A SuperLearner-enforced approach for the estimation of treatment effect in pediatric trials

Background: Randomized Clinical Trials (RCT) represent the gold standard among scientific evidence. RCTs are tailored to control selection bias and the confounding effect of baseline characteristics on the effect of treatment. However, trial conduction and enrolment procedures could be challenging, especially for rare diseases and paediatric research. In these research frameworks, the treatment effect estimation could be compromised. A potential countermeasure is to develop predictive models on the probability of the baseline disease based on previously collected observational data. Machine learning (ML) algorithms have recently become attractive in clinical research because of their flexibility and improved performance compared to standard statistical methods in developing predictive models. Objective: This manuscript proposes an ML-enforced treatment effect estimation procedure based on an ensemble SuperLearner (SL) approach, trained on historical observational data, to control the confounding effect. Methods: The REnal SCarring Urinary infEction trial served as a motivating example. Historical observational study data have been simulated through 10,000 Monte Carlo (MC) runs. Hypothetical RCTs have been also simulated, for each MC run, assuming different treatment effects of antibiotics combined with steroids. For each MC simulation, the SL tool has been applied to the simulated observational data. Furthermore, the average treatment effect (ATE), has been estimated on the trial data and adjusted for the SL predicted probability of renal scar. Results: The simulation results revealed an increased power in ATE estimation for the SL-enforced estimation compared to the unadjusted estimates for all the algorithms composing the ensemble SL

A Bayesian Sample Size Estimation Procedure Based on a B-Splines Semiparametric Elicitation Method

Sample size estimation is a fundamental element of a clinical trial, and a binomial experiment is the most common situation faced in clinical trial design. A Bayesian method to determine sample size is an alternative solution to a frequentist design, especially for studies conducted on small sample sizes. The Bayesian approach uses the available knowledge, which is translated into a prior distribution, instead of a point estimate, to perform the final inference. This procedure takes the uncertainty in data prediction entirely into account. When objective data, historical information, and literature data are not available, it may be indispensable to use expert opinion to derive the prior distribution by performing an elicitation process. Expert elicitation is the process of translating expert opinion into a prior probability distribution. We investigated the estimation of a binomial sample size providing a generalized version of the average length, coverage criteria, and worst outcome criterion. The original method was proposed by Joseph and is defined in a parametric framework based on a Beta-Binomial model. We propose a more flexible approach for binary data sample size estimation in this theoretical setting by considering parametric approaches (Beta priors) and semiparametric priors based on B-splines

Pediatric Injury Surveillance From Uncoded Emergency Department Admission Records in Italy: Machine Learning-Based Text-Mining Approach

Background: Unintentional injury is the leading cause of death in young children. Emergency department (ED) diagnoses are a useful source of information for injury epidemiological surveillance purposes. However, ED data collection systems often use free-text fields to report patient diagnoses. Machine learning techniques (MLTs) are powerful tools for automatic text classification. The MLT system is useful to improve injury surveillance by speeding up the manual free-text coding tasks of ED diagnoses. Objective: This research aims to develop a tool for automatic free-text classification of ED diagnoses to automatically identify injury cases. The automatic classification system also serves for epidemiological purposes to identify the burden of pediatric injuries in Padua, a large province in the Veneto region in the Northeast Italy. Methods: The study includes 283, 468 pediatric admissions between 2007 and 2018 to the Padova University Hospital ED, a large referral center in Northern Italy. Each record reports a diagnosis by free text. The records are standard tools for reporting patient diagnoses. An expert pediatrician manually classified a randomly extracted sample of approximately 40, 000 diagnoses. This study sample served as the gold standard to train an MLT classifier. After preprocessing, a document-term matrix was created. The machine learning classifiers, including decision tree, random forest, gradient boosting method (GBM), and support vector machine (SVM), were tuned by 4-fold cross-validation. The injury diagnoses were classified into 3 hierarchical classification tasks, as follows: injury versus noninjury (task A), intentional versus unintentional injury (task B), and type of unintentional injury (task C), according to the World Health Organization classification of injuries. Results: The SVM classifier achieved the highest performance accuracy (94.14%) in classifying injury versus noninjury cases (task A). The GBM method produced the best results (92% accuracy) for the unintentional and intentional injury classification task (task B). The highest accuracy for the unintentional injury subclassification (task C) was achieved by the SVM classifier. The SVM, random forest, and GBM algorithms performed similarly against the gold standard across different tasks. Conclusions: This study shows that MLTs are promising techniques for improving epidemiological surveillance, allowing for the automatic classification of pediatric ED free-text diagnoses. The MLTs revealed a suitable classification performance, especially for general injuries and intentional injury classification. This automatic classification could facilitate the epidemiological surveillance of pediatric injuries by also reducing the health professionals' efforts in manually classifying diagnoses for research purposes

Oral steroids for reducing kidney scarring in young children with febrile urinary tract infections: the contribution of Bayesian analysis to a randomized trial not reaching its intended sample size

Background: This study aimed to evaluate the effect of oral dexamethasone in reducing kidney scars in infants with a first febrile urinary tract infection (UTI). Methods: Children aged between 2 and 24 months with their first presumed UTI, at high risk for kidney scarring based on procalcitonin levels ( 651 ng/mL), were randomly assigned to receive dexamethasone in addition to routine care or routine care only. Kidney scars were identified by kidney scan at 6 months after initial UTI. Projections of enrollment and follow-up completion showed that the intended sample size could not be reached before funding and time to complete the study ran out. An amendment to the protocol was approved to conduct a Bayesian analysis. Results: We randomized 48 children, of whom 42 had a UTI and 18 had outcome kidney scans (instead of 128 planned). Kidney scars were found in 0/7 and 2/11 patients in the treatment and control groups respectively. The probability that dexamethasone could prevent kidney scarring was 99% in the setting of an informative prior probability distribution (which fully incorporated in the final inference the information on treatment effect provided by previous studies) and 98% in the low-informative scenario (which discounted the prior literature information by 50%). The probabilities that dexamethasone could reduce kidney scar formation by up to 20% were 61% and 53% in the informative and low-informative scenario, respectively. Conclusions: Dexamethasone is highly likely to reduce kidney scarring, with a more than 50% probability to reduce kidney scars by up to 20%. Trial registration number: EudraCT number: 2013-000388-10; registered in 2013 (prospectively registered) Graphical Abstract: [Figure not available: see fulltext.

