BackgroundHyperglycemia is associated with an increased risk of developing atrial fibrillation (AF) and atrial flutter (AFL). Sodium-glucose transporter 2 inhibitors (SGLT2i) have been reported to prevent AF/AFL in some studies, but not others. Therefore, a meta-analysis was performed to investigate whether SGLT2i use is associated with lower risks of AF/AFL.MethodsPubMed, Scopus, Web of Science, Cochrane library databases were searched for randomized placebo-controlled trials comparing SGLT2i and placebo.ResultsA total of 33 trials involving 66,685 patients were included. The serious adverse events (SAEs) of AF/AFL occurrence were significantly lower in the SGLT2i group than the placebo group (0.96% vs. 1.19%; RR 0.83; 95% CI 0.71–0.96; P = 0.01; I2 25.5%). Similarly, the SAEs of AF occurrence was significantly lower in the SGLT2i group (0.82% vs. 1.06%; RR 0.81; 95% CI 0.69–0.95; P = 0.01; I2 10.2%). The subgroup analysis showed that the reduction in AF/AFL was significant only for dapagliflozin (1.02% vs. 1.49%; RR 0.73; 95% CI 0.59–0.89; P = 0.002; I2 0%), but not for canagliflozin (1.00% vs 1.08%; RR 0.83; 95% CI 0.62–1.12; P = 0.23; I2 0%), empagliflozin (0.88% vs 0.70%; RR 1.20; 95% CI 0.76–1.90; P = 0.43; I2 0%), ertugliflozin (1.01% vs 0.96%; RR 1.08; 95% CI 0.66–1.75; P = 0.76; I2 0%), and sotagliflozin (0.16% vs 0.10%; RR 1.09; 95% CI 0.13–8.86; P = 0.93; I2 0%).ConclusionsSGLT2i use is associated with a 19.33% lower SAEs of AF/AFL compared with the placebo. Dapagliflozin users had the lowest SAEs of AF/AFL incidence. Further studies are needed to determine whether canagliflozin, empagliflozin, ertugliflozin, and sotagliflozin similarly exert protective effects against AF/AFL development.

Background The obstacle of limb motor caused by stroke, especially the decline of motor function of upper limbs, can directly affect the activities of daily living of stroke patients with hemiplegia. Based on long-term clinical practice, the treatment effect of electrical stimulation methods for stroke limb dysfunction has been widely recognized and supported by authoritative guidelines and systematic reviews. However, which electrical stimulation method is the optimum in the treatment of stroke limb dysfunction is still a controversial issue. Objective In this paper, we adopted Network Meta-Analysis (NMA) to rank the priorities of various electrical stimulation methods, so as to select the optimal electrical stimulation method and discuss its rationality in guiding clinical practice. Methods We carried out a systematic review by searching a total of 6806 studies from 8 databases and 2 clinical trial registries, and finally screened out 34 studies for further investigation. Then, pairwise meta-analysis and Bayesian network meta-analysis were employed to evaluate the effectiveness and ranking of various interventions. The primary outcome measure was Fugl-Meyer Assessment Upper Extremity (FMA-UE), and the secondary outcome measures were Modified Barthel Index (MBI) and Modified Ashworth Scale (MAS). Finally, the risk of bias, publication bias and sensitivity of the Randomized Controlled Trials (RCTs) were evaluated. Results On the basis of comprehensive rehabilitation treatment (RT), the Functional Electrical Stimulation (FES) was superior than other electrical stimulation methods in improving both FMA-UE and MBI. Meanwhile, the results indicated that the Transcutaneous Electrical Acupoint Stimulation (TEAS) was the only electrical stimulation method that showed treatment advantages in reducing MAS. Conclusion The study showed that FES had the optimal overall rehabilitation effect on upper limb dysfunction of stroke patients based on the comprehensive RT, while the treatment effect of TEAS on upper limb spasticity after stroke was the most significant.

