Dupilumab improves symptoms, quality of life, and productivity in uncontrolled persistent asthma

BACKGROUND: In a pivotal, phase 2b study (NCT01854047) in patients with uncontrolled persistent asthma, despite using medium-to-high-dose inhaled corticosteroids plus long-acting β2 agonists, dupilumab improved lung function, reduced severe exacerbations, and showed an acceptable safety profile. OBJECTIVE: To assess the impact of dupilumab on asthma control, symptoms, quality of life (QoL), and productivity. METHODS: Data are shown for the intention-to-treat population receiving dupilumab 200/300 mg every 2 weeks (doses being assessed in phase 3; NCT02414854), or placebo. Predefined analyses of total scores were conducted at week 24 for the 5-item Asthma Control Questionnaire (ACQ-5), patient-reported morning/evening (AM/PM) asthma symptoms, Asthma Quality of Life Questionnaire (AQLQ), and asthma-related productivity loss. Responder rate analyses for these measures, subgroup analyses by baseline characteristics, and asthma-related productivity loss analyses were conducted post hoc. RESULTS: Data from 465 patients were analyzed (158 placebo; 307 dupilumab). Both dupilumab doses significantly improved scores through week 24 (all outcomes, overall population). The proportion of patients meeting or exceeding the minimal clinically important difference for the overall population were significantly greater vs placebo (P \u3c .05) for ACQ-5 (range, 72.6%-76.7% vs 61.4%), for AM/PM asthma symptoms score (48.7%-54.1% vs 34.2% and 52.7%-53.5% vs 34.2%, respectively) and for AQLQ (64.0%-65.0% vs 51.3%). The effect of dupilumab was consistent across most subgroups. Productivity loss was significantly higher in placebo- vs dupilumab-treated patients (P \u3c .0001). CONCLUSION: Dupilumab produced significant, clinically meaningful improvements in asthma control, symptoms, QoL, and productivity. REGISTRATION: ClinicalTrials.gov Identifier: NCT01854047

Impact of Reporting Bias in Network Meta-Analysis of Antidepressant Placebo-Controlled Trials

BACKGROUND: Indirect comparisons of competing treatments by network meta-analysis (NMA) are increasingly in use. Reporting bias has received little attention in this context. We aimed to assess the impact of such bias in NMAs. METHODS: We used data from 74 FDA-registered placebo-controlled trials of 12 antidepressants and their 51 matching publications. For each dataset, NMA was used to estimate the effect sizes for 66 possible pair-wise comparisons of these drugs, the probabilities of being the best drug and ranking the drugs. To assess the impact of reporting bias, we compared the NMA results for the 51 published trials and those for the 74 FDA-registered trials. To assess how reporting bias affecting only one drug may affect the ranking of all drugs, we performed 12 different NMAs for hypothetical analysis. For each of these NMAs, we used published data for one drug and FDA data for the 11 other drugs. FINDINGS: Pair-wise effect sizes for drugs derived from the NMA of published data and those from the NMA of FDA data differed in absolute value by at least 100% in 30 of 66 pair-wise comparisons (45%). Depending on the dataset used, the top 3 agents differed, in composition and order. When reporting bias hypothetically affected only one drug, the affected drug ranked first in 5 of the 12 NMAs but second (n = 2), fourth (n = 1) or eighth (n = 2) in the NMA of the complete FDA network. CONCLUSIONS: In this particular network, reporting bias biased NMA-based estimates of treatments efficacy and modified ranking. The reporting bias effect in NMAs may differ from that in classical meta-analyses in that reporting bias affecting only one drug may affect the ranking of all drugs

Analyse de données textuelles d'un forum médical pour évaluer le ressenti exprimé par les internautes au sujet des antidépresseurs et des anxyolitiques

