41,086 research outputs found
Sepsis Prognostic Scores Accuracy in Predicting Adverse Outcomes in Children With Sepsis Admitted to the Pediatric Intensive Care Unit From the Emergency Department
No full textObjectiveTo compare the performance of several prognostic scores calculated in the first 24 hours of admission (day 1) in predicting mortality and morbidity among critically ill children with sepsis presenting to the pediatric emergency department (PED) and then admitted to the pediatric intensive care unit (PICU).MethodsSingle-center, retrospective cohort study in children with a diagnosis of sepsis visiting the PED and then admitted to the PICU from January 1, 2010 to December 31, 2019. Sepsis organ dysfunction scores-pediatric Sequential Organ Failure Assessment (pSOFA) (Schlapbach, Matics, Shime), quickSOFA, quickSOFA-L, Pediatric Logistic Organ Dysfunction (PELOD)-2, quickPELOD-2, and Pediatric Multiple Organ Dysfunction score-were calculated during the first 24 hours of admission (day 1) and their performance compared with systemic inflammatory response syndrome (SIRS) and severe sepsis-International Consensus Conference on Pediatric Sepsis(ICCPS)-derived criteria-using the area under the receiver operating characteristic curve. Primary outcome was PICU mortality. Secondary outcomes were: a composite of death and new disability (ie, change from baseline Pediatric Overall Performance Category score >= 1); prolonged PICU length of stay (>5 d); prolonged invasive mechanical ventilation (MV) (>3 d).ResultsAmong 60 patients with sepsis, 4 (6.7%) died, 7 (11.7%) developed new disability, 26 (43.3%) experienced prolonged length of stay, and 21 (35%) prolonged invasive MV. The prognostic ability in mortality discrimination was significantly higher for organ dysfunction scores, with PELOD-2 showing the best performance (area under the receiver operating characteristic curve, 0.924; 95% confidence interval, 0.837-1.000), significantly better than SIRS 3 criteria (0.924 vs 0.509, P = 0.009), SIRS 4 criteria (0.924 vs 0.509, P < 0.001), and severe sepsis (0.924 vs 0.527, P < 0.001). Among secondary outcomes, PELOD-2 performed significantly better than SIRS criteria and severe sepsis to predict prolonged duration of invasive MV, whereas better than severe sepsis to predict "poor outcome" (mortality or new disability).ConclusionsDay 1 organ dysfunction scores performed better in predicting mortality and morbidity outcomes than ICCPS-derived criteria. The PELOD-2 was the organ dysfunction score with the best performance for all outcomes
Handling poor accrual in pediatric trials: A simulation study using a Bayesian approach
No full textIn the conduction of trials, a common situation is related to potential difficulties in recruiting the planned sample size as provided by the study design. A Bayesian analysis of such trials might provide a framework to combine prior evidence with current evidence, and it is an accepted approach by regulatory agencies. However, especially for small trials, the Bayesian inference may be severely conditioned by the prior choices. The Renal Scarring Urinary Infection (RESCUE) trial, a pediatric trial that was a candidate for early termination due to underrecruitment, served as a motivating example to investigate the effects of the prior choices on small trial inference. The trial outcomes were simulated by assuming 50 scenarios combining different sample sizes and true absolute risk reduction (ARR). The simulated data were analyzed via the Bayesian approach using 0%, 50%, and 100% discounting factors on the beta power prior. An informative inference (0% discounting) on small samples could generate data-insensitive results. Instead, the 50% discounting factor ensured that the probability of confirming the trial outcome was higher than 80%, but only for an ARR higher than 0.17. A suitable option to maintain data relevant to the trial inference is to define a discounting factor based on the prior parameters. Nevertheless, a sensitivity analysis of the prior choices is highly recommended
Use of a Large Language Model to Identify and Classify Injuries with Free-Text Emergency Department Data
No full textThis cross-sectional study assesses the accuracy, sensitivity, and specificity of a large language model used to process unstructured, non-English emergency department (ED) data in medical records
A SuperLearner-enforced approach for the estimation of treatment effect in pediatric trials
No full textBackground: 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
No full textSample 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
Congenital malformations in neonates admitted to a neonatal intensive care unit in a low-resource setting
No full textIntroduction: Congenital malformations (CMs) are among the major causes of infant mortality in middle- and low-resource countries. This is the first study describing CMs in neonates admitted to the Neonatal Intensive Care Unit (NICU) of a referral hospital in Mozambique. Methods: We included all neonates with CMs admitted to the NICU of Beira Central Hospital from January 2015 to December 2016. CMs were classified according to the International Classification of Disease (ICD-10). All data were retrieved from medical charts. Results: CMs were found in 143/4767 (3%) neonates admitted to the NICU. The most frequent CMs were musculoskeletal (31%), neurological (18%), multiple congenital anomalies (12%), chromosomopathies (11%), cardiovascular (10%), and gastrointestinal (8%). Forty-three patients (30%) underwent corrective surgery. Overall mortality rate was 50%. Conclusions: The prevalence of CMs was 3%, with a mortality rate of 50%. Alongside implementation of antenatal screening programs, improvement on expertise and postnatal care of CMs are warranted
Analysis of Unstructured Text-Based Data Using Machine Learning Techniques: The Case of Pediatric Emergency Department Records in Nicaragua
No full textFree-text information is still widely used in emergency department (ED) records. Machine learning techniques are useful for analyzing narratives, but they have been used mostly for English-language data sets. Considering such a framework, the performance of an ML classification task of a Spanish-language ED visits database was tested. ED visits collected in the EDs of nine hospitals in Nicaragua were analyzed. Spanish-language, free-text discharge diagnoses were considered in the analysis. Five-hundred random forests were trained on a set of bootstrap samples of the whole data set (1,789 ED visits) to perform the classification task. For each one, after having identified optimal parameter value, the final validated model was trained on the whole bootstrapped data set and tested. The classification accuracies had a median of 0.783 (95% CI [0.779, 0.796]). Machine learning techniques seemed to be a promising opportunity for the exploitation of unstructured information reported in ED records in low- and middle-income Spanish-speaking countries
Increasing awareness of food-choking and nutrition in children through education of caregivers: The CHOP community intervention trial study protocol
No full textBackground: Choking is one of the leading causes of death among unintentional injuries in young children. Food choking represents a considerable public health burden, which might be reduced through increased effective preventative education programs. We present a protocol for a community intervention trial termed CHOP (CHOking Prevention project) that aimed to teach Italian families how to prevent food choking injuries and increase knowledge relating to nutrition. Methods: Italian educational facilities were enrolled. Stratified randomization blocked by geographical area was performed. Each stratum was randomized to one of three different intervention strategies or to a control group. Educational intervention was delivered in the schools by experts and certified trainers as per the following three intervention strategies: directly to families (Strategy A); to teaching staff only, who subsequently delivered the same educational intervention to families (Strategy B); to health service staff only, who then delivered the educational intervention to teaching staff, who subsequently delivered the intervention to families (Strategy C). Participants completed a questionnaire about their knowledge on the topics presented during the educational interventions (pre-, post-, and follow-up of intervention). Information from the questionnaires was synthetized into 6 indicators in order to measure how effective each intervention strategy was. Discussion: The issue of food choking injuries in children is relevant to public health. The protocol we present provides an opportunity to progress towards overcoming such challenges through a working model that can be implemented also in other countries. Trial registration: ClinicalTrials.gov NCT03218618. The study was registered on 14 July 2017. © 2019 The Author(s)
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