Phenotypic clustering of patients hospitalized in intensive cardiac care units: Insights from the ADDICT-ICCU study.

BACKGROUND: Intensive cardiac care units (ICCUs) were created to manage ventricular arrhythmias after acute coronary syndromes, but have diversified to include a more heterogeneous population, the characteristics of which are not well depicted by conventional methods. AIMS: To identify ICCU patient subgroups by phenotypic unsupervised clustering integrating clinical, biological, and echocardiographic data to reveal pathophysiological differences. METHODS: During 7-22 April 2021, we recruited all consecutive patients admitted to ICCUs in 39 centers. The primary outcome was in-hospital major adverse events (MAEs; death, resuscitated cardiac arrest or cardiogenic shock). A cluster analysis was performed using a Kamila algorithm. RESULTS: Of 1499 patients admitted to the ICCU (69.6% male, mean age 63.3±14.9 years), 67 (4.5%) experienced MAEs. Four phenogroups were identified: PG1 (n=535), typically patients with non-ST-segment elevation myocardial infarction; PG2 (n=444), younger smokers with ST-segment elevation myocardial infarction; PG3 (n=273), elderly patients with heart failure with preserved ejection fraction and conduction disturbances; PG4 (n=247), patients with acute heart failure with reduced ejection fraction. Compared to PG1, multivariable analysis revealed a higher risk of MAEs in PG2 (odds ratio [OR] 3.13, 95% confidence interval [CI] 1.16-10.0) and PG3 (OR 3.16, 95% CI 1.02-10.8), with the highest risk in PG4 (OR 20.5, 95% CI 8.7-60.8) (all P<0.05). CONCLUSIONS: Cluster analysis of clinical, biological, and echocardiographic variables identified four phenogroups of patients admitted to the ICCU that were associated with distinct prognostic profiles. TRIAL REGISTRATION: ClinicalTrials.gov identifier: NCT05063097.

Is COVID-19 Infection a Multiorganic Disease? Focus on Extrapulmonary Involvement of SARS-CoV-2.

First described in December 2019 in Wuhan (China), COVID-19 disease rapidly spread worldwide, constituting the biggest pandemic in the last 100 years. Even if SARS-CoV-2, the agent responsible for COVID-19, is mainly associated with pulmonary injury, evidence is growing that this virus can affect many organs, including the heart and vascular endothelial cells, and cause haemostasis, CNS, and kidney and gastrointestinal tract abnormalities that can impact in the disease course and prognosis. In fact, COVID-19 may affect almost all the organs. Hence, SARS-CoV-2 is essentially a systemic infection that can present a large number of clinical manifestations, and it is variable in distribution and severity, which means it is potentially life-threatening. The goal of this comprehensive review paper in the series is to give an overview of non-pulmonary involvement in COVID-19, with a special focus on underlying pathophysiological mechanisms and clinical presentation.

TAPSE/sPAP prognostic value for In-Hospital Adverse Events in Patients Hospitalized for Acute Coronary Syndrome.

AIMS: Although several studies have shown that the right ventricular to pulmonary artery (RV-PA) coupling, assessed by the ratio between tricuspid annular plane systolic excursion and systolic pulmonary artery pressure (TAPSE/sPAP) using echocardiography, is strongly associated with cardiovascular events, its prognostic value is not established in acute coronary syndrome (ACS). We aimed to assess the in-hospital prognostic value of TAPSE/sPAP among patients hospitalized for ACS in a retrospective analysis from the prospective ADDICT-ICCU study. METHODS AND RESULTS: 481 consecutive patients hospitalized in intensive cardiac care unit (mean age 65±13 years, 73% of male, 46% STEMI) for ACS (either ST-elevation [STEMI] or non-ST-elevation [NSTEMI] myocardial infarction) with TAPSE/sPAP available were included in this prospective French multicentric study (39 centers). The primary outcome was in-hospital major adverse cardiovascular events (MACEs) defined as all-cause death, resuscitated cardiac arrest or cardiogenic shock and occurred in 33 (7%) patients. ROC-curve analysis identified 0.55 mm/mmHg as the best TAPSE/sPAP cut-off to predict in-hospital MACEs. TAPSE/sPAP <0.55 was associated with in-hospital MACEs, even after adjustment with comorbidities (OR:19.1, 95%CI[7.78-54.8]), clinical severity including left ventricular ejection fraction (OR:14.4, 95%CI[5.70-41.7]) and propensity-matched population analysis (OR:22.8, 95%CI[7.83-97.2], all p<0.001). After adjustment, TAPSE/sPAP <0.55 showed the best improvement in model discrimination and reclassification above traditional prognosticators (C-statistic improvement: 0.16; global chi-square improvement: 52.8; LR-test p<0.001) with similar results for both STEMI and NSTEMI subgroups. CONCLUSION: A low RV-PA coupling defined as TAPSE/sPAP ratio <0.55 was independently associated with in-hospital MACEs and provided incremental prognostic value over traditional prognosticators in patients hospitalized for ACS. TRIAL REGISTRATION: ClinicalTrials.gov Identifier: NCT05063097.

