Predictors of start of different antidepressants in patient charts among patients with depression.
BACKGROUND
In usual psychiatric care, antidepressant treatments are selected based on physician and patient preferences rather than being randomly allocated, resulting in spurious associations between these treatments and outcome studies.


OBJECTIVE
To identify factors recorded in electronic medical chart progress notes predictive of antidepressant selection among patients who had received a depression diagnosis.


METHODS
This retrospective study sample consisted of 556 randomly selected Veterans Health Administration patients diagnosed with depression from April 1, 1999, to September 30, 2004, stratified by the antidepressant agent, geographic region, gender, and year of depression cohort entry. Predictors were obtained from administrative data, and additional variables were abstracted from electronic medical chart notes in the year prior to the start of the antidepressant in 5 categories: clinical symptoms and diagnoses, substance use, life stressors, behavioral/ideation measures (e.g., suicide attempts), and treatments received. Multinomial logistic regression analysis was used to assess the predictors associated with different antidepressant prescribing, and adjusted relative risk ratios (RRR) were reported.


RESULTS
Of the administrative data-based variables, gender, age, illicit drug abuse or dependence, and number of psychiatric medications in the prior year were significantly associated with antidepressant selection. After adjusting for administrative data-based variables, sleep problems (relative risk ratio [RRR] = 2.47) or marital issues (RRR = 2.64) identified in the charts were significantly associated with prescribing mirtazapine rather than sertraline; however, no other chart-based variables showed a significant association or an association with a large magnitude.


CONCLUSIONS
Some chart data-based variables were predictive of antidepressant selection, but we neither found many nor found them highly predictive of antidepressant selection in patients treated for depression.
University of Michigan, 3550 Rackham, Ann Arbor, MI 48109-1070. myrakim@umich.edu.
Hyungjin Myra
HM
Kim
Kara
K
Zivin
Hae Mi
HM
Choe
Clare M
CM
Stano
Dara
D
Ganoczy
Heather
H
Walters
Marcia
M
Valenstein
D000368:Aged; D000928:Antidepressive Agents / Q000627:therapeutic use*; D003863:Depression / Q000188:drug therapy*; D005260:Female; D006801:Humans; D008297:Male; D008875:Middle Aged; D012189:Retrospective Studies
D000928:Antidepressive Agents