Guideline adherence in the management of acute pulmonary oedema: Study protocol for a French survey involving cardiologists, emergency physicians and intensivists.

BACKGROUND: Because of their high morbidity and mortality, patients with acute pulmonary oedema (APE) require early recognition of symptoms, identification of precipitating factors and admission to specialized care units (cardiac critical care or intensive care). APE is at the crossroads of different specialties (cardiology, emergency medicine and intensive care medicine). Although multidisciplinary expertise and management may be a strength, it can also be a source of confusion, with unexpected heterogeneity in patient care. We hypothesized that the management of severe APE may be heterogeneous between specialties and, in some situations, may differ from international recommendations. AIM: We designed a survey to compare management of different APE phenotypes according to the physicians' medical specialty, and to compare the results with what experts would do and European guidelines. METHODS: Four clinical cases of typical APE with questions pertaining to the latest guidelines were designed by a Scientific Committee designated by the French Scientific Societies for Cardiology, Emergency Medicine and Intensive Care Medicine. We focused on oxygenation and ventilation strategies, management of precipitating factors, including timing of coronary revascularization, use of diuretics and management of diuretic resistance, and discharge coverage. From 20 June 2022 until 09 September 2022, the four cases of APE (two during hypertensive crises, two during acute coronary syndromes) were proposed to French physicians involved in APE care, and to experts, using an open online survey. To avoid any diagnostic ambiguity, the diagnosis of APE was given at the beginning of each clinical case. RESULTS: The intention is to present the results at national and international conferences and publish them in a peer-reviewed journal. CONCLUSIONS: The results of this survey are intended to pave the way for the generation of novel hypotheses for future clinical trials in case of equipoise between subsets of therapeutic procedures in APE.

Colchicine in acute myocardial infarction: cardiovascular events at 1-year follow up.

OBJECTIVE: In the COVERT-MI randomised placebo-controlled trial, oral administration of high-dose colchicine at the time of reperfusion and for 5 days in acute ST-elevated myocardial infarction did not reduce infarct size but was associated with a significant increase in left ventricular thrombus (LVT) in comparison to placebo. We aimed to assess the 1-year clinical outcomes of the study population. METHODS: This study is a follow-up analysis of the COVERT-MI study on prespecified secondary clinical endpoints at 1 year. The primary endpoint of this study was a composite of major adverse cardiovascular events (MACEs), including all-cause death, acute coronary syndromes, heart failure events, ischaemic strokes, sustained ventricular arrhythmias and acute kidney injury at 1-year follow-up. The quality of life (QOL) and the drug therapy prescription were also assessed. RESULTS: At 1 year, 192 patients (101 patients in the colchicine group, 91 in the placebo group) were followed up. Seventy-six (39.6%) MACEs were reported in the study population. There was no significant difference regarding the number of MACEs between groups: 36 (35.6%) in the colchicine group and 40 (44.1%) in the placebo group (p=0.3). There were no differences in the occurrence of ischaemic strokes between the colchicine group and the control group (3 (3%) vs 2 (2.2%), respectively, p=0.99). There was a trend towards fewer heart failure events in the colchicine group compared with the placebo group (12 (11.9%) vs 18 (19.8%), p=0.20). There was no significant difference in QOL scores at 1 year (75.8±15.7 vs 72.7±16.2 respectively, p=0.18). CONCLUSIONS: There was no significant difference between the colchicine and placebo groups at 1 year regarding MACEs, especially concerning deaths or ischaemic strokes. No excess of ischaemic adverse events was observed despite the initial increase in LVT in the colchicine group. TRIAL REGISTRATION NUMBER: NCT0315681.

Human peripheral blood mononuclear cells display a temporal evolving inflammatory profile after myocardial infarction and modify myocardial fibroblasts phenotype.

