Moghisi Reihaneh, El Morr Christo, Pace Kenneth T, Hajiha Mohammad, Huang Jimmy
School of Information Technology, York University, Toronto, ON, Canada.
School of Health Policy and Management, York University, Toronto, ON, Canada.
Interact J Med Res. 2022 Mar 16;11(1):e33357. doi: 10.2196/33357.
Shock wave lithotripsy (SWL), ureteroscopy, and percutaneous nephrolithotomy are established treatments for renal stones. Historically, SWL has been a predominant and commonly used procedure for treating upper tract renal stones smaller than 20 mm in diameter due to its noninvasive nature. However, the reported failure rate of SWL after one treatment session ranges from 30% to 89%. The failure rate can be reduced by identifying candidates likely to benefit from SWL and manage patients who are likely to fail SWL with other treatment modalities. This would enhance and optimize treatment results for SWL candidates.
We proposed to develop a machine learning model that can predict SWL outcomes to assist practitioners in the decision-making process when considering patients for stone treatment.
A data set including 58,349 SWL procedures performed during 31,569 patient visits for SWL to a single hospital between 1990 and 2016 was used to construct and validate the predictive model. The AdaBoost algorithm was applied to a data set with 17 predictive attributes related to patient demographics and stone characteristics, with success or failure as an outcome. The AdaBoost algorithm was also applied to a training data set. The generated model's performance was compared to that of 5 other machine learning algorithms, namely C4.5 decision tree, naïve Bayes, Bayesian network, K-nearest neighbors, and multilayer perceptron.
The developed model was validated with a testing data set and performed significantly better than the models generated by the other 5 predictive algorithms. The sensitivity and specificity of the model were 0.875 and 0.653, respectively, while its positive predictive value was 0.7159 and negative predictive value was 0.839. The C-statistics of the receiver operating characteristic (ROC) analysis was 0.843, which reflects an excellent test.
We have developed a rigorous machine learning model to assist physicians and decision-makers to choose patients with renal stones who are most likely to have successful SWL treatment based on their demographics and stone characteristics. The proposed machine learning model can assist physicians and decision-makers in planning for SWL treatment and allow for more effective use of limited health care resources and improve patient prognoses.
冲击波碎石术（SWL）、输尿管镜检查和经皮肾镜取石术是治疗肾结石的既定方法。从历史上看，由于SWL具有非侵入性，它一直是治疗直径小于20mm的上尿路肾结石的主要且常用的方法。然而，据报道，单次治疗后SWL的失败率在30%至89%之间。通过识别可能从SWL中受益的患者，并对可能治疗失败的患者采用其他治疗方式，可以降低失败率。这将提高并优化SWL候选患者的治疗效果。
我们提议开发一种机器学习模型，该模型可以预测SWL的治疗结果，以帮助从业者在考虑为患者进行结石治疗时进行决策。
使用一个数据集来构建和验证预测模型，该数据集包括1990年至2016年间在一家医院进行的31569次SWL患者就诊期间所实施的58349例SWL手术。将AdaBoost算法应用于一个具有17个与患者人口统计学和结石特征相关的预测属性的数据集，以成功或失败作为结果。AdaBoost算法也应用于一个训练数据集。将生成模型的性能与其他5种机器学习算法的性能进行比较，这5种算法分别是C4.5决策树、朴素贝叶斯、贝叶斯网络、K近邻和多层感知器。
所开发的模型通过一个测试数据集进行了验证，其性能明显优于由其他5种预测算法生成的模型。该模型的灵敏度和特异度分别为0.875和0.653，而其阳性预测值为0.7159，阴性预测值为0.839。受试者操作特征（ROC）分析的C统计量为0.843，这反映了一个出色的测试结果。
我们开发了一种严格的机器学习模型，以帮助医生和决策者根据患者的人口统计学和结石特征选择最有可能通过SWL获得成功治疗的肾结石患者。所提议的机器学习模型可以帮助医生和决策者规划SWL治疗，并更有效地利用有限的医疗资源，改善患者的预后。
