Nighttime heart rate predicts response to depression treatment in patients with coronary heart disease

Depression and coronary heart disease: mechanisms, interventions, and treatments

Coronary heart disease (CHD), a cardiovascular condition that poses a significant threat to human health and life, has imposed a substantial economic burden on the world. However, in contrast to conventional risk factors, depression emerges as a novel and independent risk factor for CHD. This condition impacts the onset and progression of CHD and elevates the risk of adverse cardiovascular prognostic events in those already affected by CHD. As a result, depression has garnered increasing global attention. Despite this growing awareness, the specific mechanisms through which depression contributes to the development of CHD remain unclear. Existing research suggests that depression primarily influences the inflammatory response, Hypothalamic-pituitary-adrenocortical axis (HPA) and Autonomic Nervous System (ANS) dysfunction, platelet activation, endothelial dysfunction, lipid metabolism disorders, and genetics, all of which play pivotal roles in CHD development. Furthermore, the effectiveness and safety of antidepressant treatment in CHD patients with comorbid depression and its potential impact on the prognosis of CHD patients have become subjects of controversy. Further investigation is warranted to address these unresolved questions.

The usability of daytime and night-time heart rate dynamics as digital biomarkers of depression severity

BACKGROUND

Alterations in heart rate (HR) may provide new information about physiological signatures of depression severity. This 2-year study in individuals with a history of recurrent major depressive disorder (MDD) explored the intra-individual variations in HR parameters and their relationship with depression severity.

METHODS

Data from 510 participants (Number of observations of the HR parameters = 6666) were collected from three centres in the Netherlands, Spain, and the UK, as a part of the remote assessment of disease and relapse-MDD study. We analysed the relationship between depression severity, assessed every 2 weeks with the Patient Health Questionnaire-8, with HR parameters in the week before the assessment, such as HR features during all day, resting periods during the day and at night, and activity periods during the day evaluated with a wrist-worn Fitbit device. Linear mixed models were used with random intercepts for participants and countries. Covariates included in the models were age, sex, BMI, smoking and alcohol consumption, antidepressant use and co-morbidities with other medical health conditions.

RESULTS

Decreases in HR variation during resting periods during the day were related with an increased severity of depression both in univariate and multivariate analyses. Mean HR during resting at night was higher in participants with more severe depressive symptoms.

CONCLUSIONS

Our findings demonstrate that alterations in resting HR during all day and night are associated with depression severity. These findings may provide an early warning of worsening depression symptoms which could allow clinicians to take responsive treatment measures promptly.

Internet of Things-Based ECG and Vitals Healthcare Monitoring System

Health monitoring and its associated technologies have gained enormous importance over the past few years. The electrocardiogram (ECG) has long been a popular tool for assessing and diagnosing cardiovascular diseases (CVDs). Since the literature on ECG monitoring devices is growing at an exponential rate, it is becoming difficult for researchers and healthcare professionals to select, compare, and assess the systems that meet their demands while also meeting the monitoring standards. This emphasizes the necessity for a reliable reference to guide the design, categorization, and analysis of ECG monitoring systems, which will benefit both academics and practitioners. We present a complete ECG monitoring system in this work, describing the design stages and implementation of an end-to-end solution for capturing and displaying the patient's heart signals, heart rate, blood oxygen levels, and body temperature. The data will be presented on an OLED display, a developed Android application as well as in MATLAB via serial communication. The Internet of Things (IoT) approaches have a clear advantage in tackling the problem of heart disease patient care as they can transform the service mode into a widespread one and alert the healthcare services based on the patient's physical condition. Keeping this in mind, there is also the addition of a web server for monitoring the patient's status via WiFi. The prototype, which is compliant with the electrical safety regulations and medical equipment design, was further benchmarked against a commercially available off-the-shelf device, and showed an excellent accuracy of 99.56%.

