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Disease trajectories of patients with anxiety disorders are highly diverse and approximately 60% remain chronically ill. The ability to predict disease course in individual patients would enable personalized management of these patients. This study aimed to predict recovery from anxiety disorders within 2 years applying a machine learning approach.
In total, 887 patients with anxiety disorders (panic disorder, generalized anxiety disorder, agoraphobia, or social phobia) were selected from a naturalistic cohort study. A wide array of baseline predictors (N = 569) from five domains (clinical, psychological, sociodemographic, biological, lifestyle) were used to predict recovery from anxiety disorders and recovery from all common mental disorders (CMDs: anxiety disorders, major depressive disorder, dysthymia, or alcohol dependency) at 2-year follow-up using random forest classifiers (RFCs).
At follow-up, 484 patients (54.6%) had recovered from anxiety disorders. RFCs achieved a cross-validated area-under-the-receiving-operator-characteristic-curve (AUC) of 0.67 when using the combination of all predictor domains (sensitivity: 62.0%, specificity 62.8%) for predicting recovery from anxiety disorders. Classification of recovery from CMDs yielded an AUC of 0.70 (sensitivity: 64.6%, specificity: 62.3%) when using all domains. In both cases, the clinical domain alone provided comparable performances. Feature analysis showed that prediction of recovery from anxiety disorders was primarily driven by anxiety features, whereas recovery from CMDs was primarily driven by depression features.
The current study showed moderate performance in predicting recovery from anxiety disorders over a 2-year follow-up for individual patients and indicates that anxiety features are most indicative for anxiety improvement and depression features for improvement in general.
Major depressive disorder (MDD) represents a leading cause of disability. This study examines the course of disability in patients with chronic, recurrent and remitting MDD compared to healthy controls and identifies predictors of disability in remitting MDD.
We included 914 participants from the Netherlands Study of Depression and Anxiety (NESDA). DSM-IV MDD and WHO DAS II disability were assessed at baseline and at 2, 4 and 6 years. Six-year total and domain-specific disability were analysed and compared in participants with chronic (n = 57), recurrent (n = 120), remitting (n = 127) MDD and in healthy controls (n = 430). Predictors of residual disability were identified using linear regression analysis.
At baseline, most disability was found in chronic MDD, followed by recurrent MDD, remitting MDD and healthy controls. Across diagnostic groups, most disability was found in household activities, interpersonal functioning, participation in society and cognition. A chronic course was associated with chronic disability. Symptom remission was associated with a decrease in disability, but some disability remained. In remitting MDD, higher residual disability was predicted by older age, more severe avoidance symptoms, higher disability at baseline and late symptom remission. Severity of residual disability correlated with the severity of residual depressive symptoms.
Symptomatic remission is a prerequisite for improvements in disability. However, disability persists despite symptom remission. Therefore, treatment of MDD should include an explicit focus on disability, especially on the more complex domains. To this end, treatments should promote behavioural activation and address subthreshold depressive symptoms in patients with remitted MDD.
Anxiety has been associated with new-onset cardiovascular disease (CVD),
but the quality of this relationship is unclear. Only if anxiety is a
causal, independent cardiovascular risk factor might it be a target for
To determine and examine the independent association and causality
between anxiety and incident CVD.
PubMed, EMBASE and PsycINFO databases were searched up to October 2013. A
review of Hill's criteria for causality and random effects meta-analysis
were conducted of prospective, population-based studies examining anxiety
and incident CVD in people free from CVD at baseline.
The meta-analysis comprised 37 papers (n = 1 565 699).
The follow-up ranged from 1 to 24 years. Anxiety was associated with a
52% increased incidence of CVD (hazard ratio = 1.52, 95% CI 1.36–1.71).
The risk seemed independent of traditional risk factors and depression.
The evaluation of Hill's criteria largely argued in favour of
Anxiety may be of interest for CVD prevention. Future research should
examine biological and behavioural underpinnings of the association in
order to identify targets for intervention.
This chapter emphasizes the importance of adequate care of pharmacological evidence on treating panic disorder. It focuses on the optimal first-line pharmacotherapy of panic disorder. The chapter discusses the optimal duration of maintenance therapy, and describes the optimal approach to pharmacotherapy in the treatment-refractory patient. It reviews the antidepressants and benzodiazepines with regard to efficacy in acute and long-term treatment, the side-effects and risks involved, drop-out rates, onset of action and efficacy in comorbid conditions. Given the comparable efficacy of the pharmacological classes described in acute phase treatment and the efficacy in long-term treatment, other considerations determine which agent should be considered the first-line pharmacotherapy of panic disorder. The first-line pharmacotherapy for panic disorder has been selective serotonin reuptake inhibitor (SSRIs) for some time, and there is now sufficient evidence to indicate that the SNRI venlafaxine should also be considered as a first-line agent.
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