Atonic Postpartum Hemorrhage: Blood Loss, Risk Factors, and Third Stage Management

AbstractObjectiveAtonic postpartum hemorrhage rates have increased in many industrialized countries in recent years. We examined the blood loss, risk factors, and management of the third stage of labour associated with atonic postpartum hemorrhage.MethodsWe carried out a case-control study of patients in eight tertiary care hospitals in Canada between January 2011 and December 2013. Cases were defined as women with a diagnosis of atonic postpartum hemorrhage, and controls (without postpartum hemorrhage) were matched with cases by hospital and date of delivery. Estimated blood loss, risk factors, and management of the third stage labour were compared between cases and controls. Conditional logistic regression was used to adjust for confounding.ResultsThe study included 383 cases and 383 controls. Cases had significantly higher mean estimated blood loss than controls. However, 16.7% of cases who delivered vaginally and 34.1% of cases who delivered by Caesarean section (CS) had a blood loss of < 500 mL and < 1000 mL, respectively; 8.2% of controls who delivered vaginally and 6.7% of controls who delivered by CS had blood loss consistent with a diagnosis of postpartum hemorrhage. Factors associated with atonic postpartum hemorrhage included known protective factors (e.g., delivery by CS) and risk factors (e.g., nulliparity, vaginal birth after CS). Uterotonic use was more common in cases than in controls (97.6% vs. 92.9%, P < 0.001). Delayed cord clamping was only used among those who delivered vaginally (7.7% cases vs. 14.6% controls, P = 0.06).ConclusionThere is substantial misclassification in the diagnosis of atonic postpartum hemorrhage, and this could potentially explain the observed temporal increase in postpartum hemorrhage rates

Bayesian reinforcement learning with exploration

We consider a general reinforcement learning problem and show that carefully combining the Bayesian optimal policy and an exploring policy leads to minimax sample-complexity bounds in a very general class of (history-based) environments. We also prove lower bounds and show that the new algorithm displays adaptive behaviour when the environment is easier than worst-case

COVID-19 in hospitalized lung and non-lung solid organ transplant recipients: A comparative analysis from a multicenter study

Lung transplant recipients (LTR) with coronavirus disease 2019 (COVID-19) may have higher mortality than non-lung solid organ transplant recipients (SOTR), but direct comparisons are limited. Risk factors for mortality specifically in LTR have not been explored. We performed a multicenter cohort study of adult SOTR with COVID-19 to compare mortality by 28 days between hospitalized LTR and non-lung SOTR. Multivariable logistic regression models were used to assess comorbidity-adjusted mortality among LTR vs. non-lung SOTR and to determine risk factors for death in LTR. Of 1,616 SOTR with COVID-19, 1,081 (66%) were hospitalized including 120/159 (75%) LTR and 961/1457 (66%) non-lung SOTR (p =.02). Mortality was higher among LTR compared to non-lung SOTR (24% vs. 16%, respectively, p =.032), and lung transplant was independently associated with death after adjusting for age and comorbidities (aOR 1.7, 95% CI 1.0–2.6, p =.04). Among LTR, chronic lung allograft dysfunction (aOR 3.3, 95% CI 1.0–11.3, p =.05) was the only independent risk factor for mortality and age >65 years, heart failure and obesity were not independently associated with death. Among SOTR hospitalized for COVID-19, LTR had higher mortality than non-lung SOTR. In LTR, chronic allograft dysfunction was independently associated with mortality

On the sample complexity of reinforcement learning with a generative model

Contains fulltext : 111201.pdf (preprint version ) (Open Access

On the theory of reinforcement learning : methods, convergence analysis and sample complexity

Contains fulltext : 100885.pdf (publisher's version ) (Open Access)Radboud Universiteit Nijmegen, 25 oktober 2012Promotor : Kappen, H.J. Co-promotor : Munos, R.137 p

Speedy q-learning: a computationally efficient reinforcement learning algorithm with a near optimal rate of convergence

Contains fulltext : 117240.pdf (preprint version ) (Open Access

Correcting Multivariate Auto-Regressive Models for the Influence of Unobserved Common Input

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Policy search for path integral control

Comunicació presentada a la European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD 2014), celebrada els dies 15 a 19 de setembre de 2014 a Nancy, França.Path integral (PI) control defines a general class of control problems for which the optimal control computation is equivalent to an inference problem that can be solved by evaluation of a path integral over state trajectories. However, this potential is mostly unused in real-world problems because of two main limitations: first, current approaches can typically only be applied to learn open-loop controllers and second, current sampling procedures are inefficient and not scalable to high dimensional systems. We introduce the efficient Path Integral Relative-Entropy Policy Search (PI-REPS) algorithm for learning feedback policies with PI control. Our algorithm is inspired by information theoretic policy updates that are often used in policy search. We use these updates to approximate the state trajectory distribution that is known to be optimal from the PI control theory. Our approach allows for a principled treatment of different sampling distributions and can be used to estimate many types of parametric or non-parametric feedback controllers. We show that PI-REPS significantly outperforms current methods and is able to solve tasks that are out of reach for current methods.This work was supported by the European Community Seventh Framework Programme (FP7/2007-2013) under grant agreement 270327 (CompLACS)