Soldiers' Suicide Risk Predictable With Algorithm
http://www.psychiatryadvisor.com; November 12, 2014
Researchers have developed an algorithm to help predict which soldiers have reached highest likelihood of suicide as soon as they have been hospitalized for a psychiatric condition.
Ronald C. Kessler, PhD, of Harvard Medical School, and colleagues examined 53,769 hospitalizations of active-duty soldiers from January 2004 through December 2009 with psychiatric admission diagnoses. They then sought to identify common factors inside the 68 soldiers who took their particular lives in the one year after they were discharged from the hospital.
The strongest suicide predictors included sociodemographic factors such as being male and enlisting at a later age, in addition to criminal background, areas of prior psychiatric treatment (for example the amount of antidepressant prescriptions filled in twelve months), and disorders diagnosed throughout the hospitalization, the researchers reported in JAMA Psychiatry.
Nearly 53% from the suicides inside the group were in soldiers who were in the 5% of hospitalizations with all the highest predicted suicide risk.
The researchers feel that their algorithm may help identify high-risk soldiers who may need preventive intervention.
"The high concentration of chance of suicide and also other adverse outcomes might justify targeting expanded posthospitalization interventions to soldiers classified as having highest posthospitalization suicide risk, although final determination requires careful consideration of intervention costs, comparative effectiveness, and possible side effects," they concluded.
Suicide Risk of Soldiers Predictable With Algorithm Suicide Risk of Soldiers Predictable With Algorithm
The U.S. Army experienced a sharp increase in soldier suicides starting in 2004. Administrative data reveal that some of those at highest risk are soldiers inside 12 months after inpatient treatment of a psychiatric disorder.
The objective with the study is usually to develop an actuarial risk algorithm predicting suicide inside the 12 months after U.S. Army soldier inpatient treatment of a psychiatric disorder to target expanded posthospitalization care.
There were 53,769 hospitalizations of active duty soldiers from January 1, 2004, through December 31, 2009, with psychiatric admission diagnoses. Administrative data available before hospital discharge abstracted coming from a wide range of data systems (sociodemographic, U.S. Army career, criminal justice, and medical or pharmacy) were utilized to predict suicides in the subsequent 12 months using machine learning methods (regression trees and penalized regressions) designed to evaluate cross-validated linear, nonlinear, and interactive predictive associations.