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Related Concept Videos

Receiver Operating Characteristic Plot01:15

Receiver Operating Characteristic Plot

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A ROC (Receiver Operating Characteristic) plot is a graphical tool used to assess the performance of a binary classification model by illustrating the trade-off between sensitivity (true positive rate) and specificity (false positive rate). By plotting sensitivity against 1 - specificity across various threshold settings, the ROC curve shows how well the model distinguishes between classes, with a curve closer to the top-left corner indicating a more accurate model. The area under the ROC curve...
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Early Warning Scores With and Without Artificial Intelligence.

Dana P Edelson1,2, Matthew M Churpek3, Kyle A Carey1

  • 1Section of Hospital Medicine, University of Chicago, Chicago, Illinois.

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|October 15, 2024
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The eCART artificial intelligence (AI) score demonstrated superior performance in identifying clinical deterioration compared to other AI and non-AI early warning scores. This AI tool provided earlier detection and fewer false alarms, allowing for timely intervention in hospital settings.

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Area of Science:

  • Clinical Informatics
  • Artificial Intelligence in Healthcare
  • Patient Safety

Background:

  • Early warning scores (EWS) are crucial for detecting clinical deterioration in hospitalized patients.
  • Existing EWS have varying comparative performance, necessitating further evaluation.

Purpose of the Study:

  • To compare the performance of three proprietary artificial intelligence (AI) EWS against three publicly available simple aggregated weighted scores.
  • To assess the accuracy and lead time provided by different EWS in identifying patient deterioration.

Main Methods:

  • A retrospective cohort study included over 360,000 adult medical-surgical ward encounters across seven hospitals.
  • Six EWS were evaluated: Epic Deterioration Index (EDI), Rothman Index (RI), eCARTv5 (eCART), Modified Early Warning Score (MEWS), National Early Warning Score (NEWS), and NEWS2.
  • Clinical deterioration was defined as transfer to ICU or death within 24 hours.

Main Results:

  • eCART achieved the highest area under the receiver operating characteristic curve (0.895), indicating superior discrimination.
  • NEWS2 and NEWS also showed strong performance, outperforming EDI, RI, and MEWS.
  • At moderate-risk thresholds, all scores provided a median of at least 20 hours of lead time; eCART offered the longest median lead time (11 hours) at high-risk thresholds.

Conclusions:

  • The eCART AI score demonstrated superior accuracy and provided a longer lead time for detecting clinical deterioration compared to other evaluated scores.
  • Publicly available scores like NEWS also showed significant performance, outperforming some proprietary AI tools.
  • The findings suggest a need for greater transparency and oversight in the development and implementation of EWS.