Utilization of machine learning for prediction of post-traumatic stress: a re-examination of cortisol in the prediction and pathways to non-remitting PTSD

Abstract

To date, studies of biological risk factors have revealed inconsistent relationships with subsequent post-traumatic stress disorder (PTSD). The inconsistent signal may reflect the use of data analytic tools that are ill equipped for modeling the complex interactions between biological and environmental factors that underlay post-traumatic psychopathology. Further, using symptom-based diagnostic status as the group outcome overlooks the inherent heterogeneity of PTSD, potentially contributing to failures to replicate. To examine the potential yield of novel analytic tools, we reanalyzed data from a large longitudinal study of individuals identified following trauma in the general emergency room (ER) that failed to find a linear association between cortisol response to traumatic events and subsequent PTSD. First, latent growth mixture modeling empirically identified trajectories of post-traumatic symptoms, which then were used as the study outcome. Next, support vector machines with feature selection identified sets of features with stable predictive accuracy and built robust classifiers of trajectory membership (area under the receiver operator characteristic curve (AUC)=0.82 (95% confidence interval (CI)=0.80-0.85)) that combined clinical, neuroendocrine, psychophysiological and demographic information. Finally, graph induction algorithms revealed a unique path from childhood trauma via lower cortisol during ER admission, to non-remitting PTSD. Traditional general linear modeling methods then confirmed the newly revealed association, thereby delineating a specific target population for early endocrine interventions. Advanced computational approaches offer innovative ways for uncovering clinically significant, non-shared biological signals in heterogeneous samples.

Figure 1

Latent growth mixture model identified trajectories of remitting and non-remitting PTSD. Two classes represented as the mean trajectories with random effects around LGMM identified trajectories of remitting (82.8%) and non-remitting (17.2%) post-traumatic stress based on repeated measures of the IES-R (n=155). Gray lines represent individual observations. Colored lines represent the mean trajectory. IES-R, Impact of Events Scale Revised; LGMM, latent growth mixture modeling; PTSD, post-traumatic stress disorder.

Figure 2

Classification results based on mean AUC for support vector machines with recursive feature elimination across 5 × 10-fold cross-validation. (a) Classification accuracy based on AUCs for (1) neuroendocrine features alone; (2) clinical and demographic features alone; (3) neuroendocrine, clinical and demographic features together. Each time point represents the AUC inclusive of features from the previous time point. (b) Means and s.d.'s of AUCs across 5 × 10-fold cross-validation runs. AUC, area under the receiver operator characteristic curve.

Figure 3

Causal graph around non-remitting PTSD trajectory membership derived using local-to-global learning algorithm. Note: see full description of features in methods section. (a) In graph, red lines represent negative relationships and blue lines represent positive relationships. The graph represents the local network surrounding the target variable PTSD Trajectory, identified using the HITON-PC algorithm for local-to-global learning. The local network represents the set of variables that renders all other variables in the model statistically independent of the target variable. The local network includes 4-h Urinary Cortisol, avoidance symptoms at 1 month, NE/cortisol plasma ratio and PTSD severity at 5 months. Expanding the network reveals that trajectory membership is associated with depression via PTSD severity. Further, two pathways to non-remitting post-traumatic stress are identified. (b) Path 1 indicates that individuals who do not report childhood trauma experience high sympathetic arousal and negative appraisals in the emergency room leading to the emergence of avoidance symptomatology at 1 week and 1 month, leading to non-remitting PTSD trajectory membership. Path 2 indicates that report of childhood trauma has a causal effect on PTSD non-remission through low urinary cortisol response in the emergency room. The two pathways are connected through 4-h Urinary NE. NE, norepinephrine; PTSD, post-traumatic stress disorder.