Pathophysiological response after acute myocardial infarction (AMI) is described as a three-stage model involving temporal phenotypic modifications of both immune cells and fibroblasts: a primary inflammatory phase, followed by a reparative phase and a fibrous scar maturation phase. Purinergic receptors, particularly the P2Y11 receptor, have been reported to be involved in the regulation of inflammation after ischemia and could act for the resolution of inflammation after AMI. For the first time, we characterized the immuno-inflammatory and P2Y11 expression profiles of peripheral blood mononuclear cells (PBMC) from AMI patients and analyzed the consequences of presenting these cells to cardiac fibroblasts in vitro. PBMC from 178 patients were collected at various times after reperfused ST-segment elevation AMI, from H0 to M12. Expression level of P2RY11 and genes involved in tolerogenic profile of dendritic cells and T cell polarization were evaluated by RT-PCR. P2Y11 protein expression was assessed by flow cytometry. PBMC and human cardiac fibroblasts (HCF) were cocultured and α-SMA/vimentin ratio was analyzed by flow cytometry. Within the first 48 h after AMI, expression levels of HMOX1, STAT3 and CD4 increased while IDO1 and TBX21/GATA3 ratio decreased. Concomitantly, the expression of P2RY11 increased in both T and B cells. In vitro, PBMC collected at H48 after AMI induced an increase in α-SMA/vimentin ratio in HCF. Our results suggest that human PBMC display an evolving inflammatory profile with reparative characteristics the first two days after AMI and secrete soluble mediators leading to the fibroblastic proteins modification, thus participating to myocardial fibrosis.

Dynamic CZT-SPECT: Characterizing the Lower Values of Myocardial Blood Flow and Reserve.

ABSTRACT: CZT-SPECT myocardial perfusion enables quantification of myocardial blood flow (MBF). Normal values and thresholds have been accurately defined in PET but remain unclear in SPECT. The aim of this study was to report normal MBF and myocardial flow reserve values in very low-risk patients referred for coronary artery disease screening with dynamic SPECT, in comparison with patients experiencing coronary artery disease. Eighty-four patients (31 male) were analyzed. The mean 10 years risk of fatal cardiovascular events score was 2.7% ± 1.4%. The mean global stress MBF and myocardial flow reserve were 1.6 ± 0.6 mL/min/g and 2.7 ± 0.7.

Prediction of early death after atrial fibrillation diagnosis using a machine learning approach: A French nationwide cohort study.

AIMS: Atrial fibrillation is associated with important mortality but the usual clinical risk factor based scores only modestly predict mortality. This study aimed to develop machine learning models for the prediction of death occurrence within the year following atrial fibrillation diagnosis and compare predictive ability against usual clinical risk scores. METHODS AND RESULTS: We used a nationwide cohort of 2,435,541 newly diagnosed atrial fibrillation patients seen in French hospitals from 2011 to 2019. Three machine learning models were trained to predict mortality within the first year using a training set (70% of the cohort). The best model was selected to be evaluated and compared with previously published scores on the validation set (30% of the cohort). Discrimination of the best model was evaluated using the C index. Within the first year following atrial fibrillation diagnosis, 342,005 patients (14.4%) died after a period of 83 (SD 98) days (median 37 [10-129]). The best machine learning model selected was a deep neural network with a C index of 0.785 (95% CI, 0.781-0.789) on the validation set. Compared to clinical risk scores, the selected model was superior to the CHA2DS2-VASc and HAS-BLED risk scores and superior to dedicated scores such as Charlson Comorbidity Index and Hospital Frailty Risk Score to predict death within the year following atrial fibrillation diagnosis (C indexes: 0.597; 0.562; 0.643; 0.626 respectively. P < .0001). CONCLUSION: Machine learning algorithms predict early death after atrial fibrillation diagnosis and may help clinicians to better risk stratify atrial fibrillation patients at high risk of mortality.

Phenotypes and outcomes in non-anticoagulated patients with atrial fibrillation: An unsupervised cluster analysis.

BACKGROUND: Patients with atrial fibrillation are characterized by great clinical heterogeneity and complexity. The usual classifications may not adequately characterize this population. Data-driven cluster analysis reveals different possible patient classifications. AIMS: To identify different clusters of patients with atrial fibrillation who share similar clinical phenotypes, and to evaluate the association between identified clusters and clinical outcomes, using cluster analysis. METHODS: An agglomerative hierarchical cluster analysis was performed in non-anticoagulated patients from the Loire Valley Atrial Fibrillation cohort. Associations between clusters and a composite outcome comprising stroke/systemic embolism/death and all-cause death, stroke and major bleeding were evaluated using Cox regression analyses. RESULTS: The study included 3434 non-anticoagulated patients with atrial fibrillation (mean age 70.3±17 years; 42.8% female). Three clusters were identified: cluster 1 was composed of younger patients, with a low prevalence of co-morbidities; cluster 2 included old patients with permanent atrial fibrillation, cardiac pathologies and a high burden of cardiovascular co-morbidities; cluster 3 identified old female patients with a high burden of cardiovascular co-morbidities. Compared with cluster 1, clusters 2 and 3 were independently associated with an increased risk of the composite outcome (hazard ratio 2.85, 95% confidence interval 1.32-6.16 and hazard ratio 1.52, 95% confidence interval 1.09-2.11, respectively) and all-cause death (hazard ratio 3.54, 95% confidence interval 1.49-8.43 and hazard ratio 1.88, 95% confidence interval 1.26-2.79, respectively). Cluster 3 was independently associated with an increased risk of major bleeding (hazard ratio 1.72, 95% confidence interval 1.06-2.78). CONCLUSION: Cluster analysis identified three statistically driven groups of patients with atrial fibrillation, with distinct phenotype characteristics and associated with different risks for major clinical adverse events.

