Latent profile analysis of depression in non-hospitalized elderly patients with hypertension and its influencing factors

Construction of a machine learning-based risk prediction model for depression in middle-aged and elderly hypertensive people in China: a longitudinal study

Background

Hypertension is a common chronic disease that can trigger symptoms such as anxiety and depression. Therefore, it is essential to predict their risk of depression. The aim of this study is to find the best prediction model and provide effective intervention strategies for health professionals.

Methods

The study subjects were 2733 middle-aged and older adults who participated in the China Health and Retirement Longitudinal Study (CHARLS) between 2018 and 2020. R software was used for Lasso regression analysis to screen the best predictor variables, and logistic regression, random forest and XGBoost models were constructed. Finally, the prediction efficiency of the three models was compared.

Results

In this study, 18 variables were included, and LASSO regression screened out 10 variables that were important for the establishment of the model. Among the three models, Logistic Regression model showed the best performance in various evaluation indicators.

Conclusion

The prediction model based on machine learning can accurately assess the likelihood of depression in middle-aged and elderly patients with hypertension in the next three years. And by combining Logistic regression and nomograms, we were able to provide a clear interpretation of personalized risk predictions.

Latent profile analysis of depression in US adults with obstructive sleep apnea hypopnea syndrome

Objective

This study used latent profile analysis to explore the level of depression among US adults with obstructive sleep apnea hypopnea syndrome (OSAHS) symptoms and to identify different latent categories of depression to gain insight into the characteristic differences between these categories.

Methods

The data of this study were obtained from the National Health and Nutrition Examination Survey (NHANES) database, and the subjects with OSAHS symptoms were aged 18 years and older. The latent profile analysis (LPA) method was used to fit the latent depression categories in subjects with OSAHS symptoms. The chi-square test, rank sum test, and binary logistic regression were used to analyze the influencing factors of depression subgroups in subjects with OSAHS symptoms.

Results

Three latent profiles were identified: low-level (83.7%), moderate-level (14.5%) and high-level (1.8%) depression. The scores of 9 items in the high-level depression group were higher than those in the other two groups. Among them, item 4 "feeling tired or lack of energy" had the highest score in all categories.

Conclusion

Depression in subjects with OSAHS symptoms can be divided into low-level, moderate-level and high-level depression. There are significant differences among different levels of depression in gender, marital status, PIR, BMI, smoking, general health condition, sleep duration and OSAHS symptom severity.

Network analysis of depression and anxiety symptoms and their associations with life satisfaction among Chinese hypertensive older adults: a cross-sectional study

Background

Hypertension is one of the most prevalent chronic diseases among the older adult population in China and older adults with hypertension are more susceptible to mental health problems. This study aimed to explore the network structure of depression and anxiety, and their association with life satisfaction (LS) in older adults with hypertension.

Methods

A total of 4,993 hypertensive individuals aged 60 and above were selected from the Chinese Longitudinal Healthy Longevity Survey (CLHLS 2017-2018). The design of the CLHLS study was approved by the Campus Institutional Review Board of Duke University (Pro00062871) and the Biomedical Ethics Committee of Peking University (IRB00001052-13,074). The Center for Epidemiologic Studies Depression Scale-10 (CESD-10) and the Generalized Anxiety Disorder Scale-7 (GAD-7) were used to assess depressive and anxiety symptoms. Central and bridge symptoms were identified via "Expected Influence" and "Bridge Expected Influence", respectively. Network stability was assessed using the case-dropping bootstrap technique.

Results

Network analysis identified CESD3 (Feeling blue/depressed), GAD4 (Trouble relaxing), and GAD2 (Uncontrollable worry) as the most influential central symptoms in the network of depression and anxiety. Concurrently, GAD1 (Nervousness or anxiety), CESD10 (Sleep disturbances), and CESD1 (Feeling bothered) stand as critical bridge symptoms between depression and anxiety disorders. Moreover, CESD7 (Lack of happiness) exhibited the strongest negative correlation with LS in Chinese hypertensive older adults.

Conclusion

This exploratory study represents the first investigation to examine the mutual relationship between depressive and anxiety symptoms among Chinese hypertensive older adults. Interventions addressing targeting bridge symptoms have the potential to alleviate depressive and anxiety symptoms. Furthermore, improving happiness, hope, and sleep quality in this population may mitigate the adverse effects of depression and anxiety on LS.

