In this thesis, we used qualitative and quantitative research methods to gain insight in treatment decision-making for depression- and anxiety disorders in specialized mental health care. We... Show moreIn this thesis, we used qualitative and quantitative research methods to gain insight in treatment decision-making for depression- and anxiety disorders in specialized mental health care. We identified what factors are important in the decisional process, to both patients and clinicians, and how they determine decision-making outcomes, i.e., the decision to opt for: pharmacotherapy, psychotherapy, or a combination of both. We determined what preferences patients and clinicians have regarding the treatment of depression and anxiety disorders, which treatments are selected, and which factors are involved in the formation of such treatment preferences and treatment selection. Additionally, we determined what preferences patients and clinicians have regarding their role in the decisional process, their experienced decision-making roles, and the level of concordance between preferred and experienced role of patients. Finally, we examined the extent of Shared Decision-Making (SDM) in clinical practice and explored possible target points to improve SDM. Show less
Background: Predicting the onset and course of mood and anxiety disorders is of clinical importance but remains difficult. We compared the predictive performances of traditional logistic regression... Show moreBackground: Predicting the onset and course of mood and anxiety disorders is of clinical importance but remains difficult. We compared the predictive performances of traditional logistic regression, basic probabilistic machine learning (ML) methods, and automated ML (Auto-sklearn).Methods: Data were derived from the Netherlands Study of Depression and Anxiety. We compared how well multinomial logistic regression, a naïve Bayes classifier, and Auto-sklearn predicted depression and anxiety diagnoses at a 2-, 4-, 6-, and 9-year follow up, operationalized as binary or categorical variables. Predictor sets included demographic and self-report data, which can be easily collected in clinical practice at two initial time points (baseline and 1-year follow up).Results: At baseline, participants were 42.2 years old, 66.5% were women, and 53.6% had a current mood or anxiety disorder. The three methods were similarly successful in predicting (mental) health status, with correct predictions for up to 79% (95% CI 75?81%). However, Auto-sklearn was superior when assessing a more complex dataset with individual item scores.Conclusions: Automated ML methods added only limited value, compared to traditional data modelling when predicting the onset and course of depression and anxiety. However, they hold potential for automatization and may be better suited for complex datasets. Show less
Since heterogeneity in depressed patients makes treatment decisions difficult and treatment often unsuccessful, we seek to identify certain subtypes of depression. 30 to 40% of depressed patients... Show moreSince heterogeneity in depressed patients makes treatment decisions difficult and treatment often unsuccessful, we seek to identify certain subtypes of depression. 30 to 40% of depressed patients have anger regulation problems; from irritability to anger attacks. What is the significance of anger in depression? Does it signify a subtype of depression? In the NESDA cohort, we compared a large sample of currently depressed patients with irritability to currently depressed patients without irritability. Irritable depressed patients had more symptoms of depression, more often had comorbid anxiety and had more often attempted suicide than non-irritable depressed patients. In a student sample, we investigated the role of the MAOA genotype and found that women with the high expression variant are possibly more vulnerable to anger or aggression during depression. Using acute tryptophan depletion we temporarily lowered serotonin in remitted depressed patients with and without anger regulation problems during their depression. We found no cognitive differences between the two groups, but the experiment did show us that depressed patients with anger regulation problems may be more serotonergically vulnerable. Further research is needed to elucidate best suited treatment strategies for anger regulation problems in depression Show less
