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.
Chart review of 33 medication and fluid errors identified in a pediatric emergency department through incident reports filed over a 5-year period showed the most common error was an incorrect dose of medication (35%). Incorrect recording of patient weights commonly led to an incorrect medication dose.Selbst 1999 (Link) | PubMed 10069301 | Author Search
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
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
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)
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
Evaluation of whether provider recognition of abnormal BP (greater than 90th percentile) differed before versus after the introduction of an app that extracts age, sex, height and BP data from the EHR to calculate and track a patient’s BP percentile longitudinally. The app was based on the Substitutable Medical Applications & Reusable Technology (SMART) platform and is available In the SMARTApp Gallery. Examining ~79,000 records of outpatients (primary care, endocrinology, cardiology, nephrology clinics), of which ~3500 had elevated blood pressure, showed that abnormal BP was recognized in 4.9% of visits before the app was available and 7.1% of visits afterwards. The app was used in 13% of encounters where an elevated BP was present; significantly, when the app was used, recognition of elevated BP was much higher (OR 3.17, CI 2.29-4.41).Twichell 2017 (Link) | PubMed 28493451 | Author Search
Reports the development of an automated method for identifying implausible values in pediatric EHR growth (weight and height) data, tested via data points collected in the primary care environment on over 280,000 patiets. The method compares each measurement’s z-score to a weighted moving average of prior measurements. The method had a sensitivity of 97% and a specificity of 90% for identifying implausible values compared to physician judgment, and identified almost all simulated errors.Daymont 2017 (JAMIA) | PubMed 28453637 | Author Search
A report of a prediction rule for rebound hyperbilirubinemia (return of total serum bilirubin to phototherapy threshold within 72 hours of phototherapy termination) in newborns of at least 35 weeks’ gestation. Authors studied a group of ~7000 infants, 4.6% of whom had rebound hyperbilirubinemia. The formula is calculated as: 15 points if gestational age less than 38 weeks, minus 7 × (age in days at phototherapy initiation) minus 4 × (AAP phototherapy threshold − TSB at phototherapy termination) + 50. This score in turn can be applied to a curve (pictured) to predict rebound hyperbilirubinemia.Chang 2016 (Pediatrics) | PubMed | Author Search