Mortality and Morbidity in HFrEF, HFmrEF, and HFpEF Patients with Diabetes in the Middle East

Long-term outcomes of unselected patients undergoing coronary angiography according to the presence or absence of type II diabetes mellitus

OBJECTIVE

The study investigates long-term outcomes of unselected inpatients undergoing invasive coronary angiography (CA) with and without diabetes mellitus type II (T2DM).

BACKGROUND

Due to continual shifts in demographics and advancements in treating cardiovascular disease, there has been a notable evolution in the types of patients undergoing CA over the past decades. Comprehensive data on the extended outcomes of CA patients, both with and without concurrent T2DM, remains scarce.

METHODS

Consecutive inpatients undergoing invasive CA from 2016 to 2022 were included at one institution. The prognosis of T2DM in patients undergoing CA was investigated with regard to the risk rehospitalization for heart failure (HF), acute myocardial infarction (AMI) and coronary revascularization at 36 months of follow-up. Statistical analyses included Kaplan-Meier uni- and multivariable Cox proportional regression analyses.

RESULTS

From 2016 to 2022, 7150 patients undergoing CA were included with a prevalence of T2DM of 31.2 %. Compared to non-diabetics, patients with T2DM had a higher prevalence (78.0 % vs. 64.3 %; p = 0.001) and extent (3-vessel disease: 36.9 % vs. 23.8 %; p = 0.001) of coronary artery disease (CAD). At 36 months, patients with T2DM had a higher risk rehospitalization for worsening HF (29.0 % vs. 18.2 %; p = 0.001), AMI (9.9 % vs. 6.6 %; p = 0.001), alongside with a higher need for coronary revascularization (10.7 % vs. 7.2 %; p = 0.001) compared to patients without. Even after multivariable adjustment, the risk of rehospitalization for HF (HR = 1.229; 95 % CI 1.099-1.374; p = 0.001), AMI (HR = 1.270; 95 % CI 1.052-1.534; p = 0.013) and coronary revascularization (HR = 1.457; 95 % CI 1.213-1.751; p = 0.001) was higher in patients with T2DM. Especially in patients with left ventricular ejection fraction (LVEF) ≥ 35 %, T2DM was associated with a higher risk of AMI- (HR = 1.395, 95 % CI: 1.104 - 1.763, p = 0.005) and PCI-related rehospitalization (HR = 1.442, 95 % CI: 1.185 - 1.775, p = 0.001).

CONCLUSION

In unselected patients undergoing CA, T2DM represents an independent predictor of HF-related rehospitalization, AMI- and for PCI- at 36 months.

Effect of astragalus injection on left ventricular remodeling in HFmrEF: a systematic review and meta-analysis

Objectives

The aim of this meta-analysis is to evaluate the effect of astragalus injection (AI) on left ventricular remodeling (LVR) in patients with heart failure with mildly reduced ejection fraction (HFmrEF).

Methods

The randomized controlled trials (RCTs) of AI in treating HFmrEF were retrieved from 8 major English and Chinese electronic databases, up until November 30, 2023. To evaluate the methodological quality of the included studies, the Cochrane bias risk tool and the Modified Jadad Scale were employed. Stata 17.0 software was utilized for statistical analysis, sensitivity analysis, and assessment of publication bias.

Results

Ten RCTs with 995 patients (562 males and 433 females) were identified. Meta-analysis indicated that compared to conventional treatment (CT), AI significantly improved LVR, specifically increasing left ventricular ejection fraction (LVEF, MD = 4.56, 95% CI: 3.68-5.44, p < 0.00001), decreasing left ventricular end-diastolic volume (LVEDV, MD = -7.89, 95% CI: -11.13 to -4.64, p < 0.00001), left ventricular end-diastolic diameter (LVEDD, MD = -4.18, 95% CI: -5.79 to -2.56, p < 0.00001), left ventricular end-systolic volume (LVESV, MD = -8.11, 95% CI: -11.79 to -4.43, p < 0.00001), and left ventricular end-systolic diameter (LVESD, MD = -3.42, 95% CI: -4.90 to -1.93, p < 0.00001). AI also improved clinical efficacy (RR = 4.62, 95% CI: 3.11-6.88, p < 0.00001), reduced N-terminal pro-brain natriuretic peptide (NT-pro BNP, MD = -27.94, 95% CI: -43.3 to -12.36) level, without increasing the incidence of adverse reactions (RR = 1.60, 95% CI: 0.59-4.29, p = 0.35). Sensitivity analysis confirmed the reliability of the merged results, and Begg's and Egger's tests showed no significant publication bias.

