Identifying asthma exacerbations in a pediatric emergency department: A feasibility study.
Int J Med Inform. 2007 Jul;76(7):557-64.
Sanders DL, Gregg W, Aronsky D.
Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, United States.
BACKGROUND: Asthma is a common pediatric chronic disease and is estimated to account for more than 2 million emergency department visits per year. Asthma guidelines have demonstrated improved outcomes, but remain underutilized due to several barriers. Computerized methods to automatically identify asthma exacerbations may be beneficial to initiate guideline recommended treatment, but have not been described. The goal of the study was to examine the accuracy of an algorithm to identify asthma patients at triage in real-time using only electronically available data.
METHODS: During a 9-month period, the five most frequent presenting chief complaints for Emergency Department asthma patients aged 2-18 years were identified and accounted for >95% of asthma visits: wheezing, shortness of breath, fever, cough, and dyspnea. During a following 1-month period (November 2004), medical records of all patients with one of the five chief complaints were reviewed to establish a reference standard diagnosis. An asthma identification algorithm was developed that considered only data available in electronic format at the time of triage and included the presenting chief complaint, information from the computerized problem list (past medical history; current medications, such as beta-agonists, steroids, and other asthma medications), and ICD-9 billing codes from previous encounters.
RESULTS: From 1835 Emergency Department visits, 368 visits (154 with asthma) had one of the five chief complaints and were included. A problem list was available in 203 (55.2%) and an ICD-9 code in 68 (18.5%) patients. Wheezing accounted for 56.5% of asthma visits, while fever was the most frequent chief complaint among all patients (43.8%). The asthma identification algorithm had a sensitivity of 44.8% (95% CI: 36.8-53.0%), a specificity of 91.6% (CI: 87.0-94.9%), a positive predictive value of 79.3% (CI: 69.3-87.3%) and a negative predictive value of 69.8% (CI: 64.0-75.1%). The positive and negative likelihood ratios were 5.3 (CI: 3.3-8.6) and 0.6 (CI: 0.5-0.7), respectively.
CONCLUSION: The simple identification algorithm demonstrated good accuracy for identifying asthma episodes. The algorithm may represent a promising and feasible approach to create computerized reminders or automatic triggers that can facilitate the initiation of guideline-based asthma treatment in the Emergency Department.