Nucleotide variation in central nervous system genes among male suicide attempters.

Despite marked morbidity and mortality associated with suicidal behavior, accurate identification of individuals at risk remains elusive. The goal of this study is to identify a model based on single nucleotide polymorphisms (SNPs) that discriminates between suicide attempters and non-attempters using data mining strategies. We examined functional SNPs (n = 840) of 312 brain function and development genes using data mining techniques. Two hundred seventy-seven male psychiatric patients aged 18 years or older were recruited at a University hospital psychiatric emergency room or psychiatric short stay unit. The main outcome measure was history of suicide attempts. Three SNPs of three genes (rs10944288, HTR1E; hCV8953491, GABRP; and rs707216, ACTN2) correctly classified 67% of male suicide attempters and non-attempters (0.50 sensitivity, 0.82 specificity, positive likelihood ratio = 2.80, negative likelihood ratio = 1.64). The OR for the combined three SNPs was 4.60 (95% CI: 1.31-16.10). The model's accuracy suggests that in the future similar methodologies may generate simple genetic tests with diagnostic utility in identification of suicide attempters. This strategy may uncover new pathophysiological pathways regarding the neurobiology of suicidal acts.

Variables associated with familial suicide attempts in a sample of suicide attempters.

Attempted suicide appears to be a familial behavior. This study aims to determine the variables associated with family history of attempted suicide in a large sample of suicide attempters. The sample included 539 suicide attempters 18 years or older recruited in an emergency room. The two dichotomous dependent variables were family history of suicide attempt (10%, 51/539) and of completed suicide (4%, 23/539). Independent variables were 101 clinical variables studied with two data mining techniques: Random Forest and Forward Selection. A model for family history of completed suicide could not be developed. A classificatory model for family history of attempted suicide included the use of alcohol in the intent and family history of completed suicide (sensitivity, specificity, 98.7%; and accuracy, 96.6%). This is the first study that uses a powerful new statistical methodology, data mining, in the field of familial suicidal behaviors and suggests that it may be important to study familial variables associated with alcohol use to better understand the familiality of suicide attempts.

Using data mining to explore complex clinical decisions: A study of hospitalization after a suicide attempt.

BACKGROUND: Medical education is moving toward developing guidelines using the evidence-based approach; however, controlled data are missing for answering complex treatment decisions such as those made during suicide attempts. A new set of statistical techniques called data mining (or machine learning) is being used by different industries to explore complex databases and can be used to explore large clinical databases. METHOD: The study goal was to reanalyze, using data mining techniques, a published study of which variables predicted psychiatrists' decisions to hospitalize in 509 suicide attempters over the age of 18 years who were assessed in the emergency department. Patients were recruited for the study between 1996 and 1998. Traditional multivariate statistics were compared with data mining techniques to determine variables predicting hospitalization. RESULTS: Five analyses done by psychiatric researchers using traditional statistical techniques classified 72% to 88% of patients correctly. The model developed by researchers with no psychiatric knowledge and employing data mining techniques used 5 variables (drug consumption during the attempt, relief that the attempt was not effective, lack of family support, being a housewife, and family history of suicide attempts) and classified 99% of patients correctly (99% sensitivity and 100% specificity). CONCLUSIONS: This reanalysis of a published study fundamentally tries to make the point that these new multivariate techniques, called data mining, can be used to study large clinical databases in psychiatry. Data mining techniques may be used to explore important treatment questions and outcomes in large clinical databases and to help develop guidelines for problems where controlled data are difficult to obtain. New opportunities for good clinical research may be developed by using data mining analyses.

Distinguishing the relevant features of frequent suicide attempters.

BACKGROUND: In spite of the high prevalence of suicide behaviours and the magnitude of the resultant burden, little is known about why individuals reattempt. We aim to investigate the relationships between clinical risk factors and the repetition of suicidal attempts. METHODS: 1349 suicide attempters were consecutively recruited in the Emergency Room (ER) of two academic hospitals in France and Spain. Patients were extensively assessed and demographic and clinical data obtained. Data mining was used to determine the minimal number of variables that blinded the rest in relation to the number of suicide attempts. Using this set, a probabilistic graph ranking relationships with the target variable was constructed. RESULTS: The most common diagnoses among suicide attempters were affective disorders, followed by anxiety disorders. Risk of frequent suicide attempt was highest among middle-aged subjects, and diminished progressively with advancing age of onset at first attempt. Anxiety disorders significantly increased the risk of presenting frequent suicide attempts. Pathway analysis also indicated that frequent suicide attempts were linked to greater odds for alcohol and substance abuse disorders and more intensive treatment. CONCLUSIONS: Novel statistical methods found several clinical features that were associated with a history of frequent suicide attempts. The identified pathways may promote new hypothesis-driven studies of suicide attempts and preventive strategies.