Atrial fibrillation and preexisting cognitive impairment in ischemic stroke patients: Dijon Stroke Registry.

BACKGROUND: Atrial Fibrillation (AF) is a common cause of ischemic stroke (IS), and is associated with cognitive impairment in the general population. We aimed to compare the prevalence of preexisting cognitive impairment between IS patients with and without AF, and to assess whether prior brain damage could contribute to the observed differences. METHODS: Patients with acute IS were prospectively identified from the population-based Dijon Stroke Registry, France. Patients who had a CT-scan as brain imaging modality were included in this analysis to assess the presence of preexisting leukoaraiosis, old vascular brain lesions, and cerebral atrophy. Characteristics of patients including prior-to-stroke cognitive status (normal cognition, mild cognitive impairment (MCI), or dementia) were compared between those with and without AF. RESULTS: Among 916 IS patients, 288 (31.4 %) had AF, of whom 88 had newly diagnosed AF. AF patients had more frequent prior MCI (17.8 % versus 10.2 %) or dementia (22.4 % versus 13.1 %) (p = 0.001), vascular risk factors, and preexisting brain damage. In unadjusted model, preexisting cognitive impairment was associated with AF (OR=2.24; 95 % CI: 1.49-3.37, p < 0.001 for MCI; OR=2.20; 95 % CI: 1.52-3.18, p < 0.001 for dementia). After adjustment for clinical and imaging variables, preexisting mild cognitive impairment (OR=1.87; 95 % CI: 1.06-3.32, p = 0.032) and dementia (OR=1.98; 95 % CI: 1.15-3.40, p = 0.013) were independently associated with AF. CONCLUSION: AF is a common condition in IS patients and is associated with preexisting cognitive impairment. Brain lesions visible on imaging did not seem to fully account for this association that may involve other mechanisms yet to be elucidated.

In-hospital Prognostic Value of TAPSE/sPAP in Patients Hospitalized for Acute Heart Failure.

AIMS: TAPSE/sPAP (tricuspid annular plane systolic excursion over systolic pulmonary artery pressure) assessed by echocardiography appears to be a good noninvasive approach for right ventricular to pulmonary artery coupling assessment. We aimed to assess the in-hospital prognostic value of TAPSE/sPAP among patients hospitalized for acute heart failure (AHF). METHODS AND RESULTS: 333 consecutive patients (mean age 68 ± 14 years, 70% of male, mean LVEF 44 ± 16%) hospitalized for AHF across 39 French cardiology department, with TAPSE/sPAP measured by echocardiography within the 24 first hours of hospitalization were included in this prospective study. The primary outcome was in-hospital major adverse cardiovascular events (MACEs) defined as all-cause death, resuscitated cardiac arrest or cardiogenic shock and occurred in 50 (15%) patients. Using receiver operating characteristics curves analysis, the best TAPSE/sPAP threshold for in-hospital MACEs was 0.40 mm/mmHg. TAPSE/sPAP <0.40 mm/mmHg was independently associated with in-hospital MACEs, even after adjustment with comorbidities (OR:3.75, 95%CI[1.87-7.93], p < 0.001), clinical severity (OR:2.80, 95%CI[1.36-5.95], p = 0.006). Using a 1:1 propensity-matched population, TAPSE/sPAP ratio <0.40 was associated with a higher rate of in-hospital MACEs (OR:2.98, 95%CI[1.53-6.12], p = 0.002). After adjustment, TAPSE/sPAP <0.40 showed the best improvement in model discrimination and reclassification above traditional prognostic factors (C-statistic improvement: 0.05; Chi-2 improvement: 14.4; LR-test p < 0.001). These results were consistent in an external validation cohort of 133 patients. CONCLUSION: TAPSE/sPAP < 0.40 mm/mmHg assessed by an early echocardiography during an AHF episode is independently associated with in-hospital MACEs suggesting enhanced close monitoring and strengthened HF-specific care in these patients. TRIAL REGISTRATION: ClinicalTrials.gov Identifier: NCT05063097.

