Risk of asthma in children diagnosed with bronchiolitis during infancy: Protocol of a longitudinal cohort study linking emergency department-based clinical data to provincial health administrative databases

Introduction The Canadian Bronchiolitis Epinephrine Steroid Trial (CanBEST) and the Bronchiolitis Severity Cohort (BSC) study enrolled infants with bronchiolitis during the first year of life. The CanBEST trial suggested that treatment of infants with a combined therapy of high-dose corticosteroids and nebulised epinephrine reduced the risk of admission to hospital. Our study aims to - (1) quantify the risk of developing asthma by age 5 and 10 years in children treated with high-dose corticosteroid and epinephrine for bronchiolitis during infancy, (2) identify risk factors associated with development of asthma in children with bronchiolitis during infancy, (3) develop asthma prediction models for children diagnosed with bronchiolitis during infancy. Methods and analysis We propose a longitudinal cohort study in which we will link data from the CanBEST and BSC study with routinely collected data from provincial health administrative databases. Our outcome is asthma incidence measured using a validated health administrative data algorithm. Primary exposure will be treatment with a combined therapy of high-dose corticosteroids and nebulised epinephrine for bronchiolitis. Covariates will include type of viral pathogen, disease severity, medication use, maternal, prenatal, postnatal and demographic factors and variables related to health service utilisation for acute lower respiratory tract infection. The risk associated with development of asthma in children treated with high-dose corticosteroid and epinephrine for bronchiolitis will be assessed using multivariable Cox proportional hazards regression models. Prediction models will be developed using multivariable logistic regression analysis and internally validated using a bootstrap approach. Ethics and dissemination Our study has been approved by the ethics board of all four participating sites of the CanBEST and BSC study. Finding of the study will be disseminated to the academic community and relevant stakeholders through conferences and peer-reviewed publications. Trial registration number ISRCTN56745572; Post-results

Prenatal exposure to the 2009 pandemic H1N1 influenza vaccine on health outcomes in children

Introduction During the 2009 H1N1 pandemic, less than half of pregnant women in Ontario received the recommended influenza vaccine. Commonly-cited reasons for low vaccine uptake include misconceptions about the possible impact of maternal influenza infection and vaccine safety. Providing data on previously understudied pediatric health outcomes may help increase vaccine uptake. Objectives and Approach We conducted a retrospective cohort study of all live births from November 2nd, 2009 to October 31st, 2010 using the BORN Ontario province-wide birth registry containing information on H1N1 vaccination. These data were deterministically/probabilistically linked with several health administrative databases held at the Institute for Clinical Evaluative Sciences to ascertain specific immune-related pediatric health outcomes and health services utilization over 5 years of follow-up. Negative binomial regression models were used to evaluate the association between prenatal H1N1 vaccination and outcomes. Stabilized inverse probability of treatment weights (sIPTW) derived from the propensity scores were used to adjust for potential confounding. Results The study cohort included 104,310 eligible infants, 31,310 (30%) of whom were born to H1N1-vaccinated women. Median follow-up time was 5 years. Using sIPTWs we were able to achieve good balance of baseline measured covariates across exposure groups, with no absolute standardized differences larger than 7%. The sIPTW-adjusted analyses indicated no significant associations between prenatal exposure to H1N1 vaccination and upper respiratory infections (adjusted rate ratio [aRR] 1.01; 95% confidence interval [CI] 0.98-1.03), lower respiratory infections (aRR 1.00; 95%CI 0.96-1.04), otitis media (aRR 1.04; 95%CI 1.00-1.07), all infections (aRR 1.00; 95%CI 0.98-1.03), and rates of urgent and in-patient health services utilization (aRR 1.00; 95%CI 0.98-1.02). Conclusion/Implications Our primary findings suggest there are no associations between prenatal exposure to H1N1 vaccination and (1) the development of several immune-related health outcomes in children; (2) rates of health services utilization. Furthermore, our study provides new evidence on the long-term safety of influenza vaccination during pregnancy, which is currently lacking