Conclusion

The systematic review and meta-analysis revealed that combining AI with CT improves LVR without increasing adverse events in HFmrEF patients. However, caution is needed in interpreting the results due to limited evidence. Future high-quality RCTs are needed to support these conclusions.

Systematic Review Registration

https://www.crd.york.ac.uk/prospero/, PROSPERO [CRD42022347248].

Circadian assessment of heart failure using explainable deep learning and novel multi-parameter polar images

BACKGROUND AND OBJECTIVE

Heart failure (HF) is a multi-faceted and life-threatening syndrome that affects more than 64.3 million people worldwide. Current gold-standard screening technique, echocardiography, neglects cardiovascular information regulated by the circadian rhythm and does not incorporate knowledge from patient profiles. In this study, we propose a novel multi-parameter approach to assess heart failure using heart rate variability (HRV) and patient clinical information.

METHODS

In this approach, features from 24-hour HRV and clinical information were combined as a single polar image and fed to a 2D deep learning model to infer the HF condition. The edges of the polar image correspond to the timely variation of different features, each of which carries information on the function of the heart, and internal illustrates color-coded patient clinical information.

RESULTS

Under a leave-one-subject-out cross-validation scheme and using 7,575 polar images from a multi-center cohort (American and Greek) of 303 coronary artery disease patients (median age: 58 years [50-65], median body mass index (BMI): 27.28 kg/m2 [24.91-29.41]), the model yielded mean values for the area under the receiver operating characteristics curve (AUC), sensitivity, specificity, normalized Matthews correlation coefficient (NMCC), and accuracy of 0.883, 90.68%, 95.19%, 0.93, and 92.62%, respectively. Moreover, interpretation of the model showed proper attention to key hourly intervals and clinical information for each HF stage.

CONCLUSIONS

The proposed approach could be a powerful early HF screening tool and a supplemental circadian enhancement to echocardiography which sets the basis for next-generation personalized healthcare.

Prognostic Role of Ultrasound Diagnostic Methods in Patients with Acute Decompensated Heart Failure

Objectives

To evaluate the prognostic value (total mortality + repeated hospitalization for heart failure (HF)) of ultrasound diagnostic methods in patients with acute decompensated HF (ADHF).

Methods

The subjects were patients with chronic HF, who were hospitalized for ADHF. Using ultrasound methods-lung ultrasound, ultrasound assessment of hepatic venous congestion as per the venous excess ultrasound (VExUS) protocol, and indirect elastometry-we assessed the number of B-lines, hepatic venous congestion, and liver density of the patients. Clinical outcomes were assessed using a structured telephone survey method at 1, 3, 6, and 12 months after discharge. Combined overall mortality and readmission rates associated with HF were assessed. Threshold values for different methods for detecting congestion were set as follows: the number of B-lines in ultrasound data > 5; liver density > 6.2 kPa.

Results

The subjects were 207 patients (54.1% male; mean age = 70.7 ± 12.8 years). A total of 63 (30.4%) endpoints and 23 (11.1%) deaths were detected within 364 days (IQR = 197-365). Liver density > 6.2 kPa had a hazard ratio (HR) of 1.9 (95% CI: 1.0-3.3; p = 0.029). Hepatic venous congestion (VExUS protocol) had HR of 2.8 (95% CI: 1.3-5.7; p = 0.004). There was a significant increase in the risk of overall prognostic value in the presence of congestion, identified by liver fibroelastometry + lung ultrasound (HR = 10.5, 95% CI: 2.3-46.2; p = 0.002). The ultrasound assessment of hepatic venous congestion (VExUS + lung ultrasound protocol) yielded HR of 16.7 (95% CI: 3.9-70.7; p < 0.001). For all three methods combined, the overall HR was 40.1 (95% CI: 6.6-243.1; p < 0.001).