Prevalence and characteristics of known versus newly detected atrial fibrillation in ischemic stroke: a population-based study.

BACKGROUND: Atrial fibrillation (AF) is frequently diagnosed during the acute stage of ischemic (IS), and it may reflect undiagnosed AF before stroke, thus representing a missed opportunity for stroke prevention. This population-based study aimed to assess the prevalence of known AF (KAF) and AF diagnosed early after IS (AFDAS), and to compare clinical and brain/arterial imaging characteristics between patients. METHODS: Among patients with acute IS recorded in the population-based Dijon Stroke Registry, France (2013-2020), we identified those with KAF or AFDAS. AFDAS was considered when AF was diagnosed during the initial work-up based on electrocardiograms, in-hospital continuous electrocardiographic and/or Holter monitoring. Clinical and imaging characteristics on brain CT-scan or angio-CT-scan when available including old parenchymal lesions, arterial territory of the index IS, and aortic arch, cervical and intracranial arteries atheroma were compared between groups (KAF versus AFDAS). Regression logistic models were used to assess factors associated with AFDAS (compared to KAF). RESULTS: Among 1756 IS patients, 550 (31.3%) had AF (mean age: 83.6 ±10.3 years old, 60.5% women), of whom 367 (66.7%) presented with KAF and 183 (33.3%) had AFDAS. In multivariable model, hypertension (OR=0.37; 95% CI: 0.21-0.64, p<0.001), chronic heart failure (OR=0.34; 95% CI: 0.18-0.67, p=0.002), previous stroke (OR=0.42; 95% CI: 0.26-0.67, p<0.001), and preexisting dementia (OR=0.36; 95% CI: 0.21-0.63, p<0.001), were inversely associated with AFDAS, whereas NIHSS score was associated with AFDAS (OR=1.02; 95% CI: 1.00-1.05, p=0.012). CONCLUSIONS: Our findings indicate a more advanced stage of the atrial cardiomyopathy in KAF as compared with AFDAS patients, and may thus contribute to the fact that in these latter patients AF had not been diagnosed prior to stroke. This group of patients undeniably represents a missed opportunity for stroke prevention.

Value of HAVOC and Brown ESUS-AF scores for atrial fibrillation on implantable cardiac monitors after embolic stroke of undetermined source.

OBJECTIVES: Up to 20 % of ischemic strokes are associated with overt atrial fibrillation (AF). Furthermore, silent AF was detected by an implantable cardiac monitor (ICM) in 1 in 3 cryptogenic strokes in the CRYSTAL AF study. An ESC position paper has suggested a HAVOC score ≥ 4 or a Brown ESUS-AF score ≥ 2 as criteria for ICM implantation after cryptogenic stroke, but neither of these criteria has been developed or validated in ICM populations. We assessed the performance of HAVOC and Brown ESUS-AF scores in a cohort of ICM patients implanted after embolic stroke of undetermined source (ESUS). METHODS: All patients implanted with an ICM for ESUS between February 2016 and February 2022 at two French University Hospitals were retrospectively included. Demographic data, cardiovascular risk factors, and clinical and biological data were collected after a review of electronic medical records. HAVOC and Brown ESUS-AF scores were calculated for all patients. FINDINGS: Among the 384 patients included, 106 (27 %) developed AF during a mean follow-up of 33 months. The scores performances for predicting AF during follow-up were: HAVOC= AUC: 68.5 %, C-Index: 0.662, and Brown ESUS-AF=AUC: 72.9 %, C-index 0.712. Compared with the CHA2DS2-VASc score, only the Brown ESUS-AF score showed significant improvement in NRI/IDI. Furthermore, classifying patients according to the suggested HAVOC and Brown ESUS-AF thresholds, only 24 % and 31 % of the cohort, respectively, would have received an ICM, and 58 (55 %) and 47 (44 %) of the AF patients, respectively, would not have been implanted with an ICM. CONCLUSION: HAVOC and Brown ESUS-AF scores showed close and moderate performance in predicting AF on ICM after cryptogenic stroke, with a significant lack of sensitivity. Specific risk scores should be developed and validated in large ICM cohorts.