SARS-CoV-2 testing and infection control strategies in European paediatric emergency departments during the first wave of the pandemic.

Between February and May 2020, during the first wave of the COVID-19 pandemic, paediatric emergency departments in 12 European countries were prospectively surveyed on their implementation of SARS-CoV-2 disease (COVID-19) testing and infection control strategies. All participating departments (23) implemented standardised case definitions, testing guidelines, early triage and infection control strategies early in the outbreak. Patient testing criteria initially focused on suspect cases and later began to include screening, mainly for hospital admissions. Long turnaround times for test results likely put additional strain on healthcare resources.Conclusion: Shortening turnaround times for SARS-CoV-2 tests should be a priority. Specific paediatric testing criteria are needed. What is Known: • WHO and public health authorities issued case definitions, testing and infection control recommendations for COVID-19 in January. • SARS-CoV-2 testing was made available across Europe in February. What is New: • Paediatric emergency departments implemented COVID-19-specific procedures rapidly, including case definitions, testing guidelines and early triage. • A third of surveyed departments waited more than 24 h for SARS-CoV-2 test to be reported, resulting in additional strain on resources

PCSK9 deficiency reduces insulin secretion and promotes glucose intolerance: the role of the low-density lipoprotein receptor

Aims PCSK9 loss of function genetic variants are associated with lower low-density lipoprotein cholesterol but also with higher plasma glucose levels and increased risk of Type 2 diabetes mellitus. Here, we investigated the molecular mechanisms underlying this association. Methods and results Pcsk9 KO, WT, Pcsk9/Ldlr double KO (DKO), Ldlr KO, albumin AlbCre+/Pcsk9LoxP/LoxP (liver-selective Pcsk9 knock-out mice), and AlbCre-/Pcsk9LoxP/LoxP mice were used. GTT, ITT, insulin and C-peptide plasma levels, pancreas morphology, and cholesterol accumulation in pancreatic islets were studied in the different animal models. Glucose clearance was significantly impaired in Pcsk9 KO mice fed with a standard or a high-fat diet for 20\u2009weeks compared with WT animals; insulin sensitivity, however, was not affected. A detailed analysis of pancreas morphology of Pcsk9 KO mice vs. controls revealed larger islets with increased accumulation of cholesteryl esters, paralleled by increased insulin intracellular levels and decreased plasma insulin, and C-peptide levels. This phenotype was completely reverted in Pcsk9/Ldlr DKO mice implying the low-density lipoprotein receptor (LDLR) as the proprotein convertase subtilisin/kexin Type 9 (PCSK9) target responsible for the phenotype observed. Further studies in albumin AlbCre+/Pcsk9LoxP/LoxP mice, which lack detectable circulating PCSK9, also showed a complete recovery of the phenotype, thus indicating that circulating, liver-derived PCSK9, the principal target of monoclonal antibodies, does not impact beta-cell function and insulin secretion. Conclusion PCSK9 critically controls LDLR expression in pancreas perhaps contributing to the maintenance of a proper physiological balance to limit cholesterol overload in beta cells. This effect is independent of circulating PCSK9 and is probably related to locally produced PCSK9

Preparedness and Response to Pediatric COVID-19 in European Emergency Departments : A Survey of the REPEM and PERUKI Networks

Publisher Copyright: © 2020 American College of Emergency Physicians Copyright: Copyright 2020 Elsevier B.V., All rights reserved.Study objective: We aim to describe the variability and identify gaps in preparedness and response to the coronavirus disease 2019 pandemic in European emergency departments (EDs) caring for children. Methods: A cross-sectional point-prevalence survey was developed and disseminated through the pediatric emergency medicine research networks for Europe (Research in European Pediatric Emergency Medicine) and the United Kingdom and Ireland (Paediatric Emergency Research in the United Kingdom and Ireland). We aimed to include 10 EDs for countries with greater than 20 million inhabitants and 5 EDs for less populated countries, unless the number of eligible EDs was less than 5. ED directors or their delegates completed the survey between March 20 and 21 to report practice at that time. We used descriptive statistics to analyze data. Results: Overall, 102 centers from 18 countries (86% response rate) completed the survey: 34% did not have an ED contingency plan for pandemics and 36% had never had simulations for such events. Wide variation on personal protective equipment (PPE) items was shown for recommended PPE use at pretriage and for patient assessment, with 62% of centers experiencing shortage in one or more PPE items, most frequently FFP2 and N95 masks. Only 17% of EDs had negative-pressure isolation rooms. Coronavirus disease 2019–positive ED staff was reported in 25% of centers. Conclusion: We found variation and identified gaps in preparedness and response to the coronavirus disease 2019 epidemic across European referral EDs for children. A lack in early availability of a documented contingency plan, provision of simulation training, appropriate use of PPE, and appropriate isolation facilities emerged as gaps that should be optimized to improve preparedness and inform responses to future pandemics.publishersversionPeer reviewe