Characteristics and Prognosis of Patients With Hypertensive Encephalopathy: A French Nationwide Cohort Study.

BACKGROUND: Hypertensive encephalopathy (HE) constitutes a serious condition, usually observed in patients with long-lasting hypertension. Hypertension-associated HE is sometimes differentiated from the stroke-associated hypertensive emergency. Whether prognosis of hypertension-associated and stroke-associated HE is different is unclear. METHODS: Characteristics and prognosis of HE were assessed in this nationwide retrospective cohort study in all patients with an administrative code of HE compared with age-, sex- and year of inclusion-matched controls admitted to French hospitals during the 2014 to 2022 period. RESULTS: HE was identified in 7769 patients. Chronic kidney disease (19.3%), coronary artery disease (13.8%), diabetes (22.1%), and ischemic stroke (5.2%) were frequent but thrombotic microangiopathy, hemolytic-uremic syndrome, systemic sclerosis or renal infarction were <1%. HE prognosis was poor (death: 10.4%/y, heart failure: 8.6%/y, end-stage kidney disease: 9.0%/y, ischemic stroke: 3.6%/y, hemorrhagic stroke: 1.6%/y, dementia: 4.1%/y). The risk of death was increased to a similar extent in patients with HE, regardless of the presence of known hypertension or concomitant stroke (versus patients without HE). Among patients with HE, known hypertension was significantly associated with increased risks of ischemic stroke, hemorrhagic stroke, heart failure, vascular dementia, and all-cause dementia and to a lesser extent with chronic dialysis in multivariable analyses including adjustment on concomitant stroke. CONCLUSIONS: HE remains a considerable health burden and is associated with a poor prognosis. The distinction between hypertension- versus stroke-associated HE is relevant as these 2 situations convey different risks of stroke, heart failure, vascular dementia, and end-stage kidney disease.

French expert group proposal for lipid-lowering therapy in the first 3 months after acute myocardial infarction.

In patients admitted for acute myocardial infarction (MI), it has been demonstrated that reducing LDL cholesterol (LDL-c) is associated with a reduction in major adverse cardiovascular events. We describe a consensual proposal made by a French group of experts for lipid-lowering therapy at the acute phase of acute myocardial infarction. A group of French experts comprising cardiologists, lipidologists and general practitioners prepared a proposal for a lipid-lowering strategy with a view to optimizing LDL-c levels in patients hospitalized for myocardial infarction. We describe a strategy for the use of statins, ezetimibe and and/or proprotein convertase subtilisin-kexin type 9 (PCSK9) inhibitors, with a view to reaching target LDL-c levels as early as possible. This approach, which is currently feasible in France, could considerably improve lipid management in patients after ACS, thanks to its simplicity, rapidity and the magnitude of the decrease in LDL-c that it achieves.

Clinical Phenotypes and Atrial Fibrillation Recurrences After Catheter Ablation: An Unsupervised Cluster Analysis.

Catheter ablation (CA) is a well-established treatment of atrial fibrillation (AF). Data-driven cluster analysis is able to better distinguish prognostically-relevant phenotype clusters among patients with AF. We performed a hierarchical cluster analysis in a cohort of AF patients undergoing a first CA and evaluate associations between identified clusters and recurrences of arrhythmia following ablation. The study included 209 AF patients treated with CA. A total of 3 clusters with distinct characteristics were identified. Recurrences at 1 year occurred in 27.2% in Cluster 1, 43.2% in Cluster 2 and 60.9% in Cluster 3 (P < 0.0001). Cluster classification was independently associated with arrhythmia recurrences (HR 1.58, 95% CI 1.01-2.49, P = 0.046) after adjustment for age, CHA2DS2-VASc score, left atrial volume, type of atrial fibrillation and ejection fraction. To concluded, cluster analysis identified 3 statistically-driven groups among AF patients treated with CA with different risks for arrhythmia recurrences.

Type 2 diabetes and cardiorenal syndromes. A nationwide French hospital cohort study.

