PLoS One

Estimating and selecting risk factors with extremely low prevalences of exposure for a binary outcome is a challenge because classical standard techniques, markedly logistic regression, often fail to provide meaningful results in such settings. While penalized regression methods are widely used in high-dimensional settings, we were able to show their usefulness in low-dimensional settings as well. Specifically, we demonstrate that Firth correction, ridge, the lasso and boosting all improve the estimation for low-prevalence risk factors. While the methods themselves are well-established, comparison studies are needed to assess their potential benefits in this context. This is done here using the dataset of a large unmatched case-control study from France (2005-2008) about the relationship between prescription medicines and road traffic accidents and an accompanying simulation study. Results show that the estimation of risk factors with prevalences below 0.1% can be drastically improved by using Firth correction and boosting in particular, especially for ultra-low prevalences. When a moderate number of low prevalence exposures is available, we recommend the use of penalized techniques

PLoS One

Studies using health administrative databases (HAD) may lead to biased results since information on potential confounders is often missing. Methods that integrate confounder data from cohort studies, such as multivariate imputation by chained equations (MICE) and two-stage calibration (TSC), aim to reduce confounding bias. We provide new insights into their behavior under different deviations from representativeness of the cohort. We conducted an extensive simulation study to assess the performance of these two methods under different deviations from representativeness of the cohort. We illustrate these approaches by studying the association between benzodiazepine use and fractures in the elderly using the general sample of French health insurance beneficiaries (EGB) as main database and two French cohorts (Paquid and 3C) as validation samples. When the cohort was representative from the same population as the HAD, the two methods are unbiased. TSC was more efficient and faster but its variance could be slightly underestimated when confounders were non-Gaussian. If the cohort was a subsample of the HAD (internal validation) with the probability of the subject being included in the cohort depending on both exposure and outcome, MICE was unbiased while TSC was biased. The two methods appeared biased when the inclusion probability in the cohort depended on unobserved confounders. When choosing the most appropriate method, epidemiologists should consider the origin of the cohort (internal or external validation) as well as the (anticipated or observed) selection biases of the validation sample

Development and Validation of Deep Learning Transformer Models for Building a Comprehensive and Real-time Trauma Observatory

BACKGROUND In order to study the feasibility of setting up a national trauma observatory in France, OBJECTIVE we compared the performance of several automatic language processing methods on a multi-class classification task of unstructured clinical notes. METHODS A total of 69,110 free-text clinical notes related to visits to the emergency departments of the University Hospital of Bordeaux, France, between 2012 and 2019 were manually annotated. Among those clinical notes 22,481 were traumas. We trained 4 transformer models (deep learning models that encompass attention mechanism) and compared them with the TF-IDF (Term- Frequency - Inverse Document Frequency) associated with SVM (Support Vector Machine) method. RESULTS The transformer models consistently performed better than TF-IDF/SVM. Among the transformers, the GPTanam model pre-trained with a French corpus with an additional auto-supervised learning step on 306,368 unlabeled clinical notes showed the best performance with a micro F1-score of 0.969. CONCLUSIONS The transformers proved efficient multi-class classification task on narrative and medical data. Further steps for improvement should focus on abbreviations expansion and multiple outputs multi-class classification

Jardins per a la salut

Facultat de Farmàcia, Universitat de Barcelona. Ensenyament: Grau de Farmàcia. Assignatura: Botànica farmacèutica. Curs: 2014-2015. Coordinadors: Joan Simon, Cèsar Blanché i Maria Bosch.Els materials que aquí es presenten són el recull de les fitxes botàniques de 128 espècies presents en el Jardí Ferran Soldevila de l’Edifici Històric de la UB. Els treballs han estat realitzats manera individual per part dels estudiants dels grups M-3 i T-1 de l’assignatura Botànica Farmacèutica durant els mesos de febrer a maig del curs 2014-15 com a resultat final del Projecte d’Innovació Docent «Jardins per a la salut: aprenentatge servei a Botànica farmacèutica» (codi 2014PID-UB/054). Tots els treballs s’han dut a terme a través de la plataforma de GoogleDocs i han estat tutoritzats pels professors de l’assignatura. L’objectiu principal de l’activitat ha estat fomentar l’aprenentatge autònom i col·laboratiu en Botànica farmacèutica. També s’ha pretès motivar els estudiants a través del retorn de part del seu esforç a la societat a través d’una experiència d’Aprenentatge-Servei, deixant disponible finalment el treball dels estudiants per a poder ser consultable a través d’una Web pública amb la possibilitat de poder-ho fer in-situ en el propi jardí mitjançant codis QR amb un smartphone