Conclusions

A combination of ultrasound diagnostic methods that include the number of B-lines, presence of hepatic venous congestion according to the VExUS protocol, and liver density according to indirect elastometry at discharge may have an independent prognostic value for patients with ADHF.

Prognostic Implications of Type 2 Diabetes Mellitus in Heart Failure with Mildly Reduced Ejection Fraction

BACKGROUND

Data regarding the characterization and outcomes of diabetics with heart failure with a mildly reduced ejection fraction (HFmrEF) is scarce. This study investigates the prevalence and prognostic impact of type 2 diabetes in patients with HFmrEF.

METHODS

Consecutive patients with HFmrEF (i.e., left ventricular ejection fraction 41-49% and signs and/or symptoms of HF) were retrospectively included at one institution from 2016 to 2022. Patients with type 2 diabetes (dia-betics) were compared to patients without (i.e., non-diabetics). The primary endpoint was all-cause mortality at 30 months. Statistical analyses included Kaplan-Meier, multivariable Cox regression analyses and propensity score matching.

RESULTS

A total of 2169 patients with HFmrEF were included. The overall prevalence of type 2 diabetes was 36%. Diabetics had an increased risk of 30-months all-cause mortality (35.8% vs. 28.6%; HR = 1.273; 95% CI 1.092-1.483; p = 0.002), which was confirmed after multivariable adjustment (HR = 1.234; 95% CI 1.030-1.479; p = 0.022) and propensity score matching (HR = 1.265; 95% CI 1.018-1.572; p = 0.034). Diabetics had a higher risk of HF-related rehospitalization (17.8% vs. 10.7%; HR = 1.714; 95% CI 1.355-2.169; p = 0.001). Finally, the risk of all-cause mortality was increased in diabetics treated with insulin (40.7% vs. 33.1%; log-rank p = 0.029), whereas other anti-diabetic pharmacotherapies had no prognostic impact in HFmrEF.

CONCLUSIONS

Type 2 diabetes is common and independently associated with adverse long-term prognosis in patients with HFmrEF.

Risk factors of readmission and the impact of outpatient management in heart failure patients: A national study in Japan

AIMS

Heart failure is a significant disease, and its high readmission rate is a big concern. We must identify readmission risk factors and optimize outpatient management to prevent them. This study aims to investigate the readmission risk factors, including outpatient management represented by the number of outpatient visits, and to identify the factors related to frequent outpatient visits.

METHODS AND RESULTS

We used the diagnosis-procedure-combination database between April 2016 and March 2022. Based on the number of outpatient visits within 60 days after discharge, we categorized patients into <1 visits/month, (1<, ≦2) visits/month, and <2 visits/month and observed the occurrence of 60 days readmission. We performed multiple logistic regression analyses to reveal the readmission risk factors and the association between the number of outpatient visits and readmission. As a subgroup analysis, we conducted the same research in the low- and high-readmission risk groups. We compared medical contents between (1<, ≦2) visits/month and <2 visits/month. We analysed 101 239 patients and identified the following factors as a risk of readmission: older age (P < 0.001), female (P = 0.009), longer length-of-hospital-stay (P < 0.001), artificial ventilator (P < 0.001), tolvaptan (P < 0.001), top 50% dosage of loop diuretics (P = 0.036), bottom 50% dosage of class III antiarrhythmic agents (P < 0.001), hypertension (P = 0.005), atrial fibrillation (P < 0.001), dilated cardiomyopathy (P < 0.001), valvular disease (P = 0.021), myocardial infarction (P < 0.001), diabetes (P < 0.001), and renal disease (P < 0.001). We revealed that the risk of readmission increases in <2 visits/month compared to (1<, ≦2) visits/month (P < 0.001), whereas the risk of readmission decreases in ≦1 visits/month compared with (1<, ≦2) visits/month (P < 0.001). In the subgroup analysis, we found the possibility that some risk factors are specific to the subgroup. We identified that the following factors were related to frequent outpatient visits: older age (P < 0.001), home medical care (P = 0.007), tolvaptan (P < 0.001), top 50% dosage of loop diuretics (P < 0.001), diabetes (P < 0.001), renal disease (P = 0.009), 0-2 weeks follow-up (P < 0.001), 2-4 weeks follow-up (P < 0.001), cardiac rehabilitation (P < 0.001), and echocardiography (P < 0.001).

