Cummings J M, Boullier J A, Izenberg S D, Kitchens D M, Kothandapani R V
Divisions of Urology and Trauma, Department of Surgery, University of South Alabama, Mobile, Alabama, USA.
J Urol. 2000 Aug;164(2):326-8.
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.
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.
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.
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.
尽管人们已达成共识，即位于输尿管远端的小结石很有可能自然排出，但很难预测某一特定输尿管结石在个体中是否会排出或需要干预。如果在就诊时就能准确判断结石排出的可能性，患者就能立即接受结石干预治疗，或者被告知更合适的结石排出预期时间。我们使用人工神经网络评估输尿管结石患者的数据，以预测结石是否会自然排出或需要干预。
从181例因输尿管结石引发绞痛的患者记录中收集数据。患者的输入因素包括年龄、性别、种族、婚姻状况、保险情况、结石所在侧、位置、大小、肾积水和梗阻分级、就诊前症状持续时间、血清肌酐、结石排出或干预史以及恶心、呕吐或发热情况。评估的结果为结石排出或干预情况。数据输入使用商用计算机软件创建的神经网络。
数据库中的125例患者用于训练网络。在其余用于测试的55例患者中，该网络正确预测结果的有38例（76%）。在结石自然排出的25例中，敏感性为100%。就诊前症状持续时间是网络准确预测结石排出能力的最有影响因素，其次是肾积水分级。
人工神经网络可用于准确预测输尿管结石自然排出的可能性。在就诊时使用这样的模型可能有助于确定患者是否应接受结石早期干预治疗，或者预期结石排出前需要较长时间。
