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Development and Validation of a Machine Learning COVID-19 Veteran (COVet) Deterioration Risk Score.

Sushant Govindan¹, Alexandra Spicer², Matthew Bearce¹

¹MInDSET Service Line, Kansas City Veterans Affairs Hospital, Kansas City, MO.

Critical Care Explorations

|July 19, 2024

Summary

This summary is machine-generated.

A new COVid Veteran (COVet) score accurately predicts clinical deterioration in hospitalized COVID-19 patients. This machine learning model outperforms existing scores in both Veteran and non-Veteran populations, enabling earlier interventions.

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A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

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Published on: January 11, 2020

Area of Science:

Medical Informatics
Machine Learning in Healthcare
COVID-19 Research

Background:

Clinical deterioration in hospitalized COVID-19 patients requires accurate early warning scores.
Existing scores may not adequately capture the nuances of COVID-19 progression, particularly in Veteran populations.
There is a need for a validated score developed and tested within a Veteran cohort.

Purpose of the Study:

To develop the COVid Veteran (COVet) score for predicting clinical deterioration in hospitalized Veterans with COVID-19.
To externally validate the COVet score in both Veteran and non-Veteran patient samples.
To compare the performance of the COVet score against the National Early Warning Score (NEWS).

Main Methods:

Utilized eXtreme Gradient Boosting machine learning on a large dataset of hospitalized COVID-19 patients from the Veterans Health Administration (VHA).
Included demographics, vital signs, flowsheet data, and laboratory values as predictor variables.
Assessed model performance using the area under the receiver operating characteristic curve (AUC) and compared it with NEWS.

Main Results:

The COVet score demonstrated excellent discrimination in external validation cohorts, with AUCs of 0.88 (Veteran) and 0.86 (non-Veteran).
COVet significantly outperformed NEWS in both Veteran (0.88 vs. 0.79) and non-Veteran (0.86 vs. 0.79) samples (p < 0.01).
Key predictors included eosinophil percentage, mean oxygen saturation, and worst mental status over 24 hours.

Conclusions:

A highly accurate early warning score (COVet) was developed and validated for COVID-19 patients using machine learning.
The COVet score shows superior performance compared to NEWS in both Veteran and non-Veteran populations.
This model has the potential to facilitate earlier identification and treatment, potentially improving patient outcomes.