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Resume Introduction l'insuffisance cardiaque est une pathologie frequente et grave caracterisee par des hospitalisations prolongees et recurrentes. Le but de ce travail est de colliger sur une duree de trois ans, des donnees epidemiologiques et le devenir a 6 mois d'une cohorte de patients hospitalises pour insuffisance cardiaque a l'hopital Saint Joseph (HSJ) et de les comparer aux donnees de l’Ile-de-France (IdF). Materiel et methodes Les donnees sont fournies par la Direction regionale du service medical de l’Ile-de-France, grâce a l'acces au Systeme national des donnees de sante. Le sejour d'un patient est classe « Insuffisance cardiaque » si les codes de la 10e edition de la nomenclature internationale (CIM 10) sont rapportees en diagnostic principal. Resultats Entre 2017 et 2019, un total de 1967 sejours pour insuffisance cardiaque a ete realise a HSJ. L’âge moyen de la population est de 81,4 ± 11,7 ans IC95 % [80,8 ; 81,9], la duree moyenne de sejour est de 8,6 ± 6,8 jours IC95 % [8,3 ; 8,9]. La mortalite est de 5,3 % a la phase hospitaliere, 9,6 % a 2 mois et 15,9 % a 6 mois. Le delai de rehospitalisation quand elle a lieu, est de 59,5 ± 47,5 jours IC95 % [57,4 ; 61,6], le taux de rehospitalisation toutes causes est de 23,7 % a 6 mois. La comparaison avec les donnees d'IdF portant sur 60793 sejours retrouve une population HSJ plus âgee (81,4 ± 11,7 versus 80,4 ± 12,6, p = 0,001), une duree d'hospitalisation plus courte a HSJ (8,6 ± 6,8 versus 11,3 ± 10,1 jours p Conclusion L'insuffisance cardiaque est une pathologie de sujets âges, le pronostic immediat et a 6 mois est severe, les rehospitalisation frequentes et precoces. Il existe quelques differences entre la population de HSJ et de celle d'IdF. Celles-ci peuvent etre expliquees par la diversite des structures hospitalieres, des prises en charge, de l'offre de soins, et de la transition des soins.

Congestive heart failure (CHF) is associated with prolonged and recurrent hospitalizations; the prognosis remains poor. Since 2013, the Caisse Primaire d'Assurance Maladie (CPAM) has set up a support program PRADO-IC (support program for returning home after hospitalisation for heart failure). The aim of this study was to evaluate the impact of PRADO-IC on the heart failure readmission rate and death rate at one year.From September 2016 to September 2018, all patients hospitalized for heart failure at Saint-Joseph Hospital were included in an observational study. The inclusion in PRADO-IC program was at physician's discretion. Two groups were compared according to the inclusion in PRADO-IC or not (T). The primary endpoints were the comparison of one-year mortality and heart failure readmission rate between the two groups.Six hundred and thirty-three patients were included, 262 in the PRADO-IC group and 371 in the non-PRADO group. Patients in the PRADO-IC cohort more frequently present severity criteria (age, weight, BNP level, arrhythmia, anemia, renal failure). Mortality at one year (19.5% vs 16.2%, p = 0.28) are equivalent in both groups. There were no significant differences in one-year rehospitalization rate for heart failure (HF) (35.1% in PRADO cohort vs 28% in T group, p = 0.06), the time to first hospitalization (74.5 days in PRADO vs 54.5 days in T, p = 0.55) and the length of hospitalization (6.0 days in PRADO vs 7.0 days in T, p = 0.29) between the two groups. Age, hyponatremia, anemia, cancer, HF re-hospitalization were variables linked to a risk of mortality, in a multivariable analysis.Our study shows that the PRADO-IC program concerned to the most severe patients. Despite this, the one-year mortality and the HF readmission rate are similar between the two groups.

Aims Type 2 diabetes mellitus (T2DM) is associated with high risk of heart failure. Several studies have reported asymptomatic left ventricular dysfunction (LVD) in T2DM patients with normal ejection fraction. Purpose of our study was to assess the prevalence, the type and clinical factors associated with LVD in T2DM patients by a comprehensive echocardiographic Doppler assessment including speckle tracking. Methods 200 T2DM patients without overt cardio-vascular disease were prospectively enrolled in a single center cohort study between 2018 and 2019. Results LV mass was increased in 24 patients (12%) whereas relative wall thickness (h/r) was increased in 46 patients (23%). Left atrial (LA) enlargement was observed in 27 patients (13.6%) and global longitudinal strain (GLS) was reduced in 38 patients (20.3%). By univariate analysis, LV hypertrophy or increased h/r were associated with age, renal function, hypertension and B type natriuretic peptide plasma level (BNP). LA dilation was associated with age, history of hypertension, diabetes duration and complications, insulin treatment, BNP and renal function. GLS was associated with body mass index (BMI) and in a borderline manner with diabetes duration. By multivariate analysis, hypertension was associated with LVH, and with h/r and a borderline relationship was observed for female gender (LVH) age and insulin treatment (h/r). Age, hypertension and in a borderline manner insulin treatment were associated with LA dilation. BMI and shorter diabetes duration were associated with reduced GLS. Conclusion/interpretation A high prevalence of asymptomatic cardiac dysfunction/structural abnormalities is observed in T2DM without overt cardiac disease and is associated with either age, diabetes duration or treatment and with comorbidities including hypertension and obesity. Whether these preclinical abnormalities are associated with poor outcomes deserves further studies. This article is protected by copyright. All rights reserved.

