Common and specific determinants of 9-year depression and anxiety course-trajectories: A machine-learning investigation in the Netherlands Study of Depression and Anxiety (NESDA)

Using the Super Learner algorithm to predict risk of major adverse cardiovascular events after percutaneous coronary intervention in patients with myocardial infarction

BACKGROUND

The primary treatment for patients with myocardial infarction (MI) is percutaneous coronary intervention (PCI). Despite this, the incidence of major adverse cardiovascular events (MACEs) remains a significant concern. Our study seeks to optimize PCI predictive modeling by employing an ensemble learning approach to identify the most effective combination of predictive variables.

METHODS AND RESULTS

We conducted a retrospective, non-interventional analysis of MI patient data from 2018 to 2021, focusing on those who underwent PCI. Our principal metric was the occurrence of 1-year postoperative MACEs. Variable selection was performed using lasso regression, and predictive models were developed using the Super Learner (SL) algorithm. Model performance was appraised by the area under the receiver operating characteristic curve (AUC) and the average precision (AP) score. Our cohort included 3,880 PCI patients, with 475 (12.2%) experiencing MACEs within one year. The SL model exhibited superior discriminative performance, achieving a validated AUC of 0.982 and an AP of 0.971, which markedly surpassed the traditional logistic regression models (AUC: 0.826, AP: 0.626) in the test cohort. Thirteen variables were significantly associated with the occurrence of 1-year MACEs.

CONCLUSION

Implementing the Super Learner algorithm has substantially enhanced the predictive accuracy for the risk of MACEs in MI patients. This advancement presents a promising tool for clinicians to craft individualized, data-driven interventions to better patient outcomes.

Testing the mood brightening hypothesis: Hedonic benefits of physical, outdoor, and social activities in people with anxiety, depression or both

BACKGROUND

The mood brightening hypothesis postulates that people with depressive symptoms report more positive affect (PA) and less negative affect (NA) than healthy controls after rewarding daily life activities. Whether mood brightening also occurs in people with anxiety symptoms remains unclear. This study examined effects of physical activity, being outdoors, and social activity on PA and NA across different levels of depression and anxiety symptoms in the general Dutch population.

METHODS

Participants completed an electronic diary on their smartphone, thrice daily over 30 days, to assess activities and affect (n = 430; 22,086 assessments). We compared five groups based on their scores on the Depression, Anxiety and Stress Scales: asymptomatic participants, participants with mild symptoms of depression and/or anxiety, depression symptoms, anxiety symptoms, and comorbid depression and anxiety symptoms. Multilevel linear regression models with interaction terms were used to compare the association between activities and affect in these five groups.

RESULTS

All activities were associated with increased PA and reduced NA in all groups. We found a mood brightening effect in participants with depression, as physical activity and being outdoors were associated with reduced NA. Participants with depression had increased PA and reduced NA when in social company compared to asymptomatic participants. No mood brightening effects were observed in participants with anxiety or comorbid depression and anxiety.

LIMITATIONS

Our sample included mainly women and highly educated subjects, which may limit the generalizability of our findings.

CONCLUSION

Mood brightening is specific to depression, and typically stronger when in social company.

First-onset major depression during the COVID-19 pandemic: A predictive machine learning model

BACKGROUND

This study longitudinally evaluated first-onset major depression rates during the pandemic in Italian adults without any current clinician-diagnosed psychiatric disorder and created a predictive machine learning model (MLM) to evaluate subsequent independent samples.

METHODS

An online, self-reported survey was released during two pandemic periods (May to June and September to October 2020). Provisional diagnoses of major depressive disorder (PMDD) were determined using a diagnostic algorithm based on the DSM criteria of the Patient Health Questionnaire-9 to maximize specificity. Gradient-boosted decision trees and the SHapley Additive exPlanations technique created the MLM and estimated each variable's predictive contribution.

RESULTS

There were 3532 participants in the study. The final sample included 633 participants in the first wave (FW) survey and 290 in the second (SW). First-onset PMDD was found in 7.4% of FW participants and 7.2% of the SW. The final MLM, trained on the FW, displayed a sensitivity of 76.5% and a specificity of 77.8% when tested on the SW. The main factors identified in the MLM were low resilience, being an undergraduate student, being stressed by pandemic-related conditions, and low satisfaction with usual sleep before the pandemic and support from relatives. Current smoking and taking medication for medical conditions also contributed, albeit to a lesser extent.

LIMITATIONS

Small sample size; self-report assessment; data covering 2020 only.

CONCLUSIONS

Rates of first-onset PMDD among Italians during the first phases of the pandemic were considerable. Our MLM displayed a good predictive performance, suggesting potential goals for depression-preventive interventions during public health crises.

The human physiology of well-being: A systematic review on the association between neurotransmitters, hormones, inflammatory markers, the microbiome and well-being

To understand the pathways through which well-being contributes to health, we performed a systematic review according to the Preferred Reporting Items for Systematic Review and Meta-Analysis (PRISMA) guidelines on the association between well-being and physiological markers in four categories, neurotransmitters, hormones, inflammatory markers, and microbiome. We identified 91 studies. Neurotransmitter studies (k_{number of studies}=9) reported only a possible positive association between serotonin and well-being. For the hormone studies (k = 48), a lower momentary cortisol level was related to higher well-being (meta-analytic r = -0.06), and a steeper diurnal slope of cortisol levels. Inflammatory marker studies (k = 36) reported negative or non-significant relations with well-being, with meta-analytic estimates of respectively r = -0.07 and r = -0.05 for C-reactive protein and interleukin-6. Microbiome studies (k = 4) reported inconsistent associations between different bacteria abundance and well-being. The results indicate possible but small roles of serotonin, cortisol, and inflammatory markers in explaining differences in well-being. The inconsistent and limited results for other markers and microbiome require further research. Future directions for a complete picture of the physiological factors underlying well-being are proposed.

Mechanisms and Methods to Understand Depressive Symptoms

Depressive symptoms, feelings of sadness, anger, and loss that interfere with a person's daily life, are prevalent health concerns across populations that significantly result in adverse health outcomes with direct and indirect economic burdens at a national and global level. This article aims to synthesize known mechanisms of depressive symptoms and the established and emerging methodologies used to understand depressive symptoms; implications and directions for future nursing research are discussed. A comprehensive search was performed by Cumulative Index to Nursing and Allied Health Literature, MEDLINE, and PUBMED databases between 2000-2021 to examine contributing factors of depressive symptoms. Many environmental, psychological, and physiological factors are associated with the development or increased severity of depressive symptoms (anhedonia, fatigue, sleep and appetite disturbances to depressed mood). This paper discusses biological and psychological theories that guide our understanding of depressive symptoms, as well as known biomarkers (gut microbiome, specific genes, multi-cytokine, and hormones) and established and emerging methods. Disruptions within the nervous system, hormonal and neurotransmitters levels, brain structure, gut-brain axis, leaky-gut syndrome, immune and inflammatory process, and genetic variations are significant mediating mechanisms in depressive symptomology. Nursing research and practice are at the forefront of furthering depressive symptoms' mechanisms and methods. Utilizing advanced technology and measurement tools (big data, machine learning/artificial intelligence, and multi-omic approaches) can provide insight into the psychological and biological mechanisms leading to effective intervention development. Thus, understanding depressive symptomology provides a pathway to improve patients' health outcomes, leading to reduced morbidity and mortality and the overall nation-wide economic burden.Supplemental data for this article is available online at https://doi.org/10.1080/01612840.2021.1998261 .