Left bundle branch area pacing for heart failure patients requiring cardiac resynchronization therapy: A meta-analysis.

INTRODUCTION: Left bundle branch area pacing (LBBP) is a novel conduction system pacing method to achieve effective physiological pacing and an alternative to cardiac resynchronization therapy (CRT) with biventricular pacing (BVP) for patients with heart failure with reduced ejection fraction (HFrEF). We conduted this meta-analysis and systemic review to review current data comparing BVP and LBBP in patients with HFrEF and indications for CRT. METHODS: We searched PubMed/Medline, Web of Science, and Cochrane Library from the inception of the database to November 2022. All studies that compared LBBP with BVP in patients with HFrEF and indications for CRT were included. Two reviewers performed study selection, data abstraction, and risk of bias assessment. We calculated risk ratios (RRs) with the Mantel-Haenszel method and mean difference (MD) with inverse variance using random effect models. We assessed heterogeneity using the I2 index, with I2 > 50% indicating significant heterogeneity. RESULTS: Ten studies (9 observational studies and 1 randomized controlled trial; 616 patients; 15 centers) published between 2020 and 2022 were included. We observed a shorter fluoroscopy time (MD: 9.68, 95% confidence interval [CI]: 4.49-14.87, I2 = 95%, p < .01, minutes) as well as a shorter procedural time (MD 33.68, 95% CI: 17.80-49.55, I2 = 73%, p < .01, minutes) during the implantation of LBBP CRT compared to conventional BVP CRT. LBBP was shown to have a greater reduction in QRS duration (MD 25.13, 95% CI: 20.06-30.20, I2 = 51%, p < .01, milliseconds), a greater left ventricular ejection fraction improvement (MD: 5.80, 95% CI: 4.81-6.78, I2 = 0%, p < .01, percentage), and a greater left ventricular end-diastolic diameter reduction (MD: 2.11, 95% CI: 0.12-4.10, I2 = 18%, p = .04, millimeter). There was a greater improvement in New York Heart Association function class with LBBP (MD: 0.37, 95% CI: 0.05-0.68, I2 = 61%, p = .02). LBBP was also associated with a lower risk of a composite of heart failure hospitalizations (HFH) and all-cause mortality (RR: 0.48, 95% CI: 0.25-0.90, I2 = 0%, p = .02) driven by reduced HFH (RR: 0.39, 95% CI: 0.19-0.82, I2 = 0%, p = .01). However, all-cause mortality rates were low in both groups (1.52% vs. 1.13%) and similar (RR: 0.98, 95% CI: 0.21-4.68, I2 = 0%, p = .87). CONCLUSION: This meta-analysis of primarily nonrandomized studies suggests that LBBP is associated with a greater improvement in left ventricular systolic function and a lower rate of HFH compared to BVP. There was uniformity of these findings in all of the included studies. However, it would be premature to conclude based solely on the current meta-analysis alone, given the limitations stated. Dedicated, well-designed, randomized controlled trials and observational studies are needed to elucidate better the comparative long-term efficacy and safety of LBBP CRT versus BIV CRT.

Impact of Bariatric Surgery on Inpatient Complication, Cost, and Length of Stay Following Total Hip or Knee Arthroplasty.

BACKGROUND: Morbid obesity is an important risk factor for arthroplasty and also closely associated with worse postoperative outcomes. Bariatric surgery is effective in losing weight and decreasing comorbidities associated with obesity. However, no study had demonstrated the influence of bariatric surgery on the outcome of arthroplasty in a large population. METHODS: We used 2006-2014 discharge records from the Nationwide Inpatient Sample, and identified study population and inpatient complications by International Classification of Diseases, 9th Revision, Clinical Modification diagnosis/procedure codes. Propensity score analysis was used to match total hip arthroplasty (THA) or total knee arthroplasty (TKA) patients with morbid obesity and THA or TKA patients with bariatric surgery. RESULTS: Proportion of morbid obesity in both TKA and THA patients demonstrated a rising trend, while proportion of bariatric surgery in morbidly obese TKA and THA patients remains steady after 2007. For THA patients, there was fewer pulmonary embolism, more blood transfusion and anemia, and shorter length of stay in bariatric surgery group. For TKA patients, bariatric surgery group had a lower risk of pulmonary embolism, respiratory complications, death, and shorter length of stay, but bariatric surgery group had a higher risk of blood transfusion and anemia. CONCLUSION: There is evidence that bariatric surgery prior to arthroplasty, especially THA, appears to reduce rates of pulmonary complications and length of stay. But anemia and blood transfusion seem to be more common in patients with prior bariatric surgery.

