ObjectiveTo systematically review the efficacy of Chinese medicine injection (CMI) for treating heart failure (HF).MethodsCNKI, WanFang Data, VIP, The Cochrane Library, PubMed, and EMbase databases were electronically searched from inception to January 2021 to identify randomized controlled trials (RCTs) on CMI for treating HF. Two reviewers independently screened literature, extracted data, and evaluated the risk of bias of included studies. Network meta-analysis was then performed by RevMan 5.2 software and Stata 16.0 software.ResultsA total of 47 studies were included involving 4 902 patients and 5 types of CMIs, including Shenmai, Shenfu, Yiqi Fumai (lyophilized), Shengmai, and Danhong injections. The results of network meta-analysis showed that the efficacy of combined CMIs was superior to conventional Western medicine alone. For the main efficacy, Shenmai, Shengmai, and Shenfu injections had significant advantages in improving the total clinical effectiveness. Shengmai, Shenmai, and Yiqi Fumai (lyophilized) injections were significantly more effective for reducing NT pro-BNP levels than other injections. Shenfu and Shengmai injections were significantly more effective for reducing BNP levels than other injections. Shenmai, Danhong and Shengmai injections were significantly more effective for improving the left ventricular ejection fraction than the other injections. These CMIs showed similar advantages for secondary efficacy indicators as for main efficacy indicators.ConclusionsThe combined 5 types of CMIs for treating HF can improve the clinical efficacy when compared with conventional Western medicine treatment. Shenmai injection, Yiqi Fumai injection (lyophilized), and Shengmai injection, which is part of Sheng Mai San, have clear advantages in terms of the overall curative effect or on individual indices.
Heart failure is a disease that seriously threatens human health and has become a global public health problem. Diagnostic and prognostic analysis of heart failure based on medical imaging and clinical data can reveal the progression of heart failure and reduce the risk of death of patients, which has important research value. The traditional analysis methods based on statistics and machine learning have some problems, such as insufficient model capability, poor accuracy due to prior dependence, and poor model adaptability. In recent years, with the development of artificial intelligence technology, deep learning has been gradually applied to clinical data analysis in the field of heart failure, showing a new perspective. This paper reviews the main progress, application methods and major achievements of deep learning in heart failure diagnosis, heart failure mortality and heart failure readmission, summarizes the existing problems and presents the prospects of related research to promote the clinical application of deep learning in heart failure clinical research.
Objective To systematically evaluate the prognostic prediction model for chronic heart failure patients in China, and provide reference for the construction, application, and promotion of related prognostic prediction models. Methods A comprehensive search was conducted on the studies related to prognostic prediction model for Chinese patients with chronic heart failure published in The Cochrane Library, PubMed, EMbase, Web of Science, CNKI, VIP, Wanfang, and the China Biological Medicine databases from inception to March 31, 2023. Two researchers strictly followed the inclusion and exclusion criteria to independently screen literature and extract data, and used the prediction model risk of bias assessment tool (PROBAST) to evaluate the quality of the models. Results A total of 25 studies were enrolled, including 123 prognostic prediction models for chronic heart failure patients. The area under the receiver operating characteristic curve (AUC) of the models ranged from 0.690 to 0.959. Twenty-two studies mostly used random splitting and Bootstrap for internal model validation, with an AUC range of 0.620-0.932. Seven studies conducted external validation of the model, with an AUC range of 0.720-0.874. The overall bias risk of all models was high, and the overall applicability was low. The main predictive factors included in the models were the N-terminal pro-brain natriuretic peptide, age, left ventricular ejection fraction, New York Heart Association heart function grading, and body mass index. Conclusion The quality of modeling methodology for predicting the prognosis of chronic heart failure patients in China is poor, and the predictive performance of different models varies greatly. For developed models, external validation and clinical application research should be vigorously carried out. For model development research, it is necessary to comprehensively consider various predictive factors related to disease prognosis before modeling. During modeling, large sample and prospective studies should be conducted strictly in accordance with the PROBAST standard, and the research results should be comprehensively reported using multivariate prediction model reporting guidelines to develop high-quality predictive models with strong scalability.
Abstract: Objective To build a rat model of right ventricular failure （RVF） by subcutaneous injection of Monocrotaline. Methods Forty Wistar rats were equally divided into four groups, 10 rats each group. Exp4 group: four weeks after Monocrotaline injection, experimental results were observed; Exp6 group: six weeks after Monocrotaline injection, experimental results were observed; Con4 group: four weeks after normal saline injection, experimental results were observed; Con6 group: six weeks after normal saline injection, experimental results were observed. Four and six weeks after Monocrotaline or normal saline injection respectively, the hemodynamic indexes of each pair of groups were measured. Their hearts and livers were excised to measure physiological indexes and had pathological examinations. Results Mean pulmonary arterial pressure (MPAP), maximal rate of change of right ventricular pressure (RV dp/dtmax) and right ventricular ypertrophy index in Exp4 group were higher than those in Con4 group（Plt;0.05,0.01）. Compared with Con6 group, there were obvious symptoms of RVF in Exp6 group which included the increases of heart rate, increases of central venous pressure (CVP) and MPAP, the decreases of RV dp/dtmax, the decreases of weight, the increases of liver weight/body weight ratio and right ventricular hypertrophy index, significant pleural and peritoneal effusions（P＜0.05,0.01 ）. Pathological examination of Exp6 group showed disordering and bifurcated cardiac muscle fibers, large and thickly dying cell core, enlarged transverse diameter of the cardiac muscle fibers and stroma fibrosis. Vacuolar degeneration and dissolved carcoplasm could be seen. The vessel wall of the lung arteriole thickened, intercellular layer smooth muscle cell hyperplasied, elastic fibers increased, vessel wall arteriosclerosised, lumens stenosized. Conclusion This model is simple to build and successful rate is high. It is valuable for further research.
