Department of Urology, University Hospital Zurich, Zurich, Switzerland.
Department of Urology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany.
Urolithiasis. 2022 Jun;50(3):293-302. doi: 10.1007/s00240-022-01323-4. Epub 2022 Apr 20.
In patients with symptomatic ureterolithiasis, immediate treatment of concomitant urinary tract infection (UTI) may prevent sepsis. However, urine cultures require at least 24 h to confirm or exclude UTI, and therefore, clinical variables may help to identify patients who require immediate empirical broad-spectrum antibiotics and surgical intervention. Therefore, we divided a consecutive cohort of 705 patients diagnosed with symptomatic ureterolithiasis at a single institution between 2011 and 2017 into a training (80%) and a testing cohort (20%). A machine-learning-based variable selection approach was used for the fitting of a multivariable prognostic logistic regression model. The discriminatory ability of the model was quantified by the area under the curve (AUC) of receiver-operating curves (ROC). After validation and calibration of the model, a nomogram was created, and decision curve analysis (DCA) was used to evaluate the clinical net-benefit. UTI was observed in 40 patients (6%). LASSO regression selected the variables elevated serum CRP, positive nitrite, and positive leukocyte esterase for fitting of the model with the highest discriminatory ability. In the testing cohort, model performance evaluation for prediction of UTI showed an AUC of 82 (95% CI 71.5-95.7%). Model calibration plots showed excellent calibration. DCA showed a clinically meaningful net-benefit between a threshold probability of 0 and 80% for the novel model, which was superior to the net-benefit provided by either one of its singular components. In conclusion, we developed and internally validated a logistic regression model and a corresponding highly accurate nomogram for prediction of concomitant positive midstream urine culture in patients presenting with symptomatic ureterolithiasis.
在有症状的输尿管结石患者中，立即治疗合并的尿路感染（UTI）可能可以预防败血症。然而，尿液培养需要至少 24 小时才能确认或排除 UTI，因此，临床变量可能有助于识别需要立即使用广谱经验性抗生素和手术干预的患者。因此，我们将 2011 年至 2017 年间在一家机构诊断出的 705 例有症状的输尿管结石患者连续队列分为训练组（80%）和测试组（20%）。使用基于机器学习的变量选择方法拟合多变量预后逻辑回归模型。通过接收者操作曲线（ROC）的曲线下面积（AUC）来量化模型的判别能力。在对模型进行验证和校准后，创建了一个列线图，并使用决策曲线分析（DCA）来评估临床净效益。观察到 40 例（6%）患者发生 UTI。LASSO 回归选择了升高的血清 CRP、阳性亚硝酸盐和阳性白细胞酯酶作为拟合模型的变量，以获得最高的判别能力。在测试队列中，对模型预测 UTI 的性能评估显示 AUC 为 82（95%CI 71.5-95.7%）。模型校准图显示了极好的校准度。DCA 显示在新模型的阈值概率为 0%至 80%之间具有有临床意义的净效益，优于其任何一个单一成分提供的净效益。总之，我们开发并内部验证了一个逻辑回归模型和一个相应的高度准确的列线图，用于预测有症状的输尿管结石患者同时进行的中段尿液培养阳性。