Serum extracellular vesicles promote proliferation of H9C2 cardiomyocytes by increasing miR-17-3p.

Emerging evidence showed that cardiac proliferation played a significant role in the cardiac rehabilitation and repair. Extracellular vesicles (EVs) are known to regulate multiple cell functions, whereas the role of EVs in cardiac proliferation still remains unclear. In this study, we found that serum EVs promoted cell proliferation in rat heart myoblastic H9C2 cells with significantly increased expression level of miR-17-3p. Inhibition of miR-17-3p could decrease H9C2 cells proliferation induced by serum EVs. Additionally, we found that TIMP3 was a target of miR-17-3p in H9C2 cells proliferation and the expression of TIMP3 was downregulated by serum EVs. Meanwhile, inhibition of TIMP3 increased cardiac proliferation. In conclusion, results of our study indicated that serum EVs could promote the proliferation of H9C2 cells via regulating miR-17-3p/TIMP3, which may be a potential therapeutic target for treatment of cardiac injury.

Circular RNAs in Organ Fibrosis.

Fibrosis refers to a process involving the accumulation of extracellular matrix components. It could happen in chronic organ injury or during the recovery of acute organ injury. The severity of fibrosis interferes with the function of the organ involved. Numerous studies have been carried out to explore the mechanism of fibrosis, including parenchyma injury, fibrillar ECM accumulation, fibroblast activation, microvasculature rarefaction, and a mononuclear infiltrate. Unfortunately, its underlying mechanism is at largely unknown. The studying of noncoding RNAs has provided novel insight for circRNA-miRNA-mRNA in learning disease progress. Emerging evidence has shown that circRNA is related to fibrosis activity and could potentially be a monitoring factor for fibrosis or, more excitingly, could be a target for treatment. In this chapter, we will first present the basic mechanism of organ fibrosis. Then we will focus on the recent studies about how circRNA dysregulation contributes to organ fibrosis. Finally, the advantages and potential challenges of circRNA-based therapeutics for the treatment of fibroproliferative diseases will be discussed.

Functional Role of Circular RNA in Regenerative Medicine.

Every year, millions of people around the world suffer from different forms of tissue trauma. Regenerative medicine refers to therapy that replaces the injured organ or cells. Stem cells are the frontiers and hotspots of current regenerative medicine research. Circular RNAs (circRNAs) are essential for the early development of many species. It was found that they could guide stem cell differentiation through interacting with certain microRNAs (miRNAs). Based on this concept, it is meaningful to look into how circRNAs influence stem cells and its role in regenerative medicine. In this chapter we will discuss the functional roles of circRNAs in the prevention, repair, or progression of chronic diseases, through the communication between stem cells.

An Overview of Muscle Atrophy.

Muscle is the most abundant tissue in human body, and it can be atrophy when synthesis is inferior to degradation. Muscle atrophy is prevalent as it is a complication of many diseases. Besides its devastating effects on health, it also decreases life quality and increases mortality as well. This review provides an overview of muscle atrophy, including its prevalence, economic and health burden, and clinical therapy. Its clinical therapy includes exercise training, nutritional therapy, electrical stimulation, and drugs such as testosterone and ghrelin/IGF-1 analogues. More large-scale, long-term clinical trials are needed for therapies for muscle atrophy. In addition, more therapeutic targets are highly needed.

Muscle Atrophy: Present and Future.

Muscle atrophy is the loss of muscle mass and strength, and it occurs in many diseases, such as cancer, AIDS (acquired immunodeficiency syndrome), congestive heart failure, COPD (chronic obstructive pulmonary disease), renal failure, and severe burns. Muscle atrophy accompanied by cachexia worsens patient's life quality and increases morbidity and mortality. To date there is no effective treatment on that. Here we summarize the diagnosis methods and cellular mechanisms of muscle atrophy. We also discuss the current strategies in muscle atrophy treatment and highlight the potential treatment strategies to resist muscle atrophy.

