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AMIA Annu Symp Proc. 2006; 2006: 1085.
PMCID: PMC1839276
Prospective Evaluation of a Bayesian Network for Detecting Asthma Exacerbations in a Pediatric Emergency Department
David L Sanders, MD, MS1 and Dominik Aronsky, MD, PhD1,2
1 Dept. of Biomedical Informatics
2 Dept. of Emergency Medicine Vanderbilt University Medical Center, Nashville, TN
Abstract
Early detection of asthma exacerbations may allow for automated guideline enrollment. We developed and prospectively evaluated a real-time Bayesian network to predict the presence of acute asthma after patient triage using only routinely available electronic data. 2,006 patients were enrolled, including 153 guideline-eligible patients. The area under the ROC curve was 0.971 (95% CI: 0.955 to 0.981) for identifying asthma patients. The system can be applied to identify guideline eligible patients and remind clinicians to enroll patients into guidelines.
Asthma is a common pediatric chronic disease, and exacerbations cause significant patient morbidity. Clinical guidelines exist for acute asthma care, but remain underutilized. In the Emergency Department (ED), automated identification of patients eligible for an asthma care guideline could improve use and compliance through electronic guideline triggering. We have developed and implemented a Bayesian Network (BN) for the real-time detection of asthma exacerbations in a pediatric ED immediately after patient triage. The system uses only routinely available clinical data, avoiding additional data capture from clinicians. The goal of this study was to prospectively evaluate the accuracy of the BN to identify asthma patients in real-time.
The study included all triaged and treated patients aged 2–18 years who presented to the pediatric ED of Vanderbilt Children’s Hospital during a 4-week (1/27/06 – 2/24/06) study period. We excluded patients without electronic triage documentation, who left the ED or were transferred prior to physician evaluation, or whose final assigned diagnosis could not be established through chart review. One author independently established a reference standard diagnosis for all included patients by reviewing electronic and paper-based patient records. A diagnosis of asthma was defined as an assigned final ED diagnosis of asthma, wheezing, or reactive airway disease, or a suspicion of asthma, which was later ruled out by a trial of beta-agonist administration.
The BN was developed and trained on prospectively collected data from a previous 2-month period. The BN included 10 clinical variables from the current and previous patient encounters that had documentation in the electronic medical record, including billing codes. After a patient was triaged, the BN computed in real-time the probability of an acute asthma exacerbation. We evaluated the performance of the BN by calculating the area under the receiver operating characteristic (ROC) curve. Additionally, we determined operational characteristics at a fixed level of 85% sensitivity.
RESULTS
Among the 2,006 included patient encounters, 153 visits were diagnosed with asthma exacerbation (7.6%). The area under the ROC curve was 0.971 (95% CI = 0.955 – 0.981). At a fixed sensitivity of 85%, specificity was 96.3%, positive predictive value was 65.3%, and negative predictive value was 98.7%. The positive likelihood ratio was 22.8 and the negative likelihood ratio was 0.16. At this level of performance, 69 false positive and 23 false negative classifications were made. The most common assigned diagnoses for false positive cases were pneumonia (32%), upper respiratory infection, (10%), croup (9%), and cough (7%).
DISCUSSION
The application of a Bayesian network for the early detection of asthma exacerbations demonstrated high performance. The system was able to make accurate predictions in real-time, immediately after patient triage using only routinely collected patient data available in electronic format, and without additional data entry. This demonstrates the system’s potential applicability for detection of patients with asthma exacerbations early during an ED encounter and for automatically prompting clinicians to apply asthma treatment guidelines.
Acknowledgements
The first author was supported by a NLM Training Grant T15 LM 007450-03
Articles from AMIA Annual Symposium Proceedings are provided here courtesy of
American Medical Informatics Association