Background The PRADO program was designed to improve post-discharge patient care in heart failure (HF). In this context, prediction of readmission or death in HF is of critical importance and is currently based on a limited number of variables selected by experts/literature. Artificial intelligence (AI) allows to include a non-limited and non-selected number of variables. Purpose The objective was to predict the probability of HF readmission and/or death inpatients with a new diagnosis of HF included or not in the PRADO program at the cardiologist's discretion, using medical records and machine learning models, without a priori. Methods This pilot monocentric study included all patients having a first HF admission between January 1st, 2015 and December 31, 2018, enrolled by data record (ICD-10) from the department of cardiology Paris Saint-Joseph Hospital. One thousand variables extracted from electronic health records/local hospital discharge database (PMSI) were used to create models. Data from the national PMSI were also included using the Hawkes process. Models were constructed on patients admitted between 2015 and 2017 to predict 1 and 3 months probability of HF readmission and HF readmission or mortality. Discrimination was tested using the area under the ROC curves (AUC) inpatients admitted in 2018. Results The discrimination value was limited overall ( Table 1 ). It was better for the 1 month than for the 3 months prediction of clinical outcomes. The models predicted better the composite outcome than HF readmissions and performed better for non-PRADO patients. Conclusion The predictive value of these models using AI in our population is limited and performed better at 1 month than 3 months. The better discriminating value in non-PRADO patients might result from different clinical profiles, from the arbitrary nature of the selection by cardiologists and from the limited sample size compared to the high number of variables included in the models.

Background Type 2 diabetes mellitus (DM2) is associated with a high prevalence of cardio vascular (CV) disorders including heart failure. The prevalence of asymptomatic cardiac dysfunction in DM2 remains poorly documented in patients without a history of cardio vascular disease. The purpose of DIACAR is to characterize cardiac function by doppler-echocardiography in a population of DM2 without documented CV disease. Methods One hundred and sixteen consecutive outpatients (63 males, 53 females) with DM2 were evaluated prospectively in a single center: 2D (Simpson biplane) & 3D (Heart Model Philips) left ventricular (LV) ejection fraction (EF), LV and left atrial (LA) volumes, 2D global longitudinal strain, diastolic function and LV mass index were measured. All patients with a documented history of CV disease or presenting with symptoms suggestive of any cardiac disorder were excluded. Results Patients were 57.8 ± 10.9 years old and had a history of DM2 of 12.7 ± 8.7 years. Fifty-two patients (45%) were treated with insulin. Body mass index was 29 ± 4.9 kg/m2. Mean values of the echo Doppler parameters were in the normal range ( Table 1 ). 2D Simpson EF and E/e’ were normal in all except one and two patients respectively. However, LV mass index was increased in 8/63 men and 10/53 women and LA volume index was increased in 14/116 patients whereas 2D Simpson LV volumes were all in the normal range. Finally, 2D global longitudinal strain was reduced in 44/105 patients. Conclusion In a selected population of DM2 without documented CV disease, cardiac function assessed by EF and E/e’ was in the normal range in nearly all patients. By contrast, subtle abnormalities of LV and LA morphology as well as 2D global longitudinal strain were found in a significant proportion of these asymptomatic patients. These results suggest that a systematic screening of DM2 patients by doppler- echocardiography with 2D global longitudinal strain is useful for the early detection of cardiac abnormalities.

Background Prediction of readmission or death in heart failure (HF) is of critical importance and is currently based on a limited number of variables selected by experts/literature. Artificial intelligence (AI) allows to include a non-limited and non-selected number of variables. Purpose The objective was to predict the probability of HF readmission and/or death inpatients with a new diagnosis of HF, using medical records and machine learning models, without a priori. Methods This pilot monocentric study included all patients having a first HF admission between January 1st, 2015 and December 31, 2018, enrolled by data record (ICD-10) from the department of cardiology Paris Saint-Joseph Hospital. One thousand variables extracted from electronic health records/local hospital discharge database (PMSI) were used to create models. Data from the national PMSI were also included using the Hawkes process. Models were constructed on patients admitted between 2015 and 2017 to predict 1 and 3 months probability of HF readmission (n = 905 and n = 829 respectively) and HF readmission or mortality (n = 923 and n = 868 respectively). Discrimination was tested using the area under the ROC curves (AUC) inpatients admitted in 2018. Results At 1 month, AUC was 0.56 [95%CI 0.47–0.66] (n = 294) and 0.55 [95%CI 0.47–0.64] (n = 302) for the HF readmission and the composite outcome respectively. At 3 months, AUCs were 0.51 [95%CI 0.41–0.59] (n = 224) and 0.52 [95%CI 0.45–0.60] (n = 235) respectively. Conclusion The predictive value of these models using AI in this population is limited and performed better at 1 month than 3 months. Potential biases include the accuracy of HF diagnosis (based on ICD-10) and the limited size of the derivation cohort compared to the number of variables included in the models. This approach should therefore be assessed in a larger population.

Enterococcus hirae is a rare isolate in clinical specimens. We describe a case of native aortic-valve endocarditis that was caused by Enterococcus hirae in a 72-year-old man. This is the first reported case of endocarditis due to this organism.

To date, few cases giving detailed visual descriptions of a thrombus in the pulmonary arteries or its branches in transthoracic echocardiography have been reported. We report the case of a 67-year-old man who was admitted to a hospital emergency department with a thrombus in the right pulmonary artery diagnosed by transthoracic echocardiography. The specificity of echocardiography in pulmonary embolism varies between 80% and 90% according to the series. The direct visualization of a thrombus is sometimes possible, permitting a faster diagnosis in patients with compromised hemodynamic status.