Physical Activity After Heart Surgery: Associations With Psychosocial and Sleep Factors.

BACKGROUND: Heart surgery is an effective intervention for managing heart disease, the leading cause of death globally. After surgery, physical activity is key to improving patients' quality of life and decreasing mortality, but patients are frequently physically inactive after heart surgery. OBJECTIVE: This cross-sectional pilot study aimed to examine how psychosocial and sleep factors influenced physical activity in patients after heart surgery. The mediating role of sleep factors between psychosocial factors and physical activity was also examined. METHODS: Thirty-three patients who had undergone heart surgery were recruited. Psychosocial and sleep factors and physical activity were measured using an online survey and a wrist-worn ActiGraph for 7 days and nights. RESULTS: The participants had heart surgery an average of about 7 years previously. They exceeded the recommended 150 minutes per week of moderate-intensity physical activity for Americans; however, 64% of them showed poor sleep quality (Pittsburgh Sleep Quality Index >5). Higher anxiety and depressive symptoms, lower self-efficacy, and greater sleep disturbances were associated with lower physical activity. Moreover, self-efficacy, sleep duration, sleep disturbance, sleep efficiency, and wake after sleep onset were predictors for physical activity. No mediating role of sleep factors was observed between psychosocial factors and physical activity. CONCLUSIONS: Psychosocial and sleep factors should be considered when developing and implementing physical activity strategies for patients after heart surgery. Researchers should examine the relationships among the study variables with larger samples of postsurgical cardiac patients during different periods after heart surgery.

Effects of daily sleep on physical activity after cardiac surgery.

BACKGROUND: Maintaining physical activity is challenging after cardiac surgery. Postsurgical cardiac patients often experience sleep problems showing a reciprocal interaction with physical activity. As sleep and physical activity show day-to-day variations, their daily relationships need to be assessed. However, no studies have examined daily sleep-physical activity relationships in postsurgical cardiac patients. OBJECTIVES: This study aimed to examine the effects of daily sleep factors on daily physical activity after cardiac surgery. METHODS: Among 33 patients who underwent cardiac surgery at least 10 weeks earlier, 5 sleep and 4 physical activity variables were measured using a wrist-worn ActiGraph for 7 days. Mixed-effects models were applied for data analyses. RESULTS: Most participants were male (57.6 %), non-Hispanic whites (63.6 %) who had coronary artery bypass graft surgery (54.6 %). Participants averaged 60.8 ± 10.1 years of age and 85.7 ± 91.2 months since surgery. They slept for an average of 385.6 ± 74.6 min (6.4 ± 1.2 h). Among sleep factors, greater number of awakenings (NOA) predicted lower next-day sedentary time. Higher sleep efficiency (SE) was associated with lower next-day sedentary time when not controlling for covariates. Among the psychosocial, demographic, and clinical covariates, higher comorbidity index was associated with fewer kcals expended, less daily moderate-to-vigorous physical activity, and more daily sedentary time. CONCLUSIONS: Daily SE and NOA and individual health status, including comorbidity, should be assessed over time to support improvement of daily physical activity after cardiac surgery. Researchers should examine the relationship between NOA and next-day sedentary time with larger samples. Such research should address multiple psychosocial, demographic, and clinical factors and the potential mediating role of sleep.

Validation of PiezoSleep Scoring Against EEG/EMG Sleep Scoring in Rats.

Introduction: Current methods of sleep research in rodents involve invasive surgical procedures of EEG and EMG electrodes implantation. Recently, a new method of measuring sleep, PiezoSleep, has been validated against implanted electrodes in mice and rats. PiezoSleep uses a piezoelectric film transducer to detect the rodent's movements and respiration and employs an algorithm to automatically score sleep. Here, we validate PiezoSleep scoring versus EEG/EMG implanted electrodes sleep scoring in rats. Methods: Adult male Brown Norway and Wistar Kyoto rats were implanted with bilateral stainless-steel screws into the skull for EEG recording and bilateral wire electrodes into the nuchal muscles for EMG assessment. In Brown Norway rats, the EEG/EMG electrode leads were soldered to a miniature connector plug and fixed to the skull. In Wistar Kyoto rats, the EEG/EMG leads were tunneled subcutaneously to a telemetry transmitter implanted in the flank. Rats were allowed to recover from surgery for one week. Brown Norway rats were placed in PiezoSleep cages, and had their headsets connected to cable for recording EEG/EMG signals, which were then manually scored by a human scorer in 10-sec epochs. Wistar Kyoto rats were placed in PiezoSleep cages, and EEG/EMG signals were recorded using a telemetry system (DSI). Sleep was scored automatically in 4-sec epochs using NeuroScore software. PiezoSleep software recorded and scored sleep in the rats. Results: Rats implanted with corded EEG/EMG headsets had 85.6% concurrence of sleep-wake scoring with PiezoSleep. Rats implanted with EEG/EMG telemetry had 80.8% concurrence sleep-wake scoring with PiezoSleep. Sensitivity and specificity rates were similar between the EEG/EMG recording systems. Total sleep time and hourly sleep times did not differ in all three systems. However, automatic sleep detection by NeuroScore classified more sleep during the light period compared to the PiezoSleep. Conclusion: We showed that PiezoSleep system can be a reliable alternative to both automatic and visual EEG/EMG- based sleep-wake scoring in rat.

