Entrapment and social problem-solving in suicidal behavior across the adult lifespan.

BACKGROUND: Feelings of entrapment and deficits in social problem-solving skills have been associated with risk for suicidal behavior in the context of depression. However, few studies have examined the effect of age on the association between these risk factors and suicidal behavior across most of the adult lifespan. METHODS: In a three-site study, we tested interactions of age with feelings of entrapment and social problem-solving style in 105 depressed patients with a recent suicide attempt, 95 depressed patients with no history of suicide attempt, and 97 demographically similar non-psychiatric participants (age 16-80). Attempter/non-attempter differences, age interactions, and the relative contribution of entrapment and social problem-solving style to past attempter were examined. RESULTS: Entrapment significantly interacted with age such that it discriminated past attempters from depressed non-attempters better at older ages. Social Problem-Solving Inventory (SPSI) total score and most subscales did not distinguish past attempters, but the SPSI Impulsive Style Problem-Solving was an effective discriminator of past suicide attempts across the full adult lifespan and did not interact with age. In a multipredictor model, both the entrapment by age interaction and SPSI Impulsive Style Problem-Solving score were significant predictors for the classification of attempters. LIMITATIONS: The cross-sectional nature of our research design limited conclusions that may be drawn about individual change over time or cohort effects. CONCLUSIONS: Entrapment did not distinguish past attempters at younger ages but became a better discriminator in middle to late adulthood. An impulsive problem-solving style was associated with past suicide attempts across the full adult lifespan.

The functional serotonin 1a receptor promoter polymorphism, rs6295, is associated with psychiatric illness and differences in transcription.

The G/C single-nucleotide polymorphism in the serotonin 1a receptor promoter, rs6295, has previously been linked with depression, suicide and antidepressant responsiveness. In vitro studies suggest that rs6295 may have functional effects on the expression of the serotonin 1a receptor gene (HTR1A) through altered binding of a number of transcription factors. To further explore the relationship between rs6295, mental illness and gene expression, we performed dual epidemiological and biological studies. First, we genotyped a cohort of 1412 individuals, randomly split into discovery and replication cohorts, to examine the relationship between rs6295 and five psychiatric outcomes: history of psychiatric hospitalization, history of suicide attempts, history of substance or alcohol abuse, current posttraumatic stress disorder (PTSD), current depression. We found that the rs6295G allele is associated with increased risk for substance abuse, psychiatric hospitalization and suicide attempts. Overall, exposure to either childhood or non-childhood trauma resulted in increased risk for all psychiatric outcomes, but we did not observe a significant interaction between rs6295 and trauma in modulating psychiatric outcomes. In conjunction, we also investigated the potential impact of rs6295 on HTR1A expression in postmortem human brain tissue using relative allelic expression assays. We found more mRNA produced from the C versus the G-allele of rs6295 in the prefrontal cortex (PFC), but not in the midbrain of nonpsychiatric control subjects. Further, in the fetal cortex, rs6295C allele exhibited increased relative expression as early as gestational week 18 in humans. Finally, we found that the C:G allelic expression ratio was significantly neutralized in the PFC of subjects with major depressive disorder (MDD) who committed suicide as compared with controls, indicating that normal patterns of transcription may be disrupted in MDD/suicide. These data provide a putative biological mechanism underlying the association between rs6295, trauma and mental illness. Moreover, our results suggest that rs6295 may affect transcription during both gestational development and adulthood in a region-specific manner, acting as a risk factor for psychiatric illness. These findings provide a critical framework for conceptualizing the effects of a common functional genetic variant, trauma exposure and their impact on mental health.

Life events: a complex role in the timing of suicidal behavior among depressed patients.

Suicidal behavior is often conceptualized as a response to overwhelming stress. Our model posits that given a propensity for acting on suicidal urges, stressors such as life events or major depressive episodes (MDEs) determine the timing of suicidal acts. Depressed patients (n=415) were assessed prospectively for suicide attempts and suicide, life events and MDE over 2 years. Longitudinal data were divided into 1-month intervals characterized by MDE (yes/no), suicidal behavior (yes/no) and life event scores. Marginal logistic regression models were fit, with suicidal behavior as the response variable and MDE and life event score in either the same or previous month, respectively, as time-varying covariates. Among 7843 person-months, 33% had MDE and 73% had life events. MDE increased the risk for suicidal behavior (odds ratio (OR)=4.83, P⩽0.0001). Life event scores were unrelated to the timing of suicidal behavior (OR=1.06 per 100 point increase, P=0.32), even during a MDE (OR=1.12, P=0.15). However, among those without borderline personality disorder (BPD), both health- and work-related life events were key precipitants, as was recurrent MDE, with a 13-fold effect. The relationship of life events to suicidal behavior among those with BPD was more complex. Recurrent MDE was a robust precipitant for suicidal behavior, regardless of BPD comorbidity. The specific nature of life events is key to understanding the timing of suicidal behavior. Given unanticipated results regarding the role of BPD and study limitations, these findings require replication. Of note, that MDE, a treatable risk factor, strongly predicts suicidal behaviors is cause for hope.

Machine learning and data mining: strategies for hypothesis generation.