Analysis of textual data is facilitated by the use of text mining (TM) allowing to automate content analysis, and is implemented in several application in healthcare. These include the use of TM to explore the content of posts shared online.We performed a systematique literature review to identify the application of TM in psychiatry. In addition, we used TM to explore users’ concerns of an online forum dedicated to antidepressants and anxiolytics between 2013 and 2015 analysing words frequency, cooccurences, topic models (LDA) and popularity of topics.The four TM applications in psychiatry retrieved are the analysis of patients' narratives (psychopathology), feelings expressed online, content of medical records, and biomedical literature screening. Four topics are identified on the forum: withdrawals (most frequent), escitalopram, anxiety related to treatment effect and secondary effects. While concerns around secondary effects of treatment declined, questions about withdrawals effects and changing medication increased related to several antidepressants.Content analysis of online textual data allow us to better understand major concerns of patients, support provided, and to improve the adherence of treatment.L’analyse de donnée textuelle est facilitée par l’utilisation du text mining (TM) permettant l’automatisation de l’analyse de contenu et possède de nombreuses applications en santé. L’une d’entre elles est l’utilisation du TM pour explorer le contenu des messages échangés sur Internet.Nous avons effectué une revue de la littérature systématique afin d’identifier les applications du TM en santé mentale. De plus, le TM a permis d’explorer les préoccupations des utilisateurs du forum Doctissimo.com au sujet des antidépresseurs et anxiolytiques entre 2013 et 2015 via l’analyse des fréquences des mots, des cooccurrences, de la modélisation thématique (LDA) et de la popularité des thèmes.Les quatre applications du TM en santé mentale sont l’analyse des récits des patients (psychopathologie), le ressenti exprimé sur Internet, le contenu des dossiers médicaux, et les thèmes de la littérature médicale. Quatre grands thèmes ont été identifiés sur le forum: le sevrage (le plus fréquent), l’escitalopram, l’anxiété de l’effet du traitement et les effets secondaires. Alors que les effets indésirables des traitements est un sujet qui a tendance à décroitre, les interrogations sur les effets du sevrage et le changement de traitement sont grandissantes et associées aux antidépresseurs.L’analyse du contenu d’Internet permet de comprendre les préoccupations des patients et le soutien, et améliorer l’adhérence au traitement

Text Mining Analysis of an Online Forum to Evaluate Users’ Perception about Antidepressants and Anxiolytics

L’analyse de donnée textuelle est facilitée par l’utilisation du text mining (TM) permettant l’automatisation de l’analyse de contenu et possède de nombreuses applications en santé. L’une d’entre elles est l’utilisation du TM pour explorer le contenu des messages échangés sur Internet.Nous avons effectué une revue de la littérature systématique afin d’identifier les applications du TM en santé mentale. De plus, le TM a permis d’explorer les préoccupations des utilisateurs du forum Doctissimo.com au sujet des antidépresseurs et anxiolytiques entre 2013 et 2015 via l’analyse des fréquences des mots, des cooccurrences, de la modélisation thématique (LDA) et de la popularité des thèmes.Les quatre applications du TM en santé mentale sont l’analyse des récits des patients (psychopathologie), le ressenti exprimé sur Internet, le contenu des dossiers médicaux, et les thèmes de la littérature médicale. Quatre grands thèmes ont été identifiés sur le forum: le sevrage (le plus fréquent), l’escitalopram, l’anxiété de l’effet du traitement et les effets secondaires. Alors que les effets indésirables des traitements est un sujet qui a tendance à décroitre, les interrogations sur les effets du sevrage et le changement de traitement sont grandissantes et associées aux antidépresseurs.L’analyse du contenu d’Internet permet de comprendre les préoccupations des patients et le soutien, et améliorer l’adhérence au traitement.Analysis of textual data is facilitated by the use of text mining (TM) allowing to automate content analysis, and is implemented in several application in healthcare. These include the use of TM to explore the content of posts shared online.We performed a systematique literature review to identify the application of TM in psychiatry. In addition, we used TM to explore users’ concerns of an online forum dedicated to antidepressants and anxiolytics between 2013 and 2015 analysing words frequency, cooccurences, topic models (LDA) and popularity of topics.The four TM applications in psychiatry retrieved are the analysis of patients' narratives (psychopathology), feelings expressed online, content of medical records, and biomedical literature screening. Four topics are identified on the forum: withdrawals (most frequent), escitalopram, anxiety related to treatment effect and secondary effects. While concerns around secondary effects of treatment declined, questions about withdrawals effects and changing medication increased related to several antidepressants.Content analysis of online textual data allow us to better understand major concerns of patients, support provided, and to improve the adherence of treatment

Star-shaped networks of comparisons of data from 74 US Food and Drug Administration (FDA)-registered trials of 12 antidepressants and their 51 related publications.

