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Resuscitation. 2019 Apr;137:197-204. doi: 10.1016/j.resuscitation.2019.02.030. Epub 2019 Feb 27.

A novel methodological framework for multimodality, trajectory model-based prognostication.

Author information

1
Department of Emergency Medicine, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA; Department of Critical Care Medicine, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA; Department of Neurology, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA. Electronic address: elmerjp@upmc.edu.
2
Western Pennsylvania Institute and Clinic, UPMC, Pittsburgh, PA, USA.
3
Department of Informatics and Networked Systems, University of Pittsburgh School of Computing and Information, Pittsburgh, PA, USA.
4
Department of Emergency Medicine, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA.
5
Heinz College, Carnegie Mellon University, Pittsburgh, PA, USA.

Abstract

INTRODUCTION:

Prognostic tools typically combine several time-invariant clinical predictors using regression models that yield a single, time-invariant outcome prediction. This results in considerable information loss as repeatedly or continuously sampled data are aggregated into single summary measures. We describe a method for real-time multivariate outcome prediction that accommodates both longitudinal data and time-invariant clinical characteristics.

METHODS:

We included comatose patients treated after resuscitation from cardiac arrest who underwent ≥6 h of electroencephalographic (EEG) monitoring. We used Persyst v13 (Persyst Development Corp, Prescott AZ) to generate quantitative EEG (qEEG) features and calculated hourly summaries of whole brain suppression ratio and amplitude-integrated EEG. We randomly selected half of subjects as a training sample and used the other half as a test sample. We applied group-based trajectory modeling (GBTM) to the training sample to group patients based on qEEG evolution, then estimated the relationship of group membership and clinical covariates with awakening from coma and surviving to hospital discharge using logistic regression. We used these parameters to calculate posterior probabilities of group membership (PPGMs) in the test sample, and built three prognostic models: adjusted logistic regression (no GBTM), unadjusted GBTM (no clinical covariates) and adjusted GBTM (all data). We compared these models performance characteristics.

RESULTS:

We included 723 patients. Group-specific outcome estimates from a 7-group GBTM ranged from 0 to 75%. Compared to unadjusted GBTM, adjusted GBTM calibration was significantly improved at 6 and 12 h, and time to an outcome estimate <10% and <5% were significantly shortened. Compared to simple logistic regression, adjusted GBTM identified a substantially larger proportion of subjects with outcome probability <1%.

CONCLUSIONS:

We describe a novel methodology for combining GBTM output and clinical covariates to estimate patient-specific prognosis over time. Refinement of such methods should form the basis for new avenues of prognostication research that minimize loss of clinically important information.

KEYWORDS:

Analytics; Cardiac arrest; Data; Electroencephalography; Precision medicine; Prognostication

PMID:
30825550
PMCID:
PMC6471615
[Available on 2020-04-01]
DOI:
10.1016/j.resuscitation.2019.02.030

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