AIM: Type 2 diabetes mellitus (T2DM) is a risk factor for cardiac and renal complications; its effect on cardiorenal syndromes is unknown. METHODS: In a French nationwide cohort of 5,123,193 patients hospitalized in 2012 with ≥5 years of follow-up, we assessed the effect of T2DM on cardiorenal syndrome (CRS) (using cardiorenal, renocardiac, and simultaneous subtypes) incidence and outcomes using 1:1 propensity matching. RESULTS: Among 4,605,236 adults without cardiorenal syndrome, 380,581 (8.5%) with T2DM were matched to 380,581 adults without T2DM. During follow-up, CRS occurred in 104,788 patients: simultaneous n = 25,225 (24.0%); cardiorenal n = 51,745 (49.4%); renocardiac n = 27,818 (26.5%). T2DM doubled the risk of incident CRS (1.30% versus 0.65%/year; adjusted hazard ratio (HR) for any cardiorenal syndrome: 2.14 [95% confidence interval 2.10;2.19]; renocardiac: 2.43 [2.34;2.53]; cardiorenal: 2.09 [2.03;2.15]; simultaneous: 1.94 [1.86;2.03]. Among the 26,396 adults with CRS in 2012, 11,355 (43.0%) had T2DM and were younger than non-diabetic adults (77.4 ± 9.5 versus 82.3 ± 10.0); 8,314 patients with T2DM were matched to 8,314 patients without. T2DM increased risk of: end-stage kidney disease, adjusted HR 1.50 [1.39;1.62]; myocardial infarction 1.35 [1.19;1.53]; cardiovascular death 1.20 [1.13;1.27]; heart failure 1.17 [1.12;1.21]; and all-cause death 1.09 [1.06;1.13], but not ischemic stroke. CONCLUSION: Patients with T2DM represent almost half of patients with CRS and are younger than their non-diabetic counterparts. T2DM doubles the risk of CRS and increases the risk of death, cardiovascular outcome, and end-stage kidney disease but not ischemic stroke after CRS.

Prevalence and incidence of cardiovascular and renal diseases in type 1 compared with type 2 diabetes: A nationwide French observational study of hospitalized patients.

BACKGROUND: Type 1 diabetes mellitus (T1DM) and type 2 diabetes mellitus (T2DM) increase risks of cardiovascular (CV) and renal disease compared with diabetes-free populations. There are only a few studies comparing T1DM and T2DM for the relative risk of these clinical events. METHODS: All adult patients hospitalized in French hospitals in 2013 with at least 5 years of follow-up were identified and categorized by their diabetes status. A total of 50,623 patients with T1DM (age 61.4 ± 18.6, 53% male) and 425,207 patients with T2DM (age 68.6 ± 14.3, 55% male) were followed over a median period of 5.3 years (interquartile range: 2.8 - 5.8 years). Prevalence and event rates of myocardial infarction (MI), heart failure (HF), ischemic stroke, chronic kidney disease (CKD), all-cause death and CV death were assessed with age stratification of 10-year intervals. For clinical events during follow-up, we report hazard ratios (HRs) in T1DM relative to T2DM. RESULTS: The age and sex-adjusted prevalence of CV diseases was higher in T2DM for ages above 40 years whereas the prevalence of CKD was more common in T1DM between ages 18 and 70 years. During 2,033,239 person-years of follow-up, age and sex-adjusted HR event rates comparing T1DM, versus T2DM as reference, showed that MI and HF relative risks were increased above 60 years (1.2 and 1.4 -fold). HR of ischemic stroke did not markedly differ between T1DM and T2DM. Risk of incident CKD was 2.4-fold higher in T1DM above 60 years. All-cause death HR risk was 1.1-fold higher in T1DM after 60 years and the CV death risk was 1.15-fold higher in T1DM between 60 and 69 years compared to T2DM. CONCLUSIONS: Although the crude prevalent burden of CV diseases may be lower in T1DM than in T2DM, patients with T1DM may have a higher risk of incident MI, HF, all-cause death and CV death above 60 years of age, highlighting the need for improved prevention in this population.

Cardiovascular outcomes in patients with cancer during a 5-year follow-up: Results from a French administrative database.

