Mebazaa, Alexandre

Abstract Aims Hospitalized patients with heart failure (HF) are a heterogeneous population, with multiple phenotypes proposed. Prior studies have not examined the biological phenotypes of critically ill patients with HF admitted to the contemporary cardiac intensive care unit (CICU). We aimed to leverage unsupervised machine learning to identify previously unknown HF phenotypes in a large and diverse cohort of patients with HF admitted to the CICU. Methods We screened 6008 Mayo Clinic CICU patients with an admission diagnosis of HF from 2007 to 2018 and included those without missing values for common laboratory tests. Consensus k-means clustering was performed based on 10 common admission laboratory values (potassium, chloride, anion gap, blood urea nitrogen, haemoglobin, red blood cell distribution width, mean corpuscular volume, platelet count, white blood cell count and neutrophil-to-lymphocyte ratio). In-hospital mortality was evaluated using logistic regression, and 1 year mortality was evaluated using Cox proportional hazard models after multivariable adjustment. Results Among 4877 CICU patients with HF who had complete admission laboratory data (mean age 69.4 years, 38.4% females), we identified five clusters with divergent demographics, comorbidities, laboratory values, admission diagnoses and use of critical care therapies. We labelled these clusters based on the characteristic laboratory profile of each group: uncomplicated (25.7%), iron-deficient (14.5%), cardiorenal (18.4%), inflamed (22.3%) and hypoperfused (19.2%). In-hospital mortality occurred in 10.7% and differed between the phenotypes: uncomplicated, 2.7% (reference); iron-deficient, 8.1% [adjusted odds ratio (OR) 2.18 (1.38–3.48), P < 0.001]; cardiorenal, 10.3% [adjusted OR 2.11 (1.37–3.32), P < 0.001]; inflamed, 12.5% [adjusted OR 1.79 (1.18–2.76), P = 0.007]; and hypoperfused, 21.9% [adjusted OR 4.32 (2.89–6.62), P < 0.001]. These differences in mortality between phenotypes were consistent when patients were stratified based on demographics, aetiology, admission diagnoses, mortality risk scores, shock severity and systolic function. One-year mortality occurred in 31.5% and differed between the phenotypes: uncomplicated, 11.9% (reference); inflamed, 26.8% [adjusted hazard ratio (HR) 1.56 (1.27–1.92), P < 0.001]; iron-deficient, 33.8% [adjusted HR 2.47 (2.00–3.04), P < 0.001]; cardiorenal, 41.2% [adjusted HR 2.41 (1.97–2.95), P < 0.001]; and hypoperfused, 52.3% [adjusted HR 3.43 (2.82–4.18), P < 0.001]. Similar findings were observed for post-discharge 1 year mortality. Conclusions Unsupervised machine learning clustering can identify multiple distinct clinical HF phenotypes within the CICU population that display differing mortality profiles both in-hospital and at 1 year. Mortality was lowest for the uncomplicated HF phenotype and highest for the hypoperfused phenotype. The inflamed phenotype had comparatively higher in-hospital mortality yet lower post-discharge mortality, suggesting divergent short-term and long-term prognosis.

Abstract Aims An elevated risk of adverse events persists for years in cardiogenic shock (CS) survivors with high mortality rate and physical/mental disability. This study aims to link clinical CS-survivor phenotypes with distinct late host-response patterns at intensive care unit (ICU) discharge and long-term outcomes using model-based clustering. Methods and results In the original prospective, observational, international French and European Outcome Registry in Intensive Care Units (FROG-ICU) study, ICU patients with CS on admission were identified (N = 228). Among them, 173 were discharged alive from the ICU and included in the current study. Latent class analysis was applied to identify distinct CS-survivor phenotypes at ICU discharge using 15 readily available clinical and laboratory variables. The primary endpoint was 1 year of mortality after ICU discharge. Secondary endpoints were readmission and physical/mental disability [short form-36 questionnaire (SF-36) score] within 1 year after ICU discharge. Two distinct phenotypes at ICU discharge were identified (A and B). Patients in Phenotype B (38%) were more anaemic and had higher circulating levels of lactate, sustained kidney injury, and persistent elevation in plasma markers of inflammation, myocardial fibrosis, and endothelial dysfunction compared with Phenotype A. They had also a higher rate of non-ischaemic origin of CS and right ventricular dysfunction on admission. CS survivors in Phenotype B had higher 1 year of mortality compared with Phenotype A (P = 0.045, Kaplan–Meier analysis). When adjusted for traditional risk factors (i.e. age, severity of illness, and duration of ICU stay), Phenotype B was independently associated with 1 year of mortality [adjusted hazard ratio = 2.83 (95% confidence interval 1.21–6.60); P = 0.016]. There was a significantly lower physical quality of life in Phenotype B patients at 3 months (i.e. SF-36 physical component score). Conclusions A phenotype with sustained inflammation, myocardial fibrosis, and endothelial dysfunction at ICU discharge was identified from readily available data and was independently associated with poor long-term outcomes in CS survivors.

