A Multistep Approach to Deal With Advanced Heart Failure: A Case Report on the Positive Effect of Cardiac Contractility Modulation Therapy on Pulmonary Pressure Measured by CardioMEMS

The favourable alliance between CardioMEMS and levosimendan in patients with advanced heart failure

AIMS

We report the results of a real-world study based on heart failure (HF) patients' continuous remote monitoring strategy using the CardioMEMS system to assess the impact of this device on healthcare outcomes, costs, and patients' management and quality of life.

METHODS AND RESULTS

We enrolled seven patients (69.00 ± 4.88 years; 71.43% men) with HF, implanted with CardioMEMS, and daily remote monitored to optimize both tailored adjustments of home therapy and/or hospital infusions of levosimendan. We recorded clinical, pharmacological, biochemical, and echocardiographic parameters and data on hospitalizations, emergency room access, visits, and costs. Following the implantation of CardioMEMS, we observed a 50% reduction in the total number of hospitalizations and a 68.7% reduction in the number of days in the hospital. Accordingly, improved patient quality of life was recorded with EQ-5D (pre 58.57 ± 10.29 vs. 1 year post 84.29 ± 19.02, P = 0.008). Echocardiographic data show a statistically significant improvement in both systolic pulmonary artery pressure (47.86 ± 8.67 vs. 35.14 ± 9.34, P = 0.022) and E/e' (19.33 ± 5.04 vs. 12.58 ± 3.53, P = 0.023). The Quantikine® HS High-Sensitivity Kit determined elevated interleukin-6 values at enrolment in all patients, with a statistically significant reduction after 6 months (P = 0.0211). From an economic point of view, the net savings, including the cost of CardioMEMS, were on average €1580 per patient during the entire period of observation, while the analysis performed 12 months after the implant vs. 12 months before showed a net saving of €860 per patient. The ad hoc analysis performed on the levosimendan infusions resulted in 315 days of hospital avoidance and a saving of €205 158 for the seven patients enrolled during the observation period.

CONCLUSIONS

This innovative strategy prevents unplanned access to the hospital and contributes to the efficient use of healthcare facilities, human resources, and costs.

The Ketogenic Effect of SGLT-2 Inhibitors-Beneficial or Harmful?

Sodium-glucose cotransporter-2 (SGLT-2) inhibitors, also called gliflozins or flozins, are a class of drugs that have been increasingly used in the management of type 2 diabetes mellitus (T2DM) due to their glucose-lowering, cardiovascular (CV), and renal positive effects. However, recent studies suggest that SGLT-2 inhibitors might also have a ketogenic effect, increasing ketone body production. While this can be beneficial for some patients, it may also result in several potential unfavorable effects, such as decreased bone mineral density, infections, and ketoacidosis, among others. Due to the intricate and multifaceted impact caused by SGLT-2 inhibitors, this initially anti-diabetic class of medications has been effectively used to treat both patients with chronic kidney disease (CKD) and those with heart failure (HF). Additionally, their therapeutic potential appears to extend beyond the currently investigated conditions. The objective of this review article is to present a thorough summary of the latest research on the mechanism of action of SGLT-2 inhibitors, their ketogenesis, and their potential synergy with the ketogenic diet for managing diabetes. The article particularly discusses the benefits and risks of combining SGLT-2 inhibitors with the ketogenic diet and their clinical applications and compares them with other anti-diabetic agents in terms of ketogenic effects. It also explores future directions regarding the ketogenic effects of SGLT-2 inhibitors.

Acute heart failure: mechanisms and pre-clinical models-a Scientific Statement of the ESC Working Group on Myocardial Function

While chronic heart failure (CHF) treatment has considerably improved patient prognosis and survival, the therapeutic management of acute heart failure (AHF) has remained virtually unchanged in the last decades. This is partly due to the scarcity of pre-clinical models for the pathophysiological assessment and, consequently, the limited knowledge of molecular mechanisms involved in the different AHF phenotypes. This scientific statement outlines the different trajectories from acute to CHF originating from the interaction between aetiology, genetic and environmental factors, and comorbidities. Furthermore, we discuss the potential molecular targets capable of unveiling new therapeutic perspectives to improve the outcome of the acute phase and counteracting the evolution towards CHF.