Physical Exercise Is a Potential "Medicine" for Atherosclerosis.

Cardiovascular disease (CVD) has been recognized as the number one killer for decades. The most well-known risk factor is atherosclerosis. Unlike the acuity of CVD, atherosclerosis is a chronic, progressive pathological change. This process involves inflammatory response, oxidative reaction, macrophage activity, and different interaction of inflammatory factors. Physical exercise has long been known as good for health in general. In recent studies, physical exercise has been demonstrated to be a therapeutic tool for atherosclerosis. However, its therapeutic effect has dosage-dependent effect. Un-proper over exercise might also cause damage to the heart. Here we summarize the mechanism of Physical exercise's beneficial effects and its potential clinical use.

Exercise Exerts Its Beneficial Effects on Acute Coronary Syndrome: Clinical Evidence.

Acute coronary syndrome (ACS) is characterized with high morbidity, high mortality, long hospitalization and frequent revisits. It has been the most serious coronary artery diseases in the world. A large body of clinical evidence demonstrates that exercise is associated with reduced cardiovascular disease risk. In addition, different types of exercise have become the central to most cardiac rehabilitation/risk reduction programs. However, the detailed effects of exercise in ACS is still unclear and there is still lack of evidence on which exercise regimen may be ideal for ACS. This chapter presents a brief review of the pathophysiology of ACS and the relationship between exercise and the cardiovascular system. Besides that, this chapter also provide an updated discussion of the most relevant discoveries regarding to exercise and its role in managing ACS in clinical studies.

C/EBPB-CITED4 in Exercised Heart.

C/EBPB is a crucial transcription factor, participating in a variety of biological processes including cell proliferation, differentiation and development. In the cardiovascular system, C/EBPB-CITED4 signaling is known as a signaling pathway mediating exercise-induced cardiac growth. After its exact role in exercised heart firstly reported in 2010, more and more evidence confirmed that. MicroRNA (e.g. miR-222) and many molecules (e.g. Alpha-lipoic acid) can regulate this pathway and then involve in the cardiac protection effect induced by endurance exercise training. In addition, in cardiac growth during pregnancy, C/EBPB is also a required regulator. This chapter will give an introduction of the C/EBPB-CITED4 signaling and the regulatory network based on this signaling pathway in exercised heart.

Peripartum Cardiomyopathy: Do Exosomes Play a Role?

Peripartum cardiomyopathy (PPCM) refers to irreversible cardiomyocyte damage that occurs during the last month of pregnancy, or within 5 months after giving birth. It is characterized by systolic heart failure. This life-threatening condition is relatively uncommon, but the incidence has been climbing up. Because of its high mortality, it is crucial for physicians to have high suspicious for the disease. Studies have been done to search into specific lab test and treatment for PPCM. Therapies like anti-viral, anti-inflammatory and immunosuppression regimen have been explored. New regimen like exosomes has also been explored and revealed promising effects.

Cardioprotective Effects of Exosomes and Their Potential Therapeutic Use.

Exosomes are membrane-contained vesicles released by various types of cells both in animals and human. They contain microRNAs and proteins and can travel to target cells, affecting their functions. There are specific factors on the surface of every exosomes, making sure that they will be taken up by certain type of cells. With these features, exosomes have been recognized to be one of the fundamental "messengers" for cell-cell communication. Recently, increased interest has been raised in exosomes since they were discovered to play an unneglectable role in preserving cardiac function and cardiomyocyte repair during stress. The widely explored stem cell therapy for cardiomyopathy uncovered the contribution of exosomes. Here we summarized cardioprotective effects of exosomes and their potential therapeutic use.

Therapeutic Effects of Ischemic-Preconditioned Exosomes in Cardiovascular Diseases.

Despite years of researches, cardiovascular disease (CVD) remains the most common cause of death around the world. Lots of studies showed that by pretreating with short nonfatal ischemia in in situ organ or distant organ, one could develop tolerance to the following fatal ischemia. The process is called ischemic preconditioning (IPC). IPC prepare the heart for damage by producing inflammatory signals, miRNA, neuro system stimulation and exosomes. Among them, exosomes have been gaining increasing interest since it is characterized by its capability to carry information and its specific ligand-receptor system. Here we will discuss IPC induced exosomes and its protective effects during ischemic heart disease.

