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Published on: October 23, 2020
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Comparison of parametric and nonparametric methods for outcome prediction using longitudinal data after cardiac
Jonathan Elmer1, Bobby L Jones2, Daniel S Nagin3
1Departments of Emergency Medicine, Critical Care Medicine and Neurology, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA.
Resuscitation
|February 1, 2020
Summary
Predicting patient outcomes after cardiac arrest is challenging. Group-based trajectory modeling (GBTM) offers optimal sensitivity for prognostication with a low false positive rate, providing a valuable tool for clinical decision-making.
Area of Science:
- Neurology
- Critical Care Medicine
- Biostatistics
Background:
- Predicting patient outcomes following cardiac arrest is complex.
- Quantitative electroencephalography (EEG) and baseline characteristics are crucial for prognostication.
- Group-based trajectory modeling (GBTM) has shown prior promise in this area.
Purpose of the Study:
- To implement and evaluate GBTM for outcome prediction in cardiac arrest survivors.
- To compare GBTM performance against alternative longitudinal modeling techniques.
- To provide a freely available software package for GBTM implementation.
Main Methods:
- Included 1,010 comatose cardiac arrest patients with ≥6 hours of EEG monitoring.
- Utilized clinical data and hourly electroencephalogram (EEG) suppression ratios.
- Compared frequentist GBTM, k-means clustering, and Bayesian regression models.
Main Results:
- GBTM achieved optimal sensitivity (38%) for predicting favorable outcomes (CPC 1-3) at a false positive rate (FPR) <1%.
- Alternative methods with higher sensitivity exhibited FPRs of 2-3%.
- GBTM and k-means derived similar trajectories and group outcomes.
Conclusions:
- GBTM demonstrates superior sensitivity for outcome prediction in cardiac arrest patients while maintaining a low FPR.
- The developed software facilitates accessible implementation of GBTM for clinical prognostication.
- This approach aids in refining patient-level predictions using longitudinal and time-invariant data.
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