Sex difference and intra-operative tidal volume: Insights from the LAS VEGAS study

BACKGROUND: One key element of lung-protective ventilation is the use of a low tidal volume (VT). A sex difference in use of low tidal volume ventilation (LTVV) has been described in critically ill ICU patients.OBJECTIVES: The aim of this study was to determine whether a sex difference in use of LTVV also exists in operating room patients, and if present what factors drive this difference.DESIGN, PATIENTS AND SETTING: This is a posthoc analysis of LAS VEGAS, a 1-week worldwide observational study in adults requiring intra-operative ventilation during general anaesthesia for surgery in 146 hospitals in 29 countries.MAIN OUTCOME MEASURES: Women and men were compared with respect to use of LTVV, defined as VT of 8 ml kg-1 or less predicted bodyweight (PBW). A VT was deemed 'default' if the set VT was a round number. A mediation analysis assessed which factors may explain the sex difference in use of LTVV during intra-operative ventilation.RESULTS: This analysis includes 9864 patients, of whom 5425 (55%) were women. A default VT was often set, both in women and men; mode VT was 500 ml. Median [IQR] VT was higher in women than in men (8.6 [7.7 to 9.6] vs. 7.6 [6.8 to 8.4] ml kg-1 PBW, P < 0.001). Compared with men, women were twice as likely not to receive LTVV [68.8 vs. 36.0%; relative risk ratio 2.1 (95% CI 1.9 to 2.1), P < 0.001]. In the mediation analysis, patients' height and actual body weight (ABW) explained 81 and 18% of the sex difference in use of LTVV, respectively; it was not explained by the use of a default VT.CONCLUSION: In this worldwide cohort of patients receiving intra-operative ventilation during general anaesthesia for surgery, women received a higher VT than men during intra-operative ventilation. The risk for a female not to receive LTVV during surgery was double that of males. Height and ABW were the two mediators of the sex difference in use of LTVV.TRIAL REGISTRATION: The study was registered at Clinicaltrials.gov, NCT01601223

Prevalence, associated factors and outcomes of pressure injuries in adult intensive care unit patients: the DecubICUs study

Funder: European Society of Intensive Care Medicine; doi: http://dx.doi.org/10.13039/501100013347Funder: Flemish Society for Critical Care NursesAbstract: Purpose: Intensive care unit (ICU) patients are particularly susceptible to developing pressure injuries. Epidemiologic data is however unavailable. We aimed to provide an international picture of the extent of pressure injuries and factors associated with ICU-acquired pressure injuries in adult ICU patients. Methods: International 1-day point-prevalence study; follow-up for outcome assessment until hospital discharge (maximum 12 weeks). Factors associated with ICU-acquired pressure injury and hospital mortality were assessed by generalised linear mixed-effects regression analysis. Results: Data from 13,254 patients in 1117 ICUs (90 countries) revealed 6747 pressure injuries; 3997 (59.2%) were ICU-acquired. Overall prevalence was 26.6% (95% confidence interval [CI] 25.9–27.3). ICU-acquired prevalence was 16.2% (95% CI 15.6–16.8). Sacrum (37%) and heels (19.5%) were most affected. Factors independently associated with ICU-acquired pressure injuries were older age, male sex, being underweight, emergency surgery, higher Simplified Acute Physiology Score II, Braden score 3 days, comorbidities (chronic obstructive pulmonary disease, immunodeficiency), organ support (renal replacement, mechanical ventilation on ICU admission), and being in a low or lower-middle income-economy. Gradually increasing associations with mortality were identified for increasing severity of pressure injury: stage I (odds ratio [OR] 1.5; 95% CI 1.2–1.8), stage II (OR 1.6; 95% CI 1.4–1.9), and stage III or worse (OR 2.8; 95% CI 2.3–3.3). Conclusion: Pressure injuries are common in adult ICU patients. ICU-acquired pressure injuries are associated with mainly intrinsic factors and mortality. Optimal care standards, increased awareness, appropriate resource allocation, and further research into optimal prevention are pivotal to tackle this important patient safety threat

Correction to: Prevalence, associated factors and outcomes of pressure injuries in adult intensive care unit patients: the DecubICUs study

