Study First Submitted QC: 2021-09-15
• Last Update Submitted: 2021-09-15
• Study First Posted: 2021-09-16
• Last Update Posted: 2021-09-16
• Primary Completion: 2021-12-01
• Study Completion: 2021-12-01

Recruitment & Eligibility

• Status: UNKNOWN
• Sex: All
• Target Recruitment: 500

Inclusion Criteria

Cared for in the Brigham and Women's Home Hospital study

Exclusion Criteria

Incomplete continuous monitoring data

Study & Design

• Study Type: OBSERVATIONAL
• Study Design: Not specified

Arm && Interventions

Group	Intervention	Description
Validation	Traditional vital sign alarms versus the BioVitals Index vs the National Early Warning Score 2	A subset of patients that are "held back" and used to validate the algorithm's accuracy.
Training	Traditional vital sign alarms versus the BioVitals Index vs the National Early Warning Score 2	A subset of patients that are used to train the machine learning algorithm.

Primary Outcome Measures

Name	Time	Method
Alarm burden	From admission to discharge, measured in hours, on average 5 days	The number of alarms fired per patient per hour

Secondary Outcome Measures

Name	Time	Method
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.
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).
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).
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).

Trial Locations

Locations (2)

Brigham and Women's Faulkner Hospital; 🇺🇸 Boston, Massachusetts, United States

Brigham and Women's Hospital; 🇺🇸 Boston, Massachusetts, United States