Multi-state models of transitions in depression and anxiety symptom severity and cardiovascular events in patients with coronary heart disease

Association between chronic disease status and transitions in depressive symptoms among middle-aged and older Chinese population: Insights from a Markov model-based cohort study

BACKGROUND

The relationship between chronic disease status (CDS) and transitions in depressive symptoms (DS) remains unclear. This study explores the association between CDS and DS transitions.

METHODS

This cohort study analyzed data from 8175 participants aged 45+, sourced from China Family Panel Studies (2016, 2018, 2020). DS were assessed using a brief version of Center for Epidemiologic Studies Depression Scale (CES-D). CDS was categorized into healthy, single disease, and multimorbidity. Markov models were used to estimate state transition intensities, mean sojourn times and hazard ratios (HRs).

RESULTS

DS transitions occurred between adjacent and non-adjacent states, but transition intensity between adjacent states was higher than among non-adjacent states. Self-transition intensities of severe-DS, mild-DS, and non-DS progressively increased, with average durations of 1.365, 1.482, and 7.854 years, respectively. Both single disease and multimorbidity were significantly associated with an increased risk of transitioning from non-DS to mild-DS, with multimorbidity showing a stronger association. In contrast, HRs for single diseases transitioning from mild-DS to severe-DS were significantly lower than 1. Furthermore, their HRs were almost <1 in recovery transitions but not statistically significant.

LIMITATIONS

Specific chronic diseases and their combinations were not analyzed.

CONCLUSIONS

The progression of DS exhibits various pathways. CDS is associated with DS transitions, but the roles of single disease and multimorbidity may differ across different DS progression stages. Both conditions were significantly linked to the risk of new-onset DS, with multimorbidity posing a greater association. However, this relationship is not observed in other progression stages. These findings could provide insights for early prevention and intervention for DS.

A longitudinal study on coping and emotional well-being in cardiac patients

Emotions and coping play a role in the prognosis of cardiac patients. This two-wave longitudinal study aims to analyze the ability of adaptive and maladaptive coping to predict the emotional well-being of cardiac patients after controlling for their functional physical capacity. Emotional well-being (positive and negative affect), coping strategies, and functional physical capacity were evaluated both at Time 1 (n = 253) and at Time 2 (n = 186), 8 weeks later. At Time 1, positive affect was positively predicted by adaptive coping and negatively predicted by maladaptive coping, while the opposite pattern was found when negative affect was considered. At Time 2, after controlling for sociodemographic variables and for negative affect and functional physical capacity at T1, negative affect was negatively predicted by adaptive coping and positively predicted by maladaptive coping. In addition, positive affect was only predicted by adaptive coping after controlling for functional physical capacity and positive affect at Time 1. Relationships between coping and emotional well-being remain after controlling for the functional physical capacity of cardiac patients, which has a big impact on their emotional state. Finally, it is suggested that specific modules to improve coping and emotional state of cardiac patients should be included in Cardiac Rehabilitation Programs.

Dynamic association of ambient air pollution with incidence and mortality of pulmonary hypertension: A multistate trajectory analysis

BACKGROUND

There is little evidence regarding the association between ambient air pollution and incidence and the mortality of pulmonary hypertension (PH).

METHODS

We included 494,750 participants at baseline in the UK Biobank study. Exposures to PM2.5, PM10, NO2, and NOx were estimated at geocoded participants' residential addresses, utilizing pollution data provided by UK Department for Environment, Food and Rural Affairs (DEFRA). The outcomes were the incidence and mortality of PH. We used multivariate multistate models to investigate the impacts of various ambient air pollutants on both incidence and mortality of PH.

