Acute splenic torsion in children: which is the best treatment? A case report.

Wandering spleen is a clinical entity which rarely affects children and adolescents. This condition can be asymptomatic or responsible of chronic pain, but it appears as a surgical emergency when an acute twisting occurs. The risk of post-splenectomy sepsis in the pediatric population suggests a conservative approach whenever possible, and also in case of acute torsion, most authors prefer to preserve the spleen and perform a splenopexy. The Authors describe a case of a child with acute splenic torsion, in whom a conservative surgical approach was initially adopted. The conservative option has to be balanced with the risk of prolonged thrombocytopenia, multiple transfusions and a possible second procedure to remove the spleen.

A novel approach for accurate prediction of spontaneous passage of ureteral stones: support vector machines.

The objective of this study was to optimally predict the spontaneous passage of ureteral stones in patients with renal colic by applying for the first time support vector machines (SVM), an instance of kernel methods, for classification. After reviewing the results found in the literature, we compared the performances obtained with logistic regression (LR) and accurately trained artificial neural networks (ANN) to those obtained with SVM, that is, the standard SVM, and the linear programming SVM (LP-SVM); the latter techniques show an improved performance. Moreover, we rank the prediction factors according to their importance using Fisher scores and the LP-SVM feature weights. A data set of 1163 patients affected by renal colic has been analyzed and restricted to single out a statistically coherent subset of 402 patients. Nine clinical factors are used as inputs for the classification algorithms, to predict one binary output. The algorithms are cross-validated by training and testing on randomly selected train- and test-set partitions of the data and reporting the average performance on the test sets. The SVM-based approaches obtained a sensitivity of 84.5% and a specificity of 86.9%. The feature ranking based on LP-SVM gives the highest importance to stone size, stone position and symptom duration before check-up. We propose a statistically correct way of employing LR, ANN and SVM for the prediction of spontaneous passage of ureteral stones in patients with renal colic. SVM outperformed ANN, as well as LR. This study will soon be translated into a practical software toolbox for actual clinical usage.