Importance of Tissue Doppler Evaluation in Dilated Cardiomyopathy: The Value of Diastolic Filling Pattern as a Prognostic Predictor

(1) Background: The presence of restrictive left ventricular diastolic filling pattern (LVDFP) is associated with an unfavorable prognosis in many cardiac diseases, but few data are available on the prognostic implications of this pattern in patients with dilated cardiomyopathy (DCM). We aimed to establish the main prognostic predictors at the 1- and 5-year follow-ups in DCM patients and the value of restrictive LVDFP in increasing morbidity and mortality. (2) Methods: A prospective study of 143 patients with DCM divided in non-restrictive LVDFP group (95 patients) and restrictive group (47 patients). The patients were evaluated at a 5-year follow-up through an in-patient visit during the pre-pandemic period and hybrid methods (face-to-face, teleconsultation and home monitoring with a telemedicine application) during the pandemic period. Statistical analysis compared the two groups in terms of NYHA class, quality of life, hospitalizations/emergency department (ED) visits due to HF exacerbation and total mortality. (3) Results: The mortality rate in the restrictive group was markedly higher than that in the non-restrictive group at 1 year (17.02% vs. 10.59%, respectively, p < 0.05) and at 5 years (68.08% vs. 50.53%, p < 0.05). In the restrictive group, hospitalizations/ED visits due to HF decompensations at 1 year were significantly higher (85.11% vs. 57.89%, p < 0.05), with hospitalizations for ventricular arrhythmia being almost three times higher (21.28% vs. 7.37%, respectively, p < 0.05). The percentage of patients with a favorable evolution (in terms of NYHA class and quality of life) at the 1- and 5-year follow-ups were higher in the non-restrictive LVDFP group. The main prognostic predictors in patients with DCM at the 1-year follow-up were: restrictive LVDFP, age > 75 years, markedly dilated LV, comorbidities (DM, COPD), 2nd-degree mitral regurgitation and severe pulmonary hypertension (p < 0.05). (4) Conclusions: At the 1- and 5-year follow-ups, the presence of the restrictive LVDFP in DCM patients was independently associated with a poor prognosis, being the best clinical predictor for unfavorable evolution, after adjustment for other well-established predictive parameters in DCM patients.

Artificial intelligence in cardiovascular prevention: new ways will open new doors

Prevention and effective treatment of cardiovascular disease are progressive issues that grow in tandem with the average age of the world population. Over recent decades, the potential role of artificial intelligence in cardiovascular medicine has been increasingly recognized because of the incredible amount of real-world data (RWD) regarding patient health status and healthcare delivery that can be collated from a variety of sources wherein patient information is routinely collected, including patient registries, clinical case reports, reimbursement claims and billing reports, medical devices, and electronic health records. Like any other (health) data, RWD can be analysed in accordance with high-quality research methods, and its analysis can deliver valuable patient-centric insights complementing the information obtained from conventional clinical trials. Artificial intelligence application on RWD has the potential to detect a patient's health trajectory leading to personalized medicine and tailored treatment. This article reviews the benefits of artificial intelligence in cardiovascular prevention and management, focusing on diagnostic and therapeutic improvements without neglecting the limitations of this new scientific approach.

Artificial Intelligence in Hypertension Management: An Ace up Your Sleeve

Arterial hypertension (AH) is a progressive issue that grows in importance with the increased average age of the world population. The potential role of artificial intelligence (AI) in its prevention and treatment is firmly recognized. Indeed, AI application allows personalized medicine and tailored treatment for each patient. Specifically, this article reviews the benefits of AI in AH management, pointing out diagnostic and therapeutic improvements without ignoring the limitations of this innovative scientific approach. Consequently, we conducted a detailed search on AI applications in AH: the articles (quantitative and qualitative) reviewed in this paper were obtained by searching journal databases such as PubMed and subject-specific professional websites, including Google Scholar. The search terms included artificial intelligence, artificial neural network, deep learning, machine learning, big data, arterial hypertension, blood pressure, blood pressure measurement, cardiovascular disease, and personalized medicine. Specifically, AI-based systems could help continuously monitor BP using wearable technologies; in particular, BP can be estimated from a photoplethysmograph (PPG) signal obtained from a smartphone or a smartwatch using DL. Furthermore, thanks to ML algorithms, it is possible to identify new hypertension genes for the early diagnosis of AH and the prevention of complications. Moreover, integrating AI with omics-based technologies will lead to the definition of the trajectory of the hypertensive patient and the use of the most appropriate drug. However, AI is not free from technical issues and biases, such as over/underfitting, the "black-box" nature of many ML algorithms, and patient data privacy. In conclusion, AI-based systems will change clinical practice for AH by identifying patient trajectories for new, personalized care plans and predicting patients' risks and necessary therapy adjustments due to changes in disease progression and/or therapy response.

