Prescription Errors before and after Introduction of Electronic Medication Alert System in a Pediatric Emergency Department

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

Diagnostic Errors in Primary Care Pediatrics: Project RedDE

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 BP

Rinke 2017 (Acad Pediatrics) | PubMed 28804050 | Author Search

Growth Tracking in Severely Obese or Underweight Children

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

Validity of the WHO Cutoffs for Biologically Implausible values of Weight, Height, and BMI in Children and Adolescents in NHANES from 1999 through 2012

This analysis of data on 26,480 children and adolescents taken as part of the NHANES study from 1999–2000 through 2011–2012 showed that the overall prevalence of children with biologically implausible body measurements (determined commonly accepted rules involving modified z scores) was 0.9%. Most of these were high values rather than low. Further analysis that correlated these BIVs with other body measurements suggested that the majority of these seemingly anomalous values were accurate. Using these methods to exclude BIVs tends to underestimate the prevalence of obesity in these data.

Am J Clin Nutr. 2015 Nov; 102(5): 1000–1006.
Freedman 2015 (Link) | PubMed 26377160 | Author Search

Tenfold Medication Errors: 5 Years’ Experience at a University-Affiliated Pediatric Hospital

A study of pediatric inpatient safety reports. From the abstract: “From 6643 medication-related safety reports, 252 10-fold medication errors were identified at a mean reporting rate of 0.062 per 100 total patient days. Morphine was the most frequently reported medication, and opioids were the most frequently reported drug class. Twenty-two reports described patient harm. Intravenous formulations, paper ordering, and drug-delivery pumps were frequent error enablers. Errors of dose calculation, documentation of decimal points, and confusion with zeroes were frequent contributing causes to 10-fold medication error.”

Doherty 2012 (Pediatrics) | PubMed 22473367 | Author Search

Predicting Risk of Serious Bacterial Infections in Febrile Children in the Emergency Department

A prospective study from the UK of the ability to predict serious bacterial infections (SBI) via a logistic regression model using clinical and biomarker variables. Investigators used data from 1101 children (median age 2.4 years) who had presented to an ED for fever and who had required laboratory investigation. About a quarter of this group were diagnosed with a SBI, including pneumonia. The diagnostic model discriminated well between pneumonia and no SBI and between other SBIs and no SBI. Model updating yielded good calibration with good performance at both high-risk and low-risk thresholds. Extending the model with procalcitonin and resistin yielded improvements in discrimination.

Irwin 2017 (Pediatrics) | PubMed 28679639 | Author Search

Modified Z-scores in the CDC Growth Charts

Computational methods for detecting biologically implausible values in growth data from the Center for Chronic Disease Prevention and Health Promotion, Centers for Disease Control and Prevention. Describes the calculation of z-scores and ‘modified z-scores’ in the CDC growth chart data published in 2000. A modified z value is defined, for values above the mean, as half of the difference between the value corresponding to a z-score of 2 and the mean. For values below the mean, the modified z-score is half of the difference between the value corresponding to a z-score of 2 and the mean. One expresses the modifies z-score in terms of the modified z-value. For example, for a 4-year old (48.5 months old) boy, the mean BMI is 15.62. The BMI value corresponding to a z-score of -2 is 13.74. So the modified z-value is (15.62 – 13.74)/2 = 0.94. A boy that age with a BMI of 12 would have a modified BMI z-score of (12 – 15.62)/0.94 = -3.85.

CDC 2000 (Link)

Pediatric Prehospital Medication Dosing Errors: A National Survey of Paramedics

Prehosp Emerg Care. 2017;21:185-191.

This survey of paramedics found that pediatric dosing errors in the prehospital period are common. Respondents used varied methods for estimating weight of pediatric patients in order to calculate drug doses, and they advocated for pediatric training and standardized weight estimation methods to reduce risks. These findings suggest several possible interventions to enhance pediatric medication safety in the prehospital setting.

| PubMed 28257249 | Author Search