Prediction of spontaneous ureteral calculous passage by an artificial neural network.
OBJECTIVE
Although a consensus exists that small stones presenting in the distal ureter have a good probability of spontaneous passage, it is difficult to predict in individuals whether a particular ureteral stone would pass or require intervention. If an accurate judgment were made at presentation on the likelihood of stone passage, patients would receive immediate intervention for the stone or be notified of a more appropriate time at which to expect passage. We used an artificial neural network to evaluate data in patients with ureteral calculi to predict whether a stone would pass spontaneously or require intervention.


METHODS
Data were collected from the records of 181 patients presenting with colic due to a ureteral calculus. Patient input factors included age, sex, race, marital status, insurance, stone side, level and size, hydronephrosis and obstruction grades, duration of symptoms before presentation, serum creatinine, history of stone passage or intervention and nausea, vomiting or fever. Outcomes evaluated were stone passage or intervention. Data were entered into a neural network created using commercially available computer software.


RESULTS
A set of 125 patients from the database was used for training the network. The network correctly predicted outcome in 38 of the remaining 55 patients (76%) used for testing. In the 25 cases in which stones passed spontaneously sensitivity was 100%. Duration of symptoms before presentation was the most influential factor in network ability to predict accurately stone passage, followed by hydronephrosis grade.


CONCLUSIONS
An artificial neural network may be used to predict accurately the probability of spontaneous ureteral stone passage. Using such a model at presentation may help to determine whether a patient should receive early intervention for a stone or expect a lengthy interval before stone passage.
Cummings
J M
JM
Divisions of Urology and Trauma, Department of Surgery, University of South Alabama, Mobile, Alabama, USA.
Boullier
J A
JA
Izenberg
S D
SD
Kitchens
D M
DM
Kothandapani
R V
RV
D000293:Adolescent; D000328:Adult; D000367:Age Factors; D000368:Aged; D000369:Aged, 80 and over; D002648:Child; D002675:Child, Preschool; D003404:Creatinine / Q000097:blood; D005260:Female; D006801:Humans; D007223:Infant; D008297:Male; D017533:Marital Status; D008875:Middle Aged; D016571:Neural Networks, Computer; D011336:Probability; D012075:Remission, Spontaneous; D012737:Sex Factors; D014514:Ureteral Calculi / Q000503:physiopathology*
D003404:Creatinine