Takotsubo syndrome and vaccines: a systematic review.

AIMS: Takotsubo syndrome (TTS) is a rare complication of vaccination. In this study, we sought to provide insight into the characteristics of reported TTS induced by vaccination. METHODS AND RESULTS: We did a systematic review, searching PubMed, Embase, Web of Science, Ovid MEDLINE, Journals@Ovid, and Scopus databases up to 26 April 2023 to identify case reports or case series of vaccine-induced TTS. We then extracted and summarized the data from these reports. Eighteen reports were identified, with a total of 19 patients with TTS associated with vaccinations. Of the 19 included patients, the majority were female (n = 13, 68.4%) with a mean age of 56.6 ± 21.9 years. Seventeen patients developed TTS after coronavirus disease 2019 vaccination, 14 of whom received an mRNA vaccination. Two cases of TTS occurred after influenza vaccination. Among the 19 patients, 17 (89.5%) completed transthoracic echocardiography and 16 (84.2%) underwent angiography procedures. Seven patients (36.8%) completed cardiac magnetic resonance imaging. The median time to symptom onset was 2 (inter-quartile range, 1-4) days. The most common symptoms were chest pain (68.4%), dyspnoea (57.9%), and digestive symptoms (31.6%). A total of 57.9% of patients developed nonspecific symptoms such as fatigue, myalgia, diaphoresis, and fever. Among the 16 reported cases of TTS, 15 patients (93.8%) exhibited elevated cardiac troponin levels, while among the nine reported cases, eight patients (88.9%) had elevated natriuretic peptide levels. All patients had electrocardiographic changes: ST-segment change (47.1%), T-wave inversion (58.8%), and prolonged corrected QT interval (35.3%). The most common TTS type was apical ballooning (88.2%). Treatment during hospitalization typically included beta-blockers (44.4%), angiotensin-converting enzyme inhibitors/angiotensin receptor blockers (33.3%), and diuretics (22.2%). After treatment, 81.3% of patients were discharged with improved symptoms. Among this group, nine patients (56.3%) were reported to have recovered ventricular wall motion during follow-up. Two patients (12.5%) died following vaccination without resuscitation attempts. CONCLUSIONS: TTS is a rare but potentially life-threatening complication of vaccination. Typical TTS symptoms such as chest pain and dyspnoea should be considered alarming symptoms, though nonspecific symptoms are common. The risks of such rare adverse events should be balanced against the risks of infection.

Stratifying heart failure patients with graph neural network and transformer using Electronic Health Records to optimize drug response prediction.

OBJECTIVES: Heart failure (HF) impacts millions of patients worldwide, yet the variability in treatment responses remains a major challenge for healthcare professionals. The current treatment strategies, largely derived from population based evidence, often fail to consider the unique characteristics of individual patients, resulting in suboptimal outcomes. This study aims to develop computational models that are patient-specific in predicting treatment outcomes, by utilizing a large Electronic Health Records (EHR) database. The goal is to improve drug response predictions by identifying specific HF patient subgroups that are likely to benefit from existing HF medications. MATERIALS AND METHODS: A novel, graph-based model capable of predicting treatment responses, combining Graph Neural Network and Transformer was developed. This method differs from conventional approaches by transforming a patient's EHR data into a graph structure. By defining patient subgroups based on this representation via K-Means Clustering, we were able to enhance the performance of drug response predictions. RESULTS: Leveraging EHR data from 11 627 Mayo Clinic HF patients, our model significantly outperformed traditional models in predicting drug response using NT-proBNP as a HF biomarker across five medication categories (best RMSE of 0.0043). Four distinct patient subgroups were identified with differential characteristics and outcomes, demonstrating superior predictive capabilities over existing HF subtypes (best mean RMSE of 0.0032). DISCUSSION: These results highlight the power of graph-based modeling of EHR in improving HF treatment strategies. The stratification of patients sheds light on particular patient segments that could benefit more significantly from tailored response predictions. CONCLUSIONS: Longitudinal EHR data have the potential to enhance personalized prognostic predictions through the application of graph-based AI techniques.

Using Artificial Intelligence to Learn Optimal Regimen Plan for Alzheimer's Disease.