RESULTS

During a median follow-up of 11.75 years, 2517 participants developed incident PH, and 696 died. We observed that all ambient air pollutants were associated with increased incidence of PH with different magnitudes, with adjusted hazard ratios (HRs) [95% confidence intervals (95% CIs)] for each interquartile range (IQR) increase of 1.73 (1.65, 1.81) for PM2.5, 1.70 (1.63, 1.78) for PM10, 1.42 (1.37, 1.48) for NO2, and 1.35 (1.31, 1.40) for NOx. Furthermore, PM2.5, PM10, NO2 and NO2 influenced the transition from PH to death, and the corresponding HRs (95% CIs) were 1.35 (1.25, 1.45), 1.31 (1.21, 1.41), 1.28 (1.20, 1.37) and 1.24 (1.17, 1.32), respectively.

CONCLUSION

The results of our study indicate that exposure to various ambient air pollutants might play key but differential roles in both the incidence and mortality of PH.

Screening for preclinical Alzheimer's disease: Deriving optimal policies using a partially observable Markov model

Alzheimer's Disease (AD) is believed to be the most common type of dementia. Even though screening for AD has been discussed widely, there is no screening program implemented as part of a policy in any country. Current medical research motivates focusing on the preclinical stages of the disease in a modeling initiative. We develop a partially observable Markov decision process model to determine optimal screening programs. The model contains disease free and preclinical AD partially observable states and the screening decision is taken while an individual is in one of those states. An observable diagnosed preclinical AD state is integrated along with observable mild cognitive impairment, AD and death states. Transition probabilities among states are estimated using data from Knight Alzheimer's Disease Research Center (KADRC) and relevant literature. With an objective of maximizing expected total quality-adjusted life years (QALYs), the output of the model is an optimal screening program that specifies at what points in time an individual over 50 years of age with a given risk of AD will be directed to undergo screening. The screening test used to diagnose preclinical AD has a positive disutility, is imperfect and its sensitivity and specificity are estimated using the KADRC data set. We study the impact of a potential intervention with a parameterized effectiveness and disutility on model outcomes for three different risk profiles (low, medium and high). When intervention effectiveness and disutility are at their best, the optimal screening policy is to screen every year between ages 50 and 95, with an overall QALY gain of 0.94, 1.9 and 2.9 for low, medium and high risk profiles, respectively. As intervention effectiveness diminishes and/or its disutility increases, the optimal policy changes to sporadic screening and then to never screening. Under several scenarios, some screening within the time horizon is optimal from a QALY perspective. Moreover, an in-depth analysis of costs reveals that implementing these policies are either cost-saving or cost-effective.

Resilience and Depressive Symptoms in Adults With Cardiac Disease: A Systematic Review

BACKGROUND

Depressive symptoms predict hospitalization and mortality in adults with cardiac disease. Resilience, defined as a dynamic process of positively responding to adversity, could protect against depressive symptoms in cardiac disease. No systematic review has been conducted on the relationship between these variables in this population.

OBJECTIVE

The aim of this review was to explore the association between psychological resilience and depressive symptoms in adults with cardiac disease.

METHODS

Seven databases (PubMed, EMBASE, CINAHL, PsycInfo, Web of Science, SCOPUS, and Cochrane) were searched from inception to December 2019 using the search terms "cardiac disease," "depressive symptoms," "depression," and "resilience." Inclusion criteria dictated that studies reported original research on the association between resilience and depressive symptoms in adults with a cardiac disease broadly defined. Quality ratings were performed by 2 independent raters.

RESULTS

We identified 13 studies for final review. Study sample sizes ranged from 30 to 1022 participants, average age ranged from 52 to 72 years, and all studies had majority male participants (64%-100%). Resilience and depressive symptoms were inversely related in 10 of 13 studies. The 3 studies with poor-quality sampling techniques or significant loss to follow-up found no relationship.

CONCLUSIONS

Resilience seems to protect against depression in adults with cardiac disease. Gaps in the literature include poor understanding of the direction of causality. Methods of promoting resilience need to be identified and studied.

Evaluating the transitions in care for children presenting with acute asthma to emergency departments: a retrospective cohort study

Background

Acute asthma is a common presentation to emergency departments (EDs) worldwide and, due to overcrowding, delays in treatment often occur. This study deconstructs the total ED length of stay into stages and estimates covariate effects on transition times for children presenting with asthma.