ObjectiveTo systematically review the association of body mass index (BMI) and mortality in chronic heart failure (CHF) pationts.MethodsPubMed, EMbase, The Cochrane Library, CNKI, WanFang Data and VIP databases were electronically searched to collect cohort studies about the association of BMI and mortality in CHF patients from inception to June, 2019. Two reviewers independently screened literature, extracted data and assessed the risk of bias of included studies, then, meta-analysis was performed by using Stata 12.0 software.ResultsA total of 20 cohort studies involving 91 572 CHF patients were included. The results of meta-analysis showed that, compared to patients with normal weight, underweight individuals were associated with higher mortality (HR=1.48, 95%CI 1.36 to 1.62, P<0.001), whereas overweight (HR=0.86, 95%CI 0.78 to 0.94, P=0.002) and obese (HR=0.78, 95%CI 0.68 to 0.90, P=0.001) patients were associated with lower mortality.ConclusionCurrent evidence shows that underweight is associated with a higher risk of all-cause mortality among patients with CHF, whereas overweight and obese are associated with lower risk of all-cause mortality. Due to limited quality and quantity of the included studies, more high-quality studies are required to verify above conclusions.
ObjectivesTo systematically review the efficacy and safety of ivabradine (IVA) for patients with chronic heart failure (CHF).MethodsPubMed, EMbase, The Cochrane Library, CNKI, WanFang Data and VIP databases were electronically searched to collect randomized controlled trials (RCTs) on the efficacy and safety of IVA for patients with CHF from inception to April, 2019. Two reviewers independently screened literature, extracted data and assessed risk of bias of included studies, then, meta-analysis was performed using Stata 12.0 software.ResultsA total of 22 RCTs involving 2 010 patients were included. The results of meta-analysis showed that, compared with control group, IVA group could decrease heart rate (HR) (WMD=−10.58, 95%CI −12.47 to −8.69, P=0.000) and N-terminal probrain natriuretic peptide (NT-proBNP) (WMD=−457.87, 95%CI −842.63 to −73.11, P=0.020). IVA group was superior in 6 minutes’ walk distance (6MWD) (WMD=40.49, 95%CI 27.83 to 53.15, P=0.000), left ventricular ejection fraction (LVEF) (WMD=5.11, 95%CI 3.74 to 6.48, P=0.000), left ventricular end-diastolic volume (LVEDV), left ventricular end-systolic volume (LVESV), left ventricular end-diastolic dimension (LVEDd), left ventricular end-systolic dimension (LVESd) and incidence of endpoint events with significant difference. However, the total effective rate, the incidence of adverse reactions and blood pressures were similar between two groups.ConclusionCurrent evidence shows that IVA could significantly reduce HR, improve cardiac function and exercise tolerance in CHF patients with no significant increase of adverse events. Due to limited quality and quantity of the included studies, more high-quality studies are required to verify above conclusions.
Dyspnea is the most common symptom in patients with acute heart failure syndrome (AHFS), and relieving dyspnea is an important goal in clinical practice, clinical trials and new drug regulatory approval. However, in clinical and scientific research, there is still no consensus on how to evaluate dyspnea, and there is still a lack of unified measurement methods. This article introduces the pathophysiological mechanism of dyspnea in acute heart failure, the measuring time of dyspnea, the posture of patients during measurement, the measuring conditions, and the common measurement methods of dyspnea in clinical trials and their advantages and disadvantages, so as to provide references for the selection of measurement methods of dyspnea in clinical trials of acute heart failure.
Sodium-glucose cotransporter (SGLT) -2 inhibitors is a new type of oral sugar-lowering drug. Instead of relying on insulin, it lowers blood sugar by inhibiting the reabsorption of near-curvy tube glucose, which is drained from the urine. SGLT-2 inhibitors not only have a sugar-lowering effect, but also benefit significantly in cardiovascular disease, and this drug has the advantages of permeable diuretic, reducing capacity load, and improving ventricular remodeling. SGLT-2 inhibitors can improve the diastolic function of patients with heart failure with preserved ejection fraction (HFpEF) and reduce the risk of adverse cardiovascular events. SGLT-2 inhibitors can benefit patients with HFpEF. Therefore, this article will discuss the progress of SGLT-2 inhibitors in HFpEF.