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BackgroundPostoperative atrial fibrillation (POAF) is a common complication after cardiac surgery, which is associated with age and massive bleeding. However, whether thyroid hormone (TH) level can affect POAF remains controversial.AimTo investigate the occurrence and risk factors of POAF, in particular, the preoperative TH level of patients was introduced into this study as a variable for analysis, and a column graph prediction model of POAF was constructed.MethodsPatients who underwent valve surgery in Fujian Cardiac Medical Center from January 2019 to May 2022 were retrospectively analyzed and divided into POAF group and NO-POAF group. Baseline characteristics and relevant clinical data were collected from the two groups of patients. Independent risk factors for POAF were screened using univariate analysis and binary logistic regression analysis, and a column line graph prediction model was established based on the regression analysis results, and the diagnostic efficacy and calibration of the model were evaluated using the Receiver Operating Characteristic Curve (ROC) and calibration curve.ResultsA total of 2,340 patients underwent valve surgery, excluding 1,751 patients, a total of 589 patients were included, including 89 patients in POAF group and 500 patients in NO-POAF group. The total incidence of POAF was 15.1%. The results of the Logistics regression analysis showed that gender, age, leukocytes and TSH were risk factors of POAF. The area under the ROC curve of the nomogram prediction model for POAF was 0.747 (95% CI: 0.688–0.806, P χ2 = 11.141, P = 0.194 > 0.05, the calibration curve was well fitted.ConclusionThe results of this study show that gender, age, leukocyte and TSH are risk factors of POAF, and the nomogram prediction model has a good prediction effect. Due to the limited sample size and included population, more studies are needed to validate this result.

ObjectiveTo investigate the characteristic changes in the gut microbiota of patients with type A aortic dissection (AAD) and provide a theoretical basis for future microbiome-oriented interventional studies.MethodsHigh-throughput 16S rDNA sequencing was performed on the stool samples of patients with and without (healthy control subjects) AAD. Using alpha and beta diversity analysis, we compared the gut microbiota composition of 20 patients with AAD and 20 healthy controls matched for gender, age, BMI, and geographical region. The accuracy of AAD prediction by differential microbiome was calculated using the random forest machine learning model. Targeted measurement of the plasma concentration of short-chain fatty acids (SCFAs), which are the main metabolites of the gut microbiome, was performed using gas chromatography–mass spectrometry (GC–MS). Spearman’s correlation analysis was conducted to determine the relationships of gut microbiome and SCFAs with the clinical characteristics of subjects.ResultsThe differences in gut microbiota alpha diversity between patients with AAD and the healthy controls were not statistically significant (Shannon index: p = 0.19; Chao1: p = 0.4); however, the microbiota composition (beta diversity) was significantly different between the two groups (Anosim, p = 0.001). Bacteroidota was enriched at the phylum level, and the SCFA-producing genera Prevotella, Porphyromonas, Lachnospiraceae, and Ruminococcus and inflammation-related genera Fenollaria and Sutterella were enriched at the genus level in the AAD group compared with those in the control group. The random forest model could predict AAD from gut microbiota composition with an accuracy of 87.5% and the area-under-curve (AUC) of the receiver operating characteristic curve was 0.833. The SCFA content of patients with AAD was higher than that of the control group, with the difference being statistically significant (p ConclusionTo the best of our knowledge, this is the first demonstration of the presence of significant differences in the gut microbiome of patients with AAD and healthy controls. The differential microbiome exhibited high predictive potential toward AAD and was positively correlated with inflammatory cytokines. Our results will assist in the development of preventive and therapeutic treatment methods for patients with AAD.