Prevalence and impact of recreational drug use in patients with acute cardiovascular events.

OBJECTIVE: While recreational drug use is a risk factor for cardiovascular events, its exact prevalence and prognostic impact in patients admitted for these events are not established. We aimed to assess the prevalence of recreational drug use and its association with in-hospital major adverse events (MAEs) in patients admitted to intensive cardiac care units (ICCU). METHODS: In the Addiction in Intensive Cardiac Care Units (ADDICT-ICCU) study, systematic screening for recreational drugs was performed by prospective urinary testing all patients admitted to ICCU in 39 French centres from 7 to 22 April 2021. The primary outcome was prevalence of recreational drug detection. In-hospital MAEs were defined by death, resuscitated cardiac arrest, or haemodynamic shock. RESULTS: Of 1499 consecutive patients (63±15 years, 70% male), 161 (11%) had a positive test for recreational drugs (cannabis 9.1%, opioids 2.1%, cocaine 1.7%, amphetamines 0.7%, 3,4-methylenedioxymethamphetamine (MDMA) 0.6%). Only 57% of these patients declared recreational drug use. Patients who used recreational drugs exhibited a higher MAE rate than others (13% vs 3%, respectively, p<0.001). Recreational drugs were associated with a higher rate of in-hospital MAEs after adjustment for comorbidities (OR 8.84, 95% CI 4.68 to 16.7, p<0.001). After adjustment, cannabis, cocaine, and MDMA, assessed separately, were independently associated with in-hospital MAEs. Multiple drug detection was frequent (28% of positive patients) and associated with an even higher incidence of MAEs (OR 12.7, 95% CI 4.80 to 35.6, p<0.001). CONCLUSION: The prevalence of recreational drug use in patients hospitalised in ICCU was 11%. Recreational drug detection was independently associated with worse in-hospital outcomes. CLINICAL TRIAL REGISTRATION: NCT05063097.

Distribution of atrial cardiomyopathy markers and association with atrial fibrillation detected after ischaemic stroke in the SAFAS study.

BACKGROUND: Atrial cardiomyopathy (AC) is an emerging concept explaining the pathophysiology of cardioembolic strokes in absence of atrial fibrillation (AF). A definition based on the presence of electrical abnormality (P-wave terminal force in lead V1 (PTFV1) >5000 µV×ms), N-Terminal pro-B-type natriuretic peptide (NT pro BNP) >250 pg/mL and/or indexed left atrial diameter (LADI) >3 cm/m² is currently tested in the ARCADIA (AtRial Cardiopathy and Antithrombotic Drugs In prevention After cryptogenic stroke) trial. We set out to estimate the prevalence of AC as defined in the ARCADIA trial, its determinants and its association with AF detected after stroke (AFDAS). METHODS: Stepwise screening for silent Atrial Fibrillation After Stroke (SAFAS) study prospectively included 240 ischaemic stroke patients. AC markers were complete for 192 of them and 9 were not included in this analysis because AF had been diagnosed on admission. RESULTS: A total of 183 patients were analysed, of whom 57% (104 patients) met the AC criteria (79 NT-proBNP, 47 PTFV1, 4 LADI). In the multivariate logistic regression, C reactive protein >3 mg/L (OR (95% CI) 2.60 (1.30 to 5.21), p=0.007) and age (OR (95% CI) 1.07 (1.04 to 1.10), p<0.001) were found to be independently associated with AC. After 6 months of follow-up, AFDAS was detected in 33% of AC patients and in 14% of the remaining ones (p=0.003). However, AC was not independently associated with AFDAS, contrary to left atrial volume index (>34 mL/m2, OR 2.35 (CI 1.09 to 5.06) p=0029). CONCLUSION: AC as defined in ARCADIA is mostly based on NT pro BNP elevation (76% of patients) and is associated with age and inflammation. Moreover, AC was not independently associated with AFDAS at follow-up. The ARCADIA trial, which compares aspirin to apixaban in patients with embolic strokes of undetermined source with AC markers and must, therefore be analysed in the light of these limitations. TRIAL REGISTRATION NUMBER: NCT03570060.

