Acidose lactique et metformine (étude cas-témoin appariée et régression logistique conditionnelle au CHU de Grenoble entre 2008 et 2011)

La metformine est le 1er antidiabétique oral recommandé dans le diabète de type 2. Sa complication la plus redoutée est l acidose lactique (AL). Bien qu exceptionnelle, l AL est à l origine de nombreuses restrictions d emploi et contre-indications. L objectif de ce travail est d évaluer l association entre la metformine et la survenue d une AL chez des patients diabétiques de type 2 et de préciser l importance relative de ses facteurs de risque. Dans cette étude cas-témoin appariée, tous les cas d AL survenus au CHU de Grenoble entre 2008 et 2011 (pH 5 mmol/l) ont été appariés à 2 témoins diabétiques. Les antécédents, médicaments néphrotoxiques et évènements cliniques intercurrents ont été collectés dans le dossier médical. Nous avons réalisé une régression logistique conditionnelle afin d identifier les facteurs de risque significatifs. Notre étude comprend 302 cas et 604 témoins. Les facteurs de risques significativement associés à la survenue d une AL chez les patients diabétiques sont l insuffisance rénale aiguë IRA (OR = 9,58, IC95% = [5,24 - 17,47], p<0,001), l insuffisance respiratoire aiguë (OR = 9,34, IC95% = [4,76 - 18,32], p<0,001), le sepsis (OR = 8,28, IC95% = [4,28 - 15,99], p<0,001), l insuffisance hépatocellulaire (OR = 6,51, IC95% = [2,78 - 15,25], p < 0,001) et la décompensation cardiaque aiguë (OR = 3,55, IC95% = [1,84 - 6,84], p < 0,001). La metformine n est pas considérée comme un facteur de risque significatif (OR = 1,27, IC95% = [0,73 - 2,22], p = 0,390). Il existe une interaction entre la metformine et l IRA : la metformine augmente significativement le risque d AL (OR = 1,79, IC95% = [1,09 - 2,93], p = 0,020) en cas d IRA. Notre modèle permet de quantifier l importance relative des facteurs de risque de survenue d AL, dont la metformine. Nos résultats sont en accord avec ce qui est décrit dans la littérature. L AL survient le plus souvent en présence d une maladie aiguë chez le patient diabétique de type 2. Les antécédents du patient semblent jouer un rôle modéré dans la survenue d une acidose, à part l insuffisance hépatocellulaire. Les situations à risque d atteinte rénale aiguë (déshydratation, introduction de médicaments néphrotoxiques, bas débit ) nécessitent un suivi étroit des patients traités par metformine.GRENOBLE1-BU Médecine pharm. (385162101) / SudocSudocFranceF

Leveraging the Variability of Pharmacovigilance Disproportionality Analyses to Improve Signal Detection Performances

Background: A plethora of methods and models of disproportionality analyses for safety surveillance have been developed to date without consensus nor a gold standard, leading to methodological heterogeneity and substantial variability in results. We hypothesized that this variability is inversely correlated to the robustness of a signal of disproportionate reporting (SDR) and could be used to improve signal detection performances. Methods: We used a validated reference set containing 399 true and false drug-event pairs and performed, with a frequentist and a Bayesian disproportionality method, seven types of analyses (model) for which the results were very unlikely to be related to actual differences in absolute risks of ADR. We calculated sensitivity, specificity and plotted ROC curves for each model. We then evaluated the predictive capacities of all models and assessed the impact of combining such models with the number of positive SDR for a given drug-event pair through binomial regression models. Results: We found considerable variability in disproportionality analysis results, both positive and negative SDR could be generated for 60% of all drug-event pairs depending on the model used whatever their truthfulness. Furthermore, using the number of positive SDR for a given drug-event pair largely improved the signal detection performances of all models. Conclusion: We therefore advocate for the pre-registration of protocols and the presentation of a set of secondary and sensitivity analyses instead of a unique result to avoid selective outcome reporting and because variability in the results may reflect the likelihood of a signal being a true adverse drug reaction.MIAI @ Grenoble Alpe

