Incidence Trends and Risk Prediction Nomogram for Suicidal Attempts in Patients With Major Depressive Disorder

Incidence rate and risk factors for suicide in patients with breast cancer in the USA: A surveillance, epidemiology, and end results analysis (SEER)

PURPOSE

To investigate suicide mortality and the related factors among female breast cancer patients in the United States.

METHODS

The SEER database was used to identify 716,422 patients diagnosed with breast cancer between 2010 and 2018 to calculate a standardized mortality rate (SMR). An analysis of risk factors for suicide death was conducted using the univariate and multivariate Cox proportional risk model. An estimation of suicide probability was performed through a nomogram model.

RESULTS

Compared with the expected suicide cases (n = 155) in the general population of the United States at the corresponding period (a suicide death rate of 5.71 per 100,000 person-years), the suicide rate among 716,422 breast cancer patients was followed during 2010-2018 and showed a relatively higher rate of 9.02 per 100,000 person-years. The SMR was 1.58 (95%CI: 1.39-1.79). White and other races were nine and seven times more likely to complete suicide than Black race, respectively (aHR = 9.013, 95%CI: 3.335-24.36, P < 0.001; aHR = 7.129, 95%CI: 2.317-21.931, P = 0.001); unmarried or single patients were at higher risk than married patients (aHR = 1.693, 95%CI: 1.206-2.377, P = 0.002). Patients receiving radiotherapy (aHR = 0.731, 95%CI: 0.545-0.980, P = 0.036) were less likely to complete suicide than those who did not.

CONCLUSION

Female breast cancer patients in the United States have a higher suicide rate than the general public, and the risk factors consist of non-black ethnicity, being single or unmarried, and not being treated with radiotherapy. As a result of this study, clinicians may be able to identify female breast cancer patients who are at high risk of suicide, thus providing appropriate psychological support at the early stage.

Development and validation of a nomogram for suicide attempts in patients with first-episode drug-naïve major depressive disorder

Objective

The risk of suicide can be decreased by accurately identifying high-risk suicide groups and implementing the right interventions. The aim of this study was to develop a nomogram for suicide attempts (SA) in patients with first-episode drug-naïve (FEDN) major depressive disorder (MDD).

Methods

This study undertook a cross-sectional analysis of 1,718 patients diagnosed with FEDN MDD, providing comprehensive clinical data from September 2016 to December 2018. Data on anthropometric and sociodemographic factors were gathered, and the severity of depression and anxiety was evaluated using the 17-item Hamilton Depression Scale (HAMD-17) and the Hamilton Anxiety Scale (HAMA), respectively. Additionally, thyroid hormone levels, lipid profile parameters, and fasting blood glucose (FBG) were measured. Suicide attempt (SA) history was verified based on an amalgamation of medical records, patient interviews, and family interviews. Participants were randomly divided into a training group (70%, n = 1,204) and a validation group (30%, n = 514). In the training group, LASSO analysis and multivariate regression were used to identify variables associated with SA. A nomogram was then constructed using the identified risk factors to estimate the likelihood of SA within the training group. To assess the accuracy, the area under the receiver operating characteristic curve (AUC) was utilized, and calibration plots were employed to evaluate calibration. Additionally, decision curve analysis (DCA) was performed to assess the precision of the model. Finally, internal validation was carried out using the validation group.

Results

A practical nomogram has been successfully constructed, incorporating HAMD, HAMA, thyroid stimulating hormone (TSH), thyroid peroxidase antibody (TPOAb), and systolic blood pressure (SBP) parameters, to estimate the probability of SA in Chinese patients diagnosed with FEDN MDD. The pooled area under the ROC for SA risk in both the training and validation groups was found to be 0.802 (95% CI: 0.771 to 0.832) and 0.821 (95% CI: 0.774 to 0.868), respectively. Calibration analysis revealed a satisfactory correlation between the nomogram probabilities and the actual observed probabilities. The clinical applicability of the nomogram was confirmed through decision curve analysis. To enhance accessibility for clinicians and researchers, an online version of the nomogram can be accessed at https://doctorjunjunliu.shinyapps.io/dynnomapp/.

Conclusions

We constructed and validated a nomogram for the early detection of FEDN MDD patients with a high risk of SA, thereby contributing to the implementation of effective suicide prevention programs.

