Dataset Information

Utilization of machine learning to test the impact of cognitive processing and emotion recognition on the development of PTSD following trauma exposure.

ABSTRACT:

Background

Though lifetime exposure to traumatic events is significant, only a minority of individuals develops symptoms of posttraumatic stress disorder (PTSD). Post-trauma alterations in neurocognitive and affective functioning are likely to reflect changes in underlying brain networks that are predictive of PTSD. These constructs are assumed to interact in a highly complex way. The aim of this exploratory study was to apply machine learning models to investigate the contribution of these interactions on PTSD symptom development and identify measures indicative of circuit related dysfunction.

Methods

N?=?94 participants admitted to the emergency room of an inner-city hospital after trauma exposure completed a battery of neurocognitive and emotional tests 1 month after the incident. Different machine learning algorithms were applied to predict PTSD symptom severity and clusters after 3 months based.

Results

Overall, model accuracy did not differ between PTSD clusters, though the importance of cognitive and emotional domains demonstrated both key differences and overlap. Alterations in higher-order executive functioning, speed of information processing, and processing of emotionally incongruent cues were the most important predictors.

Conclusions

Data-driven approaches are a powerful tool to investigate complex interactions and can enhance the mechanistic understanding of PTSD. The study identifies important relationships between cognitive processing and emotion recognition that may be valuable to predict and understand mechanisms of risk and resilience responses to trauma prospectively.

SUBMITTER: Augsburger M

PROVIDER: S-EPMC7310383 | biostudies-literature | 2020 Jun

REPOSITORIES: biostudies-literature

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Publications

Utilization of machine learning to test the impact of cognitive processing and emotion recognition on the development of PTSD following trauma exposure.

Augsburger Mareike M Galatzer-Levy Isaac R IR

BMC psychiatry 20200623 1

<h4>Background</h4>Though lifetime exposure to traumatic events is significant, only a minority of individuals develops symptoms of posttraumatic stress disorder (PTSD). Post-trauma alterations in neurocognitive and affective functioning are likely to reflect changes in underlying brain networks that are predictive of PTSD. These constructs are assumed to interact in a highly complex way. The aim of this exploratory study was to apply machine learning models to investigate the contribution of th ...[more]

PMID: 32576245

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Project description:Dropout rates in trauma-focused treatments for adult posttraumatic stress disorder (PTSD) are high. Most research has focused on demographic and pretreatment predictors of dropout, but findings have been inconsistent. We examined predictors of dropout in cognitive processing therapy (CPT) by coding the content of trauma narratives written in early sessions of CPT. Data are from a randomized controlled noninferiority trial of CPT and written exposure therapy (WET) in which CPT showed significantly higher dropout rates than WET (39.7% CPT vs. 6.4% WET). Participants were 51 adults with a primary diagnosis of PTSD who were receiving CPT and completed at least one of three narratives in the early sessions of CPT. Sixteen (31%) in this subsample were classified as dropouts and 35 as completers. An additional 9 participants dropped out but could not be included because they did not complete any narratives. Of the 11 participants who provided a reason for dropout, 82% reported that CPT was too distressing. The CHANGE coding system was used to code narratives for pathological trauma responses (cognitions, emotions, physiological responses) and maladaptive modes of processing (avoidance, ruminative processing, overgeneralization), each on a scale from 0 (absent) to 3 (high). Binary logistic regressions showed that, averaging across all available narratives, more negative emotions described during or around the time of the trauma predicted less dropout. More ruminative processing in the present time frame predicted lower rates of dropout, whereas more overgeneralized beliefs predicted higher rates. In the first impact statement alone, more negative emotions in the present time frame predicted lower dropout rates, but when emotional reactions had a physiological impact, dropout was higher. These findings suggest clinicians might attend to clients' written trauma narratives in CPT in order to identify indicators of dropout risk and to help increase engagement.

Project description:There is broad interest in predicting the clinical course of mental disorders from early, multimodal clinical and biological information. Current computational models, however, constitute a significant barrier to realizing this goal. The early identification of trauma survivors at risk of post-traumatic stress disorder (PTSD) is plausible given the disorder's salient onset and the abundance of putative biological and clinical risk indicators. This work evaluates the ability of Machine Learning (ML) forecasting approaches to identify and integrate a panel of unique predictive characteristics and determine their accuracy in forecasting non-remitting PTSD from information collected within 10 days of a traumatic event. Data on event characteristics, emergency department observations, and early symptoms were collected in 957 trauma survivors, followed for fifteen months. An ML feature selection algorithm identified a set of predictors that rendered all others redundant. Support Vector Machines (SVMs) as well as other ML classification algorithms were used to evaluate the forecasting accuracy of i) ML selected features, ii) all available features without selection, and iii) Acute Stress Disorder (ASD) symptoms alone. SVM also compared the prediction of a) PTSD diagnostic status at 15 months to b) posterior probability of membership in an empirically derived non-remitting PTSD symptom trajectory. Results are expressed as mean Area Under Receiver Operating Characteristics Curve (AUC). The feature selection algorithm identified 16 predictors, present in ? 95% cross-validation trials. The accuracy of predicting non-remitting PTSD from that set (AUC = .77) did not differ from predicting from all available information (AUC = .78). Predicting from ASD symptoms was not better then chance (AUC = .60). The prediction of PTSD status was less accurate than that of membership in a non-remitting trajectory (AUC = .71). ML methods may fill a critical gap in forecasting PTSD. The ability to identify and integrate unique risk indicators makes this a promising approach for developing algorithms that infer probabilistic risk of chronic posttraumatic stress psychopathology based on complex sources of biological, psychological, and social information.

Dataset Information

Utilization of machine learning to test the impact of cognitive processing and emotion recognition on the development of PTSD following trauma exposure.

Background

Methods

Results

Conclusions

Publications

Utilization of machine learning to test the impact of cognitive processing and emotion recognition on the development of PTSD following trauma exposure.

Similar Datasets

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