Methods

We extracted ED presentations in 2019 made by children in Alberta, Canada for acute asthma. We used multivariable Cox regressions in a multistate model to model transition times among the stages of start, physician initial assessment (PIA), disposition decision, and ED departure.

Results

Data from 6598 patients on 8270 ED presentations were extracted. The individual PIA time was longer (i.e., HR < 1) when time to the crowding metric (hourly PIA) was above 1 h (HR = 0.32; 95% CI:0.30,0.34), for tertiary (HR = 0.65; 95% CI:0.61,0.70) and urban EDs (HR = 0.77; 95% CI:0.70,0.84), for younger patients (HR = 0.99 per year; 95% CI:0.99,1.00), and for patients triaged less urgent/non-urgent (HR = 0.89; 95% CI:0.84,0.95). It was shorter for patients arriving by ambulance (HR = 1.22; 95% CI:1.04,1.42). Times from PIA to disposition decision were longer for tertiary (HR = 0.47; 95% CI:0.44,0.51) and urban (HR = 0.69; 95% CI:0.63,0.75) EDs, for patients triaged as resuscitation/emergent (HR = 0.51; 95% CI:0.48,0.54), and for patients arriving by ambulance (HR = 0.78; 95% CI:0.70,0.87). Times from disposition decision to ED departure were longer for patients who were admitted (HR = 0.16; 95% CI:0.13,0.20) or transferred (HR = 0.42; 95% CI:0.35,0.50), and for tertiary EDs (HR = 0.93; 95% CI:0.92,0.94).

Conclusions

All transition times were impacted by ED presentation characteristics. The sole key patient characteristic was age and it only impacted time to PIA. ED crowding demonstrated strong effects of time to PIA but not for the transition times involving disposition decision and ED departure stages.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12873-021-00550-z.

Expression of lncRNA-ANRIL before and after Treatment and Its Predictive Value for Short-Term Survival in Patients with Coronary Heart Disease

This study aimed at observing the expression of lncRNA-ANRIL (ANRIL) before and after treatment and its predictive value for short-term survival in patients with coronary heart disease (CHD). Altogether, 112 patients with CHD admitted to the hospital were enrolled as a study group (SG), which was divided into a pretreatment study group (preSG) and a posttreatment study group (postSG). Further 72 healthy people undergoing physical examinations during the same period were enrolled as a control group (CG). Peripheral blood was collected from the subjects in the three groups, to detect the expression level of serum ANRIL using quantitative reverse transcription PCR (qRT-PCR). A receiver operating characteristic (ROC) curve was plotted to evaluate the diagnostic value of ANRIL for CHD. Kaplan-Meier survival curves were plotted to analyze 3-year survival rates in high- and low-ANRIL expression groups. Cox regression was conducted to analyze independent risk factors affecting the patients. The expression level of serum ANRIL in preSG was significantly lower than those in CG and postSG (P < 0.05). According to the ROC curve, the area under the curve (AUC) of serum ANRIL for diagnosing CHD in CG was 0.894 and the optimal cutoff value was 0.639, with the sensitivity of 86.61% and the specificity of 93.67%. According to the survival curves, the 3-year overall survival rate in the high-ANRIL expression group was significantly lower than that in the low-expression group (P < 0.05). History of smoking, high total cholesterol (TC), high triglyceride (TG), high homocysteine (Hcy), and ANRIL expression were independent prognostic factors affecting the overall survival time of the patients (P < 0.05). ANRIL is poorly expressed in the peripheral blood of patients with CHD. Its detection has good sensitivity and specificity for diagnosing the disease, and its expression may be related to the poor prognosis of the patients.

