The urgency to embrace Big Data opportunities in medicine

Shortness of breath in children at the emergency department: Variability in management in Europe

OBJECTIVE

Our aim was to describe variability in resource use and hospitalization in children presenting with shortness of breath to different European Emergency Departments (EDs) and to explore possible explanations for variability.

DESIGN

The TrIAGE project, a prospective observational study based on electronic health record data.

PATIENTS AND SETTING

Consecutive paediatric emergency department visits for shortness of breath in five European hospitals in four countries (Austria, Netherlands, Portugal, United Kingdom) during a study period of 9-36 months (2012-2014).

MAIN OUTCOME MEASURES

We assessed diversity between EDs regarding resource use (diagnostic tests, therapy) and hospital admission using multivariable logistic regression analyses adjusting for potential confounding variables.

RESULTS

In total, 13,552 children were included. Of those, 7,379 were categorized as immediate/very urgent, ranging from 13-80% in the participating hospitals. Laboratory tests and X-rays were performed in 8-33% of the cases and 21-61% was treated with inhalation medication. Admission rates varied between 8-47% and PICU admission rates varied between 0.1-9%. Patient characteristics and markers of disease severity (age, sex, comorbidity, urgency, vital signs) could explain part of the observed variability in resource use and hospitalization. However, after adjusting for these characteristics, we still observed substantial variability between settings.

CONCLUSION

European EDs differ substantially regarding the resource use and hospitalization in children with shortness of breath, even when adjusting for patient characteristics. Possible explanations for this variability might be unmeasured patient characteristics such as underlying disease, differences in guideline use and adherence or different local practice patterns.

Informative missingness in electronic health record systems: the curse of knowing

Electronic health records provide a potentially valuable data source of information for developing clinical prediction models. However, missing data are common in routinely collected health data and often missingness is informative. Informative missingness can be incorporated in a clinical prediction model, for example by including a separate category of a predictor variable that has missing values. The predictive performance of such a model depends on the transportability of the missing data mechanism, which may be compromised once the model is deployed in practice and the predictive value of certain variables becomes known. Using synthetic data, this phenomenon is explained and illustrated.