Author: aspooner

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.”

[su_cite_pediatrics url_fragment = ‘129/5/916’ author = ‘Doherty’ year = ‘2012’] | 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.

[su_cite_pediatrics url_fragment = ‘140/2/e20162853’ author = ‘Irwin’ year = ‘2017’] | 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

The Effect of an Electronic Health Record–based Tool on Abnormal Pediatric Blood Pressure Recognition

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

Automated Identification of Implausible Values in Growth Data from Pediatric Electronic Health Records

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 Clinical Prediction Rule for Rebound Hyperbilirubinemia Following Inpatient Phototherapy

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.

[su_cite_pediatrics url_fragment = ‘139/3/e20162896’ author = ‘Chang’ year = ‘2016’] | PubMed 12345678 | Author Search

Measuring Prevention More Broadly: An Empirical Assessment of CHIPRA Core Measures

Using claims data from the Alabama Children’s Health Insurance Program, calculated each of four quality measures under two alternative definitions: (1) the formal claims-based guidelines outlined in the CMS Technical Specifications, and (2) a broader definition of appropriate claims for identifying preventive service use. Concludes: Differences in CHIP design and structure, across states and over time, may limit the usefulness of select claims-based core measures for detecting disparities accurately (Medicare and Medicaid Research Review).

Menachemi 2013 (Link) | PubMed 24800161 | Author Search

Body Composition and BMI Growth Charts in Children with Down Syndrome

Analysis of the use of new (2015) Down-syndrome BMI norms compared to standard CDC norms. Concludes that the general CDC norms are a better indicator of excess adiposity than the Down-syndrome-secific ones for DS children 10 years old and up.

[su_cite_pediatrics url_fragment = ‘138/4/e20160541’ author = ‘Hatch-Stein’ year = ‘2016’] | PubMed 27630073 | Author Search

Pediatric Complex Chronic Conditions Classification System Version 2: Updated for ICD-10 and Complex Medical Technology Dependence and Transplantation

An ICD-10 update of the pediatric complex chronic conditions (CCC) classification system from 2000. The system includes diagnostic and procedural codes that incorporate a new neonatal CCC category as well as domains of complexity arising from technology dependence or organ transplantation. Linked electronic supplementary material provides SAS and Stata code, plus tabular information on codes.

Feudtner 2014 (BMC Pediatrics) | PubMed 25102958 | Author Search