Background: Given the strong relationship between depression and anxiety, there is an urge to investigate their shared and specific long-term course determinants. The current study aimed to... Show moreBackground: Given the strong relationship between depression and anxiety, there is an urge to investigate their shared and specific long-term course determinants. The current study aimed to identify and compare the main determinants of the 9-year trajectories of combined and pure depression and anxiety symptom severity. Methods: Respondents with a 6-month depression and/or anxiety diagnosis (n=1,701) provided baseline data on 152 sociodemographic, clinical and biological variables. Depression and anxiety symptom severity assessed at baseline, 2-, 4-, 6- and 9-year follow-up, were used to identify data-driven course-trajectory subgroups for general psychological distress, pure depression, and pure anxiety severity scores. For each outcome (classprobability), a Superlearner (SL) algorithm identified an optimally weighted (minimum mean squared error) combination of machine-learning prediction algorithms. For each outcome, the top determinants in the SL were identified by determining variable-importance and correlations between each SL-predicted and observed outcome (rho pred) were calculated. Results: Low to high prediction correlations (rho pred: 0.41-0.91, median=0.73) were found. In the SL, important determinants of psychological distress were age, young age of onset, respiratory rate, participation disability, somatic disease, low income, minor depressive disorder and mastery score. For course of pure depression and anxiety symptom severity, similar determinants were found. Specific determinants of pure depression included several types of healthcare-use, and of pure-anxiety course included somatic arousal and psychological distress. Limitations: Limited sample size for machine learning. Conclusions: The determinants of depression- and anxiety-severity course are mostly shared. Domain-specific exceptions are healthcare use for depression and somatic arousal and distress for anxiety-severity course. Show less
Background Notwithstanding the firmly established cross-sectional association of happiness with psychiatric disorders and their symptom severity, little is known about their temporal relationships.... Show moreBackground Notwithstanding the firmly established cross-sectional association of happiness with psychiatric disorders and their symptom severity, little is known about their temporal relationships. The goal of the present study was to investigate whether happiness is predictive of subsequent psychiatric disorders and symptom severity (and vice versa). Moreover, it was examined whether changes in happiness co-occur with changes in psychiatric disorder status and symptom severity. Methods In the Netherlands Study of Depression and Anxiety (NESDA), happiness (SRH: Self-Rated Happiness scale), depressive and social anxiety disorder (CIDI: Composite Interview Diagnostic Instrument) and depressive and anxiety symptom severity (IDS: Inventory of Depressive Symptomatology; BAI: Beck Anxiety Inventory; and FQ: Fear Questionnaire) were measured in 1816 adults over a three-year period. Moreover, we focused on occurrence and remittance of 6-month recency Major Depressive Disorder (MDD) and Social Anxiety Disorders (SAD) as the two disorders most intertwined with subjective happiness. Results Interindividual differences in happiness were quite stable (ICC of .64). Higher levels of happiness predicted recovery from depression (OR = 1.41; 95% CI = 1.10-1.80), but not social anxiety disorder (OR = 1.31; 95%CI = .94-1.81), as well as non-occurrence of depression (OR = 2.41; 95%CI = 1.98-2.94) and SAD (OR = 2.93; 95%CI = 2.29-3.77) in participants without MDD, respectively SAD at baseline. Higher levels of happiness also predicted a reduction of IDS depression (sr = - 0.08; 95%CI = -0.10 - -0.04), and BAI (sr = - 0.09; 95%CI = -0.12 - -0.05) and FQ (sr = - 0.06; 95%CI = -0.09 - -0.04) anxiety symptom scores. Conversely, presence of affective disorders, as well as higher depression and anxiety symptom severity at baseline predicted a subsequent reduction of self-reported happiness (with marginal to small sr values varying between -.04 (presence of SAD) to -.17 (depression severity on the IDS)). Moreover, changes in happiness were associated with changes in psychiatric disorders and their symptom severity, in particular with depression severity on the IDS (sr = - 0.46; 95%CI = -.50 - -.42). Conclusions Results support the view of rather stable interindividual differences in subjective happiness, although level of happiness is inversely associated with changes in psychiatric disorders and their symptom severity, in particular depressive disorder and depression severity. Show less
The main aim of this thesis is to explore risk factors associated to an increased risk of adverse outcomes for heart failure (HF) patients and improve the early re-admission or mortality prediction... Show moreThe main aim of this thesis is to explore risk factors associated to an increased risk of adverse outcomes for heart failure (HF) patients and improve the early re-admission or mortality prediction in HF. Data from two studies (OPERA-HF study in the UK and SAPHIRE study in US) has been used to explore a wide range of variables as potential risk factors. We found that depression is a significant and independent predictor of all-cause mortality among HF patients. Depression was also significantly associated with recurrent events: unplanned readmission or mortality. Other psychosocial or non-clinical variables independently associated with increasing risk of recurrent events in the year following discharge after a HF hospital admission were: presence of frailty, moderate-to-severe anxiety, living alone and the presence of cognitive impairment. We then used data from the OPERA-HF study to develop a 30-day composite outcome model and to explore the added predictive value of non-clinical predictors to early outcomes: 30-day unplanned readmission or mortality. The performance of the model improved by including physical frailty and social support next to clinical variables. The transportability of the model to a different geography was proved in the external validation of the model on the SAPHIRE study data. Show less