Heart rate and heart rate variability as outcomes and longitudinal moderators of treatment for pain across follow-up in Veterans with Gulf War illness

AIMS

Accumulating evidence suggests Gulf War illness (GWI) is characterised by autonomic nervous system dysfunction (higher heart rate [HR], lower heart rate variability [HRV]). Yoga - an ancient mind-body practice combining mindfulness, breathwork, and physical postures - is proposed to improve autonomic dysfunction yet this remains untested in GWI. We aimed to determine (i) whether HR and HRV improve among Veterans with GWI receiving either yoga or cognitive behavioural therapy (CBT) for pain; and (ii) whether baseline autonomic functioning predicts treatment-related pain outcomes across follow-up.

MAIN METHODS

We present secondary analyses of 24-hour ambulatory cardiac data (mean HR, square root of the mean squared differences between successive R-R intervals [RMSSD], high frequency power [HF-HFV], and low-to-high frequency ratio [LF/HF] extracted from a 5-min window during the first hour of sleep) from our randomised controlled trial of yoga versus CBT for pain among Veterans with GWI (ClinicalTrials.govNCT02378025; N = 75).

KEY FINDINGS

Veterans who received CBT tended towards higher mean HR at end-of-treatment. Better autonomic function (lower mean HR, higher RMSSD/HF-HRV) at baseline predicted greater reductions in pain across follow-up, regardless of treatment group. Better baseline autonomic function (mid-range-to-high RMSSD/HF-HRV) also predicted greater pain reductions with yoga, while worse baseline autonomic function (higher mean HR, lower RMSSD/HF-HRV) predicted greater pain reductions with CBT.

SIGNIFICANCE

To our knowledge, this is the first study to suggest that among Veterans with GWI, HR may increase with CBT yet remain stable with yoga. Furthermore, HR and HRV moderated pain outcome across follow-up for yoga and CBT.

El Kassabi H, Ismail H, Nujum Navaz A. ECG Monitoring Systems: Review, Architecture, Processes, and Key Challenges

Health monitoring and its related technologies is an attractive research area. The electrocardiogram (ECG) has always been a popular measurement scheme to assess and diagnose cardiovascular diseases (CVDs). The number of ECG monitoring systems in the literature is expanding exponentially. Hence, it is very hard for researchers and healthcare experts to choose, compare, and evaluate systems that serve their needs and fulfill the monitoring requirements. This accentuates the need for a verified reference guiding the design, classification, and analysis of ECG monitoring systems, serving both researchers and professionals in the field. In this paper, we propose a comprehensive, expert-verified taxonomy of ECG monitoring systems and conduct an extensive, systematic review of the literature. This provides evidence-based support for critically understanding ECG monitoring systems' components, contexts, features, and challenges. Hence, a generic architectural model for ECG monitoring systems is proposed, an extensive analysis of ECG monitoring systems' value chain is conducted, and a thorough review of the relevant literature, classified against the experts' taxonomy, is presented, highlighting challenges and current trends. Finally, we identify key challenges and emphasize the importance of smart monitoring systems that leverage new technologies, including deep learning, artificial intelligence (AI), Big Data and Internet of Things (IoT), to provide efficient, cost-aware, and fully connected monitoring systems.

Entropy-Based Measures of Hypnopompic Heart Rate Variability Contribute to the Automatic Prediction of Cardiovascular Events

Surges in sympathetic activity should be a major contributor to the frequent occurrence of cardiovascular events towards the end of nocturnal sleep. We aimed to investigate whether the analysis of hypnopompic heart rate variability (HRV) could assist in the prediction of cardiovascular disease (CVD). 2217 baseline CVD-free subjects were identified and divided into CVD group and non-CVD group, according to the presence of CVD during a follow-up visit. HRV measures derived from time domain analysis, frequency domain analysis and nonlinear analysis were employed to characterize cardiac functioning. Machine learning models for both long-term and short-term CVD prediction were then constructed, based on hypnopompic HRV metrics and other typical CVD risk factors. CVD was associated with significant alterations in hypnopompic HRV. An accuracy of 81.4% was achieved in short-term prediction of CVD, demonstrating a 10.7% increase compared with long-term prediction. There was a decline of more than 6% in the predictive performance of short-term CVD outcomes without HRV metrics. The complexity of hypnopompic HRV, measured by entropy-based indices, contributed considerably to the prediction and achieved greater importance in the proposed models than conventional HRV measures. Our findings suggest that Hypnopompic HRV assists the prediction of CVD outcomes, especially the occurrence of CVD event within two years.