BACKGROUND: Limited data are available regarding the optimal management and prognosis of patients with cancer who develop an acute myocardial infarction. AIM: The objective of this study was to analyse the characteristics and outcomes of patients according to cancer and myocardial infarction occurrence. METHODS: Based on the French administrative hospital discharge database, the study collected information for all consecutive patients seen in French hospitals in 2013, excluding those with a history of myocardial infarction. The population was divided into two groups according to their history of cancer. We studied the following outcomes: all-cause and cardiovascular mortality; acute myocardial infarction; and ischaemic stroke. Data were collected after a 5-year follow-up. RESULTS: Between 2013 and 2019, 3,381,472 patients were seen in French hospitals; among them, 3,323,757 had no history of myocardial infarction. Patients with a history of cancer (n=497,593) had higher incidences of all-cause mortality (17.82%/year vs 3.79%/year), cardiovascular mortality (1.61%/year vs 1.17%/year), myocardial infarction (0.82%/year vs 0.61%/year) and ischaemic stroke (0.91%/year vs 0.62%/year) compared with patients without cancer (n=2,826,164). After performing an adjusted competing-risk analysis, the cumulative incidence of acute myocardial infarction, cardiovascular death and ischaemic stroke incidence was found to be lower in patients with a history of cancer, whereas death of non-cardiac origin was more prevalent in patients with a history of cancer. CONCLUSIONS: In this observational study, we have shown that patients with cancer have a higher incidence of all-cause mortality, cardiovascular mortality and myocardial infarction. However, multivariable analysis showed a lower cumulative incidence of these events.

High Prevalence of Geriatric Conditions Among Older Adults With Cardiovascular Disease.

As the population ages, the global cardiovascular disease burden will continue to increase, particularly among older adults. Increases in life expectancy and better cardiovascular care have significantly reshaped the epidemiology of cardiovascular disease and have created new patient profiles. The combination of older age, multiple comorbidities, polypharmacy, frailty, and adverse noncardiovascular outcomes is challenging our routine clinical practice in this field. In this review, we examine noncardiovascular factors that statistically interact in a relevant way with health status and quality of life in older people with cardiovascular disease. We focused on specific geriatric conditions (multimorbidity, polypharmacy, geriatric syndromes, and frailty) that are responsible for a major risk of functional decline and have an important impact on the overall prognosis in this patient population.

Anti-Inflammatory Drug Candidates for Prevention and Treatment of Cardiovascular Diseases.

Incidence and mortality rates for cardiovascular disease are declining, but it still remains a major cause of morbidity and mortality. Drug treatments to slow the progression of atherosclerosis focus on reducing cholesterol levels. The paradigm shift to consider atherosclerosis an inflammatory disease by itself has led to the development of new treatments. In this article, we discuss the pathophysiology of inflammation and focus attention on therapeutics targeting different inflammatory pathways of atherosclerosis and myocardial infarction. In atherosclerosis, colchicine is included in new recommendations, and eight randomized clinical trials are testing new drugs in different inflammatory pathways. After a myocardial infarction, no drug has shown a significant benefit, but we present four randomized clinical trials with new treatments targeting inflammation.

Prediction of incident atrial fibrillation in post-stroke patients using machine learning: a French nationwide study.

BACKGROUND: Targeting ischemic strokes patients at risk of incident atrial fibrillation (AF) for prolonged cardiac monitoring and oral anticoagulation remains a challenge. Clinical risk scores have been developed to predict post-stroke AF with suboptimal performances. Machine learning (ML) models are developing in the field of AF prediction and may be used to discriminate post-stroke patients at risk of new onset AF. This study aimed to evaluate ML models for the prediction of AF and to compare predictive ability to usual clinical scores. METHODS: Based on a French nationwide cohort of 240,459 ischemic stroke patients without AF at baseline from 2009 to 2012, ML models were trained on a train set and the best model was selected to be evaluate on the test set. Discrimination of the best model was evaluated using the C index. We finally compared our best model with previously described clinical scores. RESULTS: During a mean follow-up of 7.9 ± 11.5 months, 14,095 patients (mean age 77.6 ± 10.6; 50.3% female) developed incident AF. After training, the best ML model selected was a deep neural network with a C index of 0.77 (95% CI 0.76-0.78) on the test set. Compared to traditional clinical scores, the selected model was statistically significantly superior to the CHA2DS2-VASc score, Framingham risk score, HAVOC score and C2HEST score (P < 0.0001). The ability to predict AF was improved as shown by net reclassification index increase (P < 0.0001) and decision curve analysis. CONCLUSIONS: ML algorithms predict incident AF post-stroke with a better ability than previously developed clinical scores. AF: atrial fibrillation; DNN: deep neural network; IS: ischemic stroke; KNN: K-nearest neighbors; LR: logistic regression; RFC: random forest classifier; XGBoost: extreme gradient boosting.