Minimally invasive colopexy for pediatric Chilaiditi syndrome.

Chilaiditi syndrome is a rare disorder characterized by abdominal pain, respiratory distress, constipation, and vomiting in association with Chilaiditi's sign. Chilaiditi's sign is the finding on plain roentgenogram of colonic interposition between the liver and diaphragm and is usually asymptomatic. Surgery is typically reserved for cases of catastrophic colonic volvulus or perforation because of the syndrome. We present a case of a 6-year-old boy who presented with Chilaiditi syndrome and resulting failure to thrive because of severe abdominal pain and vomiting, which did not improve with laxatives and dietary changes. He underwent a laparoscopic gastrostomy tube placement and laparoscopic colopexy of the transverse colon to the falciform ligament and anterior abdominal wall. Postoperatively, his symptoms resolved completely, as did his failure to thrive. His gastrostomy tube was removed 3 months after surgery and never required use. This is the first case of Chilaiditi syndrome in the pediatric literature we are aware of that was treated with an elective, minimally invasive colopexy. In cases of severe Chilaiditi syndrome refractory to medical treatment, a minimally invasive colopexy should be considered as a possible treatment option and potentially offered before development of life-threatening complications such as volvulus or perforation.

The use of prognostic indicators in the development of a statistical model predictive for adrenal insufficiency in trauma patients.

We performed a retrospective chart review of trauma patients admitted to Palmetto Richland Memorial Hospital and identified 63 cases of adrenal insufficiency along with 65 trauma patient controls. Two statistical models, a neural network and a multiple logistic regression, were developed to predict patients with increased risk of developing adrenal insufficiency. Each model had 11 selected independent variables, along with patient demographic data, to make a probabilistic prediction of patient outcome. The neural network model was trained with 102 patients to identify linear and nonlinear relationships that could yield a predictive capability. The neural network achieved an accuracy of 71 per cent. The logistic regression model achieved an accuracy of 82 per cent. With these models, we have shown the feasibility of a method to more accurately screen patients with an increased risk of adrenal insufficiency. This ability should allow earlier identification and treatment of patients with adrenal insufficiency. Further development with a larger database is needed to improve the accuracy of the present models.