The Prescription Pattern of Heart Failure Medications in Reduced, Mildly Reduced, and Preserved Ejection Fractions

A substantial proportion of patients with heart failure (HF) receive suboptimal guideline-recommended therapy. We aimed to identify the factors leading to suboptimal drug prescription in HF and according to HF phenotypes. This retrospective, single-centre observational cohort study included 702 patients admitted for worsening HF (HF with a reduced ejection fraction [HFrEF], n = 198; HF with a mildly reduced EF [HFmrEF], n = 122; and HF with a preserved EF [HFpEF], n = 382). A score based on the prescription and dose percentage of ACEi/ARBs, β-blockers, and MRAs at discharge was calculated (a total score ranging from zero to six). Approximately 70% of patients received ACEi/ARBs/ARNi, 80% of patients received β-blockers, and 20% received MRAs. The mean HF drug dose was approximately 50% of the recommended dose, irrespective of the HF phenotype. Ischaemic heart disease was associated with a higher prescription score (ranging from 0.4 to 1) compared to no history of ischaemic heart disease, irrespective of the left ventricular EF (LVEF) level. A lower prescription score was associated with older age and male sex in HFrEF and diabetes in HFmrEF. The overall ability of the models to predict the optimal drug dose, including key HF variables (including natriuretic peptides at admission), was poor (R2 < 0.25). A higher prescription score was associated with a lower risk of re-hospitalization and death (HR: 0.75 (0.57−0.97), p = 0.03), irrespective of phenotype (p-interaction = 0.41). Despite very different HF management guidelines according to LVEF, the prescription pattern of HF drugs is poorly related to LVEF and clinical characteristics, thus suggesting that physician-driven factors may be involved in the setting of therapeutic inertia. It may also be related to drug intolerance or clinical stability that is not predicted by the patients’ profiles.

Challenges for Management of Dilated Cardiomyopathy during COVID-19 Pandemic-A Telemedicine Application

BACKGROUND AND OBJECTIVES

The 2019 coronavirus pandemic (COVID-19) represented a significant challenge for the medical community. The first aim of this study was to examine the COVID-19 impact on the follow-up of patients with dilated cardiomyopathy (DCM) and to establish the advantages of multiparametric home monitoring. Also, we tried to establish the main prognostic predictors at 2-years follow-up and the value of LV diastolic filling pattern (LVDFP) in increasing mortality and morbidity.

MATERIALS AND METHODS

We conducted a prospective study of 142 patients with DCM assessed by in-patient visit in the pre-pandemic period and hybrid (face-to-face, online consultation and telemedicine home monitoring with a dedicated application) during the pandemic period. The statistical analysis compared the strategy used in the pre-pandemic with management during the pandemic, in terms of clinical assessment, hospitalizations/emergency room visits due to HF exacerbation and total mortality.

RESULTS

We did not observe significant changes in blood pressure (BP), heart rate (FC), weight and symptoms or an increased rate of adverse drug events between the two periods. We successfully titrated HF medications with close monitoring of HF decompensations, which were similar in number, but were mostly managed at home during the pandemic. There was also no statistically significant difference in emergency room visits due to severe decompensated HF. Mortality in the first and second year of follow-up was between 12.0 and 13%, similar in the pre-pandemic and pandemic periods, but significantly higher in patients with restrictive LVDFP. Clinical improvement or stability after 2 years was more frequent in patients with nonrestrictive LVDFP. The main prognostic predictors at 1 and 2-years follow-up were: the restrictive LVDFP, significantly dilated LV, comorbidities (DM, COPD), older age, associated severe mitral regurgitation and pulmonary hypertension.

CONCLUSIONS

The pandemic restrictions determined a marked decrease of the healthcare use, but no significant change in the clinical status of DCM patients under multiparametric home monitoring. At 2-years follow-up, the presence of the restrictive LVDFP was associated with an increased risk of death and with a worse clinical status in DCM patients.

Clinical Support through Telemedicine in Heart Failure Outpatients during the COVID-19 Pandemic Period: Results of a 12-Months Follow Up

Background: Heart failure (HF) patients are predisposed to recurrences and disease destabilizations, especially during the COVID-19 outbreak period. In this scenario, telemedicine could be a proper way to ensure continuous care. The purpose of the study was to compare two modalities of HF outpatients’ follow up, the traditional in-person visits and telephone consultations, during the COVID-19 pandemic period in Italy. Methods: We conducted an observational study on consecutive HF outpatients. The follow up period was 12 months, starting from the beginning of the COVID-19 Italy lockdown. According to the follow up modality, and after the propensity matching score, patients were divided into two groups: those in G1 (n = 92) were managed with traditional in-person visits and those in G2 (n = 92) were managed with telephone consultation. Major adverse cardiovascular events (MACE) were the primary endpoints. Secondary endpoints were overall mortality, cardiovascular death, cardiovascular hospitalization, and hospitalization due to HF. Results: No significant differences between G1 and G2 have been observed regarding MACE (p = 0.65), cardiovascular death (p = 0.39), overall mortality (p = 0.85), hospitalization due to acute HF (p = 0.07), and cardiovascular hospitalization (p = 0.4). Survival analysis performed by the Kaplan–Meier method also did not show significant differences between G1 and G2. Conclusions: Telephone consultations represented a valid option to manage HF outpatients during COVID-19 pandemic, comparable to traditional in-person visits.