Multimorbidity phenotypes in patients presenting to the emergency department with possible acute coronary syndrome.

BACKGROUND: Multimorbidity (> 2 conditions) increases the risk of adverse outcomes and challenges health care systems for patients with acute coronary syndrome (ACS). These complications may be partially attributed to ACS clinical care which is driven by single-disease-based practice guidelines; current guidelines do not consider multimorbidity. OBJECTIVES: To identify multimorbidity phenotypes (combinations of conditions) with suspected ACS. We hypothesized that: 1) subgroups of patients with similar multimorbidity phenotypes could be identified, 2) classes would differ according to diagnosis, and 3) class membership would differ by sex, age, functional status, family history, and discharge diagnosis. METHODS: This was a secondary analysis of data from a large multi-site clinical study of patients with suspected ACS. Conditions were determined by items on the Charlson Comorbidity Index and the ACS Patient Information Questionnaire. Latent class analysis was used to identify phenotypes. RESULTS: The sample (n = 935) was predominantly male (68%) and middle-aged (mean= 59 years). Four multimorbidity phenotypes were identified: 1) high multimorbidity (Class 1) included hyperlipidemia, hypertension (HTN), obesity, diabetes, and respiratory disorders (COPD or asthma); 2) low multimorbidity (Class 2) included only obesity; 3) cardiovascular multimorbidity (Class 3) included HTN, hyperlipidemia, and coronary heart disease; and 4) cardio-oncology multimorbidity (Class 4) included HTN, hyperlipidemia, and cancer. Patients ruled-in for ACS primarily clustered in Classes 3 and 4 (OR 2.82, 95% CI 1.95-4.05, p = 0.001 and OR 1.76, 95% CI 1.13-2.74, p = 0.01). CONCLUSION: Identifying and understanding multimorbidity phenotypes may assist with risk-stratification and better triage of high-risk patients in the emergency department.

Whole blood transcriptome analysis using RNA sequencing in individuals with insomnia disorder and good sleepers: a pilot study.

BACKGROUND: Insomnia is a highly prevalent condition that is associated with negative health outcomes, yet little is known about the underlying molecular mechanisms. METHOD: RNA sequencing was conducted using blood samples from 15 individuals with primary insomnia and 15 age- and gender-matched good sleeper controls. The RNA library was sequenced with 150 base pair paired-ends on the Illumina NovaSeq-6000 platform. Alignment was performed using human reference genome hg38. Differential gene expression analysis was performed using DESeq2 following alignment, using log fold change ±0.50, and had a false discovery rate p-value <0.05. Pathway analysis was performed using Ingenuity Pathway Analysis. RESULTS: We found 288 differentially expressed genes in insomnia patients when compared to controls. Upregulated genes included LINC02224 (Long Intergenic Non-Protein Coding RNA 2224), DUX4L9 (Double Homeobox 4 Like 9), and TUSC3 (Tumor Suppressor Candidate 3) and down regulated genes included CTXN2 (Cortexin 2), CSMD1 (CUB And Sushi Multiple Domains 1), and SLC12A1 (Solute Carrier Family 12 Member 1). Ingenuity® Pathway Analysis (IPA) revealed 3 associated networks (score>40) with genes and hubs related to inflammation (nuclear factor-kB), oxidative stress (Mitochondrial complex 1) and ubiquitination. CONCLUSION: Differentially expressed genes in this analysis are functionally associated with inflammation and immune response, mitochondrial and metabolic processes. Further research into the transcriptomic changes in insomnia is needed to understand related pathways to the disorder and provide new avenues for diagnostics and therapeutics.

Persistent Fatigue in Hematopoietic Stem Cell Transplantation Survivors.

