Suicide Risk Prediction in Cancer Patients

Fang Tang

Status: Completed

Conditions: Cancer

Study type: Observational

Funder types: Other

Identifiers

NCT06167720

YXLL-KY-2023(139)

Details and patient eligibility

About

Previous studies have found that the suicide risk of cancer patients is influenced by socioeconomic factors, clinical characteristics, and environmental factors. But prediction model with multiple predictors for suicide risk in cancer patients is limited.

The aim of this study is to assess the association of socioeconomic factors, clinical characteristics and meteorological factors with cancer patients' suicide, based on retrospective cohorts, and to establish a suicide risk prediction model with multiple predictors for cancer patients.

Full description

Cancer is a serious public health concern, with almost 10 million people dying from cancer in 2020. Previous studies have reported that cancer patients are more likely to die by suicide than the general public, especially in the six months to one year following cancer diagnosis. Since suicide is a result of the interaction of various factors such as socioeconomic factors, clinical characteristics, and environmental factors, it is necessary to construct a multivariate prediction model to predict the suicide risk in cancer patients.

A retrospective cohort of cancer patients based on the Surveillance, Epidemiology, and End Results (SEER) program database was used to assess the association of socioeconomic factors, clinical characteristics and meteorological factors with cancer patients' suicide, and to establish prediction model with multiple predictors for cancer patients. Another retrospective cohort conducted from Shandong Multi-Center Healthcare Big Data Platform (SMCHBDP) was used to verify the predictive ability and generalization ability of the prediction model.

Enrollment: 176,000 patients

Sex: All

Volunteers: No Healthy Volunteers

Inclusion criteria

1. Cancer patients in SEER database and SMCHBDP

Exclusion criteria

1. No certain cause of death
2. Missing area code
3. Lost to follow-up

Trial design

176,000 participants in 2 patient groups

Cancer patients cohort in SEER database

Description:

A retrospective cohort of cancer patients based on the Surveillance, Epidemiology, and End Results (SEER) program database was used to assess the association of socioeconomic factors, clinical characteristics and meteorological factors with cancer patients' suicide, and to establish prediction model with multiple predictors for cancer patient

Cancer patients cohort in SMCHBDP

Description:

Retrospective cohort conducted from Shandong Multi-Center Healthcare Big Data Platform (SMCHBDP) was used to verify the predictive ability and generalization ability of the prediction model.