How well can post-traumatic stress disorder be predicted from pre-trauma risk factors? An exploratory study in the WHO World Mental Health Surveys

Abstract

Post-traumatic stress disorder (PTSD) should be one of the most preventable mental disorders, since many people exposed to traumatic experiences (TEs) could be targeted in first response settings in the immediate aftermath of exposure for preventive intervention. However, these interventions are costly and the proportion of TE-exposed people who develop PTSD is small. To be cost-effective, risk prediction rules are needed to target high-risk people in the immediate aftermath of a TE. Although a number of studies have been carried out to examine prospective predictors of PTSD among people recently exposed to TEs, most were either small or focused on a narrow sample, making it unclear how well PTSD can be predicted in the total population of people exposed to TEs. The current report investigates this issue in a large sample based on the World Health Organization (WHO)'s World Mental Health Surveys. Retrospective reports were obtained on the predictors of PTSD associated with 47,466 TE exposures in representative community surveys carried out in 24 countries. Machine learning methods (random forests, penalized regression, super learner) were used to develop a model predicting PTSD from information about TE type, sociodemographics, and prior histories of cumulative TE exposure and DSM-IV disorders. DSM-IV PTSD prevalence was 4.0% across the 47,466 TE exposures. 95.6% of these PTSD cases were associated with the 10.0% of exposures (i.e., 4,747) classified by machine learning algorithm as having highest predicted PTSD risk. The 47,466 exposures were divided into 20 ventiles (20 groups of equal size) ranked by predicted PTSD risk. PTSD occurred after 56.3% of the TEs in the highest-risk ventile, 20.0% of the TEs in the second highest ventile, and 0.0-1.3% of the TEs in the 18 remaining ventiles. These patterns of differential risk were quite stable across demographic-geographic sub-samples. These results demonstrate that a sensitive risk algorithm can be created using data collected in the immediate aftermath of TE exposure to target people at highest risk of PTSD. However, validation of the algorithm is needed in prospective samples, and additional work is warranted to refine the algorithm both in terms of determining a minimum required predictor set and developing a practical administration and scoring protocol that can be used in routine clinical practice.

Peritraumatic distress fully mediates the relationship between posttraumatic stress symptoms preoperative and three months postoperative in patients undergoing spine surgery

“…Not only is the linkage between peritraumatic distress and PTSD symptoms the strongest, as found in other studies (Brewin et al, 2000; Kessler et al, 2014; Ozer et al, 2003; Trickey et al, 2012), but also the relationship between pre- and posttrauma PSS is no longer significant when peritraumatic distress is controlled for as mediating factor. This effect could be shown for main symptom clusters of PTSD, intrusions, avoidance and hyperarousal, as well as general PSS, proving that no over- or undermodulation on symptom level occurs.…”

“…Based on the study by Kessler et al (2014), posttraumatic symptomatology before the potential trauma, namely the surgical procedure, was considered a risk factor. The short screening instrument used in this study, the PTSS-10, can be criticized for containing a range of symptoms not specific for PTSD, such as sleeping problems.…”

“…Patients undergoing elective spine surgery received questionnaires within one week before, one week after and three months after the potentially traumatic event ‘surgery’. Posttraumatic stress symptoms (PSS) prior to the surgery were investigated as a pretrauma variable, since posttraumatic stress symptomatology prior to the exposure of a new traumatic event was a strong predictor of posttraumatic stress in prior studies (Jubran et al, 2010; Kessler et al, 2014). Peritraumatic distress in general was analysed as a peritrauma variable, because peritraumatic distress has been shown to predict posttraumatic stress better than peritraumatic dissociation (Bui et al, 2010; Ladois-Do Pilar Rei et al, 2012).…”

“…Kessler et al (2014) analysed the predictive value of pretrauma variables in a large study involving individuals from 24 countries and found that posttraumatic stress symptomatology prior to exposure to a new traumatic event was the most powerful predictor.…”

Machine learning in mental health: A systematic scoping review of methods and applications

“…Regression [281,282], DT [282], SVM [282], RF [281], Super Learner [281] Interview [282], Survey [281] Psychiatric Emergency BN [269], DT [269], SVM [269] Social Media [269] Psychiatric Stressors NLP [283], Named-entity recognition [283] Clinical Notes [283] Psychosis Regression [284], RF [285] Clinical Assessment [285], Electronic Health Records [284] Social Support LIWC [267], SVM [267] Social Media [267] Stress Cluster analysis [286], Sentiment Analysis [270], SVM [270] Clinical Assessment [286], Social Media [270] Substance Use PCA [265], NLP [265], RF [285] Social Media [265], Clinical Assessment [285] Suicide/Self-Harm ARM [271], DT [271], Genetic Algorithm [287], NB [268,271], RF [268,271], Regression [268,271,[288][289][290], SVM …”