Incidence and predictors of pacemaker implantation at follow-up after reversible high-degree sinus node dysfunction or atrioventricular block.

BACKGROUND: A pacemaker implantation is not indicated in cases of reversible high-degree symptomatic sinus node dysfunction (SND) and atrioventricular block (AVB). However, it remains uncertain whether these reversible automaticity/conduction disorders may recur in some patients at follow-up, in the absence of reversible cause. This retrospective study aimed to determine the incidence and predictive factors of permanent pacemaker (PPM) implantation at follow-up and after reversible high-degree SND/AVB. METHODS: Based on medical electronic files codes, we identified patients who were hospitalized in our cardiac intensive care unit between January 2003 and December 2020 due to reversible high-degree SND/AVB and who were discharged from the hospital alive and without PPM implantation. Acute myocardial infarction and post-cardiac surgery patients were excluded. We categorized the patients according to the need for PPM at follow-up due to non-reversible high-degree SND/AVB. RESULTS: Of the 93 patients included, 26 patients (28%) were readmitted for PPM implantation at follow-up after hospital discharge. Among baseline characteristics, compared with patients who did not have high-degree SND/AVB recurrence, those who had subsequent PPM implantation had less frequent previous hypertension (70% vs. 46%, p = .031). Regarding the initial causes of reversible SND/AVB, isolated hyperkalemia was found more often in the patients readmitted for PPM (19% vs. 3% vs. p = .017). Moreover, recurrence of high-degree SND/AVB was significantly associated with the presence of intraventricular conduction disorders (either bundle branch block or left bundle branch hemiblock) on ECG at discharge (36% in patients without PPM vs. 68% in PPM patients, p = .012). CONCLUSION: Almost one third of the patients discharged alive from the hospital after a reversible high-degree SND/AVB needed a pacemaker implantation at follow-up. Complete bundle branch block or left bundle branch hemiblock on discharge ECG after recovery of atrioventricular conduction and/or sinus automaticity was associated with a greater risk of recurrence leading to pacemaker implantation.

Automatic classification of patients with myocardial infarction or myocarditis based only on clinical data: A quick response.

BACKGROUND: In acute cardiovascular disease management, the delay between the admission in a hospital emergency department and the assessment of the disease from a Delayed Enhancement cardiac MRI (DE-MRI) scan is one of the barriers for an immediate management of patients with suspected myocardial infarction or myocarditis. OBJECTIVES: This work targets patients who arrive at the hospital with chest pain and are suspected of having a myocardial infarction or a myocarditis. The main objective is to classify these patients based solely on clinical data in order to provide an early accurate diagnosis. METHODS: Machine learning (ML) and ensemble approaches have been used to construct a framework to automatically classify the patients according to their clinical conditions. 10-fold cross-validation is used during the model's training to avoid overfitting. Approaches such as Stratified, Over-sampling, Under-sampling, NearMiss, and SMOTE were tested in order to address the imbalance of the data (i.e. proportion of cases per pathology). The ground truth is provided by a DE-MRI exam (normal exam, myocarditis or myocardial infarction). RESULTS: The stacked generalization technique with Over-sampling seems to be the best one providing more than 97% of accuracy corresponding to 11 wrong classifications among 537 cases. Generally speaking, ensemble classifiers such as Stacking provided the best prediction. The five most important features are troponin, age, tobacco, sex and FEVG calculated from echocardiography. CONCLUSION: Our study provides a reliable approach to classify the patients in emergency department between myocarditis, myocardial infarction or other patient condition from only clinical information, considering DE-MRI as ground-truth. Among the different machine learning and ensemble techniques tested, the stacked generalization technique is the best one providing an accuracy of 97.4%. This automatic classification could provide a quick answer before imaging exam such as cardiovascular MRI depending on the patient's condition.

Efficiency and effectiveness of intensive multidisciplinary follow-up of patients with stroke/TIA or myocardial infarction compared to usual monitoring: protocol of a pragmatic randomised clinical trial. DiVa (Dijon vascular) study.

