Abstract
Background Asthma exacerbation leading to emergency department (ED) visit is prevalent, an indicator
of poor control of asthma, and is a potentially preventable clinical outcome.
Objective We propose to utilize multiple data elements available in electronic medical records
(EMRs) and claims database to create separate algorithms with high validity for clinical
and research purposes to identify asthma exacerbation-related ED visit among the general
population.
Methods We performed a retrospective study with inclusion criteria of patients aged 4 to
40 years, a visit to Geisinger ED from January 1, 2006, to October 28, 2013, with
asthma on their problem list. Different electronic data elements including chief complaints,
vitals, season, smoking, medication use, and discharge diagnoses were obtained to
create the algorithm. A stratified random sample was generated to select the charts
for review. Chart review was performed to classify patients with asthma-related ED
visit, that is, the gold standard. Two reviewers performed the chart review and validation
was done on a small subset.
Results There were 966 eligible ED visits in the EMR sample and 731 in the claims sample.
Agreement between reviewers was 95.45% and kappa statistic was 0.91. Mean age of the
EMR sample was 22 years, and mostly white (93%). Multiple models conventionally used
in studies were evaluated and the final model chosen included principal diagnosis,
bronchodilator, and steroid use for both algorithms, chief complaints for EMR, and
secondary diagnosis for claims. Area under the curve was 0.93 (95% confidence interval:
0.91–0.94) and 0.94 (0.93–0.96), respectively, for EMR and claims data, with positive
predictive value of > 94%. The algorithms are visually presented using nomograms.
Conclusion We were able to develop two separate algorithms for EMR and claims to identify asthma
exacerbation-related ED visit with excellent diagnostic ability and varying discrimination
threshold for clinical and research purposes.
Keywords
system improvement - disease management - clinical research informatics - allergy
and immunology - clinical data management