Background: Alzheimer's Disease (AD) is a progressive neurological disorder with no specific curative medications. While only a few medications are approved by FDA (i.e., donepezil, galantamine, rivastigmine, and memantine) to relieve symptoms (e.g., cognitive decline), sophisticated clinical skills are crucial to optimize the appropriate regimens given the multiple coexisting comorbidities in this patient population. Objective: Here, we propose a study to leverage reinforcement learning (RL) to learn the clinicians' decisions for AD patients based on the longitude records from Electronic Health Records (EHR). Methods: In this study, we withdraw 1,736 patients fulfilling our criteria, from the Alzheimer's Disease Neuroimaging Initiative(ADNI) database. We focused on the two most frequent concomitant diseases, depression, and hypertension, thus resulting in five main cohorts, 1) whole data, 2) AD-only, 3) AD-hypertension, 4) AD-depression, and 5) AD-hypertension-depression. We modeled the treatment learning into an RL problem by defining the three factors (i.e., states, action, and reward) in RL in multiple strategies, where a regression model and a decision tree are developed to generate states, six main medications extracted (i.e., no drugs, cholinesterase inhibitors, memantine, hypertension drugs, a combination of cholinesterase inhibitors and memantine, and supplements or other drugs) are for action, and Mini-Mental State Exam (MMSE) scores are for reward. Results: Given the proper dataset, the RL model can generate an optimal policy (regimen plan) that outperforms the clinician's treatment regimen. With the smallest data samples, the optimal-policy (i.e., policy iteration and Q-learning) gained a lesser reward than the clinician's policy (mean -2.68 and -2.76 vs . -2.66, respectively), but it gained more reward once the data size increased (mean -3.56 and -2.48 vs . -3.57, respectively). Conclusions: Our results highlight the potential of using RL to generate the optimal treatment based on the patients' longitude records. Our work can lead the path toward the development of RL-based decision support systems which could facilitate the daily practice to manage Alzheimer's disease with comorbidities.

Association of atrial fibrillation burden with in-hospital outcomes in patients with Takotsubo cardiomyopathy.

BACKGROUND: The effects of atrial fibrillation (AF) and its burden on in-hospital mortality in patients with Takotsubo cardiomyopathy (TCM) are unclear. Here, we examined the effect of AF and paroxysmal AF on in-hospital outcomes in patients with TCM. METHODS: We used ICD-10 codes to retrospectively identify patients with a primary diagnosis of TCM in the National Inpatient Sample database 2016-2018. We compared in-hospital outcomes in TCM patients with and without AF before and after propensity score matching. The effect of AF burden on outcomes was assessed in patients with paroxysmal AF and no AF. RESULTS: Of the 4,733 patients with a primary diagnosis of TCM, 650 (13.7%) had AF, and 4,083 (86.3%) did not. Of TCM patients with AF, 368 (56.6%) had paroxysmal AF. In-hospital mortality was higher in patients with AF before (3.4% vs 1.2%, P <  0.001) and after propensity matching (3.4% vs 1.7%, P = 0.021) but did not differ between the paroxysmal AF and the no AF groups (P = 0.205). In the matched cohorts, both AF and paroxysmal AF groups were associated with a higher rate of cardiogenic shock (AF, P < 0.001; paroxysmal AF, P < 0.001), ventricular arrhythmia (AF, P = 0.002; paroxysmal AF, P = 0.02), acute kidney injury (AF, P = 0.007; paroxysmal AF, P = 0.008), and acute respiratory failure (AF, P < 0.001; paroxysmal AF, P < 0.001) compared with the no AF group. CONCLUSIONS: Although AF was associated with increased in-hospital mortality, paroxysmal AF did not affect in-hospital mortality, suggesting a higher AF burden is associated with worse clinical outcome in patients with TCM.

Predicting Physiological Response in Heart Failure Management: A Graph Representation Learning Approach using Electronic Health Records.