A low-tech, cost-effective and efficient method for safeguarding genetic diversity by direct cryopreservation of poultry embryonic reproductive cells

Chickens are an important resource for smallholder farmers who raise locally adapted, genetically distinct breeds for eggs and meat. The development of efficient reproductive technologies to conserve and regenerate chicken breeds safeguards existing biodiversity and secures poultry genetic resources for climate resilience, biosecurity, and future food production. The majority of the over 1600 breeds of chicken are raised in low and lower to middle income countries under resource-limited, small-scale production systems, which necessitates a low-tech, cost-effective means of conserving diversity is needed. Here, we validate a simple biobanking technique using cryopreserved embryonic chicken gonads. The gonads are quickly isolated, visually sexed, pooled by sex, and cryopreserved. Subsequently, the stored material is thawed and dissociated before injection into sterile host chicken embryos. By using pooled GFP and RFP-labelled donor gonadal cells and Sire Dam Surrogate mating, we demonstrate that chicks deriving entirely from male and female donor germ cells are hatched. This technology will enable ongoing efforts to conserve chicken genetic diversity for both commercial and smallholder farmers, and to preserve existing genetic resources at poultry research facilities

MetaCAM: Ensemble-Based Class Activation Map

The need for clear, trustworthy explanations of deep learning model predictions is essential for high-criticality fields, such as medicine and biometric identification. Class Activation Maps (CAMs) are an increasingly popular category of visual explanation methods for Convolutional Neural Networks (CNNs). However, the performance of individual CAMs depends largely on experimental parameters such as the selected image, target class, and model. Here, we propose MetaCAM, an ensemble-based method for combining multiple existing CAM methods based on the consensus of the top-k% most highly activated pixels across component CAMs. We perform experiments to quantifiably determine the optimal combination of 11 CAMs for a given MetaCAM experiment. A new method denoted Cumulative Residual Effect (CRE) is proposed to summarize large-scale ensemble-based experiments. We also present adaptive thresholding and demonstrate how it can be applied to individual CAMs to improve their performance, measured using pixel perturbation method Remove and Debias (ROAD). Lastly, we show that MetaCAM outperforms existing CAMs and refines the most salient regions of images used for model predictions. In a specific example, MetaCAM improved ROAD performance to 0.393 compared to 11 individual CAMs with ranges from -0.101-0.172, demonstrating the importance of combining CAMs through an ensembling method and adaptive thresholding.Comment: 9 page

Deep ROC Analysis and AUC as Balanced Average Accuracy to Improve Model Selection, Understanding and Interpretation

Optimal performance is critical for decision-making tasks from medicine to autonomous driving, however common performance measures may be too general or too specific. For binary classifiers, diagnostic tests or prognosis at a timepoint, measures such as the area under the receiver operating characteristic curve, or the area under the precision recall curve, are too general because they include unrealistic decision thresholds. On the other hand, measures such as accuracy, sensitivity or the F1 score are measures at a single threshold that reflect an individual single probability or predicted risk, rather than a range of individuals or risk. We propose a method in between, deep ROC analysis, that examines groups of probabilities or predicted risks for more insightful analysis. We translate esoteric measures into familiar terms: AUC and the normalized concordant partial AUC are balanced average accuracy (a new finding); the normalized partial AUC is average sensitivity; and the normalized horizontal partial AUC is average specificity. Along with post-test measures, we provide a method that can improve model selection in some cases and provide interpretation and assurance for patients in each risk group. We demonstrate deep ROC analysis in two case studies and provide a toolkit in Python.Comment: 14 pages, 6 Figures, submitted to IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), currently under revie

Metabolic profiles derived from residual blood spot samples: A longitudinal analysis [version 1; referees: 2 approved]

