Development and validation of a model for predicting the risk of suicide in patients with cancer

Dynamic prediction of lung cancer suicide risk based on meteorological factors and clinical characteristics：A landmarking analysis approach

Suicide is a severe public health issue globally. Accurately identifying high-risk lung cancer patients for suicidal behavior and taking timely intervention measures has become a focus of current research. This study intended to construct dynamic prediction models for identifying suicide risk among lung cancer patients. Patients were sourced from the Surveillance, Epidemiology, and End Results database, while meteorological data was acquired from the Centers for Disease Control and Prevention. This cohort comprised 455, 708 eligible lung cancer patients from January 1979 to December 2011. A Cox proportional hazard regression model based on landmarking approach was employed to explore the impact of meteorological factors and clinical characteristics on suicide among lung cancer patients, and to build dynamic prediction models for the suicide risk of these patients. Additionally, subgroup analyses were conducted by age and sex. The model's performance was evaluated using the C-index, Brier score, area under curve (AUC) and calibration plot. During the study period, there were 666 deaths by suicide among lung cancer patients. Multivariable Cox results from the dynamic prediction model indicated that age, marital status, race, sex, primary site, stage, monthly average daily sunlight, and monthly average temperature were significant predictors of suicide. The dynamic prediction model demonstrated well consistency and discrimination capabilities. Subgroup analyses revealed that the association of monthly average daily sunlight and monthly average temperature with suicide remained significant among female and younger lung cancer patients. The dynamic prediction model can effectively incorporate covariates with time-varying to predict lung cancer patients' suicide death. The results of this study have significant implications for assessing lung cancer individuals' suicide risk.

Pandemic Grief and Suicidal Ideation in Latin American Countries: A Network Analysis

This study aimed to characterize the network structure of pandemic grief symptoms and suicidal ideation in 2174 people from eight Latin American countries. Pandemic grief and suicidal ideation were measured using the Pandemic Grief Scale and a single item, respectively. Network analysis provides an in-depth characterization of symptom-symptom interactions within mental disorders. The results indicated that, "desire to die," "apathy" and "absence of sense of life" are the most central symptoms in a pandemic grief symptom network; therefore, these symptoms could be focal elements for preventive and treatment efforts. Suicidal ideation, the wish to die, and the absence of meaning in life had the strongest relationship. In general, the network structure did not differ among the participating countries. It identifies specific symptoms within the network that may increase the likelihood of their co-occurrence and is useful at the therapeutic level.

Suicidal ideation in patients with cancer: Its prevalence and results of structural equation modelling

OBJECTIVE

Patients with cancer have a higher risk of suicidal ideation (SI) and suicidality than the general population. This study was designed to investigate the prevalence of SI and its association with psychosocial and sociodemographic factors and tumour entity.

METHODS

In this observational cross-sectional study, 4372 adult patients with different cancer entities were enrolled. We assessed the outcome variables (i.e. SI, depressive and anxiety symptoms, mental and physical fatigue and sociodemographic data) using self-report questionnaires. Data were analysed via descriptive statistics, binomial logistic regression and structural equation modelling (SEM).

RESULTS

Among all patients, 627 (14.3%) reported SI, of whom 12.8% reported SI on several days, 0.9% on half of the days and 0.6% nearly every day. Age, anxiety, mental fatigue and the Patient Health Questionnaire-9 items 'feeling down, depressed and hopeless', 'feeling bad about oneself' and 'slowing or agitation' were significant predictors of SI. SEM, including all significant predictors with a latent depressiveness-demoralisation variable, explained 30.3% variance of SI, showing a good fit.

CONCLUSIONS

Our results showed that a significant number of patients with cancer show SI. Future long-term studies are needed to address the differential contribution of depression and demoralisation on SI in patients with cancer.