Comparative Analysis of 16S rRNA Gene and Metagenome Sequencing in Pediatric Gut Microbiomes.

The colonization of the human gut microbiome begins at birth, and over time, these microbial communities become increasingly complex. Most of what we currently know about the human microbiome, especially in early stages of development, was described using culture-independent sequencing methods that allow us to identify the taxonomic composition of microbial communities using genomic techniques, such as amplicon or shotgun metagenomic sequencing. Each method has distinct tradeoffs, but there has not been a direct comparison of the utility of these methods in stool samples from very young children, which have different features than those of adults. We compared the effects of profiling the human infant gut microbiome with 16S rRNA amplicon vs. shotgun metagenomic sequencing techniques in 338 fecal samples; younger than 15, 15-30, and older than 30 months of age. We demonstrate that observed changes in alpha-diversity and beta-diversity with age occur to similar extents using both profiling methods. We also show that 16S rRNA profiling identified a larger number of genera and we find several genera that are missed or underrepresented by each profiling method. We present the link between alpha diversity and shotgun metagenomic sequencing depth for children of different ages. These findings provide a guide for selecting an appropriate method and sequencing depth for the three studied age groups.

A non-interventional comparison of rivaroxaban with standard of care for thromboprophylaxis after major orthopaedic surgery in 17,701 patients with propensity score adjustment.

Rivaroxaban demonstrated superior efficacy and a similar safety profile to enoxaparin for the prevention of venous thromboembolism in the phase III RECORD programme in patients undergoing elective hip or knee replacement surgery. The XAMOS study investigated adverse events, including bleeding and thromboembolic events, in patients receiving rivaroxaban for thromboprophylaxis in routine clinical practice. XAMOS was a non-interventional, open-label cohort study in patients undergoing major orthopaedic surgery of the hip or knee (predominantly elective arthroplasty), in which rivaroxaban was compared with other pharmacological thromboprophylaxis. All adverse events were documented, including symptomatic thromboembolic and bleeding events. Crude and adjusted incidences based on propensity score subclasses were calculated and compared between the rivaroxaban and standard-of-care groups. A total of 17,701 patients were enrolled from 252 centres in 37 countries. Crude incidences of symptomatic thromboembolic events three months after surgery in the safety population were 0.89% in the rivaroxaban group (n=8,778) and 1.35% in the standard-of-care group (n=8,635; odds ratio [OR] 0.65; 95% confidence interval [CI] 0.49-0.87), and 0.91% and 1.31% (weighted) in the propensity score-adjusted analysis (OR 0.69; 95% CI 0.56-0.85), respectively. Treatment-emergent major bleeding events (as defined in the RECORD studies) occurred in 0.40% and 0.34% of patients in the rivaroxaban and standard-of-care groups in the safety population (OR 1.19; 95% CI 0.73-1.95), and in 0.44% versus 0.33% (weighted) in the propensity score-adjusted analysis (OR 1.35; 95% CI 0.94-1.93), respectively.This study in unselected patients confirmed the favourable benefit-risk profile of rivaroxaban seen in the RECORD programme.

Métodos de puntuación de propensión para crear una distribución equilibrada de las covariables en los estudios observacionales / Propensity score methods for creating covariate balance in observational studies

La asignación aleatoria del tratamiento en los experimentos divide a los pacientes en grupos de tratamiento que están aproximadamente equilibrados en cuanto a las covariables basales. Sin embargo, en los estudios observacionales, en los que la asignación del tratamiento no es aleatoria, los pacientes de los grupos de tratamiento activo y de control difieren a menudo en covariables cruciales que están relacionadas con las variables de respuesta. Estos desequilibrios en las covariables pueden conducir a estimaciones sesgadas del efecto del tratamiento. La puntuación de propensión (propensity score) es la probabilidad de que a un paciente con unas características basales específicas se le asigne el tratamiento activo, y no el control. Aunque las puntuaciones de propensión son desconocidas en los estudios observacionales, al parear o subclasificar a los pacientes según las puntuaciones de propensión estimadas, podemos diseñar estudios observacionales que sean análogos a los experimentos aleatorios, con un equilibrio aproximado entre pacientes en cuanto a las covariables observadas. Los diseños de estudios observacionales basados en puntuaciones de propensión estimadas pueden producir estimaciones aproximadamente insesgadas del efecto del tratamiento. Una cuestión crucial es que los diseños de puntuación de propensión deben crearse sin tener acceso a las respuestas, imitando la separación entre el diseño del estudio y el análisis de las respuestas que es propia de los experimentos aleatorios. En este artículo se describen el marco conceptual de las respuestas potenciales para la inferencia causal y las mejores prácticas para el diseño de estudios observacionales con puntuaciones de propensión. Comentamos el uso de puntuaciones de propensión en dos estudios en los que se evaluaron la efectividad y los riesgos de los fármacos antifibrinolíticos durante las cirugías cardiacas (AU)

Randomization of treatment assignment in experiments generates treatment groups with approximately balanced baseline covariates. However, in observational studies, where treatment assignment is not random, patients in the active treatment and control groups often differ on crucial covariates that are related to outcomes. These covariate imbalances can lead to biased treatment effect estimates. The propensity score is the probability that a patient with particular baseline characteristics is assigned to active treatment rather than control. Though propensity scores are unknown in observational studies, by matching or subclassifying patients on estimated propensity scores, we can design observational studies that parallel randomized experiments, with approximate balance on observed covariates. Observational study designs based on estimated propensity scores can generate approximately unbiased treatment effect estimates. Critically, propensity score designs should be created without access to outcomes, mirroring the separation of study design and outcome analysis in randomized experiments. This paper describes the potential outcomes framework for causal inference and best practices for designing observational studies with propensity scores. We discuss the use of propensity scores in two studies assessing the effectiveness and risks of antifibrinolytic drugs during cardiac surgery (AU)

[Propensity score methods for creating covariate balance in observational studies]. / Métodos de puntuación de propensión para crear una distribución equilibrada de las covariables en los estudios observacionales.

Randomization of treatment assignment in experiments generates treatment groups with approximately balanced baseline covariates. However, in observational studies, where treatment assignment is not random, patients in the active treatment and control groups often differ on crucial covariates that are related to outcomes. These covariate imbalances can lead to biased treatment effect estimates. The propensity score is the probability that a patient with particular baseline characteristics is assigned to active treatment rather than control. Though propensity scores are unknown in observational studies, by matching or subclassifying patients on estimated propensity scores, we can design observational studies that parallel randomized experiments, with approximate balance on observed covariates. Observational study designs based on estimated propensity scores can generate approximately unbiased treatment effect estimates. Critically, propensity score designs should be created without access to outcomes, mirroring the separation of study design and outcome analysis in randomized experiments. This paper describes the potential outcomes framework for causal inference and best practices for designing observational studies with propensity scores. We discuss the use of propensity scores in two studies assessing the effectiveness and risks of antifibrinolytic drugs during cardiac surgery.