BACKGROUND: Fatigue is highly prevalent after hematopoietic stem cell transplantation (HCT). It has been described as intense and may last for years following treatment. OBJECTIVE: The aim of this study is to compare fatigue, physical activity, sleep, emotional distress, cognitive function, and biological measures in HCT survivors with persistent fatigue (n = 25) with age- and gender-matched healthy controls with occasional tiredness (n = 25). METHODS: Data were collected using (a) objective, real-time assessments of physical activity and sleep over 7 days; (b) patient-reported fatigue assessments; (c) computerized objective testing of cognitive functioning; and (d) biological measures. Differences between groups were examined using multivariate analysis of variance. RESULTS: Survivors of HCT reported increased physical (P < .001), mental (P < .001), and overall (P < .001) fatigue as well as increased anxiety (P < .05) and depression (P < .01) compared with healthy controls. Red blood cell (RBC) levels were significantly lower in HCT survivors (P < .001). Levels of RBC for both groups, however, were in the normal range. Tumor necrosis factor-α (P < .001) and interleukin-6 (P < .05) levels were significantly higher in HCT survivors. CONCLUSIONS: Persistent fatigue in HCT survivors compared with healthy controls with occasional tiredness is accompanied by increased anxiety and depression along with decreased RBC counts. Elevated tumor necrosis factor-α and interleukin-6 levels may be important biomarkers. IMPLICATIONS FOR PRACTICE: This study provides preliminary support for the conceptualization of fatigue as existing on a continuum, with tiredness anchoring one end and exhaustion the other. Persistent fatigue experienced by HCT survivors is more severe than the occasional tiredness of everyday life.

Fatigue, inflammation, and projected mortality in heart failure.

BACKGROUND: Fatigue is a prominent and poorly understood symptom of heart failure with reduced ejection fraction (HFrEF). The purpose of this study was to determine whether fatigue correlated with immune biomarkers and prognosis. METHODS/RESULTS: In patients with HFrEF (N = 59) and healthy controls (N = 25), we prospectively measured fatigue (Profile of Mood States), depressive symptoms (Patient Health Questionnaire-8), sleep quality (Pittsburgh Sleep Quality Index), and immune biomarkers (plasma C-reactive protein [CRP], tumor necrosis factor-α [TNFα], and interleukins [IL-6 and IL-10]). Seattle Heart Failure Model (SHFM) mortality risk scores were determined. Patients with HFrEF had significantly greater fatigue and depressive symptoms and poorer sleep quality compared to control subjects. When controlling for depressive symptoms, however, fatigue did not differ significantly between patients with HFrEF and controls. Patients with HFrEF had significantly lower levels of IL-10 compared to controls. Cytokines did not correlate significantly with fatigue, but fatigue was significantly associated with higher SHFM scores. CONCLUSIONS: Depressive symptoms were an important covariate of fatigue in patients with HFrEF. Our study findings were the first to show a positive association between fatigue and the SHFM score, indicating that fatigue was associated with poorer prognosis.

Fatigue with systolic heart failure.

BACKGROUND AND RESEARCH OBJECTIVE: Fatigue is one of the most prevalent symptoms in persons with systolic heart failure (HF). There remains insufficient information about the physiological and psychosocial underpinnings of fatigue in HF. The specific aims of this study were to (1) determine the psychometric properties of 2 fatigue questionnaires in patients with HF, (2) compare fatigue in patients with HF to published scores of healthy adults and patients with cancer undergoing treatment, and (3) identify the physiological (eg, hemoglobin, B-type natriuretic peptide, body mass index, and ejection fraction) and psychosocial (eg, depressed mood) correlates of fatigue in HF. SUBJECTS AND METHODS: A convenience sample of 87 HF outpatients was recruited from 2 urban medical centers. Patients completed the Fatigue Symptom Inventory, Profile of Mood States, and Short Form-36 Health Survey. RESULTS AND CONCLUSIONS: Patients with HF and patients with cancer reported similar levels of fatigue, and both patient groups reported significantly more fatigue than did healthy adults. Physical functioning and hemoglobin categories explained 30% of the variance in Fatigue Symptom Inventory-Interference Scale scores, whereas depressed mood and physical functioning explained 47% of the variance in Profile of Mood States Fatigue subscale scores. Patients with HF experienced substantial fatigue that is comparable with cancer-related fatigue. Low physical functioning, depressed mood, and low hemoglobin level were associated with HF-related fatigue.

Endocarditis after valve replacement surgery. Early recognition and treatment are essential to averting deadly complications.

The incidence of infective endocarditis, an inflammation of the heart valves, the endocardium, or both that's caused by bacterial, viral, or fungal agents, continues to rise despite advances in treatment. People with prosthetic valves are among those at highest risk, and mortality rates can be extremely high, especially in cases of early onset. Diagnosis is challenging, in part because signs and symptoms associated with systemic infections can mask signs of the underlying endocarditis. This article outlines the causes and effects of prosthetic valve-related endocarditis (PVE), describes its signs and symptoms, discusses the leading diagnostic tests and criteria, and addresses prevention and treatment, as well as nursing implications.