Prediction of Patient Deterioration Using Machine Learning

Study Overview
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Brief Summary
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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.

Official Title
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Prediction of Patient Deterioration Using Machine Learning

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

Intervention / Treatment
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* Other: Traditional vital sign alarms versus the BioVitals Index vs the National Early Warning Score 2


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

Ages Eligible for Study
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Minimum Age: 18 Years

Sexes Eligible for Study
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All

Accepts Healthy Volunteers
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No

Study Plan
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How is the study designed?
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Arms and Interventions

| Participant Group/Arm | Intervention/Treatment |
| --- | --- |
| Training<br>A subset of patients that are used to train the machine learning algorithm. | Other: Traditional vital sign alarms versus the BioVitals Index vs the National Early Warning Score 2<br>* 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<br>|
| Validation<br>A subset of patients that are held back and used to validate the algorithm's accuracy. | Other: Traditional vital sign alarms versus the BioVitals Index vs the National Early Warning Score 2<br>* 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<br>|

What is the study measuring?
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Primary Outcome Measures

| Outcome Measure | Measure Description | Time Frame |
| --- | --- | --- |
| Alarm burden | The number of alarms fired per patient per hour | From admission to discharge, measured in hours, on average 5 days | 

Secondary Outcome Measures

| Outcome Measure | Measure Description | Time Frame |
| --- | --- | --- |
| 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 | 
| 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 | 
| 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 | 
| 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 | 
| 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 |