OBJECTIVES: To review a contemporary approach to the management of patients with cardiogenic shock (CS). DATA SOURCES: We reviewed salient medical literature regarding CS. STUDY SELECTION: We included professional society scientific statements and clinical studies examining outcomes in patients with CS, with a focus on randomized clinical trials. DATA EXTRACTION: We extracted salient study results and scientific statement recommendations regarding the management of CS. DATA SYNTHESIS: Professional society recommendations were integrated with evaluated studies. CONCLUSIONS: CS results in short-term mortality exceeding 30% despite standard therapy. While acute myocardial infarction (AMI) has been the focus of most CS research, heart failure-related CS now predominates at many centers. CS can present with a wide spectrum of shock severity, including patients who are normotensive despite ongoing hypoperfusion. The Society for Cardiovascular Angiography and Intervention Shock Classification categorizes patients with or at risk of CS according to shock severity, which predicts mortality. The CS population includes a heterogeneous mix of phenotypes defined by ventricular function, hemodynamic profile, biomarkers, and other clinical variables. Integrating the shock severity and CS phenotype with nonmodifiable risk factors for mortality can guide clinical decision-making and prognostication. Identifying and treating the cause of CS is crucial for success, including early culprit vessel revascularization for AMI. Vasopressors and inotropes titrated to restore arterial pressure and perfusion are the cornerstone of initial medical therapy for CS. Temporary mechanical circulatory support (MCS) is indicated for appropriately selected patients as a bridge to recovery, decision, durable MCS, or heart transplant. Randomized controlled trials have not demonstrated better survival with the routine use of temporary MCS in patients with CS. Accordingly, a multidisciplinary team-based approach should be used to tailor the type of hemodynamic support to each individual CS patient’s needs based on shock severity, phenotype, and exit strategy.

Aims Remote monitoring of pulmonary artery pressure has reduced heart failure (HF) hospitalizations in chronic HF as elevation of pulmonary artery pressure provides information that can guide treatment. The venous system is characterized by high capacitance, thus substantial increases in intravascular volume can occur before filling pressures increase. The inferior vena cava (IVC) is a highly compliant venous conduit and thus a candidate for early detection of change in intravascular volume. We aimed to compare IVC cross‐sectional area using a novel sensor with cardiac filling pressures during experimental manipulation of volume status, vascular tone, and cardiac function. Methods and results Experiments were conducted in sheep to manipulate volume status (colloid infusion), vascular tone (nitroglycerin infusion) and cardiac function (rapid cardiac pacing). A wireless implantable IVC sensor was validated ex‐vivo and in‐vivo , and then used to measure the cross‐sectional area of the IVC. Right‐ and left‐sided cardiac filling pressures were obtained via right heart catheterization. The IVC sensor provided highly accurate and precise measurements of cross‐sectional area in ex‐vivo and in‐vivo validation. IVC area changes were more sensitive than the corresponding changes in cardiac filling pressures during colloid infusion ( p < 0.001), vasodilatation ( p < 0.001) and cardiac dysfunction induced by rapid pacing ( p ≤ 0.02). Conclusions Inferior vena cava area can be remotely and accurately measured in real time with a wireless implantable sensor. Changes in IVC area are more sensitive than corresponding changes in filling pressures following experimental volume loading and fluid redistribution. Additional research is warranted to understand if remote monitoring of the IVC may have advantages over pressure‐based monitors in HF.

Abstract There is an unmet need for effective treatment strategies to reduce morbidity and mortality in patients with heart failure with preserved ejection fraction (HFpEF). Until recently, attention in patients with HFpEF was almost exclusively focused on the left side. However, it is now increasingly recognized that right heart dysfunction is common and contributes importantly to poor prognosis in HFpEF. More insights into the development of right heart dysfunction in HFpEF may aid to our knowledge about this complex disease and may eventually lead to better treatments to improve outcomes in these patients. In this position paper from the Heart Failure Association of the European Society of Cardiology, the Committee on Heart Failure with Preserved Ejection Fraction reviews the prevalence, diagnosis, and pathophysiology of right heart dysfunction and failure in patients with HFpEF. Finally, potential treatment strategies, important knowledge gaps and future directions regarding the right side in HFpEF are discussed.