The original version of this article unfortunately contained a mistake

Prevalence, associated factors and outcomes of pressure injuries in adult intensive care unit patients: the DecubICUs study

Funder: European Society of Intensive Care Medicine; doi: http://dx.doi.org/10.13039/501100013347Funder: Flemish Society for Critical Care NursesAbstract: Purpose: Intensive care unit (ICU) patients are particularly susceptible to developing pressure injuries. Epidemiologic data is however unavailable. We aimed to provide an international picture of the extent of pressure injuries and factors associated with ICU-acquired pressure injuries in adult ICU patients. Methods: International 1-day point-prevalence study; follow-up for outcome assessment until hospital discharge (maximum 12 weeks). Factors associated with ICU-acquired pressure injury and hospital mortality were assessed by generalised linear mixed-effects regression analysis. Results: Data from 13,254 patients in 1117 ICUs (90 countries) revealed 6747 pressure injuries; 3997 (59.2%) were ICU-acquired. Overall prevalence was 26.6% (95% confidence interval [CI] 25.9–27.3). ICU-acquired prevalence was 16.2% (95% CI 15.6–16.8). Sacrum (37%) and heels (19.5%) were most affected. Factors independently associated with ICU-acquired pressure injuries were older age, male sex, being underweight, emergency surgery, higher Simplified Acute Physiology Score II, Braden score 3 days, comorbidities (chronic obstructive pulmonary disease, immunodeficiency), organ support (renal replacement, mechanical ventilation on ICU admission), and being in a low or lower-middle income-economy. Gradually increasing associations with mortality were identified for increasing severity of pressure injury: stage I (odds ratio [OR] 1.5; 95% CI 1.2–1.8), stage II (OR 1.6; 95% CI 1.4–1.9), and stage III or worse (OR 2.8; 95% CI 2.3–3.3). Conclusion: Pressure injuries are common in adult ICU patients. ICU-acquired pressure injuries are associated with mainly intrinsic factors and mortality. Optimal care standards, increased awareness, appropriate resource allocation, and further research into optimal prevention are pivotal to tackle this important patient safety threat

Contribution to model selection via Lasso penalization in Epidemiology

Mes travaux portent principalement sur le développement, l’adaptation, l’implémentation et l’application de méthodes statistiques de sélection de modèle. Ma principale contribution consiste à adapter des méthodes de l'apprentissage statistique supervisé qui sont devenues très populaires lors de la dernière décennie, les régressions pénalisées de type Lasso, à l'analyse de données issues d'études épidémiologiques. L'enjeu est de s'attaquer aux problèmes des données volumineuses (\textit{Big Data}) tout en respectant les objectifs et spécificités de la discipline. Le volume important se réfère ici au fait que le nombre d'observations et/ou le nombre de variables est bien plus important que celui qui était classique dans le domaine, sans exclure le cas où le nombre de variables est supérieur au nombre d'observations (données de grande dimension). Le contexte de la pratique épidémiologique est en plein changement avec les évolutions technologiques et la conséquente disponibilité croissante des Big Data. Le Système National des Données de Santé (SNDS), regroupant les principales bases de données de santé publique existantes en France, constitue un exemple de Big Data en santé. Le données ``omiques'' (génomiques, transcriptomiques, protéomiques, métabolomiques, microbiomiques, mycobiomiques, viromiques,$\ldots$) issues des avancées des techniques de séquençage à haut débit constituent un autre exemple de Big Data en santé. Enfin, les mesures de l'\textit{exposome} (par opposition aux facteurs génétiques), qui désigne en épidémiologie l’ensemble des expositions environnementales que subit un individu au long de sa vie peut également constituer une source de Big Data.Ce document s'articule autour de trois chapitres. Il résume mon activité de recherche depuis 2005, soit depuis mon recrutement à l’Université de Bordeaux après ma thèse. Le premier chapitre est une introduction générale dans laquelle je contextualise, motive et énonce la problématique abordée tout au long de mes recherches. Le deuxième chapitre est consacré à mes travaux en lien avec les études sur les traumatismes accidentels et expositions médicamenteuses à partir des données du SNDS. Le troisième chapitre est consacré à mes travaux en lien avec des études biomédicales: la prédiction de la charge virale censurée par un seuil de détection à partir des mutations du VIH, d'une part, et l'automatisation de la détection des seuils d'anomalie des hémogrammes en population générale, d'autre part