Alghafees Mohammad A, Abdul Rab Saleha, Aljurayyad Abdulaziz S, Alotaibi Tariq S, Sabbah Belal Nedal, Seyam Raouf M, Aldosari Lama H, Alomar Mohammad A
College of Medicine, King Saud Bin Abdulaziz University for Health Sciences, Riyadh, Saudi Arabia.
College of Medicine, Alfaisal University, Riyadh, Saudi Arabia.
Ann Med Surg (Lond). 2022 Nov 17;84:104957. doi: 10.1016/j.amsu.2022.104957. eCollection 2022 Dec.
Machine learning techniques have been used extensively in the field of clinical medicine, especially when used for the construction of prediction models. The aim of the study was to use machine learning to predict the stone-free status after percutaneous nephrolithotomy (PCNL).
This is a retrospective cohort study of 137 patients. Data from adult patients who underwent PCNL at our institute were used for the purpose of this study. Three supervised machine learning algorithms were employed: Logistic Regression, XGBoost Regressor, and Random Forests. A set of variables comprising independent attributes including age, gender, body mass index (BMI), chronic kidney disease (CKD), hypertension (HTN), diabetes mellitus, gout, renal and stone factors (previous surgery, stone location, size, and staghorn status), and pre-operative surgical factors (infections, stent, hemoglobin, creatinine, and bacteriuria) were entered.
137 patients were identified. The majority were males (65.4%; n = 89), aged 50 years and above (41.9%; n = 57). The stone-free status (SFS) rate was 86% (n = 118). An inverse relation was detected between SFS, and CKD and HTN. The accuracies were 71.4%, 74.5% and 75% using Logistic Regression, XGBoost, and Random Forest algorithms, respectively. Stone size, pre-operative hemoglobin, pre-operative creatinine, and stone type were the most important factors in predicting the SFS following PCNL.
The Random Forest model showed the highest efficacy in predicting SFS. We developed an effective machine learning model to assist physicians and other healthcare professionals in selecting patients with renal stones who are most likely to have successful PCNL treatment based on their demographics and stone characteristics. Larger multicenter studies are needed to develop more powerful algorithms, such as deep learning and other AI subsets.
机器学习技术已在临床医学领域广泛应用，尤其是用于构建预测模型时。本研究的目的是使用机器学习预测经皮肾镜取石术（PCNL）后的无结石状态。
这是一项对137例患者的回顾性队列研究。本研究使用了在我们研究所接受PCNL的成年患者的数据。采用了三种监督式机器学习算法：逻辑回归、XGBoost回归器和随机森林。输入了一组包含独立属性的变量，包括年龄、性别、体重指数（BMI）、慢性肾脏病（CKD）、高血压（HTN）、糖尿病、痛风、肾脏和结石因素（既往手术、结石位置、大小和鹿角状结石状态）以及术前手术因素（感染、支架、血红蛋白、肌酐和菌尿）。
共纳入137例患者。大多数为男性（65.4%；n = 89），年龄在50岁及以上（41.9%；n = 57）。无结石状态（SFS）率为86%（n = 118）。检测到SFS与CKD和HTN之间呈负相关。使用逻辑回归、XGBoost和随机森林算法时的准确率分别为71.4%、74.5%和75%。结石大小、术前血红蛋白、术前肌酐和结石类型是预测PCNL术后SFS的最重要因素。
随机森林模型在预测SFS方面显示出最高的效能。我们开发了一种有效的机器学习模型，以协助医生和其他医疗保健专业人员根据患者的人口统计学特征和结石特征，选择最有可能成功接受PCNL治疗的肾结石患者。需要开展更大规模的多中心研究来开发更强大的算法，如深度学习和其他人工智能子集。
