Background: Depression is one of the most common mental disorders that affects >300 million people worldwide. There is a shortage of providers trained in the provision of mental health care, and the nursing workforce is essential in filling this gap. The diagnosis of depression relies heavily on self-reported symptoms and clinical interviews, which are subject to implicit biases. The omics methods, including genomics, transcriptomics, epigenomics, and microbiomics, are novel methods for identifying the biological underpinnings of depression. Machine learning is used to analyze genomic data that includes large, heterogeneous, and multidimensional data sets. Objective: This scoping review aims to review the existing literature on machine learning methods for omics data analysis to identify individuals with depression, with the goal of providing insight into alternative objective and driven insights into the diagnostic process for depression. Methods: This scoping review was reported following the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews) guidelines. Searches were conducted in 3 databases to identify relevant publications. A total of 3 independent researchers performed screening, and discrepancies were resolved by consensus. Critical appraisal was performed using the Joanna Briggs Institute Critical Appraisal Checklist for Analytical Cross-Sectional Studies. Results: The screening process identified 15 relevant papers. The omics methods included genomics, transcriptomics, epigenomics, multiomics, and microbiomics, and machine learning methods included random forest, support vector machine, k-nearest neighbor, and artificial neural network. Conclusions: The findings of this scoping review indicate that the omics methods had similar performance in identifying omics variants associated with depression. All machine learning methods performed well based on their performance metrics. When variants in omics data are associated with an increased risk of depression, the important next step is for clinicians, especially nurses, to assess individuals for symptoms of depression and provide a diagnosis and any necessary treatment.

Aims: Atrial fibrillation (AF) symptom relief is a primary indication for catheter ablation, but AF symptom resolution is not well characterized. The study objective was to describe AF symptom documentation in electronic health records (EHRs) pre- and post-ablation and identify correlates of post-ablation symptoms. Methods and results: We conducted a retrospective cohort study using EHRs of patients with AF (n = 1293), undergoing ablation in a large, urban health system from 2010 to 2020. We extracted symptom data from clinical notes using a natural language processing algorithm (F score: 0.81). We used Cochran’s Q tests with post-hoc McNemar’s tests to determine differences in symptom prevalence pre- and post-ablation. We used logistic regression models to estimate the adjusted odds of symptom resolution by personal or clinical characteristics at 6 and 12 months post-ablation. In fully adjusted models, at 12 months post-ablation patients, patients with heart failure had significantly lower odds of dyspnoea resolution [odds ratio (OR) 0.38, 95% confidence interval (CI) 0.25–0.57], oedema resolution (OR 0.37, 95% CI 0.25–0.56), and fatigue resolution (OR 0.54, 95% CI 0.34–0.85), but higher odds of palpitations resolution (OR 1.90, 95% CI 1.25–2.89) compared with those without heart failure. Age 65 and older, female sex, Black or African American race, smoking history, and antiarrhythmic use were also associated with lower odds of resolution of specific symptoms at 6 and 12 months. Conclusion: The post-ablation symptom patterns are heterogeneous. Findings warrant confirmation with larger, more representative data sets, which may be informative for patients whose primary goal for undergoing an ablation is symptom relief.

(1) The prevalence of depression is two times higher in women than men. Black women have an increased risk of depression due to stressors such as low socioeconomic status and perceived discrimination. Depression is likely influenced by both genetic and environmental factors. Psychosocial stressors can influence DNA methylation (DNAm), leading to changes in gene expression and ultimately, depression. The objective of this study was to examine associations between DNAm and depressive symptoms in Black women. (2) This study was a secondary analysis of data from the Intergenerational Impact of Genetic and Psychological Factors on Blood Pressure (InterGEN) Study. Perceived discrimination was assessed using Krieger’s Experiences of Discrimination and Waelde’s Race-Related Events Scale, and participants were screened for depressive symptoms with the Beck Depression Inventory. Raw data from saliva samples were analyzed using the Illumina Infinium Epic (850 K) BeadChip and then preprocessed in RStudio. (3) Differential methylation analysis identified DNAm sites and regions associated with depressive symptoms. Six DNAm sites had a q-value less than 0.05. Additionally, of the 25 regions identified, 12 were associated with neurological diseases or disorders. (4) These findings suggest that there is a neurological component to depression, which should be considered during treatment.