背景:诊断与膀胱出口梗阻(BOO)相关的非神经源性男性下尿路症状的根本原因具有挑战性。视频尿动力学研究(VUDS)和压力流量研究(PFS)都是BOO的侵入性诊断方法。VUDS可以更精确地区分男性BOO的病因,比如良性前列腺梗阻,原发性膀胱颈梗阻,和功能失调的排尿,可能优于PFS。
目的:这些检查的侵入性凸显了开发非侵入性预测模型的必要性,以促进BOO诊断并减少侵入性程序的必要性。
方法:我们对一组药物难治性男性进行了回顾性研究,2001年至2022年接受VUDS的怀疑BOO的非神经源性下尿路症状。总的来说,开发了2个BOO预测模型-1基于国际连续性协会的定义(国际连续性协会定义的膀胱出口梗阻;ICS-BOO),另一个基于视频尿动力学研究诊断的膀胱出口梗阻(VBOO)。将患者队列随机分为训练集和测试集进行分析。共有6种机器学习算法,包括逻辑回归,用于模型开发。在模型开发过程中,我们首先在训练集上使用重复的5倍交叉验证进行开发验证,然后测试验证以评估模型在独立测试集上的性能.这两个模型都实现为基于纸张的列线图,并集成到基于网络的人工智能预测工具中,以帮助临床决策。
结果:在307例患者中,26.7%(n=82)符合ICS-BOO标准,82.1%(n=252)被诊断为VBOO。ICS-BOO预测模型在开发验证中的受试者工作特征曲线(AUC)下的平均面积为0.74(SD0.09),平均准确度为0.76(SD0.04),AUC和准确度分别为0.86和0.77,在测试验证中。VBOO预测模型的内部平均AUC为0.71(SD0.06),平均准确度为0.77(SD0.06),AUC和准确度分别为0.72和0.76,外部。当两种模型预测都应用于同一患者时,他们的综合见解可以显着增强临床决策并简化诊断途径。通过双模型预测方法,如果两个模型都正向预测BOO,提示所有病例实际上是由药物难治性原发性膀胱颈梗阻或良性前列腺梗阻引起的,可以考虑手术干预。因此,对于100名(32.6%)患者,VUDS可能是不必要的。相反,当ICS-BOO预测是负面的,而VBOO预测是正面的,表明不同的病因,建议VUDS而不是PFS用于精确诊断和指导后续治疗。准确识别51.1%(47/92)的VUDS患者。
结论:预测ICS-BOO和VBOO的2种机器学习模型,基于6个无创临床参数,表现出值得称赞的歧视表现。使用双模型预测方法,当两个模型都有积极的预测时,可以避免VUDS,协助男性BOO诊断并减少对此类侵入性手术的需要。
BACKGROUND: Diagnosing underlying causes of nonneurogenic male lower urinary tract symptoms associated with bladder outlet obstruction (BOO) is challenging. Video-urodynamic studies (VUDS) and pressure-flow studies (PFS) are both invasive diagnostic methods for BOO. VUDS can more precisely differentiate etiologies of male BOO, such as benign prostatic obstruction, primary bladder neck obstruction, and dysfunctional voiding, potentially outperforming PFS.
OBJECTIVE: These examinations\' invasive nature highlights the need for developing noninvasive predictive models to facilitate BOO diagnosis and reduce the necessity for invasive procedures.
METHODS: We conducted a retrospective
study with a cohort of men with medication-refractory, nonneurogenic lower urinary tract symptoms suspected of BOO who underwent VUDS from 2001 to 2022. In total, 2 BOO predictive models were developed-1 based on the International Continence Society\'s definition (International Continence Society-defined bladder outlet obstruction; ICS-BOO) and the other on video-urodynamic studies-diagnosed bladder outlet obstruction (VBOO). The patient cohort was randomly split into training and test sets for analysis. A total of 6 machine learning algorithms, including logistic regression, were used for model development. During model development, we first performed development validation using repeated 5-fold cross-validation on the training set and then test validation to assess the model\'s performance on an independent test set. Both models were implemented as paper-based nomograms and integrated into a web-based artificial intelligence prediction tool to aid clinical decision-making.
RESULTS: Among 307 patients, 26.7% (n=82) met the ICS-BOO criteria, while 82.1% (n=252) were diagnosed with VBOO. The ICS-BOO prediction model had a mean area under the receiver operating characteristic curve (AUC) of 0.74 (SD 0.09) and mean accuracy of 0.76 (SD 0.04) in development validation and AUC and accuracy of 0.86 and 0.77, respectively, in test validation. The VBOO prediction model yielded a mean AUC of 0.71 (SD 0.06) and mean accuracy of 0.77 (SD 0.06) internally, with AUC and accuracy of 0.72 and 0.76, respectively, externally. When both models\' predictions are applied to the same patient, their combined insights can significantly enhance clinical decision-making and simplify the diagnostic pathway. By the dual-model prediction approach, if both models positively predict BOO, suggesting all cases actually resulted from medication-refractory primary bladder neck obstruction or benign prostatic obstruction, surgical intervention may be considered. Thus, VUDS might be unnecessary for 100 (32.6%) patients. Conversely, when ICS-BOO predictions are negative but VBOO predictions are positive, indicating varied etiology, VUDS rather than PFS is advised for precise diagnosis and guiding subsequent therapy, accurately identifying 51.1% (47/92) of patients for VUDS.
CONCLUSIONS: The 2 machine learning models predicting ICS-BOO and VBOO, based on 6 noninvasive clinical parameters, demonstrate commendable discrimination performance. Using the dual-model prediction approach, when both models predict positively, VUDS may be avoided, assisting in male BOO diagnosis and reducing the need for such invasive procedures.