BackgroundPostoperative delirium (POD) is an acute brain dysfunction that is frequently observed in patients undergoing cardiac surgery. Increasing evidence indicates POD is related to higher mortality among cardiac surgical patients, but the results remain controversial. Moreover, a quantitative evaluation of the influence of POD on hospital days, intensive care unit (ICU) time, and mechanical ventilation (MV) time has not been performed.ObjectiveThis study aimed to evaluate the correlation between POD and outcomes in patients undergoing cardiac surgery by a systematic review and meta-analysis.Materials and methodsA total of 7 electronic databases (Cochrane Library, PubMed, EMBASE, CINAHL Complete, MEDLINE, Wan-fang database, and China National Knowledge Infrastructure) were searched from January 1980 to July 20, 2021, with language restrictions to English and Chinese, to estimate the impact of the POD on outcome in patients who underwent cardiac surgery. The meta-analysis was registered with PROSPERO (Registration: CRD42021228767).ResultsForty-two eligible studies with 19785 patients were identified. 3368 (17.0%) patients were in the delirium group and 16417 (83%) were in the non-delirium group. The meta-analysis showed that compared to patients without POD, patients with POD had 2.77-fold higher mortality (OR = 2.77, 95% CI 1.86–4.11, P < 0.001), 5.70-fold higher MV (>24h) rate (OR = 5.70, 95% CI 2.93–11.09, P < 0.001); and longer MV time (SMD = 0.83, 95% CI 0.57–1.09, P < 0.001), ICU time (SMD = 0.91, 95% CI 0.60–1.22, P < 0.001), hospital days (SMD = 0.62, 95% CI 0.48–0.76, P < 0.001).ConclusionThe synthesized evidence suggests that POD is causally related to the increased risk of mortality, prolonged length of ICU and hospital stay, and a longer duration of MV time. Future research should focus on the interventions for POD, to reduce the incidence.Systematic review registration[www.crd.york.ac.uk/PROSPERO], identifier [CRD42021228767].

Background: Medication adherence is crucial for patients with mechanical heart valve replacement. Although families functioning is positively associated with medication adherence, little is known about the underlying mechanisms.Objective: To test whether family functioning affects medication adherence through illness perceptions and whether this mediating effect was moderated by medication literacy.Methods: 319 patients after mechanical heart valve replacement were included in this cross-sectional study from June 2021 to October 2021. Data regarding family functioning, illness perceptions, medication adherence, and medication literacy were collected through questionnaires. The moderated mediation model was examined by Hayes’s PROCESS macro, based on the bootstrapping method.Results: The results revealed illness perceptions partially mediated the association of family functioning on medication adherence [β = 0.08, 95% confidence intervals: (0.04, 0.12)], and this effect was stronger for patients with low medication literacy than those with high literacy [β = −0.36, 95% CI: (−0.50, −0.22)]. Furthermore, the relationship between family functioning and medication adherence was only significant in patients with low medication literacy [β= 0.36, 95% CI: (0.23, 0.50)].Conclusion: The mediating effect of illness perceptions between family functioning and medication adherence was moderated by medication literacy. Efforts to improve medication adherence by targeting at improving family functioning may be more effective when considering illness perceptions, especially for patients with limited medication literacy.

Perceived social support (PSS) has been shown to be positively related to self-reported quality of life (QoL) as well as to emotion regulation strategy. In the present study, we compared a QoL index between Chinese fishermen (N = 507) and local villagers (N = 192) and examined whether PSS moderates the relationship between emotion regulation and QoL in our sample of Chinese ocean-going fishermen. Fishermen’s QoL was found to be poorer than that of local villagers. Structural equation modeling (SEM) confirmed that cognitive reappraisal of emotion regulation had a positive predictive effect on QoL, while expression suppression of emotion regulation had a negative predictive effect on QoL. Using, latent moderated structural equations (LMS), we further confirmed that PSS moderates the relationship between emotion regulation and QoL. Simple slope analysis revealed that emotional regulation can predict QoL in a high-PSS context but not in a low-PSS context.

This study examined pathways of influence between work stress, depressive symptoms, nicotine dependence, expressive suppression, and cognitive reappraisal in fishermen with smoking habits in Qionghai, Hainan province, China (N = 1068). These fishermen responded to multiple assessments a week before leaving on a deep-sea fishing trip, including a Mental Stressor Investigation Questionnaire (MSIQ), the Center for Epidemiological Studies Depression Scale (CES-D), the Russell Reason for Smoking Questionnaire (RRSQ), and an Emotion Regulation Questionnaire (ERQ). Structural equation modeling (SEM) analyses of the collected data in Mplus 7 showed that work stress and nicotine dependence were independent predictors of depressive symptoms. The relationship between work stress and depressive symptoms was found to be partially mediated by nicotine dependence and be moderated by cognitive reappraisal. The evidence suggests it advantageous to examine the need of work stress, nicotine dependence, and cognitive reappraisal when attempting to understand depressive symptoms in fishermen with a smoking habit. These findings suggest that improving nicotine dependence through work stress management and training in cognitive reappraisal could be utilized as effective modalities for improving depressive symptoms.