Garbas Karolina, Zapała Łukasz, Ślusarczyk Aleksander, Piekarczyk Hanna, Piecha Tomasz, Radziszewski Piotr
Department of General, Oncological and Functional Urology, Medical University of Warsaw, Lindleya 4, 02-005, Warsaw, Poland.
BMC Urol. 2025 Mar 6;25(1):44. doi: 10.1186/s12894-025-01722-w.
To evaluate and synthesize existing evidence on non-invasive methods for diagnosing detrusor underactivity (DU) in men presenting with lower urinary tract symptoms (LUTS), focusing on their feasibility and diagnostic accuracy.
A systematic search of PubMed, Scopus, and Web of Science databases was conducted for original articles reporting on non-invasive diagnostic tests for DU in men with LUTS. Data extraction focuses on study characteristics, diagnostic methods, and accuracy. The risk of bias was assessed using the QUADAS-2 tool.
Eighteen studies involving 7390 patients, of whom 3194 were diagnosed with DU, were included in our analysis. The evaluated diagnostic methods included ultrasound parameters, biomarkers, uroflowmetry results, symptom questionnaires, and clinical characteristics. Developed models, including those based on artificial intelligence (AI), and nomograms were also assessed. The symptom questionnaire DUA-SQ showed the highest sensitivity of 95.8%, while ultrasound measurements, such as detrusor wall thickness showed 100% specificity but limited sensitivity (42%). Models incorporating clinical variables achieved sensitivity rates of over 75%. Uroflowmetry parameters, particularly presence of "sawtooth" and "interrupted" waveforms, demonstrated sensitivity of 80% and specificity of 87%. Biomarkers, including serum adiponectin and urine NO/ATP ratio, achieved sensitivity of 79% and 88.5%, respectively. AI models showed potential, with sensitivities ranging from 65.9% to 79.7%. Due to the poor quality of the studies and data heterogeneity, meta-analysis was not performed.
Non-invasive diagnostic methods for DU, particularly DUA-SQ, ultrasound measurements, and AI models, demonstrate potential, though their accuracies vary. Further research is needed to standardize these methods and enhance their diagnostic reliability.
The study protocol was registered with PROSPERO (CRD42024556425).
not applicable.
评估并综合现有证据,以了解用于诊断下尿路症状(LUTS)男性逼尿肌活动低下(DU)的非侵入性方法,重点关注其可行性和诊断准确性。
对PubMed、Scopus和Web of Science数据库进行系统检索,以查找报告针对LUTS男性进行DU非侵入性诊断测试的原始文章。数据提取侧重于研究特征、诊断方法和准确性。使用QUADAS-2工具评估偏倚风险。
我们的分析纳入了18项研究,涉及7390名患者,其中3194名被诊断为DU。评估的诊断方法包括超声参数、生物标志物、尿流率结果、症状问卷和临床特征。还评估了开发的模型,包括基于人工智能(AI)的模型和列线图。症状问卷DUA-SQ显示出最高敏感性,为95.8%,而超声测量,如逼尿肌壁厚度,显示出100%的特异性,但敏感性有限(42%)。纳入临床变量的模型实现了超过75%的敏感性率。尿流率参数,特别是“锯齿状”和“中断”波形的存在,显示出80%的敏感性和87%的特异性。生物标志物,包括血清脂联素和尿NO/ATP比值,分别实现了79%和88.5%的敏感性。AI模型显示出潜力,敏感性范围为65.9%至79.7%。由于研究质量差和数据异质性,未进行荟萃分析。
DU的非侵入性诊断方法,特别是DUA-SQ、超声测量和AI模型,显示出潜力,尽管它们的准确性各不相同。需要进一步研究来规范这些方法并提高其诊断可靠性。
该研究方案已在PROSPERO(CRD42024556425)注册。
不适用。