INTRODUCTION: The ongoing ageing population is associated with an increase in the number of patients suffering a stroke, transient ischaemic attack (TIA) or myocardial infarction (MI). In these patients, implementing secondary prevention is a critical challenge and new strategies need to be developed to close the gap between clinical practice and evidence-based recommendations. We describe the protocol of a randomised clinical trial that aims to evaluate the efficiency and effectiveness of an intensive multidisciplinary follow-up of patients compared with standard care. METHODS AND ANALYSIS: The DiVa study is a randomised, prospective, controlled, multicentre trial including patients >18 years old with a first or recurrent stroke (ischaemic or haemorrhagic) or TIA, or a type I or II MI, managed in one of the participating hospitals of the study area, with a survival expectancy >12 months. Patients will be randomised with an allocation ratio of 1:1 in two parallel groups: one group assigned to a multidisciplinary, nurse-based and pharmacist-based 2-year follow-up in association with general practitioners, neurologists and cardiologists versus one group with usual follow-up. In each group for each disease (stroke/TIA or MI), 430 patients will be enrolled (total of 1720 patients) over 3 years. The primary outcome will be the incremental cost-utility ratio at 24 months between intensive and standard follow-up in a society perspective. Secondary outcomes will include the incremental cost-utility ratio at 6 and 12 months, the incremental cost-effectiveness ratio at 24 months, reduction at 6, 12 and 24 months of the rates of death, unscheduled rehospitalisation and iatrogenic complications, changes in quality of life, net budgetary impact at 5 years of the intensive follow-up on the national health insurance perspective and analysis of factors having positive or negative effects on the implementation of the project in the study area. ETHICS AND DISSEMINATION: Ethical approval was obtained and all patients receive information about the study and give their consent to participate before randomisation. Results of the main trial and each of the secondary analyses will be submitted for publication in a peer-reviewed journal. TRIAL REGISTRATION NUMBER: ClinicalTrials.gov Identifier: NCT04188457. Registered on 6 December 2019.

Incremental value of the combined brain-cardiac CT protocol on prediction of atrial fibrillation after stroke.

Introduction: Atrial fibrillation (AF) is one of the most common causes of ischemic stroke. It is essential to target patients at highest risk of AF detected after stroke (AFDAS), who should benefit from a prolonged rhythm screening strategy. Cardiac-CT angiography (CCTA) was added to the stroke protocol used in our institution in 2018. We sought to assess, for AFDAS, the predictive value of atrial cardiopathy markers by a CCTA performed on admission for acute ischemic stroke. Patients and Methods: From November 2018 to October 2019, consecutive stroke patients with no history of AF were included. Let atrial volume (LAV), epicardial adipose tissue (EAT) attenuation and volume, and LAA characteristics were measured on CCTA. The primary endpoint was the presence of AFDAS at follow-up, diagnosed by continuous electrocardiographic monitoring, long-term external Holter monitoring during hospital stay, or implantable cardiac monitor (ICM). Results: Sixty of the 247 included patients developed AFDAS. Multivariable analysis shows independent predictors of AFDAS: age >80 years (HR 2.46; 95%CI (1.23-4.92), p = 0.011), indexed LAV >45 mL/m2 (HR 2.58; 95%CI (1.19-5.62), p = 0.017), EAT attenuation > -85HU (HR 2.16; 95%CI (1.13-4.15), p = 0.021) and LAA thrombus (HR 2.50; 95%CI (1.06-5.93), p = 0.037). Added consecutively to AFDAS prediction AS5F score (combining age and NIHSS >5), these markers had an incrementally better predictive value compared with the global Chi2 of the initial model (p = 0.001, 0.035, and 0.015 respectively). Discussion and conclusion: Adding CCTA to the acute stroke protocol to assess markers of atrial cardiopathy associated with AFDAS may help to better stratify the AF screening strategy, including the use of an ICM.

Machine learning-based scoring system to predict in-hospital outcomes in patients hospitalized with COVID-19.

