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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.