Evaluating depression with multimodal wristband-type wearable device: screening and assessing patient severity utilizing machine-learning

OBJECTIVE

We aimed to develop a machine learning algorithm to screen for depression and assess severity based on data from wearable devices.

METHODS

We used a wearable device that calculates steps, energy expenditure, body movement, sleep time, heart rate, skin temperature, and ultraviolet light exposure. Depressed patients and healthy volunteers wore the device continuously for the study period. The modalities were compared hourly between patients and healthy volunteers. XGBoost was used to build machine learning models and 10-fold cross-validation was applied for the validation.

RESULTS

Forty-five depressed patients and 41 healthy controls participated, creating a combined 5,250 days' worth of data. Heart rate, steps, and sleep were significantly different between patients and healthy volunteers in some comparisons. Similar differences were also observed longitudinally when patients' symptoms improved. Based on seven days' data, the model identified symptomatic patients with 0.76 accuracy and predicted Hamilton Depression Rating Scale-17 scores with a 0.61 correlation coefficient. Skin temperature, sleep time-related features, and the correlation of those modalities were the most significant features in machine learning.

LIMITATIONS

The small number of subjects who participated in this study may have weakened the statistical significance of the study. There are differences in the demographic data among groups although we performed a correction for multiple comparisons. Validation in independent datasets was not performed, although 10-fold cross validation with the internal data was conducted.

CONCLUSION

The results indicated that utilizing wearable devices and machine learning may be useful in identifying depression as well as assessing severity.

Measurement Structure of the Pittsburgh Sleep Quality Index and Its Association with Health Functioning in Patients with Coronary Heart Disease

Research on the underlying structure of sleep measures in patients with coronary heart disease (CHD) is lacking. Existing research on sleep and health outcomes primarily focused on only one dimension of sleep (e.g., sleep duration), leaving other aspects unexamined. To address this gap, this study examined the measurement structure of Pittsburgh Sleep Quality Index (PSQI) and its associations with health-related quality of life among CHD patients. Participants were 167 CHD patients from a cardiac wellness program. Confirmatory factor analysis revealed that the two-factor structure with sleep efficiency and perceived sleep quality best fitted the data. Concurrent validity analyses with structural equation modeling showed that, when considered simultaneously, perceived sleep quality, but not sleep efficiency, was significantly associated with emotional, physical, and social quality of life. Findings demonstrated that the PSQI consists of two moderately correlated factors that are differentially associated with separate health domains in cardiac patients.

Somatic and sociodemographic predictors of depression outcome among depressed patients with coronary artery disease - a secondary analysis of the SPIRR-CAD study

BACKGROUND

Depressive symptoms are common in patients with coronary artery disease (CAD) and are associated with an unfavourable outcome. Establishing prognostic patient profiles prior to the beginning of mental health care may facilitate higher efficacy of targeted interventions. The aim of the current study was to identify sociodemographic and somatic predictors of depression outcome among depressed patients with CAD.

METHODS

Based on the dataset of the multicentre SPIRR-CAD randomised controlled trial (n = 570 patients with CAD and ≥ 8 points on the Hospital Anxiety and Depression Scale (HADS)), 141 potential sociodemographic and somatic predictors of the change in the HADS-D depression score from baseline to 18-month-follow-up were derived in two different ways. We screened for univariable association with response, using either analysis of (co)variance or logistic regression, respectively, both adjusted for baseline HADS-D value and treatment group. To guard against overfitting, multivariable association was evaluated by a linear or binomial (generalised) linear model with lasso regularisation, a machine learning approach. Outcome measures were the change in continuous HADS-D depression scores, as well as three established binary criteria. The Charlson Comorbidity Index (CCI) was calculated to assess possible influences of comorbidities on our results and was also entered in our machine learning approach.

