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.
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.
Authors: Marcia Valenstein; Hyungjin Myra Kim; Dara Ganoczy; Daniel Eisenberg; Paul N Pfeiffer; Karen Downing; Katherine Hoggatt; Mark Ilgen; Karen L Austin; Kara Zivin; Frederic C Blow; John F McCarthy Journal: J Clin Psychopharmacol Date: 2012-06 Impact factor: 3.153
Authors: Hyungjin Myra Kim; Kara Zivin; Dara Ganoczy; Paul Pfeiffer; Katherine Hoggatt; John F McCarthy; Karen Downing; Marcia Valenstein Journal: Pharmacoepidemiol Drug Saf Date: 2010-10 Impact factor: 2.890
Authors: Mark Zimmerman; Michael Posternak; Michael Friedman; Naureen Attiullah; Scott Baymiller; Robert Boland; Stacie Berlowitz; Shahzad Rahman; Kirsten Uy; Steve Singer Journal: Am J Psychiatry Date: 2004-07 Impact factor: 18.112
Authors: Hyungjin Myra Kim; Eric G Smith; Claire M Stano; Dara Ganoczy; Kara Zivin; Heather Walters; Marcia Valenstein Journal: BMC Health Serv Res Date: 2012-01-23 Impact factor: 2.655
Authors: Andrea C Fernandes; David Chandran; Mizanur Khondoker; Michael Dewey; Hitesh Shetty; Rina Dutta; Robert Stewart Journal: BMJ Open Date: 2018-09-05 Impact factor: 2.692