<p>The central node represents the placebo, and each leaf node represents an antidepressant agent. Each node diameter is proportional to the number of patients who received the antidepressant agent; each connecting line width is proportional to the number of trials that addressed the comparison.</p

Scatterplot of estimates of relative efficacy for 66 pair-wise comparisons of the 12 antidepressant agents with one another derived from network meta-analyses of data from 74 FDA-registered trials and their 51 trial publications.

<p>Data are effect sizes. Positive effect sizes indicate that drug A has higher efficacy than drug B. The two areas above the uppermost dotted line (labeled +100%) and below the lowest dotted line (labeled −100%) correspond to cases in which an effect size derived from the network meta-analysis of the 51 published trials differed in absolute value from that derived from the network meta-analysis of the 74 FDA-registered trials by at least 100%. The two areas between the 2 upper dotted lines (labeled +50%) and between the 2 lower dotted lines (labeled −50%) correspond to cases in which an effect size derived from the network meta-analysis of the 51 published trials differed in absolute value from that derived from the network meta-analysis of the 74 FDA-registered trials by at least 50%. Red-colored points refer to cases in which agent B was superior to agent A by one network meta-analysis and A was superior to B by the other network meta-analysis.</p

Probabilities of being the best among competing antidepressant agents when reporting bias affects one specific agent.

<p>The first stacked bar at the left corresponds to the network meta-analysis free of reporting biases (ie, with the data from the 74 FDA-registered trials). The other stacked bars correspond to the 12 network meta-analyses in which reporting bias hypothetically affects one specific agent in turn. For instance, for mirtazapine, we used the 6 published trials (out of 10 FDA-registered trials), with published effect sizes, and data from the 64 FDA-registered trials for the other 11 agents, which resulted in an incomplete FDA network of 70 trials; the probability of mirtazapine being the best was 80.6% with data from the incomplete FDA network and 7.3% with data from the 74 FDA-registered trials. For the sake of clarity, we presented in each analysis the 3 drugs with the 3 highest probabilities of being the best among competing antidepressant agents. Bup: Bupropion; Cit: Citalopram; Dul : Duloxetine ; Esc: Escitalopram; Flu: Fluoxetine; Mir: Mirtazapine ; Nef: Nefazodone ; Par: Paroxetine; Par CR: Paroxetine CR; Ser: Sertraline; Ven: Venlafaxine; VenXR: Venlafaxine XR.</p

Summary effect sizes for the 12 comparisons of each antidepressant agent and placebo.

<p>Weighted mean effect-size values for each drug were derived using a random-effects model with the method of DerSimonian and Laird. N: number of trials; SMD (95%CI): summary standardized mean difference of drug vs. placebo derived from random effects meta-analysis (95% confidence interval); Τ² (SE): between-trial variance as a measure of heterogeneity in meta-analysis (standard error); NA: not assessable.</p

Probabilities that each antidepressant drug is the best according to network meta-analyses of data from 74 FDA-registered trials or 51 published trials with published effect sizes.

<p>For instance, for mirtazapine, the probability of being the best was 7.3% and 30.2% according to network-meta-analysis of the 74 FDA-registered trials and 51 published trials with published effect sizes, respectively. Drugs for which the probability of being the best was <5% for both published and FDA data are not labeled (blue area).</p

Une longue histoire

L’intérêt porté au paysage est d’abord une préoccupation des sociétés actuelles. Tout à la fois protégé, menacé, transformé rapidement, il est devenu une composante des politiques territoriales. Enjeu, il est aussi un sujet d’études dont se sont emparées les sciences qui travaillent sur les dynamiques spatiales et temporelles. Car c’est aujourd’hui une évidence, le paysage n’est pas une donnée invariable (même dit naturel ou sauvage), un simple décor : hybride, il est le produit des sociétés et des milieux géographiques, une longue construction. Le colloque organisé à Carcassonne, aux Archives départementales de l’Aude, les 23 et 24 mai 2008 a traité d’Une longue histoire : la construction des paysage méridionaux. L’objet du questionnement et du débat, c’est le paysage tel qu’il est, tel qu’il s’élabore, mais aussi tel qu’il est perçu ; la construction mentale, autant que l’édification physique. Les contributions rassemblées font appel aux différentes approches disciplinaires et auscultent l’espace du sud de la France, de l’Antiquité romaine au XXIe siècle