Patient characteristics, validity of clinical diagnoses and Outcomes Associated with Suicidality in Inpatients with Symptoms of Depression (OASIS-D): design, procedures and outcomes

BACKGROUND

Suicidality, ranging from passive suicidal thoughts to suicide attempt, is common in major depressive disorder (MDD). However, relatively little is known about patient, illness and treatment characteristics in those with co-occurring MDD and suicidality, including the timing of and factors associated with the offset, continuation or reemergence of suicidality. Here, we present the background, rationale, design and hypotheses of the Patient Characteristics, Validity of Clinical Diagnoses and Outcomes Associated with Suicidality in Inpatients with Symptoms of Depression (OASIS-D) study, an investigator-initiated, observational study, funded by Janssen-Cilag GmbH.

METHODS/RESULTS

OASIS-D is an eight-site, six-month, cohort study of patients aged 18-75 hospitalized with MDD. Divided into three sub-studies and patient populations (PPs), OASIS-D will (i) systematically characterize approximately 4500 consecutively hospitalized patients with any form of unipolar depressive episode (PP1), (ii) evaluate the validity of the clinical diagnosis of moderate or severe unipolar depressive episode with the Mini-International Neuropsychiatric Interview (M.I.N.I.) and present suicidality (at least passive suicidal thoughts) present ≥ 48 h after admission with the Sheehan-Suicide Tracking Scale (S-STS), assessing also predictors of the diagnostic concordance/discordance of MDD in around 500 inpatients (PP2), and (iii) characterize and prospectively follow for 6 months 315 inpatients with a research-verified moderate or severe unipolar depressive episode and at least passive suicidal thoughts ≥ 48 h after admission, evaluating treatment and illness/response patterns at baseline, hospital discharge, 3 and 6 months. Exploratory objectives will describe the association between the number of days with suicidality and utilization of outpatient and inpatient care services, and structured assessments of factors influencing the risk of self-injurious behavior without suicidal intent, and of continuous, intermittent or remitted suicidality during the 6-month observation period.

CONCLUSION

Despite their frequency and clinical relevance, relatively little is known about patient and treatment characteristics of individuals with MDD and suicidality, including factors moderating and mediating the outcome of both MDD and suicidality. Results of the OASIS-D study are hoped to improve the understanding of the frequency, correlates and 6-month naturalistic treatment and outcome trajectories of different levels of suicidality in hospitalized adults with MDD and suicidality.

TRIAL REGISTRATION

NCT04404309 [ClinicalTrials.gov].

Differences in the prevalence and clinical correlates of comorbid suicide attempts in patients with early- and late-onset major depressive disorder

Objective

There are many studies on differences in the onset age of major depressive disorder (MDD) patients. However, study on differences in clinical correlates of suicide attempts between early- and late-onset MDD patients is limited. The aim of this study was to investigate the differences in the prevalence and clinical correlates of suicide attempts in patients with early- and late-onset MDD in China.

Methods

A total of 1718 adult outpatients with MDD were recruited. Demographic and clinical data were collected. The 17-item Hamilton Rating Scale for Depression (HAMD-17), Hamilton Anxiety Rating Scale (HAMA), Positive and Negative Syndrome Scale (PANSS) positive subscale, and Clinical Global Impression-Severity (CGI-S) Scales were used to assess their depressive, anxiety, psychotic symptoms, and the severity of the clinical symptoms, respectively.

Results

The prevalence of suicide attempts was higher in late-onset MDD patients (291/1369, 21.3%) than in early-onset MDD patients (55/349, 15.8%) (p = 0.023). However after Bonferroni correction no significant difference was found in the prevalence of suicide attempts in late-onset and late-onset MDD patients (p > 0.05). In both early- and late-onset groups, univariate analysis showed that the following characteristics were significantly associated with suicide attempts: HAMA, HAMD and PANSS positive subscale scores, thyroid stimulating hormone (TSH) levels, blood glucose levels, systolic blood pressure (SBP), and diastolic blood pressure (DBP). In both the early- and late-onset groups, the prevalence rates of severe anxiety disorder and psychotic symptoms were significantly higher in the suicide attempt group than in the non-suicide attempt group. In regression analysis, disease duration, TSH levels and HAMA score were independently associated with suicide attempts in the early-onset group, while TSH levels, HAMA and HAMD score were independently associated with suicide attempts in the late-onset group.

Conclusion

This study suggests that suicide attempts are not frequent in early-onset outpatients with MDD compared with late-onset, and some clinical correlates are associated with suicide attempt in early- and late-onset MDD.

