TY  - JOUR
PY  - 2020//
TI  - Identifying risk factors for suicidal ideation across a large community healthcare system
JO  - Journal of affective disorders
A1  - Schriver, Emily
A1  - Lieblich, Shari
A1  - AlRabiah, Reem
A1  - Mowery, Danielle L.
A1  - Brown, Lily A.
SP  - 1038
EP  - 1045
VL  - 276
IS  - 
N2  - BACKGROUND: Suicide is the tenth leading cause of death in the United States. Several studies have leveraged electronic health record (EHR) data to predict suicide risk in veteran and military samples; however, few studies have investigated suicide risk factors in a large-scale community health population.     METHODS: Clinical data was queried for 9,811 patients from the Penn Medicine Health System who had completed a Patient Health Questionnaire-9 (PHQ-9) documented in the EHR between January 2017 and June 2019. Patient demographics, PHQ-9 scores, and psychiatric comorbidities were extracted from the EHR. Univariate and multivariable logistic regressions were applied to determine significant risk factors associated with suicide ideation responses from the PHQ-9.     RESULTS: One-quarter (25.8%% of patients endorsed suicide ideation. Univariate analysis found 22 risk factors of suicide ideation. Multivariable logistic regression found significant positive associations (Odds Ratio, (95% Confidence Interval)) with the following: younger ages less than 18 years: 2.1, (1.69, 2.60) and 19-24 years: 1.55, (1.29, 1.87)), single marital status (1.22, (1.08, 1.38)), African American (1.22, (1.08, 1.38)), non-commercial insurance (1.16, (1.03, 1.31)), multiple comorbidities (1 comorbidity (1.65, (1.32, 2.07); 2 comorbidities (2.07, (1.61, 2.64)), 3+ comorbidities (2.49, (1.87, 3.33))), bipolar disorders (Type I: 1.38, (1.14, 1.67) and Type II: 1.94, (1.52, 2.49)), depressive disorders (1.70, (1.49, 1.94)), obsessive compulsive disorder (OCD) (1.43, (1.08, 1.90)), and stress disorders (1.53, (1.33, 1.76)).     CONCLUSION: Community EHR information can be used to predict suicidal ideation. This information can be used to design tools for identifying patients at risk for suicide in real-time.<p />  <p>Language: en</p>
LA  - en
SN  - 0165-0327
UR  - http://dx.doi.org/10.1016/j.jad.2020.07.047
ID  - ref1
ER  -