Comparison of computer-based facial recognition software of facial images against standard, manual examination in fetal alcohol spectrum disorders. Areas-under-the-curve values for individual receiver-operating characteristic curves revealed the computer-aided system to be comparable to the manual method in detecting patients with FASD. Cases of alcohol-related neurodevelopmental disorder were identified more efficiently by the computer-aided system in comparison to the manual method.Valentine 2017 (Pediatrics) | PubMed 29187580 | Author Search
An evaluation of the Modified Checklist for Autism in Toddlers (M-CHAT) With Follow-up Interview (M-CHAT/F) as administered by primary-care pediatricians during typical checkups. Comparing the PCP performance to that of trained research assistants, sensitivity, specificity, positive predictive value (PPV), and overall accuracy for M-CHAT/F showed significant equivalence.Sturner 2016 (Pediatrics) | PubMed 27542847 | Author Search
A retrospective study of 79,000 ED encounters at a children’s hospital and two general hospitals. The intent of the study was to characterize the frequency of weight errors and to determine of the children’s hospital was any better at correcting errors than the general hospitals. The findings were that weight errors were uncommon (0.63% of all weights, as defined by the weight being a new extreme value on the growth chart) in the 3 EDs, but they led to identifiable weight-based medication-dosing errors with the potential to cause harm. The rates of error where similar across hospitals, and it looked like the children’s hospital was slightly better at intercepting errors once they were committed. Common weight errors included the weight in pounds being substituted for the weight in kilograms and decimal placement errors.Hirata 2017 (Link) | PubMed 28976456 | Author Search
Aimed to describe trends in length-for-age, weight-for-age, weight-for-length, and early childhood weight gain among US children aged 6 to 23 months from NHANES data ranging from 1976 to 2014. Between 1976–1980 and 2011–2014, there were no significant trends in low or high weight-for-age and weight-for-length, whereas the percent with high length-for-age decreased (5.5% to 3.7%). Non-Hispanic black children gained more weight between birth and survey participation in 2011–2014 versus 1988–1994.Akinbami 2017 (Pediatrics) | PubMed 28213608 | Author Search
Downloadable PDFs for plotting growth for children with Trisomy 21, based on data collected in the Down Syndrome Growing Up Study (DSGS) published in 2015. This study collected data from a convenience sample of patients with Down syndrome.CDC 2017 (Link)
The objective of this was to compare prescription error rates before and after introduction of computerized provider order entry on a pediatric emergency department. Error assessment was limited to errors with potential to cause life-threatening injury, failure of therapy, or an adverse drug effect. 29.6% of prescriptions generated an alert, with an almost 90% false-positive rate. 7,268 medication orders before and 7,292 after were compared, showing a significant reduction in the errors per 100 prescriptions (10.4 before vs. 7.3 after; absolute risk reduction = 3.1, 95% confidence interval [CI] = 2.2 to 4.0)Sethuraman 2015 (Link) | PubMed 25998704 | Author Search
This study examined the prevalence of three diagnostic errors or missed opportunities to diagnose in pediatric primary care practices (adolescent depression, elevated blood pressure, and sexually-transmitted infection lab results). They found that these errors were common. Providers did not follow up abnormal STI laboratory values for 11% of patients and did not address adolescent depression in 62% of visits. Providers did not document recognition of an elevated BP in 51% of patients with elevated BPRinke 2017 (Acad Pediatrics) | PubMed 28804050 | Author Search
Report on experience using a body-mass index chart that may be better suited for plotting children at the extremes of the growth spectrum. This growth chart uses a “modified z score,” proposed by the CDC, which expresses variations from the median in terms of a unit equal to one half the difference between 0 and +2 z scores (for measures above the mean) and one half the difference between 0 and -2 z scores (for measures below the mean) for any given age and gender.
If one uses modified z score as the y-axis against age, ordinary BMI changes fall along a curve that is much close to a straight line, so outliers should be easier to spot in this circumstance.
Authors illustrate the advantages of using an age-vs-BMI chart with modified z-score isobars over the standard CDC 2000 charts and over the modified charts showing the percentage of the 95th percentile of BMI.Chambers 2017 (Pediatrics) | PubMed 29114063 | Author Search