Construction d'un score d'événement à court terme pour les insuffisants cardiaques

International audienceL'insuffisance cardiaque (IC) est un problème majeur de santé publique. Afin d'identifier les patients à risque de décéder ou d'être hospitalisé pour progression de l'IC à court terme, nous avons construit un score d'événement d'IC par l'intermédiaire d'une méthode d'ensemble, en utilisant deux règles de classification différentes, la régression logistique et l'analyse discriminante linéaire de données mixtes, des échantillons bootstrap, et en introduisant un aléa dans la construction des prédicteurs par une sélection aléatoire de variables. L'intervalle de variation du score a été ramené sur une échelle de 0 à 100. Enfin, nous définissons une mesure du risque d'événement associé au score par un odds-ratio et mesurons l'importance des variables et des groupes de variables en utilisant les coefficients standardisés

Score de risque d'événement et score en ligne pour des insuffisants cardiaques

International audienceOn présente la construction d'un score de risque d'événement à court terme pour des insuffisants cardiaques. On suppose ensuite que les données de patients arrivent de façon continue et que l'on veut actualiser en ligne la fonction de score. On étudie en particulier l'estimation en ligne des paramètres d'un mo-dèle de régression linéaire par un processus de gradient stochastique en utilisant des données standardisées en ligne au lieu des données brutes

Visit-to-visit blood pressure variation is associated with outcomes in a U-shaped fashion in patients with myocardial infarction complicated with systolic dysfunction and/or heart failure: findings from the EPHESUS and OPTIMAAL trials

Background: Visit-to-visit office blood pressure variation (BPV) has prognostic implications independent from mean BP across several populations in the cardiovascular field. The association of BPV with outcomes in patients with myocardial infarction (MI) with systolic dysfunction and/or heart failure is yet to be determined. Methods: Two independent cohorts were assessed: the EPHESUS and the OPTIMAAL trials with a total of more than 12 000 patients. The primary outcome was all-cause death. BPV was calculated as a coefficient of variation, that is, the ratio of the SD to the mean BP along the postbaseline follow-up. Cox regression models were used to determine the associations between BPV and events. Results: Compared with the middle and lower BPV tertiles, patients in the upper BPV tertile were older, more often women, hypertensive, diabetic, with peripheral artery disease, and had more frequent use of loop diuretics and ACEi/ARBs. They also had lower LVEF, hemoglobin, and eGFR (all P < 0.001). BPV was independently associated with worse prognosis in a U-shaped manner. In the EPHESUS trial, both low and high BPV were associated with higher rates of death (and also cardiovascular death and the composite of cardiovascular death/ cardiovascular hospitalization): adjusted hazard ratio (95% CI) for the outcome of death is 1.99 (1.68–2.36) for high BPV and is 1.60 (1.35–1.90) for low BPV. Similar results were observed in the OPTIMAAL trial population. Conclusion: In two independent cohorts of MI patients with systolic dysfunction and/or heart failure, BPV was associated with worse prognosis in a U-shaped manner independently of the mean BP

Individualizing treatment choices in the systolic blood pressure intervention trial.

BACKGROUND: Any treatment decision should be tailored to the individual patients' characteristics. A personalized approach aims to help better selecting the patients who are likely to benefit most from a treatment decision. In the systolic blood pressure intervention trial, intensive treatment reduced the rate of major cardiovascular events, but increased the rate of serious adverse events (SAEs). OBJECTIVES: To assess the trade-off between efficacy and safety to simultaneously quantify an individual patient's absolute benefit and absolute harm, helping clinicians making better therapeutic choices in daily practice. METHODS: Multivariable Poisson regression models were used to identify independent risk factors for: primary composite cardiovascular outcome and major SAEs = safety. Estimates from the models were used to quantify each individual risk. RESULTS: Subclinical cardiovascular disease, number of antihypertensive agents, current smoking, age, urine albumin-to-creatinine ratio, and serum creatinine were associated with increased risk of both primary outcome events and SAEs. Triglycerides were associated with increased primary outcome events only, and chronic kidney disease and female sex with SAEs only. The models were well calibrated and showed good performance (c-index for safety = 0.69 and c-index for efficacy = 0.72). For the primary outcome, there is a steep gradient in risk by fifths of the predicted model and a similar gradient exists for the safety outcome predicted model. Mortality within 1 year of an efficacy outcome (as assessed by the Kaplan-Meier method) was nearly three-fold higher than following a safety outcome (21.9 vs. 7.5%). If one judges the clinical importance of efficacy and safety outcomes based on their 1-year mortality, then there is a net benefit of intensive therapy for almost all patients. CONCLUSION: Antihypertensive treatment intensification is associated with lower cardiovascular event rates; however, it increases the risk of adverse events. However, having adverse events has less weight when it comes to therapeutic decisions and antihypertensive therapy intensification is beneficial for the great majority of patients included in the systolic blood pressure intervention trial