Heterogeneity of work alienation and its relationship with job embeddedness among Chinese nurses: a cross-sectional study using latent profile analysis

OBJECTIVE

To identify the distinct profiles of work alienation among Chinese nurses, examine the demographic factors associated with profile memberships, and then explore the relationship between latent categories of work alienation and job embeddedness.

METHODS

A cross-sectional survey of 523 nurses was conducted from July to August 2023. Latent profile analysis (LPA) was performed to identify distinct profiles of nurses based on three aspects: powerlessness, helplessness, and meaningfulness. A multinomial logistic regression analysis was conducted to explore the predictors of profile membership. Hierarchical regression analysis was carried out to examine the association between profile memberships and job embeddedness.

RESULTS

Three subgroups of work alienation of nurses were identified: 23.1%, 57.8%, and 19.1% in the low work alienation group (profile 1), the moderate work alienation group (profile 3), and the high work alienation group (profile 2), respectively. Nurses with college degrees were more likely to be grouped into moderate work alienation. Nurses who did not work night shifts were more likely to have low or moderate levels of work alienation. Nurses earning 2,000-3,000 and 3,001-5,000 yuan per month were likely to be in the low work alienation group. The different categories of work alienation significantly predicted job embeddedness among nurses (ΔR2 = 0.103, p < 0.001).

CONCLUSIONS

Work alienation has an important impact on clinical nurses' job embeddedness. Nursing managers should pay attention to the differences in individual work alienation status and adopt reasonable management strategies to improve the level of job embeddedness, ensure the quality of care, and reduce nursing turnover.

Latent profile analysis of depression among empty nesters in China

OBJECTIVES

The study aimed to explore the depression profile of empty nesters and to identify heterogeneous subgroups in the elderly population. It explored the influencing factors of depression in elderly people with different depression profiles, with a view to provide a reference basis for improving the depression situation of empty-nesting elderly people.

METHODS

This study used the Chinese Longitudinal Healthy Lifespan Survey (CLHLS) survey data, with empty nesters over 60 as the research subjects. Latent profile analysis (LPA) was used to fit potential classes of depression in empty nesters; chi-square tests, Kruskal-Wallis, and multinomial logistic regression were used to explore the factors influencing different depression profiles in older adults.

RESULTS

A total of 4481 subjects were included in this study and were classified as low-level (11.6 %), moderate-level (51.6 %), and high-level (36.8 %). Compared to the low-level, the influencing factors for the high-level were IADL, anxiety, self-rated health, exercise, and education; and the influencing factors for the medium level group were anxiety, self-rated health, drink, and education. Factors influencing high-level relative to the mid-level group were IADL, anxiety, residence, self-rated health, exercise, and limited in activities.

LIMITATIONS

The CESD-10, as a screening tool, could not completely determine the presence of depression in high levels of empty nesters.

CONCLUSIONS

Psychological problems arising from depression among empty nesters seriously affected their overall health, and targeted intervention strategies should be developed for different categories of older adults to improve depression and enhance health-related quality of life.

Post-stroke fatigue: a review of development, prevalence, predisposing factors, measurements, and treatments

Background

Post-stroke fatigue (PSF) is a ubiquitous and overwhelming symptom for most stroke survivors. However, there are no effective management strategies for PSF, which is partly due to our limited understanding.

Objective

In this paper, we review the development, prevalence, predisposing factors, measurements, and treatments of PSF.

Results

PSF is an independent symptom after stroke, with a prevalence ranging from 42 to 53%, which depends on the selection of measurement tools and stroke characteristics. It is affected by biological, physical, and psychological factors, among which inflammation may play a key role.

Conclusion

Numerous but non-specific evaluation measurement tools limit the management of PSF. In clinical practice, it may be beneficial to identify PSF by combining scales and objective indexes, such as walking tests and electromyographic examinations. There are no evidence-based interventions to improve PSF. However, increasing evidence suggests that transcranial direct-current stimulation and mindfulness-based interventions may become promising treatments. Further studies are urgently needed to better understand the etiology of PSF, thereby providing the basis for developing new measurement tools and targeted treatments.