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Completed

NCT Number: NCT05045742

Prediction of Patient Deterioration Using Machine Learning

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.

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Key information

Conditions

Infection Anticoagulants; Increased Asthma Atrial Fibrillation Rapid Bronchial Diseases Cardiovascular Diseases Chronic Disease Chronic Kidney Diseases Chronic Obstructive Pulmonary Disease Disease Attributes Female Urogenital Diseases Female Urogenital Diseases and Pregnancy Complications Gout Flare Heart Diseases Heart Failure Hypersensitivity Hypersensitivity, Immediate Hypertension Hypertensive Crisis Hypertensive Urgency Immune System Diseases Infections Kidney Diseases Lung Diseases Lung Diseases, Obstructive Male Urogenital Diseases Pathologic Processes Pathological Conditions, Signs and Symptoms Pulmonary Disease, Chronic Obstructive Renal Insufficiency Renal Insufficiency, Chronic Respiratory Hypersensitivity Respiratory Tract Diseases Urogenital Diseases Urologic Diseases Vascular Diseases

Age range

18 year and older

Sex eligibility

All sexes

Study type

Observational

Primary location

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

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Who can participate

Healthy volunteers accepted: No

Only the study team can determine whether someone qualifies for participation.

Inclusion criteria

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

Exclusion criteria

Incomplete continuous monitoring data

Treatment and study plan

Traditional vital sign alarms versus the BioVitals Index vs the National Early Warning Score 2

Other

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

Primary outcomes

  1. Alarm burden

    Time frame: From admission to discharge, measured in hours, on average 5 days

    The number of alarms fired per patient per hour

Secondary outcomes

  1. Sensitivity for recognition of a safety composite

    Time frame: 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).

  2. Specificity for recognition of a safety composite

    Time frame: 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).

  3. Positive predictive value for recognition of a safety composite

    Time frame: 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).

  4. Negative predictive value for recognition of a safety composite

    Time frame: 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).

  5. Rate of alarms with clinical utility

    Time frame: 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.

Sponsors and collaborators

Lead sponsor

Brigham and Women's Hospital

Other

Collaborators

  • Biofourmis Inc.

Registry information

Important dates

Study start
2021
Primary completion
2025
Study completion
2026
First posted
Sep 16, 2021
Registry last updated
Mar 17, 2026

OpenTrials presents study information sourced from ClinicalTrials.gov. The official registry record should be consulted for the latest information.

View the official ClinicalTrials.gov record (opens in a new tab)

This listing is for discovery and informational purposes only. It is not medical advice, does not guarantee that a study is recruiting, and does not determine eligibility. Contact the study team and a qualified healthcare professional when considering participation.

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