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

Acute pain management in children: A survey of Italian pediatricians

Background: Current guidelines recommend assessing and relieving pain in all children and in all instances; yet, in clinical practice, management is frequently suboptimal. We investigated the attitude of Italian family pediatricians towards the evaluation and treatment of different types of acute pain in children aged 7-12 years. Methods: This is a cross-sectional study based on a 17-question survey accessible online from October 2017 to October 2018. Responders had to describe cases of children suffering from any type of acute pain among headache, sore throat, musculoskeletal/post-traumatic pain, and earache. Children's characteristics, pain assessment modalities and therapeutic approaches were queried. The following tests were used: Z-proportion to evaluate the distribution of categorical data; chi-squared and Kruskall-Wallis to explore data heterogeneity across groups; Mann-Whitney for head-to-head comparisons. Results: Overall, 929 pediatricians presented 6335 cases uniformly distributed across the types examined. Pain was more frequently of moderate intensity (42.2%, P < 0.001) and short duration (within some days: 98.4%, P < 0.001). Only 50.1% of responders used an algometric scale to measure pain and 60.5% always prescribed a treatment. In children with mild-moderate pain (N = 4438), the most commonly used first-line non-opioids were ibuprofen (53.3%) and acetaminophen (44.4%). Importantly, a non-recommended dosage was prescribed in only 5.3% of acetaminophen-treated cases (overdosing). Among the misconceptions emerged, there were the following: I) ibuprofen and acetaminophen have different efficacy and safety profiles (when choosing the non-opioid, effectiveness weighted more for ibuprofen [79.7% vs 74.3%, P < 0.001] and tolerability for acetaminophen [74.0% vs 55.4%, P < 0.001]); ii) ibuprofen must be taken after meals to prevent gastric toxicities (52.5%); ibuprofen and acetaminophen can be used combined/alternated for persisting mild-moderate pain (16.1%). In case of moderate-severe pain not completely controlled by opioids, ibuprofen and acetaminophen were the most used add-on medications, with ibuprofen being much more prescribed than acetaminophen (65.2% vs 23.7%, respectively) overall and in all pain types. Conclusions: Several gaps exist between the current practice of pain assessment and treatment and recommendations. Further efforts are needed to raise awareness and improve education on the possible exposure of the child to short- A nd long-term consequences in case of suboptimal pain management

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

Relationship between postpartum uterine involution and biomarkers of inflammation and oxidative stress in clinically healthy mares (Equus caballus)

To test the hypothesis that delayed/impaired uterine involution could be associated with oxinflammation, westudied the progression of the uterine involution in association with some biomarkers of inflammation andoxidative stress in clinically healthy mares (N\ubc26) during early postpartum. The examination of the repro-ductive tract was performed on Days 7 and 21 after foaling. Uterine involution was assessed considering: a) theincrease of the gravid uterine horn diameter (GUHD) compared with diameter recorded before pregnancy duringthe previous breeding season; b) the level of endometrial edema (EE); c) the degree of accumulation of intra-uterinefluid (IUFA); d) the status of the cervix (CS). Inflammation and oxidative stress were studied by measuringserum amyloid A (SAA), cortisol, DHEA, AOPP, protein carbonyl groups, malondialdheyde (MDA) and thiols inplasma on Days 7 and 21. By Day 21 after parturition, a significant improvement (P<0.01) was observed forGUHD and EE; while IUFA increased in six animals. Plasma SAA and DHEA concentrations were higher when theclinical parameters indicated a lower degree of uterine involution. On Day 7, the cortisol/DHEA ratio was lower inanimals with higher degree of EE. Plasma AOPP and MDA concentrations were significantly lower (P<0.05) inanimals with the lower GUHD. On Day 21, plasma MDA concentrations were significantly lower (P<0.05) inanimals with the lower IUFA. Our data suggest that a mild condition of inflammation and oxidative stress occur inmares with delayed/impaired uterine involution

Point-of-care testing allows successful simultaneous screening of sickle cell disease, HIV, and tuberculosis for households in rural Guinea-Bissau, West Africa

Diagnosis of noncommunicable genetic diseases like sickle cell disease (SCD) and com municable diseases such as human immunodeficiency virus (HIV) or tuberculosis (TB) is often difficult in rural areas of Africa due to the lack of infrastructures, trained staff, or capacity to involve families living in remote areas. The availability of point-of-care (POC) tests for the above diseases offers the opportunity to build joint programs to tackle all conditions. We report successful simultaneous screening of SCD, HIV, and TB utilizing POC tests in 898 subjects in Fanhe, in rural Guinea-Bissau. Adherence was 100% and all diagnosed subjects were enrolled in care program

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.

PRIMO SPORT Surroundings and activities just right for growing up well

Concept and "how to use" of a playground designed for improving modor development in 0-6 y old children. To be used by parent and educators looking for information and recommendation from international health agencies and from scientific publication