PURPOSE Preventative strategies and surgical treatment for urolithiasis depend on stone composition. However, stone composition is often unknown until the stone is passed or surgically managed. Given that stone composition likely reflects the physiological parameters during its formation, we used clinical data from stone formers to predict stone composition. MATERIAL AND METHODS Stone composition, 24-hour urine, serum biochemistry, patient demographic and medical history were prospectively collected from 777 kidney stone patients. Data were used to train gradient boosted machine and logistic regression models to distinguish calcium vs non-calcium, calcium oxalate monohydrate vs dihydrate, and calcium oxalate vs calcium phosphate vs uric acid stone types. Model performance was evaluated using kappa score and the influence of each predictor variable was assessed. RESULTS The calcium vs non-calcium model successfully differentiated stone types with a kappa of 0.5231. The most influential predictors were 24-hour urine calcium, blood urate and phosphate. The calcium oxalate monohydrate vs dihydrate model is the first of its kind and could discriminate stone types with a kappa of 0.2042. The key predictors were 24-hour urine urea, calcium, and oxalate. The multiclass model had a kappa of 0.3023 and the top predictors were age, and 24-hour urine calcium and creatinine. CONCLUSIONS Clinical data can be leveraged with machine learning algorithms to predict stone composition, which may help urologists determine stone type and guide their management plan before stone treatment. Investigating the most influential predictors of each classifier may improve the understanding of key clinical features of urolithiasis and shed light on the pathophysiology.

中文翻译：

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使用机器学习和临床数据对尿路结石成分进行预测建模：对治疗策略和病理生理学见解的影响。

目的 尿石症的预防策略和手术治疗取决于结石的成分。然而，在结石排出或通过手术处理之前，结石的成分通常是未知的。鉴于结石成分可能反映了其形成过程中的生理参数，我们使用结石形成者的临床数据来预测结石成分。材料和方法 前瞻性地收集 777 名肾结石患者的结石成分、24 小时尿液、血清生化、患者人口统计学和病史。数据用于训练梯度增强机器和逻辑回归模型，以区分钙与非钙、一水草酸钙与二水合物、以及草酸钙与磷酸钙与尿酸结石类型。使用 kappa 评分评估模型性能，并评估每个预测变量的影响。结果 钙与非钙模型成功区分结石类型，kappa 为 0.5231。最有影响力的预测因素是 24 小时尿钙、血尿酸和磷酸盐。一水草酸钙与二水草酸钙模型是同类模型中的第一个，可以以 0.2042 的 kappa 区分结石类型。关键预测因子是 24 小时尿素、钙和草酸盐。多类模型的 kappa 为 0.3023，最重要的预测因素是年龄、24 小时尿钙和肌酐。结论 可以利用机器学习算法利用临床数据来预测结石成分，这可以帮助泌尿科医生确定结石类型并在结石治疗前指导他们的管理计划。研究每个分类器中最有影响力的预测因子可以提高对尿石症关键临床特征的理解并阐明病理生理学。