Incidence and clinical impact of infective endocarditis after transcatheter aortic valve implantation

The aim of this study is to describe the characteristics of infective endocarditis (IE) after transcatheter aortic valve implantation (TAVI). This study was performed using the GAMES database, a national prospective registry of consecutive patients with IE in 26 Spanish hospitals. Of the 739 cases of IE diagnosed during the study, 1.3% were post-TAVI IE, and these 10 cases, contributed by five centres, represented 1.1% of the 952 TAVIs performed. Mean age was 80 years. All valves were implanted transfemorally. IE appeared a median of 139 days after implantation. The mean age-adjusted Charlson comorbidity index was 5.45. Chronic kidney disease was frequent (five patients), as were atrial fibrillation (five patients), chronic obstructive pulmonary disease (four patients), and ischaemic heart disease (four patients). Six patients presented aortic valve involvement, and four only mitral valve involvement; the latter group had a higher percentage of prosthetic mitral valves (0% vs. 50%). Vegetations were found in seven cases, and four presented embolism. One patient underwent surgery. Five patients died during follow-up: two of these patients died during the admission in which the valve was implanted. Conclusions: IE is a rare but severe complication after TAVI which affects about 1% of patients and entails a relatively high mortality rate. IE occurred during the first year in nine of the 10 patients.

Health care-associated native valve endocarditis: importance of non-nosocomial acquisition.

BACKGROUND: The clinical profile and outcome of nosocomial and non-nosocomial health care-associated native valve endocarditis are not well defined. OBJECTIVE: To compare the characteristics and outcomes of community-associated and nosocomial and non-nosocomial health care-associated native valve endocarditis. DESIGN: Prospective cohort study. SETTING: 61 hospitals in 28 countries. PATIENTS: Patients with definite native valve endocarditis and no history of injection drug use who were enrolled in the ICE-PCS (International Collaboration on Endocarditis Prospective Cohort Study) from June 2000 to August 2005. MEASUREMENTS: Clinical and echocardiographic findings, microbiology, complications, and mortality. RESULTS: Health care-associated native valve endocarditis was present in 557 (34%) of 1622 patients (303 with nosocomial infection [54%] and 254 with non-nosocomial infection [46%]). Staphylococcus aureus was the most common cause of health care-associated infection (nosocomial, 47%; non-nosocomial, 42%; P = 0.30); a high proportion of patients had methicillin-resistant S. aureus (nosocomial, 57%; non-nosocomial, 41%; P = 0.014). Fewer patients with health care-associated native valve endocarditis had cardiac surgery (41% vs. 51% of community-associated cases; P < 0.001), but more of the former patients died (25% vs. 13%; P < 0.001). Multivariable analysis confirmed greater mortality associated with health care-associated native valve endocarditis (incidence risk ratio, 1.28 [95% CI, 1.02 to 1.59]). LIMITATIONS: Patients were treated at hospitals with cardiac surgery programs. The results may not be generalizable to patients receiving care in other types of facilities or to those with prosthetic valves or past injection drug use. CONCLUSION: More than one third of cases of native valve endocarditis in non-injection drug users involve contact with health care, and non-nosocomial infection is common, especially in the United States. Clinicians should recognize that outpatients with extensive out-of-hospital health care contacts who develop endocarditis have clinical characteristics and outcomes similar to those of patients with nosocomial infection. PRIMARY FUNDING SOURCE: None.