Effect of eplerenone on extracellular cardiac matrix biomarkers in patients with acute ST-elevation myocardial infarction without heart failure: insights from the randomized double-blind REMINDER Study

Objective: Aldosterone stimulates cardiac collagen synthesis. Circulating biomarkers of collagen turnover provide a useful tool for the assessment of cardiac remodeling in patients with an acute myocardial infarction (MI).  Methods: The REMINDER trial assessed the effect of eplerenone in patients with an acute ST-elevation Myocardial Infarction (STEMI) without known heart failure (HF), when initiated within 24 h of symptom onset. The primary outcome was almost totally (>90%) driven by natriuretic peptide (NP) thresholds after 1-month post-MI (it also included a composite of cardiovascular death or re-hospitalization or new onset HF or sustained ventricular tachycardia or fibrillation or LVEF ≤40% after 1-month post-MI). This secondary analysis aims to assess the extracellular matrix marker (ECMM) levels with regards to: (1) patients` characteristics; (2) determinants; (3) and eplerenone effect.  Results: Serum levels of ECMM were measured in 526 (52%) of the 1012 patients enrolled in the REMINDER trial. Patients with procollagen type III N-terminal propeptide (PIIINP) above the median were older and had worse renal function (p < 0.05). Worse renal function was associated with increased levels of PIIINP (standardized β ≈ 0.20, p < 0.05). Eplerenone reduced PIIINP when the levels of this biomarker were above the median of 3.9 ng/mL (0.13 ± 1.48 vs. -0.37 ± 1.56 ng/mL, p = 0.008). Higher levels of PIIINP were independently associated with higher proportion of NP above the prespecified thresholds (HR = 1.95, 95% CI 1.16-3.29, p = 0.012).  Conclusions: Eplerenone effectively reduces PIIINP levels when baseline values were above the median. Eplerenone may limit ECMM formation in post-MI without HF

COVID-19 symptoms at hospital admission vary with age and sex: results from the ISARIC prospective multinational observational study

Background: The ISARIC prospective multinational observational study is the largest cohort of hospitalized patients with COVID-19. We present relationships of age, sex, and nationality to presenting symptoms. Methods: International, prospective observational study of 60 109 hospitalized symptomatic patients with laboratory-confirmed COVID-19 recruited from 43 countries between 30 January and 3 August 2020. Logistic regression was performed to evaluate relationships of age and sex to published COVID-19 case definitions and the most commonly reported symptoms. Results: ‘Typical’ symptoms of fever (69%), cough (68%) and shortness of breath (66%) were the most commonly reported. 92% of patients experienced at least one of these. Prevalence of typical symptoms was greatest in 30- to 60-year-olds (respectively 80, 79, 69%; at least one 95%). They were reported less frequently in children (≤ 18 years: 69, 48, 23; 85%), older adults (≥ 70 years: 61, 62, 65; 90%), and women (66, 66, 64; 90%; vs. men 71, 70, 67; 93%, each P &lt; 0.001). The most common atypical presentations under 60 years of age were nausea and vomiting and abdominal pain, and over 60 years was confusion. Regression models showed significant differences in symptoms with sex, age and country. Interpretation: This international collaboration has allowed us to report reliable symptom data from the largest cohort of patients admitted to hospital with COVID-19. Adults over 60 and children admitted to hospital with COVID-19 are less likely to present with typical symptoms. Nausea and vomiting are common atypical presentations under 30 years. Confusion is a frequent atypical presentation of COVID-19 in adults over 60 years. Women are less likely to experience typical symptoms than men

Aide à la décision médicale et télémédecine dans le suivi de l’insuffisance cardiaque