Background: Secondary use of newborn screening dried blood spot samples include use for biomedical or epidemiological research. However, the effects of storage conditions on archival samples requires further examination. The objective of this study was to determine the utility of residual newborn samples for deriving reliable metabolic gestational age estimates. Methods: Residual newborn dried blood spot samples that had been stored for 2-, 4-, 6-, or 12-months in temperature controlled (21°C) conditions were re-analyzed for the full panel of newborn screening analytes offered by a provincial newborn screening lab in Ottawa, Canada. Data from re-analyzed samples were compared to corresponding baseline newborn screening values for absolute agreement, and Pearson and intraclass correlation. Performance of a gestational age estimation algorithm originally developed from baseline newborn screening values was then validated on data derived from stored samples. Results: A total of 307 samples were used for this study. 17-hydroxyprogesterone and newborn hemoglobin profiles measured by immunoassay and high-performance liquid chromatography, respectively, were among the most stable markers across all time points of analysis. Acylcarnitines exhibited the greatest degree of variation in stability upon repeat measurement. The largest shifts in newborn analyte profiles and the poorest performance of metabolic gestational age algorithms were observed when samples were analyzed 12-months after sample collection. Conclusions: Duration of sample storage, independent of temperature and humidity, affects newborn screening profiles and gestational age estimates derived from metabolic gestational dating algorithms. When considering use of dried blood spot samples either for clinical or research purposes, care should be taken when interpreting data stemming from secondary use

Health services use among children diagnosed with medium-chain acyl-CoA dehydrogenase deficiency through newborn screening: A cohort study in Ontario, Canada

Background: We describe early health services utilization for children diagnosed with medium-chain acyl-CoA dehydrogenase (MCAD) deficiency through newborn screening in Ontario, Canada, relative to a screen negative comparison cohort. Methods: Eligible children were identified via newborn screening between April 1, 2006 and March 31, 2010. Age-stratified rates of physician encounters, emergency department (ED) visits and inpatient hospitalizations to March 31, 2012 were compared using incidence rate ratios (IRR) and incidence rate differences (IRD). We used negative binomial regression to adjust IRRs for sex, gestational age, birth weight, socioeconomic status and rural/urban residence. Results: Throughout the first few years of life, children with MCAD deficiency (n = 40) experienced statistically significantly higher rates of physician encounters, ED visits, and hospital stays compared with the screen negative cohort. The highest rates of ED visits and hospitalizations in the MCAD deficiency cohort occurred from 6 months to 2 years of age (ED use: 2.1-2.5 visits per child per year; hospitalization: 0.5-0.6 visits per child per year), after which rates gradually declined. Conclusions: This study confirms that young children with MCAD deficiency use health services more frequently than the general population throughout the first few years of life. Rates of service use in this population gradually diminish after 24 months of age

The utility and predictive value of combinations of low penetrance genes for screening and risk prediction of colorectal cancer

Despite the fact that colorectal cancer (CRC) is a highly treatable form of cancer if detected early, a very low proportion of the eligible population undergoes screening for this form of cancer. Integrating a genomic screening profile as a component of existing screening programs for CRC could potentially improve the effectiveness of population screening by allowing the assignment of individuals to different types and intensities of screening and also by potentially increasing the uptake of existing screening programs. We evaluated the utility and predictive value of genomic profiling as applied to CRC, and as a potential component of a population-based cancer screening program. We generated simulated data representing a typical North American population including a variety of genetic profiles, with a range of relative risks and prevalences for individual risk genes. We then used these data to estimate parameters characterizing the predictive value of a logistic regression model built on genetic markers for CRC. Meta-analyses of genetic associations with CRC were used in building science to inform the simulation work, and to select genetic variants to include in logistic regression model-building using data from the ARCTIC study in Ontario, which included 1,200 CRC cases and a similar number of cancer-free population-based controls. Our simulations demonstrate that for reasonable assumptions involving modest relative risks for individual genetic variants, that substantial predictive power can be achieved when risk variants are common (e.g., prevalence > 20%) and data for enough risk variants are available (e.g., ~140–160). Pilot work in population data shows modest, but statistically significant predictive utility for a small collection of risk variants, smaller in effect than age and gender alone in predicting an individual’s CRC risk. Further genotyping and many more samples will be required, and indeed the discovery of many more risk loci associated with CRC before the question of the potential utility of germline genomic profiling can be definitively answered