Empty stomach together with menstrual bleeding as predictors of committed suicides among women of reproductive age: What a primary physician must know

Background

Women of reproductive age group (WoRAG) are among the most vulnerable groups to suicide in India. The present study intended to develop a mathematical model to differentiate suicides from homicides among WoRAG.

Methods

It was a cross-sectional study based on a record review of autopsy at Patna, India, from 2016 to 2021. The cause of deaths was ascertained by autopsies and other records independently by two investigators to reduce the interobserver bias. Independent variables were tested with confirmed suicides to calculate statistically significant association. These variables were further used for developing prediction models for the suicides by multivariate logistic regression analysis.

Results

Out of total of 520 autopsies of WoRAG performed by investigators, the cause of death has been confirmed for 62. Of them, 30 were confirmed as suicides. In univariate analysis, suicides were associated with the menstrual bleed (OR 35 CI 6.9,179), gastric emptying (OR 3.9 CI 1.2,12.8), hanging, poisoning, and drowning as mode of death (OR 435 CI 37.4,5061.9). By logistic regression, three prediction models were built to predict suicide; Model I: gastric emptying, Model II: menstrual bleed, and Model III: including both. The area under the curve (AUC) for Models I, II, and III was 0.67 (95%CI 0.34,0.99), 0.92 (95%CI 0.75,1.00), and 0.94 (95%CI 0.82,1.00), respectively. The AUC of Model III differs significantly from that of Model I (P value 0.03) but not with Model II (P value 0.11).

Conclusion

Menstrual bleed, gastric emptying, and mode of death may be used as a supplement tool in ascertaining the cause of death among WoRAG in medical and legal proceedings.

A Systematic Review of Behavior-Outcome Psychological Assessments as Correlates of Suicidality

AIM

Identifying correlates of suicidality is an important goal for suicide researchers because these correlates may predict suicidal behaviors. Psychological tasks that assess sensitivity to the outcomes of actions (i.e., consequence-based learning) have been commonly used by researchers seeking to identify correlates of suicidality. This is likely due to the straightforward integration of the tasks within most theoretical frameworks for understanding suicidality. Contextual factors have been shown to have a substantial effect on responding in behavior-outcome tasks. However, the direct relevance of these factors as determinants of behavior in suicide research is not clear. Thus, the purpose of this review was to assess the role of context in tasks involving behavior-outcome relations in suicide research.

METHODS

Four databases were searched using terms from general learning theory. Articles that featured evaluation of tasks with hypothetical or real outcomes to differentiate suicidality were included.

RESULTS

Eighty-two studies met inclusion criteria. Across studies there were 27 different tasks. Most instances of tasks across studies involved rewards (76.9%), while others emphasized punishment (15.7%), social (5.6%), or virtual suicide (1.8%) outcomes. Differentiation of suicidality was detected by 43.4%, 64.7%, 83.3%, and 50% of tasks featuring reward, punishment, social contexts, and virtual suicide respectively. All but five studies were retrospective.

CONCLUSION

Tasks that more closely mimic contexts and outcomes related to suicide appear to produce more pronounced differentiation of people with suicidality from people without suicidality. The lack of prospective designs is an important limitation of the literature.HIGHLIGHTSTasks that involve punishment or social outcomes better discriminate suicidality.Reward-based tasks are overused in suicide research.The conditioning hypothesis of suicidality is closely aligned with the literature.Only 5 of 82 studies incorporated prospective measures.

Predictors of suicide attempt in patients with obsessive-compulsive disorder: an exploratory study with machine learning analysis

BACKGROUND

Patients with obsessive-compulsive disorder (OCD) are at increased risk for suicide attempt (SA) compared to the general population. However, the significant risk factors for SA in this population remains unclear - whether these factors are associated with the disorder itself or related to extrinsic factors, such as comorbidities and sociodemographic variables. This study aimed to identify predictors of SA in OCD patients using a machine learning algorithm.

METHODS

A total of 959 outpatients with OCD were included. An elastic net model was performed to recognize the predictors of SA among OCD patients, using clinical and sociodemographic variables.

RESULTS

The prevalence of SA in our sample was 10.8%. Relevant predictors of SA founded by the elastic net algorithm were the following: previous suicide planning, previous suicide thoughts, lifetime depressive episode, and intermittent explosive disorder. Our elastic net model had a good performance and found an area under the curve of 0.95.

CONCLUSIONS

This is the first study to evaluate risk factors for SA among OCD patients using machine learning algorithms. Our results demonstrate an accurate risk algorithm can be created using clinical and sociodemographic variables. All aspects of suicidal phenomena need to be carefully investigated by clinicians in every evaluation of OCD patients. Particular attention should be given to comorbidity with depressive symptoms.