Predicting suicide attempts among people with diabetes using a large multicenter electronic health records dataset

Abstract

Objective People with diabetes have a higher risk of suicidal behaviors than the general population. However, few studies have focused on understanding this relationship. We investigated risk factors and predicted suicide attempts in people with diabetes using Least Absolute Shrinkage and Selection Operator (LASSO) regression. Method Data was retrieved from Cerner Real-World Data and included over 3 million diabetes patients in the study. LASSO regression was applied to identify associated factors. Gender, diabetes-type, and depression-specific LASSO regression models were analyzed. Results There were 7764 subjects diagnosed with suicide attempts with an average age of 45. Risk factors for suicide attempts in diabetes patients were American Indian or Alaska Native race ( β = 0.637), receiving atypical antipsychotic agents ( β = 0.704), benzodiazepines ( β = 0.784), or antihistamines ( β = 0.528). Amyotrophy was negatively associated with suicide attempts in males ( β = 2.025); in contrast, amyotrophy significantly increased the risk in females ( β = 3.339). Using a MAOI was negatively related to suicide attempts in T1DM patients ( β = 7.304). Age less than 20 was positively associated with suicide attempts in depressed ( β = 2.093) and non-depressed patients ( β = 1.497). The LASSO model achieved a 94.4% AUC and 87.4% F1 score. Conclusions To our knowledge, this is the first study to use LASSO regression to identify risk factors for suicide attempts in patients with diabetes. The shrinkage technique successfully reduced the number of variables in the model to improve the fit. Further research is needed to determine cause-and-effect relationships. The results may help providers to identify high-risk groups for suicide attempt among diabetic patients.