Heart failure management is challenging due to the complex and heterogenous nature of its pathophysiology which makes the conventional treatments based on the "one size fits all" ideology not suitable. Coupling the longitudinal medical data with novel deep learning and network-based analytics will enable identifying the distinct patient phenotypic characteristics to help individualize the treatment regimen through the accurate prediction of the physiological response. In this study, we develop a graph representation learning framework that integrates the heterogeneous clinical events in the electronic health records (EHR) as graph format data, in which the patient-specific patterns and features are naturally infused for personalized predictions of lab test response. The framework includes a novel Graph Transformer Network that is equipped with a self-attention mechanism to model the underlying spatial interdependencies among the clinical events characterizing the cardiac physiological interactions in the heart failure treatment and a graph neural network (GNN) layer to incorporate the explicit temporality of each clinical event, that would help summarize the therapeutic effects induced on the physiological variables, and subsequently on the patient's health status as the heart failure condition progresses over time. We introduce a global attention mask that is computed based on event co-occurrences and is aggregated across all patient records to enhance the guidance of neighbor selection in graph representation learning. We test the feasibility of our model through detailed quantitative and qualitative evaluations on observational EHR data.

Artificial Intelligence-based Efficacy Prediction of Phase 3 Clinical Trial for Repurposing Heart Failure Therapies.

Introduction: Drug repurposing involves finding new therapeutic uses for already approved drugs, which can save costs as their pharmacokinetics and pharmacodynamics are already known. Predicting efficacy based on clinical endpoints is valuable for designing phase 3 trials and making Go/No-Go decisions, given the potential for confounding effects in phase 2. Objectives: This study aims to predict the efficacy of the repurposed Heart Failure (HF) drugs for the Phase 3 Clinical Trial. Methods: Our study presents a comprehensive framework for predicting drug efficacy in phase 3 trials, which combines drug-target prediction using biomedical knowledgebases with statistical analysis of real-world data. We developed a novel drug-target prediction model that uses low-dimensional representations of drug chemical structures and gene sequences, and biomedical knowledgebase. Furthermore, we conducted statistical analyses of electronic health records to assess the effectiveness of repurposed drugs in relation to clinical measurements (e.g., NT-proBNP). Results: We identified 24 repurposed drugs (9 with a positive effect and 15 with a non-positive) for heart failure from 266 phase 3 clinical trials. We used 25 genes related to heart failure for drug-target prediction, as well as electronic health records (EHR) from the Mayo Clinic for screening, which contained over 58,000 heart failure patients treated with various drugs and categorized by heart failure subtypes. Our proposed drug-target predictive model performed exceptionally well in all seven tests in the BETA benchmark compared to the six cutting-edge baseline methods (i.e., best performed in 266 out of 404 tasks). For the overall prediction of the 24 drugs, our model achieved an AUCROC of 82.59% and PRAUC (average precision) of 73.39%. Conclusion: The study demonstrated exceptional results in predicting the efficacy of repurposed drugs for phase 3 clinical trials, highlighting the potential of this method to facilitate computational drug repurposing.

Using artificial intelligence to learn optimal regimen plan for Alzheimer's disease.

BACKGROUND: Alzheimer's disease (AD) is a progressive neurological disorder with no specific curative medications. Sophisticated clinical skills are crucial to optimize treatment regimens given the multiple coexisting comorbidities in the patient population. OBJECTIVE: Here, we propose a study to leverage reinforcement learning (RL) to learn the clinicians' decisions for AD patients based on the longitude data from electronic health records. METHODS: In this study, we selected 1736 patients from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database. We focused on the two most frequent concomitant diseases-depression, and hypertension, thus creating 5 data cohorts (ie, Whole Data, AD, AD-Hypertension, AD-Depression, and AD-Depression-Hypertension). We modeled the treatment learning into an RL problem by defining states, actions, and rewards. We built a regression model and decision tree to generate multiple states, used six combinations of medications (ie, cholinesterase inhibitors, memantine, memantine-cholinesterase inhibitors, hypertension drugs, supplements, or no drugs) as actions, and Mini-Mental State Exam (MMSE) scores as rewards. RESULTS: Given the proper dataset, the RL model can generate an optimal policy (regimen plan) that outperforms the clinician's treatment regimen. Optimal policies (ie, policy iteration and Q-learning) had lower rewards than the clinician's policy (mean -3.03 and -2.93 vs. -2.93, respectively) for smaller datasets but had higher rewards for larger datasets (mean -4.68 and -2.82 vs. -4.57, respectively). CONCLUSIONS: Our results highlight the potential of using RL to generate the optimal treatment based on the patients' longitude records. Our work can lead the path towards developing RL-based decision support systems that could help manage AD with comorbidities.