title: [Prediction of calculus clearance after extracorporeal shock wave lithotripsy of calculi in the inferior kidney calices. Application of the artificial neural network].
abstract: The purpose of this retrospective study was to define prognostic factors which determine the stone clearance (SC) for lower caliceal stones after extracorporeal shock wave lithotripsy (ESWL) and to compare the prediction accuracy of artificial neural network analysis (ANNA) and standard computational methods. Since January 1995, 321 renal units in 310 patients with single or multiple inferior caliceal calculi of all sizes and compositions have been treated with ESWL (Lithotriptor: Piezolith 2500, Wolf company). The classification accuracy of ANNA in the test set was 94%, with a sensitivity of 95%, a specificity of 92%, and a receiver operating characteristic curve area of 0.966, results significantly better than those yielded by a logistic regression analysis (classification accuracy 77%, sensitivity 75%, specificity 81%, and ROC curve area 0.779). Patients with lower renal caliceal stones appear to have the best chance of successful ESWL when their body mass index (BMI) and urinary transport (UT) are normal, the infundibular width (IW) is 5 mm or more, and the infundibular ureteropelvic angle (IUPA) is 45 degrees or more. Stone size and composition, as factors of SC, are not statistically significant. After determining the angle, width, and UT in patients with optimal age and body mass suitable for ESWL, SC can be achieved irrespective of stone size and composition.
authors.affiliation: Klinik für Urologie und Kinderurologie, Krankenhaus Nordwest, Stiftung Hospital zum Heiligen Geist, Akademisches Lehrkrankenhau, Johann-Wolfgang-Goethe-Universität, Frankfurt am Main. Poulakis@em.uni-frankfurt.de
authors.forename: V
authors.initials: V
authors.lastname: Poulakis
authors.forename: U
authors.initials: U
authors.lastname: Witzsch
authors.forename: J
authors.initials: J
authors.lastname: Remplick
authors.forename: S
authors.initials: S
authors.lastname: Sihler
authors.forename: E
authors.initials: E
authors.lastname: Becht
mesh_terms: D000293:Adolescent; D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D005260:Female; D006801:Humans; D007669:Kidney Calculi / Q000628:therapy*; D007670:Kidney Calices; D008096:Lithotripsy / Q000706:statistics & numerical data; D008297:Male; D008875:Middle Aged; D016571:Neural Networks, Computer; D011379:Prognosis; D012372:ROC Curve; D012189:Retrospective Studies