Numerous studies over the past decade have pointed to the fre quent occurrence of trauma and neglect in the childhood expe rience of patients with Borderline Personality Disorder (BPD). Advancing research on Posttraumatic Stress Disorder (PTSD) and Dissociative Disorders offers further insight into under standing shared clinical phenomena with BPD. Drawing upon attachment theory, models of learned helplessness, and early primate deprivation, empirical, theoretical and clinical data are integrated to conceptualize the role of trauma and neglect in the etiology of BPD.One hundred years after Breuer and Freud postulated a traumatic etiology of the hysterical neuroses in their Studies on Hysteria (1893-1895), there is still considerable debate about the exact role of trauma in the etiology of what we now call Borderline Personality Disorder (BPD). While Freud retreated from his original proposition, a wealth of recent literature has reestablished its importance in the development of BPD. Any sensible review of this literature must begin with a warning that we are still working with hypotheses, and our understanding is far from complete. Those who neatly package their theories now may be humbled by research that emerges over the next fifty years. Nevertheless, clinical and scientific interests dictate that we develop working hypotheses based on available data, to more effectively understand and treat our patients. The following account re mains highly speculative.There is a wealth of retrospectively gathered data associating significant childhood trauma, especially before the age of 10, and BPD (reviewed by Gunderson & Sabo, 1993a). Recent examination of the reliability of memo ries of abuse raises questions about whether these memories are reliable

Suicide is the tenth leading cause of death in the United States (US). An early-warning system (EWS) for suicide attempt could prove valuable for identifying those at risk of suicide attempts, and analyzing the contribution of repeated attempts to the risk of eventual death by suicide. In this study we sought to develop an EWS for high-risk suicide attempt patients through the development of a population-based risk stratification surveillance system. Advanced machine-learning algorithms and deep neural networks were utilized to build models with the data from electronic health records (EHRs). A final risk score was calculated for each individual and calibrated to indicate the probability of a suicide attempt in the following 1-year time period. Risk scores were subjected to individual-level analysis in order to aid in the interpretation of the results for health-care providers managing the at-risk cohorts. The 1-year suicide attempt risk model attained an area under the curve (AUC ROC) of 0.792 and 0.769 in the retrospective and prospective cohorts, respectively. The suicide attempt rate in the "very high risk" category was 60 times greater than the population baseline when tested in the prospective cohorts. Mental health disorders including depression, bipolar disorders and anxiety, along with substance abuse, impulse control disorders, clinical utilization indicators, and socioeconomic determinants were recognized as significant features associated with incident suicide attempt.

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