Strategies for generating knowledge in medicine have included observation of associations in clinical or research settings and more recently, development of pathophysiological models based on molecular biology. Although critically important, they limit hypothesis generation to an incremental pace. Machine learning and data mining are alternative approaches to identifying new vistas to pursue, as is already evident in the literature. In concert with these analytic strategies, novel approaches to data collection can enhance the hypothesis pipeline as well. In data farming, data are obtained in an 'organic' way, in the sense that it is entered by patients themselves and available for harvesting. In contrast, in evidence farming (EF), it is the provider who enters medical data about individual patients. EF differs from regular electronic medical record systems because frontline providers can use it to learn from their own past experience. In addition to the possibility of generating large databases with farming approaches, it is likely that we can further harness the power of large data sets collected using either farming or more standard techniques through implementation of data-mining and machine-learning strategies. Exploiting large databases to develop new hypotheses regarding neurobiological and genetic underpinnings of psychiatric illness is useful in itself, but also affords the opportunity to identify novel mechanisms to be targeted in drug discovery and development.

Evaluation of clinical prognostic models for suicide attempts after a major depressive episode.

OBJECTIVE: In this study, we compare the performance of prognostic models of increasing complexity for prediction of future suicide attempt. METHOD: Using data from a 2-year prospective study of 304 depressed subjects, a series of Cox proportional hazard regression models were developed to predict future suicide attempt. The models were evaluated in terms of discrimination (the ability to rank subjects in order of risk), calibration (accuracy of predicted probabilities of attempt), and sensitivity and specificity of risk group stratification based on cross-validated predicted probabilities. RESULTS: Although an additive model with past attempt, smoking status, and suicidal ideation achieved 75% (cross-validated) sensitivity and specificity, models that performed best in terms of discrimination included interactions between predictor variables. CONCLUSION: As several models had similar predictive power, clinical considerations and ease of interpretation may have a significant role in the final stage of model selection for assessing future suicide attempt risk.

Lower cortisol levels in depressed patients with comorbid post-traumatic stress disorder.

Post-traumatic stress disorder (PTSD) is often comorbid with major depressive episodes (MDEs) and both conditions carry a higher rate of suicidal behavior. Hypothalamic-pituitary-adrenal (HPA) axis and serotonin abnormalities are associated with both conditions and suicidal behavior, but their inter-relation is not known. We determined cortisol response to placebo or fenfluramine in MDE, MDE and PTSD (MDE+PTSD), and healthy volunteers (HVs) and examined the relation of cortisol responses to suicidal behavior. A total of 58 medication-free patients with MDE (13 had MDE+PTSD) and 24 HVs were studied. They received placebo on the first day and fenfluramine on the second day. Cortisol levels were drawn before challenge and for 5 h thereafter. The MDE+PTSD group had the lowest plasma cortisol, the MDE group had the highest, and HVs had intermediate levels. There were no group differences in cortisol response to fenfluramine. Suicidal behavior, sex, and childhood history of abuse were not predictors of baseline or postchallenge plasma cortisol. Cortisol levels increased with age. This study finds elevated cortisol levels in MDE and is the first report of lower cortisol levels in MDE+PTSD. The findings underscore the impact of comorbidity of PTSD with MDE and highlight the importance of considering comorbidity in psychobiology.

Experiences with ARTEMIS--an Internet-based telemedicine system.

ARTEMIS is one of the first systems to exploit the Internet/Intranet technologies for exchanging patient information among health care providers. The primary project goal was to develop and demonstrate a regional telehealth environment specifically to support real-time consultations among health care providers via a computer network, provide secure access to multi-media patient records and discharge summaries, facilitate authentication/digital sign-off, multi-media mail-based referrals, and network-based dictation/transcription. A prototype is deployed in southern West Virginia in a Community Care Network (CCN). The CCN consists of providers, hospitals, clinics, laboratories, that make up one "Virtual" clinic on the "Intranet". ARTEMIS employs new technologies such as Java and JavaScript for the browser, and CORBA-based "middleware" for interoperability at the server-end. Several experiments were designed for evaluating the impact of ARTEMIS on patient care. In this paper we discuss the challenges we faced and the means by which we plan to meet these challenges. We conclude by outlining new thrust areas in which we are concentrating in our next phase of development of ARTEMIS.

Evaluation of Community Care Network (CCN) system in a rural health care setting.

Concurrent Engineering Research Center (CERC), under the sponsorship of NLM (National Library of Medicine) is in the process of developing a computerized patient record system for a clinical environment distributed in rural West Virginia. This realization of the CCN (Community Care Network), besides providing computer-based patient records accessible from a chain of clinics and one hospital, supports collaborative health care processes like referral and consulting. To evaluate the effectiveness of the system, a study was designed and is in the process of being executed. Three surveys were designed to provide subjective measures, and four experiments for collecting objective data. Data collection is taking place in several phases: baseline data are collected before the system is deployed; the process is repeated with minimal changes three, then six months later or as often as new versions of the system are installed. Results are then to be compared, using whenever possible matching techniques (i.e. the preliminary data collected on a provider will be matched with the data collected later on the same provider). Surveys are conducted through questionnaires distributed to providers and nurses and person-to-person interviews of the patients. The time spent on patient-chart related activities is measured by work-sampling, aided by a computer application running on a laptop PC. Information about missing patient record parts is collected by the providers, the frequency by which new features of the computerized system are used will be logged by the system itself and clinical outcome measures will be studied from the results of the clinics' own patient chart audits. Preliminary results of the surveys and plans for the immediate and distant future are discussed at the end of the paper.