BACKGROUND: The evolution of patients hospitalized with coronavirus disease 2019 (COVID-19) is still hard to predict, even after several months of dealing with the pandemic. AIMS: To develop and validate a score to predict outcomes in patients hospitalized with COVID-19. METHODS: All consecutive adults hospitalized for COVID-19 from February to April 2020 were included in a nationwide observational study. Primary composite outcome was transfer to an intensive care unit from an emergency department or conventional ward, or in-hospital death. A score that estimates the risk of experiencing the primary outcome was constructed from a derivation cohort using stacked LASSO (Least Absolute Shrinkage and Selection Operator), and was tested in a validation cohort. RESULTS: Among 2873 patients analysed (57.9% men; 66.6±17.0 years), the primary outcome occurred in 838 (29.2%) patients: 551 (19.2%) were transferred to an intensive care unit; and 287 (10.0%) died in-hospital without transfer to an intensive care unit. Using stacked LASSO, we identified 11 variables independently associated with the primary outcome in multivariable analysis in the derivation cohort (n=2313), including demographics (sex), triage vitals (body temperature, dyspnoea, respiratory rate, fraction of inspired oxygen, blood oxygen saturation) and biological variables (pH, platelets, C-reactive protein, aspartate aminotransferase, estimated glomerular filtration rate). The Critical COVID-19 France (CCF) risk score was then developed, and displayed accurate calibration and discrimination in the derivation cohort, with C-statistics of 0.78 (95% confidence interval 0.75-0.80). The CCF risk score performed significantly better (i.e. higher C-statistics) than the usual critical care risk scores. CONCLUSIONS: The CCF risk score was built using data collected routinely at hospital admission to predict outcomes in patients with COVID-19. This score holds promise to improve early triage of patients and allocation of healthcare resources.

Impact of the First COVID-19 Wave on French Hospitalizations for Myocardial Infarction and Stroke: A Retrospective Cohort Study.

The COVID-19 pandemic modified the management of myocardial infarction (MI) and stroke. We aimed to evaluate the effect of the COVID-19 pandemic on the volume and spatial distribution of hospitalizations for MI and stroke, before, during and after the first nationwide lockdown in France in 2020, compared with 2019. Hospitalization data were extracted from the French National Discharge database. Patient's characteristics were compared according to COVID-19 status. Changes in hospitalization rates over time were measured using interrupted time series analysis. Possible spatial patterns of over or under-hospitalization rates were investigated using Moran's indices. We observed a rapid and significant drop in hospitalizations just before the beginning of the lockdown with a nadir at 36.5% for MI and 31.2% for stroke. Hospitalization volumes returned to those seen in 2019 four weeks after the end of the lockdown, except for MI, which rebounded excessively. Older age, male sex, elevated rate of hypertension, diabetes, obesity and mortality characterized COVID-19 patients. There was no evidence of a change in the spatial pattern of over- or under-hospitalization clusters over the three periods. After a steep drop, only MI showed a significant rebound after the first lockdown with no change in the spatial distribution of hospitalizations.

Multimodal Approach for the Prediction of Atrial Fibrillation Detected After Stroke: SAFAS Study.

Background: Intensive screening for atrial fibrillation (AF) has led to a better recognition of this cause in stroke patients. However, it is currently debated whether AF Detected After Stroke (AFDAS) has the same pathophysiology and embolic risk as prior-to-stroke AF. We thus aimed to systematically approach AFDAS using a multimodal approach combining clinical, imaging, biological and electrocardiographic markers. Methods: Patients without previously known AF admitted to the Dijon University Hospital (France) stroke unit for acute ischemic stroke were prospectively enrolled. The primary endpoint was the presence of AFDAS at 6 months, diagnosed through admission ECG, continuous electrocardiographic monitoring, long-term external Holter during the hospital stay, or implantable cardiac monitor if clinically indicated after discharge. Results: Of the 240 included patients, 77 (32%) developed AFDAS. Compared with sinus rhythm patients, those developing AFDAS were older, more often women and less often active smokers. AFDAS patients had higher blood levels of NT-proBNP, osteoprotegerin, galectin-3, GDF-15 and ST2, as well as increased left atrial indexed volume and lower left ventricular ejection fraction. After multivariable analysis, galectin-3 ≧ 9 ng/ml [OR 3.10; 95% CI (1.03-9.254), p = 0.042], NT-proBNP ≧ 290 pg/ml [OR 3.950; 95% CI (1.754-8.892, p = 0.001], OPG ≥ 887 pg/ml [OR 2.338; 95% CI (1.015-5.620), p = 0.046) and LAVI ≥ 33.5 ml/m2 [OR 2.982; 95% CI (1.342-6.625), p = 0.007] were independently associated with AFDAS. Conclusion: A multimodal approach combining imaging, electrocardiography and original biological markers resulted in good predictive models for AFDAS. These results also suggest that AFDAS is probably related to an underlying atrial cardiopathy. Clinical Trial Registration: [www.ClinicalTrials.gov], identifier [NCT03570060].

