Longitudinal studies of PTSD: Overview of findings and methods

Department of Psychiatry, Center for Traumatic Stress Studies, Hadassah University Hospital, Ein Kerem Campus, Jerusalem 91120, Israel.
CNS spectrums (Impact Factor: 2.71). 09/2006; 11(8):589-602.
Source: PubMed

ABSTRACT Posttraumatic stress disorder (PTSD) has a discernible starting point and typical course, hence the particular appropriateness of longitudinal research in this disorder. This review outlines the salient findings of longitudinal studies published between 1988 and 2004. Studies have evaluated risk factors and risk indicators of PTSD, the disorder's trajectory, comorbid disorders and the predictive role of acute stress disorder. More recent studies used advanced data analytic methods to explore the sequence of causation that leads to chronic PTSD. Advantages and limitations of longitudinal methods are discussed.

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Available from: Arieh Y Shalev, Sep 27, 2015
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    • "Systematic reviews have indicated that nearly 40% of those with PTSD have a chronic course, and only a very small fraction (3.5%) of new PTSD cases appeared after 3 months (Peleg & Shalev, 2006; Santiago et al., 2013). Several studies have examined whether MDD comorbidity can complicate its course or delay its onset. "
    Nordic psychology 09/2015; · 0.18 Impact Factor
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    • "Chronic PTSD is prevalent, distressful, and debilitating (Kessler, 2000) and often follows an unremitting course (Galatzer-Levy et al., 2013; Peleg and Shalev, 2006). The early manifestations may provide sufficient information to identify individuals at risk for chronic PTSD. "
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    ABSTRACT: a b s t r a c t 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 ap-proaches to identify and integrate a panel of unique predictive characteristics and determine their ac-curacy in forecasting non-remitting PTSD from information collected within10 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 ¼ .77) did not differ from predicting from all available information (AUC ¼ .78). Predicting from ASD symptoms was not better then chance (AUC ¼ .60). The prediction of PTSD status was less accurate than that of membership in a non-remitting trajectory (AUC ¼ .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.
    Journal of Psychiatric Research 09/2014; 59. DOI:10.1016/j.jpsychires.2014.08.017 · 3.96 Impact Factor
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    • "Most survivors recover from brief traumatic stress, but 17–25% have posttraumatic stress disorder (PTSD) months later (Beck & Coffey, 2007; Daniels et al., 2012; Ehlers, Mayou, & Bryant, 1998). Further evidence also links the initial stress symptoms to subsequent symptoms of PTSD months or years after the MVA (King, King, Salgado, & Shalev, 2003; Norris, 2006; Peleg & Shalev, 2006). Negative emotions (e.g., fear, guilt, and helplessness) and Ineffective cognitive processing may contribute to the development of PTSD (Brewin & Holmes, 2003; Liberzon & Sripada, 2008). "
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    ABSTRACT: This study documented family/friend support to patients in the Emergency Department (ED), including bedside visits and transportation of patients from the ED after discharge, and measured depression, anxiety, and stress symptoms within 2 weeks, 1 month, and 3 months after motor vehicle accidents. Stress and depression symptoms significantly decreased during the initial three months. Family/friend visitation in the ED was negatively associated with anxiety and depression symptoms within 2 weeks and with stress symptoms months after trauma. This pilot study suggests family/friend visitation in the ED is associated with fewer mental health issues in the months following an accident.
    Social Work in Mental Health 01/2014; 12(1):69-80. DOI:10.1080/15332985.2013.841611
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