PURPOSE: Most infants undergoing an ultrasound to rule out pyloric stenosis will have a negative study, suggesting the low accuracy of clinical assessment. The purpose of this study was to evaluate the feasibility of using a Bayesian network to improve the accuracy of diagnosing pyloric stenosis. METHODS: Records of 118 infants undergoing an ultrasound to rule out pyloric stenosis were reviewed. Data from 88 (75%) infants were used to train a Bayesian decision network that predicted the probability of pyloric stenosis using risk factors, signs, and symptoms of the disease. The emergency department records of the remaining 28 (25%) infants were used to test the network. Two groups of pediatric surgeons and pediatric emergency medicine physicians were asked to predict the probability of pyloric stenosis in the testing set: (1) physicians using the network and (2) physicians using only emergency department records. Accuracy was evaluated using area under the ROC curve (discrimination) and Hosmer-Lemeshow (H-L) c-statistic (calibration). RESULTS: Physicians using the Bayesian decision network better predicted the probability of pyloric stenosis among infants in the testing set than those not using the network (ROC 0.973 vs 0.882; H-L c-statistic 3.9 [P > .05] vs 24.3 [P < .05]). Physicians using the network would have ordered 22% fewer ultrasounds and missed no cases of pyloric stenosis. CONCLUSIONS: The use of a Bayesian decision network may improve the accuracy of physicians diagnosing infants with possible pyloric stenosis. Use of this decision tool may safely reduce the need for imaging among infants with suspected pyloric stenosis.
PURPOSE: Most infants undergoing an ultrasound to rule out pyloric stenosis will have a negative study, suggesting the low accuracy of clinical assessment. The purpose of this study was to evaluate the feasibility of using a Bayesian network to improve the accuracy of diagnosing pyloric stenosis. METHODS: Records of 118 infants undergoing an ultrasound to rule out pyloric stenosis were reviewed. Data from 88 (75%) infants were used to train a Bayesian decision network that predicted the probability of pyloric stenosis using risk factors, signs, and symptoms of the disease. The emergency department records of the remaining 28 (25%) infants were used to test the network. Two groups of pediatric surgeons and pediatric emergency medicine physicians were asked to predict the probability of pyloric stenosis in the testing set: (1) physicians using the network and (2) physicians using only emergency department records. Accuracy was evaluated using area under the ROC curve (discrimination) and Hosmer-Lemeshow (H-L) c-statistic (calibration). RESULTS: Physicians using the Bayesian decision network better predicted the probability of pyloric stenosis among infants in the testing set than those not using the network (ROC 0.973 vs 0.882; H-L c-statistic 3.9 [P > .05] vs 24.3 [P < .05]). Physicians using the network would have ordered 22% fewer ultrasounds and missed no cases of pyloric stenosis. CONCLUSIONS: The use of a Bayesian decision network may improve the accuracy of physicians diagnosing infants with possible pyloric stenosis. Use of this decision tool may safely reduce the need for imaging among infants with suspected pyloric stenosis.
Authors: Paola Sebastiani; Vikki G Nolan; Clinton T Baldwin; Maria M Abad-Grau; Ling Wang; Adeboye H Adewoye; Lillian C McMahon; Lindsay A Farrer; James G Taylor; Gregory J Kato; Mark T Gladwin; Martin H Steinberg Journal: Blood Date: 2007-06-28 Impact factor: 22.113