The Utility of Clinical and Laboratory Data for Predicting Intraabdominal Injury Among Children

Abstract

Physical examination combined with selected laboratory studies can be used to predict the risk of IAI accurately among children who sustain blunt trauma. Application of these findings may be useful in reducing costs and improving the accuracy of diagnosing IAI among children.

Delay in Diagnosis and Treatment of Blunt Intestinal Perforation Does Not Adversely Affect Prognosis in the Pediatric Trauma Patient: Retracted

“…Because physical examination and the presence of abdominal tenderness are important factors in determining when a BII is present, children with TBI may be more difficult to examine a factor which would tend toward a delay in diagnosis and intervention. 17,18 Almost half of the children presented to a referring hospital initially and were then transferred to the pediatric trauma center, with an average transfer time of just more than 6 hours. Many times the delay in transfer was secondary to the time required to obtain an abdominal CT scan, because more than one fourth of the patients had a CT scan before transfer.…”

Delay in diagnosis and treatment of blunt intestinal injury does not adversely affect prognosis in the pediatric trauma patient

“…Of the 11 deaths in the lessthan-6-hour group, 8 were from the concomitant head injury. Because physical examination and the presence of peritonitis are important factors in determining when a BII is present, children with TBI may be more difficult to examine, a factor which would tend toward a delay in diagnosis and intervention [17,18].…”

Models developed by three techniques did not achieve acceptable prediction of binary trauma outcomes

“…If left to ''grow'' unchecked, these trees become ''bushy,'' with nodes having few cases, and may not generalize well; several ''pruning'' techniques are available [21]. We employed an implementation [22] of the CART (Classification and Regression Trees) algorithm [12,23], previously applied to trauma data [24][25][26][27][28][29][30]. When employed with categoric outcomes, CART by default minimizes the Gini impurity criterion [23], a measure of variability within the subgroups created at each stage of the tree.…”