CONCLUSIONS

This study comprehensively identified risk factors for readmission and found outpatient visit is personalized by readmission risk. There is still room to optimize outpatient management. We suggest optimizing outpatient management according to our identified characteristics.

Regional expert opinion: Management of heart failure with preserved ejection fraction in the Middle East, North Africa and Turkey

Although epidemiological data on heart failure (HF) with preserved ejection fraction (HFpEF) are scarce in the Middle East, North Africa and Turkey (MENAT) region, Lancet Global Burden of Disease estimated the prevalence of HF in the MENAT region in 2019 to be 0.78%, versus 0.71% globally. There is also a high incidence of HFpEF risk factors and co-morbidities in the region, including coronary artery disease, diabetes, obesity, hypertension, anaemia and chronic kidney disease. For instance, 14.5-16.2% of adults in the region reportedly have diabetes, versus 7.0% in Europe. Together with increasing life expectancy, this may contribute towards a higher burden of HFpEF in the region than currently reported. This paper aims to describe the epidemiology and burden of HFpEF in the MENAT region, including unique risk factors and co-morbidities. It highlights challenges with diagnosing HFpEF, such as the prioritization of HF with reduced ejection fraction (HFrEF), the specific profile of HFpEF patients in the region and barriers to effective management associated with the healthcare system. Guidance is given on the diagnosis, prevention and management of HFpEF, including the emerging role of sodium-glucose co-transporter-2 inhibitors. Given the high burden of HFpEF coupled with the fact that its prevalence is likely to be underestimated, healthcare professionals need to be alert to its signs and symptoms and to manage patients accordingly. Historically, HFpEF treatments have focused on managing co-morbidities and symptoms, but new agents are now available with proven effects on outcomes in patients with HFpEF.

Risk Factors with 30-Day Readmission and the Impact of Length of Hospital Stay on It in Patients with Heart Failure: A Retrospective Observational Study Using a Japanese National Database

Heart failure is a major disease, and its 30-day readmission (readmission within 30-day after discharge) negatively impacts patients and society. Thus, we need to stratify the risk and prevent readmission. We aimed to investigate risk factors associated with 30-day readmission and examine the impact of length of hospital stay (LOS) on 30-day readmission. Using the Diagnosis-Procedure-Combination database from April 2018 to March 2021, we conducted multiple logistic regression to investigate risk factors with 30-day readmission. Also, we conducted subgroup analysis in the short LOS group. To examine the association between LOS and 30-day readmission, we performed propensity score matching between the short and middle LOS groups. As a result, we categorized 10,283 patients and 169,842 patients into the readmission group and the no-readmission group. We identified the following factors as the risk of readmission: short LOS, female, smoking, older age, lower body mass index, lower barthel index, artificial ventilator, beta-blockers, thiazides, tolvaptan, loop diuretics, carperitides, class Ⅲ antiarrhythmic agents, myocardial infarction, diabetes, renal disease, atrial fibrillation, dilated cardiomyopathy, and discharge to home. As a subgroup analysis in the short LOS group, we revealed that the short LOS group risk factors differed from overall. After propensity score matching in the short LOS group and middle LOS group, 37,199 pairs were matched, and we revealed that shorter LOS increases the risk of readmission. These results demonstrated that shortened LOS increases 30-day readmission, and risk factors are unique to each LOS. We suggest stratifying the readmission risk and being careful with early discharge.

Deep Learning Predicts Heart Failure With Preserved, Mid-Range, and Reduced Left Ventricular Ejection Fraction From Patient Clinical Profiles

