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.

Chang 2016 (Pediatrics) | PubMed | Author Search

Impact of Electronic Health Record-Based Alerts on Influenza Vaccination for Children With Asthma

A prospective, cluster-randomized trial of the use of a flu-shot reminder alert in pediatric primary care, showing only modest effects. Vaccination opportunities in children with asthma increased from 14.4% to 18.6% at intervention sites and from 12.7% to 16.3% at control sites, a 0.6% greater improvement. The authors conclude that if one is using an immunization reminder system, “the addition of an influenza reminder system may be helpful, especially in the setting of urban resident-teaching practices.”

Fiks 2009 (Pediatrics) | PubMed 19564296 | Author Search

New Technologies as a Strategy to Decrease Medication Errors: How Do they Affect Adults and Children Differently?

A review of techniques used to reduce medication errors in pediatrics. Within the limitations of the heterogeneous system that makes up information technology for child health, the authors conclude that CPOE and accompanying decision support can help but also creates new types of error (World Journal of Pediatrics).

Ruano 2015 (Link) | PubMed 26684316 | Author Search

Implementation of Electronic Clinical Decision Support for Pediatric Appendicitis

pas_scoreStudy of the implementation of clinical decision support on the rates of CT scanning for pediatric patients presenting with abdominal pain (and possible appendicitis) to 2 large, tertiary emergency departments. Decision support in this case consisted of an order set, a web-based risk stratification tool, and an alert presented at the time of CT ordering to guide practitioners in the use of the latter tools. Results showed about a 50% decrease in the rate of CT scan ordering, with no increase in missed appendicitis or other complications. In the studied population, 28% of patients had appendicitis.

Kharbanda 2016 (Pediatrics) | PubMed 27244781 | Author Search

Standardized Clinical Pathways for Hospitalized Children and Outcomes

15 clinical pathways

Analysis of admissions on any of 15 care pathways to Seattle Children’s Hospital over ~4 years that showed a slowing in the rate of rise of costs of care. Pathways were implemented in the electronic health record via order sets and accompanying decision support. Data on order-set use shows no clear trend toward broad increases in usage rates; for a few conditions, order-set usage improved over time (croup went from 38% to 68% over ~2 years) but most were low, stable usage rates, and some went down (cellulitis [42–>27%], femur fracture [94—>82%]). Length-of-stay data showed that LOS fell slightly for some conditions over time (0.03 days saved overall for each month following the implementation of the pathway). Quality-of-life measurement was unchanged.

Lion 2016 (Pediatrics) | PubMed 27002007 | Author Search

Multicentre Validation of the Bedside Paediatric Early Warning System Score: A Severity of Illness Score to Detect Evolving Critical Illness in Hospitalised Children

2016-01-15_08-34-33Multicentre (4 hospitals) case-control study to validate the Bedside PEWS score (2,074 patients) in the prediction of cardiopulmonary arrest. The median maximum Bedside PEWS scores for the 12 hours ending 1 hour before the clinical deterioration event were 8 in case patients and 2 in control patients.

Parshuram 2011 (Link) | PubMed 21812993 | Author Search

Predicting Discharge Dates From the NICU Using Progress Note Data

Reports a machine-learning algorithm for predicting discharge from text in daily NICU progress notes. The authors postulate that knowing when a patient is medically ready to go home may make it easier to plan for non-medical needs. This research contrasts with studies that focus on explicitly predicting when a patient is medically ready to go home. In either case, it is important to know when a patient is ready, especially with long stays like one sees in NICUs.

Temple 2015 (Pediatrics) | PubMed 26216319 | Author Search