Psychological changes after coronary angiographic intervention: pre-post comparison and follow-up

Epidemiological studies demonstrate the relevance of cardiovascular diseases for health policies and medical care, especially coronary heart diseases and myocardial infarction. Research has shown that a significant proportion of patients undergoing coronary angiography suffer from clinically relevant mental stress. The aim of this study is to investigate to what extent the psychological state of cardiology patients changes in short- and mid-term periods after coronary angiography has been performed. The study design is naturalistic, longitudinal and comparative about consecutively admitted patients undergoing coronary angiography (N = 419; consenting patients fulfilling inclusion criteria n = 68) at four measurement points: before and after angiography and 6 weeks and 6 months after discharge. The statistical analysis includes paired t-tests, chi-square tests, effect sizes and random effects regression models. The sample was representative of the target population. The prevalence of risk factors were: 84% heart attack, 31% diabetes and 84% hypertension. There were no angiographic pathological findings in 12% of the sample. The neuroticism levels of the sample was higher than in the general population. There were almost no pre-post differences for depression, anxiety, psychological well-being, self-efficacy, resilience or locus of control. At the mid-term, well-being and anxiety decreased and internal locus of control increased. Neuroticism was negatively and extraversion and openness were positively associated with mental state and resources. The sample showed persistent adverse subsyndromal depressivity. At the mid-term, patients realised that their prognosis also depends on their own behaviour (internal attribution). Special psychosomatic attention should be given to people with subsyndromal depression, higher emotional instability and those with angina pectoris symptoms displaying normal coronary angiography.

Roles of Anxiety and Depression in Predicting Cardiovascular Disease Among Patients With Type 2 Diabetes Mellitus: A Machine Learning Approach

Cardiovascular disease (CVD) is a major complication of type 2 diabetes mellitus (T2DM). In addition to traditional risk factors, psychological determinants play an important role in CVD risk. This study applied Deep Neural Network (DNN) to develop a CVD risk prediction model and explored the bio-psycho-social contributors to the CVD risk among patients with T2DM. From 2017 to 2020, 834 patients with T2DM were recruited from the Department of Endocrinology, Affiliated Hospital of Harbin Medical University, China. In this cross-sectional study, the patients' bio-psycho-social information was collected through clinical examinations and questionnaires. The dataset was randomly split into a 75% train set and a 25% test set. DNN was implemented at the best performance on the train set and applied on the test set. The receiver operating characteristic curve (ROC) analysis was used to evaluate the model performance. Of participants, 272 (32.6%) were diagnosed with CVD. The developed ensemble model for CVD risk achieved an area under curve score of 0.91, accuracy of 87.50%, sensitivity of 88.06%, and specificity of 87.23%. Among patients with T2DM, the top five predictors in the CVD risk model were body mass index, anxiety, depression, total cholesterol, and systolic blood pressure. In summary, machine learning models can provide an automated identification mechanism for patients at CVD risk. Integrated treatment measures should be taken in health management, including clinical care, mental health improvement, and health behavior promotion.

The Continuum Between Temperament and Mental Illness as Dynamical Phases and Transitions

The full range of biopsychosocial complexity is mind-boggling, spanning a vast range of spatiotemporal scales with complicated vertical, horizontal, and diagonal feedback interactions between contributing systems. It is unlikely that such complexity can be dealt with by a single model. One approach is to focus on a narrower range of phenomena which involve fewer systems but still cover the range of spatiotemporal scales. The suggestion is to focus on the relationship between temperament in healthy individuals and mental illness, which have been conjectured to lie along a continuum of neurobehavioral regulation involving neurochemical regulatory systems (e.g., monoamine and acetylcholine, opiate receptors, neuropeptides, oxytocin), and cortical regulatory systems (e.g., prefrontal, limbic). Temperament and mental illness are quintessentially dynamical phenomena, and need to be addressed in dynamical terms. A meteorological metaphor suggests similarities between temperament and chronic mental illness and climate, between individual behaviors and weather, and acute mental illness and frontal weather events. The transition from normative temperament to chronic mental illness is analogous to climate change. This leads to the conjecture that temperament and chronic mental illness describe distinct, high level, dynamical phases. This suggests approaching biopsychosocial complexity through the study of dynamical phases, their order and control parameters, and their phase transitions. Unlike transitions in physical systems, these biopsychosocial phase transitions involve information and semiotics. The application of complex adaptive dynamical systems theory has led to a host of markers including geometrical markers (periodicity, intermittency, recurrence, chaos) and analytical markers such as fluctuation spectroscopy, scaling, entropy, recurrence time. Clinically accessible biomarkers, in particular heart rate variability and activity markers have been suggested to distinguish these dynamical phases and to signal the presence of transitional states. A particular formal model of these dynamical phases will be presented based upon the process algebra, which has been used to model information flow in complex systems. In particular it describes the dual influences of energy and information on the dynamics of complex systems. The process algebra model is well-suited for dealing with the particular dynamical features of the continuum, which include transience, contextuality, and emergence. These dynamical phases will be described using the process algebra model and implications for clinical practice will be discussed.

