Automated Classification Model for Hemodynamic Instability After Anesthesia Induction
Author Information
Author(s): Kho Eline, Immink Rogier V., van der Ster Bjorn J.P., van der Ven Ward H., Schenk Jimmy, Hollmann Markus W., Tol Johan T.M., Terwindt Lotte E., Vlaar Alexander P.J., Veelo Denise P.
Primary Institution: Amsterdam UMC, University of Amsterdam
Hypothesis
Can undesirable postinduction blood pressure decreases, defined as hemodynamic instability, be classified with an automated classification model based on blood pressures?
Conclusion
The developed model can effectively differentiate between clinically relevant hemodynamic instability and stable patients.
Supporting Evidence
- 78 patients were classified as hemodynamically unstable and 279 as stable.
- The model achieved an AUROC of 0.96, indicating excellent performance.
- Interrater agreement among experts was 0.92, showing high consistency in labeling.
Takeaway
Doctors can now use a computer program to quickly tell if a patient is having serious blood pressure problems after anesthesia, which helps them take action faster.
Methodology
This prospective study included 375 adult elective noncardiac surgery patients, measuring noninvasive blood pressure continuously before and after induction, and developing a classification model based on expert ratings.
Potential Biases
The reliance on expert labeling may introduce subjective bias in classifying hemodynamic instability.
Limitations
The study lacked a strict induction protocol, leading to variability in induction agents and potential biases in BP measurement.
Participant Demographics
The study included 375 adult patients, with a near-equal gender distribution and a median age of 57 years.
Statistical Information
P-Value
<0.001
Confidence Interval
95% CI, 4–11
Statistical Significance
p<0.001
Digital Object Identifier (DOI)
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