RESULTS

Higher age (p = 0.002), unknown previous myocardial infarction (p = 0.013), and a higher heart rate variability during numeracy tests (p = .020) were univariably associated with a favourable depression outcome, whereas hyperuricemia (p ≤ 0.003), higher triglycerides (p = 0.014), NYHA class III (p ≤ 0.028), state after resuscitation (p ≤ 0.042), intake of thyroid hormones (p = 0.007), antidiabetic drugs (p = 0.015), analgesic drugs (p = 0.027), beta blockers (p = 0.035), uric acid drugs (p ≤ 0.039), and anticholinergic drugs (p = 0.045) were associated with an adverse effect on the HADS-D depression score. In all analyses, no significant differences between study arms could be found and physical comorbidities also had no significant influence on our results.

CONCLUSION

Our findings may contribute to identification of somatic and sociodemographic predictors of depression outcome in patients with CAD. The unexpected effects of specific medication require further clarification and further research is needed to establish a causal association between depression outcome and our predictors.

TRIAL REGISTRATION

www.clinicaltrials.gov   NCT00705965 (registered 27th of June, 2008). www.isrctn.com   ISRCTN76240576 (registered 27th of March, 2008).

Metabolic features and regulation of the healing cycle-A new model for chronic disease pathogenesis and treatment

Without healing, multicellular life on Earth would not exist. Without healing, one injury predisposes to another, leading to disability, chronic disease, accelerated aging, and death. Over 60% of adults and 30% of children and teens in the United States now live with a chronic illness. Advances in mass spectrometry and metabolomics have given scientists a new lens for studying health and disease. This study defines the healing cycle in metabolic terms and reframes the pathophysiology of chronic illness as the result of metabolic signaling abnormalities that block healing and cause the normal stages of the cell danger response (CDR) to persist abnormally. Once an injury occurs, active progress through the stages of healing is driven by sequential changes in cellular bioenergetics and the disposition of oxygen and carbon skeletons used for fuel, signaling, defense, repair, and recovery. >100 chronic illnesses can be organized into three persistent stages of the CDR. One hundred and two targetable chemosensory G-protein coupled and ionotropic receptors are presented that regulate the CDR and healing. Metabokines are signaling molecules derived from metabolism that regulate these receptors. Reframing the pathogenesis of chronic illness in this way, as a systems problem that maintains disease, rather than focusing on remote trigger(s) that caused the initial injury, permits new research to focus on novel signaling therapies to unblock the healing cycle, and restore health when other approaches have failed.

Residual Symptoms After Treatment for Depression in Patients With Coronary Heart Disease

OBJECTIVE

Depression is associated with an increased risk of mortality in patients with coronary heart disease (CHD). The risk may be reduced in patients who remit with adequate treatment, but few patients achieve complete remission. The purpose of this study was to identify the symptoms that persist despite aggressive treatment for depression in patients with CHD.

METHODS

One hundred twenty-five patients with stable CHD who met the DSM-IV criteria for a moderate-to-severe major depressive episode completed treatment with cognitive behavior therapy, either alone or combined with an antidepressant, for up to 16 weeks. Depression symptoms were assessed at baseline and after 16 weeks of treatment.

RESULTS

The M (SD) Beck Depression Inventory scores were 30.0 (8.6) at baseline and 8.3 (7.5) at 16 weeks. Seventy seven (61%) of the participants who completed treatment met remission criteria (Hamilton Rating Scale for Depression ≤7) at 16 weeks. Loss of energy and fatigue were the most common posttreatment symptoms both in remitters (n = 44, 57%; n = 34, 44.2%) and nonremitters (n = 42, 87.5%; n = 35, 72.9%). These symptoms were not predicted by baseline depression severity, anxiety, demographic, or medical variables including inflammatory markers or cardiac functioning or by medical events during depression treatment.

CONCLUSIONS

Fatigue and loss of energy often persist in patients with CHD even after otherwise successful treatment for major depression. These residual symptoms may increase the risks of relapse and mortality. Development of effective interventions for these persistent symptoms is a priority for future research.