Long-term trajectories of anxiety and depression in patients with stable coronary heart disease and risk of subsequent cardiovascular events

BACKGROUND

Anxiety and depression seem to be under-recognized in their importance and are often not incorporated in subsequent prevention strategies in routine clinical care of coronary heart disease.

METHODS

The KAROLA cohort included coronary heart disease patients participating in an in-patient rehabilitation program (years 1999/2000) and followed after 1, 3, 6, 8, 10, 13, and 15 years. We identified anxiety and depression trajectories based on the hospital anxiety and depression scale subdomains using joint latent class mixture time-to-event models. We included cardiovascular (CV) events and non-CV mortality as competing endpoints.

RESULTS

We included 1,109 patients (15.4% female; mean age, 59.4 (standard deviation [SD] = 8.0) years) with baseline covariate data. Over a median follow-up of 14.8 years, participants experienced 324 subsequent CV events. We identified four anxiety and depression trajectory classes, a low-stable class (52.2% and 69.6% of patients for anxiety and depression, respectively), moderate-stable class (37.6% and 23.8%), increasing class (2.3% and 3.3%), and high-stable/high-decreasing class (7.9% and 3.3%). The hazard ratio (HR) for subsequent CV events for the increasing anxiety class was 2.13 (95% confidence interval [CI], 0.61; 7.45) compared with the low-stable class after covariate adjustment. Patients following the high-decreasing anxiety trajectory showed an HR of 1.72 (95% CI, 1.11; 2.68) and patients following the high-stable depression trajectory an HR of 2.47 (95% CI, 1.35; 4.54).

CONCLUSIONS

Chronic high anxiety and depression trajectory classes were associated with increased risk of subsequent CV events. Assessments of both symptoms of anxiety and depression during long-term routine medical care are recommended to identify patients who would benefit from appropriate interventions.

Non-HDL cholesterol level and depression among Canadian elderly—a cross-sectional analysis of the baseline data from the CLSA

To explore whether non-high-density-lipoprotein cholesterol (non-HDL-c) is associated with depression, a total of 26 819 Canadians aged 45–85 from the Canadian Longitudinal Study on Aging (CLSA) were included in analysis. Non-HDL-c, the difference between total-c and HDL-c, was categorized into five levels, i.e., <2.6, 2.6 to <3.7, 3.7 to <4.8, 4.8 to 5.7, and ≥5.7 mmol/L. History of clinical depression was collected by questionnaire at an in-home interview, and current potential depression status was determined by CES-D10 (Center for Epidemiological Studies Depression Scale 10 questions version) score, i.e., ≥10 vs. <10. Logistic continuation ratio model for ordinal data was used to estimate the odds of being at or above a higher non-HDL-c category for depression status. Compared with those without clinical depression history and currently undepressed, the adjusted odds ratios (95% CI) were 1.09 (1.02, 1.17) for those without clinical depression history but currently depressed, 1.05 (0.98, 1.12) for those had clinical depression history but currently undepressed, and 1.21 (1.10, 1.32) for those had clinical depression history and currently depressed. The average of non-HDL-c for four depression groups were 3.64, 3.71, 3.69, and 3.82 mmol/L, respectively, and group 4 was statistically higher than others ( p < 0.001). In conclusion, people with both current depression and a history clinical depression are at an increased risk of having high level of non-HDL-c.