Innate Sensing of HIV-Infected Cells

Cell-free HIV-1 virions are poor stimulators of type I interferon (IFN) production. We examined here how HIV-infected cells are recognized by plasmacytoid dendritic cells (pDCs) and by other cells. We show that infected lymphocytes are more potent inducers of IFN than virions. There are target cell-type differences in the recognition of infected lymphocytes. In primary pDCs and pDC-like cells, recognition occurs in large part through TLR7, as demonstrated by the use of inhibitors and by TLR7 silencing. Donor cells expressing replication-defective viruses, carrying mutated reverse transcriptase, integrase or nucleocapsid proteins induced IFN production by target cells as potently as wild-type virus. In contrast, Env-deleted or fusion defective HIV-1 mutants were less efficient, suggesting that in addition to TLR7, cytoplasmic cellular sensors may also mediate sensing of infected cells. Furthermore, in a model of TLR7-negative cells, we demonstrate that the IRF3 pathway, through a process requiring access of incoming viral material to the cytoplasm, allows sensing of HIV-infected lymphocytes. Therefore, detection of HIV-infected lymphocytes occurs through both endosomal and cytoplasmic pathways. Characterization of the mechanisms of innate recognition of HIV-infected cells allows a better understanding of the pathogenic and exacerbated immunologic events associated with HIV infection

Angiotensin-converting enzyme and dipeptidyl peptidase-4 inhibitor–induced angioedema: A disproportionality analysis of the WHO pharmacovigilance database

International audienc

Lactic Acidosis in Diabetic Population: Is Metformin Implicated? Results of a Matched Case-Control Study Performed on the Type 2 Diabetes Population of Grenoble Hospital University

International audienc

Reply to ‘Overuse or underuse of methylphenidate in adults in France: commentary on Pauly et al . 2018’ by Weibel et al .

International audienc

Nodular Regenerative Hyperplasia Induced by Trastuzumab Emtansine: Role of Emtansine?

Trastuzumab is a monoclonal antibody targeted against the Human Epidermal Growth Factor Receptor 2 (HER2) overexpressed in some breast cancer. This targeted therapy significantly improves the prognosis of these cancers. Recently an anti-HER2 antibodydrug conjugate was shaped in order to facilitate the targeted delivery of potent cytotoxic drug to cancer cells and to reduce resistance. This formulation, called trastuzumab emtansine (T-DM1), consists of the monoclonal antibody trastuzumab linked to a cytotoxic drug (a derivative of maytansine) via a chemical linker. Little is known about adverse reactions due to this new formulation. Herein we described the case of a woman suffering from a HER2-positive breast cancer, treated with trastuzumab for 30 months followed by T-DM1 monotherapy. After 12 months of T-DM1 treatment, a nodular regenerative hyperplasia confirmed by liver biopsy occurred. T-DM1 was stopped and medical imagery showed a resolution of the nodular regenerative hyperplasia. Unfortunately, hepatic metastasis progressed. Few cases of nodular regenerative hyperplasia induced by T-DM1 have been described so far. Further studies are needed to explore pathogenesis of nodular regenerative hyperplasia with this new antibody-drug conjugate treatment

Scalable Machine Learning for Predicting At-Risk Profiles Upon Hospital Admission

International audienceWe show how the analysis of very large amounts of drug prescription data make it possible to detect, on the day of hospital admission, patients at risk of developing complications during their hospital stay. We explore, for the first time, to which extent volume and variety of big prescription data help in constructing predictive models for the automatic detection of at-risk profiles.Our methodology is designed to validate our claims that: (1) drug prescription data on the day of admission contain rich information about the patient's situation and perspectives of evolution, and (2) the various perspectives of big medical data (such as veracity, volume, variety) help in extracting this information.We build binary classification models to identify at-risk patient profiles. We use a distributed architecture to ensure scalability of model construction with large volumes of medical records and clinical data. We report on practical experiments with real data of millions of patients and hundreds of hospitals. We demonstrate how the fine-grained analysis of such big data can improve the detection of at-risk patients, making it possible to construct more accurate predictive models that significantly benefit from volume and variety, while satisfying important criteria to be deployed in hospitals

Reported Adverse Drug Reactions Associated With the Use of Hydroxychloroquine and Chloroquine During the COVID-19 Pandemic

International audienc