This thesis is part of the "Handle your heart" project aimed at developing a drug prescription assistance device for heart failure patients. In a first part, a study was conducted to highlight the prognostic value of an estimation of plasma volume or its variations for predicting major short-term cardiovascular events. Two classification rules were used, logistic regression and linear discriminant analysis, each preceded by a stepwise variable selection. Three indices to measure the improvement in discrimination ability by adding the biomarker of interest were used. In a second part, in order to identify patients at short-term risk of dying or being hospitalized for progression of heart failure, a short-term event risk score was constructed by an ensemble method, two classification rules, logistic regression and linear discriminant analysis of mixed data, bootstrap samples, and by randomly selecting predictors. We define an event risk measure by an odds-ratio and a measure of the importance of variables and groups of variables using standardized coefficients. We show a property of linear discriminant analysis of mixed data. This methodology for constructing a risk score can be implemented as part of online learning, using stochastic gradient algorithms to update online the predictors. We address the problem of sequential multidimensional linear regression, particularly in the case of a data stream, using a stochastic approximation process. To avoid the phenomenon of numerical explosion which can be encountered and to reduce the computing time in order to take into account a maximum of arriving data, we propose to use a process with online standardized data instead of raw data and to use of several observations per step or all observations until the current step. We define three processes and study their almost sure convergence, one with a variable step-size, an averaged process with a constant step-size, a process with a constant or variable step-size and the use of all observations until the current step without storing them. These processes are compared to classical processes on 11 datasets. The third defined process with constant step-size typically yields the best resultsCette thèse s’inscrit dans le cadre du projet "Prendre votre cœur en mains" visant à développer un dispositif médical d’aide à la prescription médicamenteuse pour les insuffisants cardiaques. Dans une première partie, une étude a été menée afin de mettre en évidence la valeur pronostique d’une estimation du volume plasmatique ou de ses variations pour la prédiction des événements cardiovasculaires majeurs à court terme. Deux règles de classification ont été utilisées, la régression logistique et l’analyse discriminante linéaire, chacune précédée d’une phase de sélection pas à pas des variables. Trois indices permettant de mesurer l’amélioration de la capacité de discrimination par ajout du biomarqueur d’intérêt ont été utilisés. Dans une seconde partie, afin d’identifier les patients à risque de décéder ou d’être hospitalisé pour progression de l’insuffisance cardiaque à court terme, un score d’événement a été construit par une méthode d’ensemble, en utilisant deux règles de classification, la régression logistique et l’analyse discriminante linéaire de données mixtes, des échantillons bootstrap et en sélectionnant aléatoirement les prédicteurs. Nous définissons une mesure du risque d’événement par un odds-ratio et une mesure de l’importance des variables et des groupes de variables. Nous montrons une propriété de l’analyse discriminante linéaire de données mixtes. Cette méthode peut être mise en œuvre dans le cadre de l’apprentissage en ligne, en utilisant des algorithmes de gradient stochastique pour mettre à jour en ligne les prédicteurs. Nous traitons le problème de la régression linéaire multidimensionnelle séquentielle, en particulier dans le cas d’un flux de données, en utilisant un processus d’approximation stochastique. Pour éviter le phénomène d’explosion numérique et réduire le temps de calcul pour prendre en compte un maximum de données entrantes, nous proposons d’utiliser un processus avec des données standardisées en ligne au lieu des données brutes et d’utiliser plusieurs observations à chaque étape ou toutes les observations jusqu’à l’étape courante sans avoir à les stocker. Nous définissons trois processus et en étudions la convergence presque sûre, un avec un pas variable, un processus moyennisé avec un pas constant, un processus avec un pas constant ou variable et l’utilisation de toutes les observations jusqu’à l’étape courante. Ces processus sont comparés à des processus classiques sur 11 jeux de données. Le troisième processus à pas constant est celui qui donne généralement les meilleurs résultat

Aide à la décision médicale et télémédecine dans le suivi de l’insuffisance cardiaque