Using different types of visual reaction time measurements for assessing cognitive difficulties in depression

There is a need for objective, easy and relatively short methods to diagnose cognition in depression. We have constructed a set of simple visual tasks using three different ways of speed measuring: paper-pencil-based, computer-based, and eye-tracking based. We used a single case design with 22 participants. A clinical group counted 11 patients with major depression examined two times (first examination without medication and second after three months of medical treatment) together with a group of 11 matched healthy controls. Cognitive difficulties were observable in all the checked levels of performance. The weakest in all tasks were patients before medication, some improvement was observed after medical treatment, but not matching the level of healthy controls. Cognitive difficulties were not eliminated by medical treatment as quickly as emotional disturbances were. The observed difficulties could be interpreted in terms of psychomotor retardation, a typical symptom in depression, which proved to be mainly cognitive as the analysis of differences in reaction times and the first saccade latencies concluded. The analysis of simple visual reaction times on several stages turned out to be a promising method to measure the cognitive state in persons with mood disorders and cognitive convalescence during major depressive disorder treatment.

Identification of risk factors for attempted suicide by self-poisoning and a nomogram to predict self-poisoning suicide

Purpose

Suicide is a global concern, especially among young people. Suicide prediction models have the potential to make it easier to identify patients who are at a high risk of suicide, but they have very little predictive power when there is a positive value for suicide mortality. Therefore, the aim of the study is to uncover potential risk factors associated with suicide by self-poisoning and further to provide a trustworthy nomogram to predict self-poisoning suicide among poisoned patients.

Methods

This study prospectively enrolled 237 patients who were treated for poisoning at the Fifth Medical Center of PLA General Hospital (Beijing) between May 2021 and May 2022. Patient's basic characteristics, daily activities, mental health status, and history of psychological illnesses were gathered to examine their predictive power for self-poisoning suicide. On developing a prediction model, patients were split 8:2 into a training (n = 196) group and a validation (n = 41) group at random via computer. The training group worked on model development, while the validation group worked on model validation. In this study, the Hosmer and Lemeshow test, accuracy, and area under the curve were the primary evaluation criteria. Shapley Additive exPlanations (SHAP) was determined to evaluate feature importance. To make the prediction model easy for researchers to utilize, it was presented in nomogram format. Two risk groups of patients were identified based on the ideal cut-off value.

Results

Of all poisoned patients, 64.6% committed suicide by self-poisoning. With regard to self-poisoning attempted suicide, multivariate analysis demonstrated that female gender, smoking, generalized anxiety disorder-7 (GAD-7), and beck hopelessness scale-20 (BHS-20) were significant risk factors, whereas married status, relatively higher education level, a sedentary time of 1–3 h per day, higher sport frequency per week, higher monthly income were significant protective features. The nomogram contained each of the aforementioned nine features. In the training group, the area under curve (AUC) of the nomogram was up to 0.938 (0.904–0.972), whereas in the validation group, it reached a maximum of 0.974 (0.937–1.000). Corresponding accuracy rates were up to 0.883 and 0.927, respectively, and the P-values for the Hosmer and Lemeshow test were 0.178 and 0.346, respectively. SHAP demonstrated that the top three most important features were BHS-20, GAD-7, and marital status. Based on the best cut-off value of the nomogram (40%), patients in the high-risk group had a nearly six-time larger likelihood of committing suicide by self-poisoning than patients in the low-risk group (88.68 vs. 15.38%, P &lt; 0.001). The dynamic nomogram was made available at the following address: https://xiaobo.shinyapps.io/Nomogramselfpoisoningsuicide/.

Conclusions

This study proposes a prediction model to stratify patients at a high risk of suicide by self-poisoning and to guide individual preventive strategies. Patients in the high-risk group require further mental health counseling to alleviate anxiety and hopelessness, healthy lifestyle like quitting smoking and exercising more, and restriction of access to poison and psychiatric drugs.

A nomogram of suicidal ideation among men who have sex with men in China: Based on the integrated motivational-volitional model of suicidal behavior

Introduction

Men who have sex with men (MSM) are a high-risk group for suicide, with a much higher prevalence of suicidal ideation (SI) than the general population and male population. The aim of this study was to explore the risk factors influencing the development of SI and to develop and validate a nomogram among MSM.

