Service de Biostatistique Bioinformatique

Sainte-Foy-lès-Lyon, France

Service de Biostatistique Bioinformatique

Sainte-Foy-lès-Lyon, France
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Dupont C.,Hopital de la Croix Rousse | Dupont C.,Health Services and Performance Research HESPER EA 7425 | Winer N.,University of Nantes | Rabilloud M.,Service de Biostatistique Bioinformatique | And 18 more authors.
European Journal of Obstetrics Gynecology and Reproductive Biology | Year: 2017

Objective Suboptimal care contributes to perinatal morbidity and mortality. We investigated the effects of a multifaceted program designed to improve obstetric practices and outcomes. Study design A cluster-randomized trial was conducted from October 2008 to November 2010 in 95 French maternity units randomized either to receive an information intervention about published guidelines or left to apply them freely. The intervention combined an outreach visit with a morbidity/mortality conference (MMC) to review perinatal morbidity/mortality cases. Within the intervention group, the units were randomized to have MMCs with or without clinical psychologists. The primary outcome was the rate of suboptimal care among perinatal morbidity/mortality cases. The secondary outcomes included the rate of suboptimal care among cases of morbidity, the rate of suboptimal care among cases of mortality, the rate of avoidable morbidity and/or mortality cases, and the incidence of, morbidity and/or mortality. A mixed logistic regression model with random intercept was used to quantify the effect of the intervention on the main outcome. Results The study reviewed 2459 cases of morbidity or mortality among 165,353 births. The rate of suboptimal care among morbidity plus mortality cases was not significantly lower in the intervention than in the control group (8.1% vs. 10.6%, OR [95% CI]: 0.75 [0.50-1.12], p = 0.15. However, the cases of suboptimal care among morbidity cases were significantly lower in the intervention group (7.6% vs. 11.5%, 0.62 [0.40-0.94], p = 0.02); the incidence of perinatal morbidity was also lower (7.0 vs. 8.1‰, p = 0.01). No differences were found between psychologist-backed and the other units. Conclusions The intervention reduced the rate of suboptimal care mainly in morbidity cases and the incidence of morbidity but did not succeed in improving morbidity plus mortality combined. More clear-cut results regarding mortality require a longer study period and the inclusion of structures that intervene before and after the delivery room. (ClinicalTrials.gov ID: NCT02584166) © 2017


Dridi N.,French National Center for Scientific Research | Dridi N.,Gabes University | Giremus A.,French National Center for Scientific Research | Giovannelli J.-F.,French National Center for Scientific Research | And 13 more authors.
Eurasip Journal on Bioinformatics and Systems Biology | Year: 2017

This paper addresses the question of biomarker discovery in proteomics. Given clinical data regarding a list of proteins for a set of individuals, the tackled problem is to extract a short subset of proteins the concentrations of which are an indicator of the biological status (healthy or pathological). In this paper, it is formulated as a specific instance of variable selection. The originality is that the proteins are not investigated one after the other but the best partition between discriminant and non-discriminant proteins is directly sought. In this way, correlations between the proteins are intrinsically taken into account in the decision. The developed strategy is derived in a Bayesian setting, and the decision is optimal in the sense that it minimizes a global mean error. It is finally based on the posterior probabilities of the partitions. The main difficulty is to calculate these probabilities since they are based on the so-called evidence that require marginalization of all the unknown model parameters. Two models are presented that relate the status to the protein concentrations, depending whether the latter are biomarkers or not. The first model accounts for biological variabilities by assuming that the concentrations are Gaussian distributed with a mean and a covariance matrix that depend on the status only for the biomarkers. The second one is an extension that also takes into account the technical variabilities that may significantly impact the observed concentrations. The main contributions of the paper are: (1) a new Bayesian formulation of the biomarker selection problem, (2) the closed-form expression of the posterior probabilities in the noiseless case, and (3) a suitable approximated solution in the noisy case. The methods are numerically assessed and compared to the state-of-the-art methods (t test, LASSO, Battacharyya distance, FOHSIC) on synthetic and real data from proteins quantified in human serum by mass spectrometry in selected reaction monitoring mode. © 2017, The Author(s).

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