Background: The predictive power of the CHADS2 and CHA2DS2-VASc scores for the presence of Left atrial thrombus (LAT)/ spontaneous echo contrast (SEC) in non-valvular atrial fibrillation (NVAF) is modest. The aim of this analysis is to define clinical and ultrasonic variables associated with LAT/SEC and to propose nomograms for individual risk prediction.Methods: Data on 1,813 consecutive NVAF patients who underwent transesophageal echocardiography (TEE) from January 2016 to January 2021 were collected. The univariate and multivariate logistic regression analyses were used to construct a nomogram. We examined the predictive ability of the risk scores by calculating the area under the curve (AUC). Moreover, the performance of the nomogram was assessed with respect to calibration, discrimination, and clinical usefulness.Results: LAT/SEC was found in 260 (21.0%) and 124 (21.6%) patients in the training and validation cohorts, respectively. On multivariate analysis, independent factors for LAT/SEC were Age, left atrial diameter (LAD), left ventricular ejection fraction (LVEF), hypertension (HTN), previous stroke or transient ischemic attack, Non-paroxysmal AF and a nomogram was built based on these variables. The calibration curve for the probability of LAT/SEC showed good prediction agreement with actual observation. The nomogram achieved good concordance indexes of 0.836 and 0.794 in predicting LAT/SEC in the training and validation cohorts, respectively. Decision curve analysis demonstrated that the nomogram would be clinically useful.Conclusions: In this study, a nomogram was constructed that incorporated six characteristics of NVAF patients. The nomogram may be of great value for the prediction of LAT/SEC in NVAF patients.

Objective: Both serum uric acid (SUA) levels and left atrium diameter (LAD) associate with AF. However, the influence of SUA status for the associated risk of AF related to LAD in hypertension patients is currently unknown.Methods: We retrospectively analyzed a hospital-based sample of 9,618 hypertension patients. Standard electrocardiograms were performed on all patients and were interpreted by expert electro-physiologists.Results: Overall 1,028 (10.69%) patients had AF out of 9,618 patients. In men >65 years of age, the prevalence of AF in the1st, 2nd, and 3rd tertiles of SUA among those grouped in the third tertile of LAD were 9, 12.3, and 21.7%, respectively. In the hyperuricemia group, the OR (95% CI) of AF for the highest tertile of LAD in men ≤ 65 years of age was 3.150 (1.756, 5.651; P < 0.001). Similarly, the hyperuricemic men in the 3rd LAD tertile had a higher likelihood of AF than those belonging to the 1st tertile. The ORs and (95% CIs) were 3.150 (1.756, 5.651; P < 0.001) and 5.522 (2.932, 10.400; P ≤ 0.001) for patients ≤ 65 and >65 years of age. An increase in SUA values was significantly associated with an increased likelihood of AF among women at the top tertiles of LAD, with the OR (95% CI) = 4.593 (1.857, 11.358; P = 0.001). Also, men> 65 years of age with large LAD, present at the third tertile of SUA, had a higher likelihood of AF, with the OR (95% CI) = 2.427 (1.039, 5.667; P < 0.05).Conclusion: SUA levels and LAD are associated with AF in patients with hypertension and the risk of AF associated with LAD increases among those with hyperuricemia.

Introduction: Understanding the epidemiology of cardiovascular disease (CVD) related comorbidity is a key strategy for improving the outcomes of patients with cancer. Therefore, this study aimed to assess the distribution of cardiovascular comorbidities and cardiovascular risk factors (CVRF) among five cancer sites.Methods: This is a single-centered, cross-sectional study performed in Dalian, China. Between 2008 and 2018, all newly diagnosed cancer in the First Affiliated Hospital of Dalian Medical University, China were screened. Clinical data were extracted from a comprehensive electronic health record system.Results: 35861 patients with lung, colorectal, gastric, breast, and thyroid cancer were collected retrospectively. The most prevalent CVDs in descending order were hypertension (21.9%), followed by coronary heart disease (6.5%), atrial fibrillation (2.9%), and heart failure (1%). The prevalence of hypertension significantly varies between lung (21.3%), colorectal (27.3%), gastric (22.5%), breast (16.7%), and thyroid cancer (22.4%) (P < 0.001). CVRF varies with cancer sites. Age, sex, total cholesterol, triglyceride, low-density lipoprotein cholesterol, systolic blood pressure, smoking, alcohol use, and diabetes mellitus (DM) are common risk factors associated with CVD at different cancer sites. The association between DM and presence of CVD was strong in breast (odds ratio [OR] = 4.472, 95% confidence interval [CI]: 3.075–6.504, P < 0.001), lung (OR = 3.943; 95% CI: 3.270–4.754, P < 0.001), colorectal (OR = 3.049; 95% CI: 2.326–3.996, P < 0.001), and gastric (OR = 2.508; 95% CI: 1.927–3.264, P < 0.001) cancer.Conclusion: Cancer patients had a significant burden of CVD and increased CVRF. The prevalence of CVRF and CVD comorbidity differ for cancer types. DM remains significantly associated with CVD at different cancer sites except for thyroid cancer.