Background: Left ventricular ejection fraction (LVEF) is the gold standard for evaluating heart failure (HF) in coronary artery disease (CAD) patients. It is an essential metric in categorizing HF patients as preserved (HFpEF), mid-range (HFmEF), and reduced (HFrEF) ejection fraction but differs, depending on whether the ASE/EACVI or ESC guidelines are used to classify HF. Objectives: We sought to investigate the effectiveness of using deep learning as an automated tool to predict LVEF from patient clinical profiles using regression and classification trained models. We further investigate the effect of utilizing other LVEF-based thresholds to examine the discrimination ability of deep learning between HF categories grouped with narrower ranges. Methods: Data from 303 CAD patients were obtained from American and Greek patient databases and categorized based on the American Society of Echocardiography and the European Association of Cardiovascular Imaging (ASE/EACVI) guidelines into HFpEF (EF > 55%), HFmEF (50% ≤ EF ≤ 55%), and HFrEF (EF < 50%). Clinical profiles included 13 demographical and clinical markers grouped as cardiovascular risk factors, medication, and history. The most significant and important markers were determined using linear regression fitting and Chi-squared test combined with a novel dimensionality reduction algorithm based on arc radial visualization (ArcViz). Two deep learning-based models were then developed and trained using convolutional neural networks (CNN) to estimate LVEF levels from the clinical information and for classification into one of three LVEF-based HF categories. Results: A total of seven clinical markers were found important for discriminating between the three HF categories. Using statistical analysis, diabetes, diuretics medication, and prior myocardial infarction were found statistically significant (p < 0.001). Furthermore, age, body mass index (BMI), anti-arrhythmics medication, and previous ventricular tachycardia were found important after projections on the ArcViz convex hull with an average nearest centroid (NC) accuracy of 94%. The regression model estimated LVEF levels successfully with an overall accuracy of 90%, average root mean square error (RMSE) of 4.13, and correlation coefficient of 0.85. A significant improvement was then obtained with the classification model, which predicted HF categories with an accuracy ≥93%, sensitivity ≥89%, 1-specificity <5%, and average area under the receiver operating characteristics curve (AUROC) of 0.98. Conclusions: Our study suggests the potential of implementing deep learning-based models clinically to ensure faster, yet accurate, automatic prediction of HF based on the ASE/EACVI LVEF guidelines with only clinical profiles and corresponding information as input to the models. Invasive, expensive, and time-consuming clinical testing could thus be avoided, enabling reduced stress in patients and simpler triage for further intervention.

Emerging Role of Adipocyte Dysfunction in Inducing Heart Failure Among Obese Patients With Prediabetes and Known Diabetes Mellitus

Adipose tissue dysfunction is a predictor for cardiovascular (CV) events and heart failure (HF) in patient population with obesity, metabolic syndrome, and known type 2 diabetes mellitus. Previous preclinical and clinical studies have yielded controversial findings regarding the role of accumulation of adipose tissue various types in CV risk and HF-related clinical outcomes in obese patients. There is evidence for direct impact of infiltration of epicardial adipocytes into the underlying myocardium to induce adverse cardiac remodeling and mediate HF development and atrial fibrillation. Additionally, perivascular adipocytes accumulation is responsible for release of proinflammatory adipocytokines (adiponectin, leptin, resistin), stimulation of oxidative stress, macrophage phenotype switching, and worsening vascular reparation, which all lead to microvascular inflammation, endothelial dysfunction, atherosclerosis acceleration, and finally to increase in CV mortality. However, systemic effects of white and brown adipose tissue can be different, and adipogenesis including browning of adipose tissue and deficiency of anti-inflammatory adipocytokines (visfatin, omentin, zinc-α2-glycoprotein, glypican-4) was frequently associated with adipose triglyceride lipase augmentation, altered glucose homeostasis, resistance to insulin of skeletal muscles, increased cardiomyocyte apoptosis, lowered survival, and weak function of progenitor endothelial cells, which could significantly influence on HF development, as well as end-organ fibrosis and multiple comorbidities. The exact underlying mechanisms for these effects are not fully understood, while they are essential to help develop improved treatment strategies. The aim of the review is to summarize the evidence showing that adipocyte dysfunction may induce the onset of HF and support advance of HF through different biological mechanisms involving inflammation, pericardial, and perivascular adipose tissue accumulation, adverse and electrical cardiac remodeling, and skeletal muscle dysfunction. The unbalancing effects of natriuretic peptides, neprilysin, and components of renin–angiotensin system, as exacerbating cause of altered adipocytokine signaling on myocardium and vasculature, in obesity patients at high risk of HF are disputed. The profile of proinflammatory and anti-inflammatory adipocytokines as promising biomarker for HF risk stratification is discussed in the review.