This thesis is part of the "Handle your heart" project aimed at developing a drug prescription assistance device for heart failure patients. In a first part, a study was conducted to highlight the prognostic value of an estimation of plasma volume or its variations for predicting major short-term cardiovascular events. Two classification rules were used, logistic regression and linear discriminant analysis, each preceded by a stepwise variable selection. Three indices to measure the improvement in discrimination ability by adding the biomarker of interest were used. In a second part, in order to identify patients at short-term risk of dying or being hospitalized for progression of heart failure, a short-term event risk score was constructed by an ensemble method, two classification rules, logistic regression and linear discriminant analysis of mixed data, bootstrap samples, and by randomly selecting predictors. We define an event risk measure by an odds-ratio and a measure of the importance of variables and groups of variables using standardized coefficients. We show a property of linear discriminant analysis of mixed data. This methodology for constructing a risk score can be implemented as part of online learning, using stochastic gradient algorithms to update online the predictors. We address the problem of sequential multidimensional linear regression, particularly in the case of a data stream, using a stochastic approximation process. To avoid the phenomenon of numerical explosion which can be encountered and to reduce the computing time in order to take into account a maximum of arriving data, we propose to use a process with online standardized data instead of raw data and to use of several observations per step or all observations until the current step. We define three processes and study their almost sure convergence, one with a variable step-size, an averaged process with a constant step-size, a process with a constant or variable step-size and the use of all observations until the current step without storing them. These processes are compared to classical processes on 11 datasets. The third defined process with constant step-size typically yields the best resultsCette thèse s’inscrit dans le cadre du projet "Prendre votre cœur en mains" visant à développer un dispositif médical d’aide à la prescription médicamenteuse pour les insuffisants cardiaques. Dans une première partie, une étude a été menée afin de mettre en évidence la valeur pronostique d’une estimation du volume plasmatique ou de ses variations pour la prédiction des événements cardiovasculaires majeurs à court terme. Deux règles de classification ont été utilisées, la régression logistique et l’analyse discriminante linéaire, chacune précédée d’une phase de sélection pas à pas des variables. Trois indices permettant de mesurer l’amélioration de la capacité de discrimination par ajout du biomarqueur d’intérêt ont été utilisés. Dans une seconde partie, afin d’identifier les patients à risque de décéder ou d’être hospitalisé pour progression de l’insuffisance cardiaque à court terme, un score d’événement a été construit par une méthode d’ensemble, en utilisant deux règles de classification, la régression logistique et l’analyse discriminante linéaire de données mixtes, des échantillons bootstrap et en sélectionnant aléatoirement les prédicteurs. Nous définissons une mesure du risque d’événement par un odds-ratio et une mesure de l’importance des variables et des groupes de variables. Nous montrons une propriété de l’analyse discriminante linéaire de données mixtes. Cette méthode peut être mise en œuvre dans le cadre de l’apprentissage en ligne, en utilisant des algorithmes de gradient stochastique pour mettre à jour en ligne les prédicteurs. Nous traitons le problème de la régression linéaire multidimensionnelle séquentielle, en particulier dans le cas d’un flux de données, en utilisant un processus d’approximation stochastique. Pour éviter le phénomène d’explosion numérique et réduire le temps de calcul pour prendre en compte un maximum de données entrantes, nous proposons d’utiliser un processus avec des données standardisées en ligne au lieu des données brutes et d’utiliser plusieurs observations à chaque étape ou toutes les observations jusqu’à l’étape courante sans avoir à les stocker. Nous définissons trois processus et en étudions la convergence presque sûre, un avec un pas variable, un processus moyennisé avec un pas constant, un processus avec un pas constant ou variable et l’utilisation de toutes les observations jusqu’à l’étape courante. Ces processus sont comparés à des processus classiques sur 11 jeux de données. Le troisième processus à pas constant est celui qui donne généralement les meilleurs résultat

Sequential linear regression with online standardized data

International audienceThe present study addresses the problem of sequential least square multidimensional linear regression, particularly in the case of a data stream, using a stochastic approximation process. To avoid the phenomenon of numerical explosion which can be encountered and to reduce the computing time in order to take into account a maximum of arriving data, we propose using a process with online standardized data instead of raw data and the use of several observations per step or all observations until the current step. Herein, we define and study the almost sure convergence of three processes with online standardized data: a classical process with a variable step-size and use of a varying number of observations per step, an averaged process with a constant step-size and use of a varying number of observations per step, and a process with a variable or constant step-size and use of all observations until the current step. Their convergence is obtained under more general assumptions than classical ones. These processes are compared to classical processes on 11 datasets for a fixed total number of observations used and thereafter for a fixed processing time. Analyses indicate that the third-defined process typically yields the best results

Methodology for Constructing a Short-Term Event Risk Score in Heart Failure Patients

International audienceWe present a methodology for constructing a short-term event risk score in heart failure patients from an ensemble predictor, using bootstrap samples, two different classification rules, logistic regression and linear discriminant analysis for mixed data, continuous or categorical, and random selection of explanatory variables to build individual predictors. We define a measure of the importance of each variable in the score and an event risk measure by an odds-ratio. Moreover, we establish a property of linear discriminant analysis for mixed data. This methodology is applied to EPHESUS trial patients on whom biological, clinical and medical history variables were measured