Genomics to elucidate the molecular basis of calcific aortic valve disease

Le rétrécissement valvulaire aortique (RVA) est causé par une calcification et une fibrose progressive de la valve aortique. Le risque de développer la maladie augmente avec l’âge. À cause de l’augmentation de l’espérance de vie, le RVA est devenu un problème de santé publique. Le RVA est fatal en absence de traitement médical. Actuellement, la chirurgie est le seul traitement pour le stade sévère de la maladie, mais près de 50% des individus avec RVA n’y sont pas éligibles, principalement due à la présence de comorbidités. Plusieurs processus biologiques ont été associés à la maladie, mais les voies moléculaires spécifiques et les gènes impliqués dans le développement et la progression du RVA ne sont pas connus. Il est donc urgent de découvrir les gènes de susceptibilité pour le RVA afin d’identifier les personnes à risque ainsi que les biomarqueurs et les cibles thérapeutiques pouvant mener au développement de médicaments pour inverser ou limiter la progression de la maladie. L’objectif de cette thèse de doctorat était d’identifier la base moléculaire du RVA. Des approches modernes en génomique, incluant l’étude de gènes candidats et le criblage génomique par association (GWAS), ont été réalisées à l’aide de collections d’ADN provenant d’un grand nombre de patients bien caractérisés pour le RVA. Des études complémentaires en transciptomique ont comparé le profil d’expression global des gènes entre des valves calcifiées et non-calcifiées à l’aide de biopuces à ADN et de séquençage de l’ARN. Une première étude a identifié des variations dans le gène NOTCH1 et suggère pour la première fois la présence d’un polymorphisme commun dans ce gène conférant une susceptibilité au RVA. La deuxième étude a combiné par méta-analyse deux GWAS de patients provenant de la ville de Québec et Paris (France) aux données transcriptomiques. Cette étude de génomique intégrative a confirmé le rôle de RUNX2 dans le RVA et a permis l’identification d’un nouveau gène de susceptibilité, CACNA1C. Les troisième et quatrième études sur l’expression des gènes ont permis de mieux comprendre les bases moléculaires de la calcification des valves aortiques bicuspides et ainsi d’identifier de nouvelles cibles thérapeutiques pour le RVA. Les données générées par ce projet sont la base de futures découvertes importantes qui permettront d’améliorer les options de traitement et la qualité de vie des patients atteints du RVA.

Chest Breadths to Predict Individuals' Age - A Case Based View

It is well known that rib cage dimensions depend on the gender and vary with the age of the individual. Under this setting it is therefore possible to assume that a computational approach to the problem may be thought out and, consequently, this work will focus on the development of an Artificial Intelligence grounded decision support system to predict individual’s age, based on such measurements. On the one hand, using some basic image processing techniques it were extracted such descriptions from chest X-rays (i.e., its maximum width and height). On the other hand, the computational framework was built on top of a Logic Programming Case Base approach to knowledge representation and reasoning, which caters for the handling of incomplete, unknown, or even contradictory information. Furthermore, clustering methods based on similarity analysis among cases were used to distinguish and aggregate collections of historical data in order to reduce the search space, therefore enhancing the cases retrieval and the overall computational process. The accuracy of the proposed model is satisfactory, close to 90%.

A Case Based Methodology for Problem Solving aiming at Knee Osteoarthritis Detection

Knee osteoarthritis is the most common type of arthritis and a major cause of impaired mobility and disability for the ageing populations. Therefore, due to the increasing prevalence of the malady, it is expected that clinical and scientific practices had to be set in order to detect the problem in its early stages. Thus, this work will be focused on the improvement of methodologies for problem solving aiming at the development of Artificial Intelligence based decision support system to detect knee osteoarthritis. The framework is built on top of a Logic Programming approach to Knowledge Representation and Reasoning, complemented with a Case Based approach to computing that caters for the handling of incomplete, unknown, or even self-contradictory information.

Age Prediction through Pelvis X-Ray Images - A Case Based approach to Problem Solving

It is well known that the dimensions of the pelvic bones depend on the gender and vary with the age of the individual. Indeed, and as a matter of fact, this work will focus on the development of an intelligent decision support system to predict individual’s age based on pelvis’ dimensions criteria. On the one hand, some basic image processing technics were applied in order to extract the relevant features from pelvic X-rays. On the other hand, the computational framework presented here was built on top of a Logic Programming approach to knowledge representation and reasoning, that caters for the handling of incomplete, unknown, or even self-contradictory information, complemented with a Case Base approach to computing.

An Artificial Intelligence Approach to Individuals' Age Assessment

A link between patterns of pelvic growth and human life history is supported by the finding that, cross-culturally, variation in maturation rates of female pelvis are correlated with variation in ages of menarche and first reproduction, i.e., it is well known that the human dimensions of the pelvic bones depend on the gender and vary with the age. Indeed, one feature in which humans appear to be unique is the prolonged growth of the pelvis after the age of sexual maturity. Both the total superoinferior length and mediolateral breadth of the pelvis continues to grow markedly after puberty, and do not reach adult proportions until the late teens years. This continuation of growth is accomplished by relatively late fusion of the separate centers of ossification that form the bones of the pelvis. Hence, in this work we will focus on the development of an intelligent decision support system to predict individual’s age based on a pelvis' dimensions criteria. Some basic image processing techniques were applied in order to extract the relevant features from pelvic X-rays, being the computational framework built on top of a Logic Programming approach to Knowledge Representation and Reasoning that caters for the handling of incomplete, unknown, or even self-contradictory information, complemented with a Case Base approach to computing.