David L Sanders1, Dominik Aronsky. 1. Depart. of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, USA.
Abstract
OBJECTIVE: To develop and evaluate a Bayesian network to identify patients eligible for an asthma-care guideline using only data available electronically at the time of patient triage. POPULATION: Consecutive patients 2-18 years old who presented to a pediatric emergency department during a 2-month period. METHODS: A network was developed and evaluated using clinical data from patient visits. An independent reference standard for asthma guideline eligibility was established and verified for each patient through chart review. Outcome measures were area under the receiver operating characteristic curve, sensitivity, specificity, predictive values, and likelihood ratios. RESULTS: We enrolled 3,023 patient visits, including 385 who were eligible for guideline-based care. Area under the receiver operating curve for the network was 0.959 (95% CI = 0.933 - 0.977). At a fixed 90% sensitivity, specificity was 88.3%, positive predictive value was 44.7% and negative predictive value was 98.8%. The positive likelihood ratio was 7.69 and the negative likelihood ratio was 0.11. CONCLUSION: The Bayesian network was able to detect patients eligible for an asthma guideline with high accuracy suggesting that this technique could be used to automatically initiate guideline use for eligible patients.
OBJECTIVE: To develop and evaluate a Bayesian network to identify patients eligible for an asthma-care guideline using only data available electronically at the time of patient triage. POPULATION: Consecutive patients 2-18 years old who presented to a pediatric emergency department during a 2-month period. METHODS: A network was developed and evaluated using clinical data from patient visits. An independent reference standard for asthma guideline eligibility was established and verified for each patient through chart review. Outcome measures were area under the receiver operating characteristic curve, sensitivity, specificity, predictive values, and likelihood ratios. RESULTS: We enrolled 3,023 patient visits, including 385 who were eligible for guideline-based care. Area under the receiver operating curve for the network was 0.959 (95% CI = 0.933 - 0.977). At a fixed 90% sensitivity, specificity was 88.3%, positive predictive value was 44.7% and negative predictive value was 98.8%. The positive likelihood ratio was 7.69 and the negative likelihood ratio was 0.11. CONCLUSION: The Bayesian network was able to detect patients eligible for an asthma guideline with high accuracy suggesting that this technique could be used to automatically initiate guideline use for eligible patients.
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