Background Insomnia as one of the dominant diseases of traditional Chinese medicine (TCM) has been extensively studied in recent years. To explore the novel approaches of research on TCM diagnosis and treatment, this paper presents a strategy for the research of insomnia based on machine learning. Methods First of all, 654 insomnia cases have been collected from an experienced doctor of TCM as sample data. Secondly, in the light of the characteristics of TCM diagnosis and treatment, the contents of research samples have been divided into four parts: the basic information, the four diagnostic methods, the treatment based on syndrome differentiation and the main prescription. And then, these four parts have been analyzed by three analysis methods, including frequency analysis, association rules and hierarchical cluster analysis. Finally, a comprehensive study of the whole four parts has been conducted by random forest. Results Researches of the above four parts revealed some essential connections. Simultaneously, based on the algorithm model established by the random forest, the accuracy of predicting the main prescription by the combinations of the four diagnostic methods and the treatment based on syndrome differentiation was 0.85. Furthermore, having been extracted features through applying the random forest, the syndrome differentiation of five zang-organs was proven to be the most significant parameter of the TCM diagnosis and treatment. Conclusions The results indicate that the machine learning methods are worthy of being adopted to study the dominant diseases of TCM for exploring the crucial rules of the diagnosis and treatment.

Background: Insomnia as one of the dominant diseases of traditional Chinese medicine (TCM) has been extensively studied in recent years. To explore the novel approaches of research on TCM diagnosis and treatment, this paper presents a strategy for the research of insomnia based on machine learning. Methods: First of all, 654 insomnia cases have been collected from an experienced doctor of TCM as sample data. Secondly, in the light of the characteristics of TCM diagnosis and treatment, the contents of research samples have been divided into four parts: the basic information, the four diagnostic methods, the treatment based on syndrome differentiation and the main prescription. And then, these four parts have been analyzed by three analysis methods, including frequency analysis, association rules and hierarchical cluster analysis. Finally, a comprehensive study of the whole four parts has been conducted by random forest. Results: Researches of the above four parts revealed some essential connections. Simultaneously, based on the algorithm model established by the random forest, the accuracy of predicting the main prescription by the combinations of the four diagnostic methods and the treatment based on syndrome differentiation was 0.85. Furthermore, having been extracted features through applying the random forest, the syndrome differentiation of five zang-organs was proven to be the most significant parameter of the TCM diagnosis and treatment.Conclusions: The results indicate that the machine learning methods are worthy of being adopted to study the dominant diseases of TCM for exploring the crucial rules of the diagnosis and treatment.

Abstract Background: Insomnia as one of the dominant diseases of Traditional Chinese medicine (TCM) has been extensively studied in recent years. To explore the novel approaches of research on TCM diagnosis and treatment, this paper presents a strategy for the research of insomnia based on machine learning. Methods: First of all, 654 insomnia cases have been collected from an experienced doctor of TCM as sample data. Secondly, in the light of the characteristics of TCM diagnosis and treatment, the contents of research samples have been divided into four parts: the basic information, the four diagnostic methods, the treatment based on syndrome differentiation and the main prescription. And then, these four parts have been analyzed by three analysis methods, including frequency analysis, association rules and hierarchical cluster analysis. Finally, a comprehensive study of the whole four parts was conducted by random forest. Results: Analysis of the four parts revealed some significant relationships. Simultaneously, based on the algorithm model established by the random forest, the accuracy of predicting the main prescription by the combination of the four diagnostic methods and the treatment based on syndrome differentiation was 0.85. Furthermore, the syndrome differentiation of five zang-organs was proven to be the most significant parameter after the features extraction by the random forest.Conclusions: The results indicate that the machine learning method can be adopted to study the dominant diseases of TCM to explore the crucial rules of the diagnosis and the treatment.