Trends of Myocarditis and Endocarditis Cases before, during, and after the First Complete COVID-19-Related Lockdown in 2020 in France.

BACKGROUND: The impact of the COVID-19 pandemic on hospitalization for cardiac infections is not well known. We aimed to evaluate the nationwide trends in hospital stays for myocarditis and endocarditis cases before, during and after the nationwide lockdown for the COVID-19 pandemic in France. We then aimed to describe the proportion of myocarditis and endocarditis patients with and without COVID-19 and their clinical characteristics. METHODS: Hospitalized cases of cardiac infection were extracted from the French National Discharge database, which collects the medical records of all patients discharged from all public and private hospitals in France. Age, sex, and available cardiovascular risk factors were compared between stays with and without COVID-19 during the lockdown. RESULTS: The number of myocarditis cases was 11% higher in 2020, compared to the average of the three prior years. In 2020, 439 of 3727 cases of myocarditis were associated with COVID-19. For endocarditis, there was an increase in cases by 7% in 2020 versus prior years. For endocarditis, 3% (240 of 8128 cases) of patients with endocarditis had COVID-19. For myocarditis, older age, hypertension, diabetes, obesity, and atrial fibrillation were more frequent in patients with COVID-19 than in those without. For endocarditis, only hypertension was more frequent in patients with COVID-19 than in those without. CONCLUSION: Our study reports an increase in hospitalizations for both myocarditis and endocarditis in 2020, possibly related to the COVID-19 pandemic. Interestingly, the trends differ according to the COVID-19 status. Knowledge of the factors associating myocarditis or endocarditis and COVID-19 may improve the quality and the type of monitoring for people with COVID-19, the identification of patients at risk of cardiac infections, and the treatment of COVID-19 patients.

Deep learning methods for automatic evaluation of delayed enhancement-MRI. The results of the EMIDEC challenge.

A key factor for assessing the state of the heart after myocardial infarction (MI) is to measure whether the myocardium segment is viable after reperfusion or revascularization therapy. Delayed enhancement-MRI or DE-MRI, which is performed 10 min after injection of the contrast agent, provides high contrast between viable and nonviable myocardium and is therefore a method of choice to evaluate the extent of MI. To automatically assess myocardial status, the results of the EMIDEC challenge that focused on this task are presented in this paper. The challenge's main objectives were twofold. First, to evaluate if deep learning methods can distinguish between non-infarct and pathological exams, i.e. exams with or without hyperenhanced area. Second, to automatically calculate the extent of myocardial infarction. The publicly available database consists of 150 exams divided into 50 cases without any hyperenhanced area after injection of a contrast agent and 100 cases with myocardial infarction (and then with a hyperenhanced area on DE-MRI), whatever their inclusion in the cardiac emergency department. Along with MRI, clinical characteristics are also provided. The obtained results issued from several works show that the automatic classification of an exam is a reachable task (the best method providing an accuracy of 0.92), and the automatic segmentation of the myocardium is possible. However, the segmentation of the diseased area needs to be improved, mainly due to the small size of these areas and the lack of contrast with the surrounding structures.

Prediction of Myocardial Infarction From Patient Features With Machine Learning.

This study proposes machine learning-based models to automatically evaluate the severity of myocardial infarction (MI) from physiological, clinical, and paraclinical features. Two types of machine learning models are investigated for the MI assessment: the classification models classify the presence of the infarct and the persistent microvascular obstruction (PMO), and the regression models quantify the Percentage of Infarcted Myocardium (PIM) of patients suspected of having an acute MI during their reception in the emergency department. The ground truth labels for these supervised models are derived from the corresponding Delayed Enhancement MRI (DE-MRI) exams and manual annotations of the myocardium and scar tissues. Experiments were conducted on 150 cases and evaluated with cross-validation. Results showed that for the MI (PMO inclusive) and the PMO (infarct exclusive), the best models obtained respectively a mean error of 0.056 and 0.012 for the quantification, and 88.67 and 77.33% for the classification accuracy of the state of the myocardium. The study of the features' importance also revealed that the troponin value had the strongest correlation to the severity of the MI among the 12 selected features. For the proposal's translational perspective, in cardiac emergencies, qualitative and quantitative analysis can be obtained prior to the achievement of MRI by relying only on conventional tests and patient features, thus, providing an objective reference for further treatment by physicians.