Methods

A cross-sectional study was conducted in 915 MSM from Shanghai, Shenyang, Shenzhen and Kunming, China using the snowball sampling method. The integrated motivational-volitional (IMV) model of suicidal behavior was used as a theoretical framework to collect different potential influencing factors of SI including diathesis-environment-life events factors and psychosocial factors. The risk factors of SI were screened by logistic regression analysis, and a nomogram for predicting SI were developed. Model properties including discrimination, calibration and decision curves were evaluated to validate the prediction model.

Results

882 MSM were included in the statistical analysis, with a lifetime prevalence of SI of 34.4% (303/882). Logistic regression analysis showed that age group, sexual orientation disclosure, high-risk sexual behavior, entrapment, defeat and interpersonal needs were associated with SI. A nomogram was constructed based on the above six predictors. AUC values of ROC for prediction model were 0.761 (0.641-0.770) and 0.754 (0.565-0.822) in the training set (n = 662) and validation set (n = 220), respectively. And there was no statistical difference of the AUC values between the two sets (P > 0.05). The calibration plots of the prediction model in both sets fit well with the ideal model (P > 0.05). The decision curves demonstrated that the threshold probability of prediction model in training set was 1-85%, whereas in validation set was 1-63%.

Conclusions

The lifetime prevalence of SI among Chinese MSM is high. The nomogram can serve as a useful tool to predict the development of SI among MSM. Defeat, entrapment and interpersonal needs, as significant predictors of SI, can be measured to identify SI in advance. Early assessment of SI and the enhancement of psychosocial interventions are important to prevent suicide-related behaviors. Future studies could incorporate more variables of interest to refine the prediction model to better guide behavioral and psychological intervention strategies among MSM.

Development and validation of a nomogram for predicting the risk of mental health problems of factory workers and miners

OBJECTIVE

A nomogram for predicting the risk of mental health problems was established in a population of factory workers and miners, in order to quickly calculate the probability of a worker suffering from mental health problems.

METHODS

A cross-sectional survey of 7500 factory workers and miners in Urumqi was conducted by means of an electronic questionnaire using cluster sampling method. Participants were randomly assigned to the training group (70%) and the validation group (30%). Questionnaire-based survey was conducted to collect information. A least absolute shrinkage and selection operator (LASSO) regression model was used to screen the predictors related to the risk of mental health problems of the training group. Multivariate logistic regression analysis was applied to construct the prediction model. Calibration plots and receiver operating characteristic-derived area under the curve (AUC) were used for model validation. Decision curve analysis was applied to calculate the net benefit of the screening model.

RESULTS

A total of 7118 participants met the inclusion criteria and the data were randomly divided into a training group (n=4955) and a validation group (n=2163) in a ratio of 3:1. A total of 23 characteristics were included in this study and LASSO regression selected 12 characteristics such as education, professional title, age, Chinese Maslach Burnout Inventory, effort-reward imbalance, asbestos dust, hypertension, diabetes, working hours per day, working years, marital status and work schedule as predictors for the construction of the nomogram. In the validation group, the Brier score was 0.176, the calibration slope was 0.970 and the calibration curve of nomogram showed a good fit. The AUC of training group and verification group were 0.785 and 0.784, respectively.

CONCLUSION

The nomogram combining these 12 characteristics can be used to predict the risk of suffering mental health problems, providing a useful tool for quickly and accurately screening the risk of mental health problems.

Predicting the risk of suicide attempt in a depressed population: Development and assessment of an efficient predictive nomogram

The purpose of this study was to develop and validate a user-friendly suicide attempt risk nomogram in depression, supporting timely interventions by clinicians. We collected clinical data of 273 depressed patients from January 2020 to January 2021. Suicide attempt was assessed conducting the Mini International Neuropsychiatric Interview. First, optimized features were filtrated through the least absolute shrinkage and selection operator regression analysis. Subsequently, we selected variables with nonzero coefficients and entered them into multiple logistic regression model and nomogram function to construct a visual predicting suicide attempt model. Additionally, the C-index, calibration plot and decision curve analysis, were applied to assess discrimination, calibration, and clinical practicability. Finally, the bootstrapping validation was applied to assess internal validation. Finally, eleven clinical features are screened out in the prediction nomogram. The model presented tiptop calibration and pleasant discrimination with a C-index of 0.853. A towering C-index value, up to 0.799, could also be attained in the interval validation analysis. In addition, decision curve analysis exhibited that our predictive model is clinically effective when the threshold is no less than 1%. These results demonstrate this predictive model was helpful for clinicians assessing the inpatient's suicide attempt recently and implementing individualized treatment strategies.