Risk factors for concomitant positive midstream urine culture in patients presenting with symptomatic ureterolithiasis

Urinary sepsis due to upper urinary tract obstruction is most commonly caused by ureteral stones [1] and has a high risk of potentially serious complications such as septic shock and/or disseminated intravascular coagulopathy [2‐4]. Thus, the high mortality rate of up to 26% emphasizes the importance for an immediate treatment of concomitant urinary tract infection (UTI) in patients presenting with symptomatic ureterolithiasis. Therefore, accurate identification of these patients could help to guide clinical decision-making and reduce the rate of sepsis development. Moreover, particularly in an era of increasing antibiotic resistances, the number of unnecessary antibiotic treatments could be reduced [5].

However, early diagnosis and treatment of concomitant UTI is challenging, as the incubation time of urine cultures, the gold standard for diagnosis of UTI, usually requires at least 24 h. Therefore, for diagnosis of a concomitant UTI in patients with symptomatic ureterolithiasis, clinicians frequently base their diagnostic and clinical assessment on the subjective interpretation of symptoms, and physical and laboratory findings. However, this approach bears the risk of over- or undertreatment with antibiotics and/or emergent surgical interventions [6]. While previous studies identified several blood and urine laboratory biomarkers for the prediction of UTI, a combined analysis incorporating multiple biomarkers into a multivariable predictive model has not yet been performed [7‐18].

We therefore analyzed a consecutive cohort of patients and used a machine-learning-based approach to identify the variables that offer the highest discriminatory power. We hypothesized that this approach will enable the development of a logistic regression model that can accurately identify patients at risk of concomitant UTI by predicting a positive midstream urine culture at time of admission and guide clinical decision-making.

After applying the exclusion criteria, a cohort of 705 patients was available for analysis. Patient characteristics, clinical and radiological findings, treatment and outcomes of all patients stratified by occurrence of UTI, and training/testing cohort are summarized in Table 1. The laboratory findings and their corresponding reference ranges or cut-off values are displayed in Table 2. In the total cohort, UTI was observed in 40 patients (5.7%). These patients had a significant higher rate of dysuria (28 vs. 12%, p = 0.008), higher pulse rate (86 bpm (IQR 64–96) vs. 72 bpm (IQR 64–83), p = 0.009), lower diastolic blood pressure [median 82 mmHg (IQR 71–90) vs. 88 mmHg (IQR 78–96), p = 0.006], and higher body temperature [36.9 °C (IQR 36.4–37.6 °C) vs. 36.7 °C (IQR 36.4–37.0 °C), p = 0.046]. Furthermore, patients with UTI had elevated serum levels of CRP (57 vs 23%, p < 0.001), leukocytes (55 vs. 42%, p = 0.021), neutrophil granulocytes (63 vs. 39%, p = 0.04), and creatinine (40 vs. 22%, p = 0.015). On urinary dipstick analysis, UTI patients had significant higher rates of positive nitrite (30 vs. 0.5%, p < 0.001) and positive leukocyte esterase (70 vs. 13%, p < 0.001). Patients with UTI also required longer inpatient stays compared to patients without UTI (median 4.5 vs 0 days, p < 0.001). While the rate of development of sepsis was higher in UTI patients, this did not reach statistical significance (5 vs. 0.8%, p = 0.055). Patients with UTI underwent significantly more subsequent surgical interventions (75 vs. 27%, p < 0.001) and received more often empirical antibiotic treatment (72 vs. 12%, p < 0.001). With the exception of the position of the biggest stone on CT scan as well as hemoglobin and thrombocytes levels, all baseline characteristics and the rate of UTI were equally distributed between the training and the testing cohort.

In the current study, we developed and internally validated an accurate and easy-to-use nomogram for prediction of concomitant UTI in patients presenting with symptomatic ureterolithiasis. Using a machine-learning approach, we found that elevated levels of serum CRP as well as positive nitrite and leukocytes esterase on urinary dipstick analysis can accurately identify patients at risk of developing UTI. A 200-fold bootstrap corrected AUC of 81.6% (95% CI 71.5–95.7%) was demonstrated during our internal validation in a cohort that was not used during the development of our model.

As all predictive parameters identified in our model are part of the routine examination in patients presenting with symptomatic ureterolithiasis, we feel that our model is not only easy-to-use but will also be accessible to a broad community of physicians. This is of clinical importance, as there are no existing recommendations on predictive parameters and their thresholds for immediate diagnosis of concomitant UTI. However, individual and subjective interpretation of laboratory markers by clinicians may expose patients to unnecessary empirical antibiotic therapy and/or emergent surgical intervention, while increasing sepsis-related morbidity in case of missed diagnosis.

Results from our multivariable logistic regression analysis confirm the previous univariable findings for early detection of UTI [8, 12, 13, 15]. Nitrite has shown to deliver a low sensitivity of 16.7% but high specificity of 99.5% to diagnose UTI [8, 15]. The low sensitivity of nitrite can be explained by the exclusive detection of Gram-negative rod bacteria [22], which is also reflected in the relatively low probability of approximately 0.35 in our nomogram for an existing UTI when nitrite is found positive. This highlights the need for additional predictors to detect non-nitrite producing bacteria. Indeed, the combination of nitrite and urinary leukocytes esterase as the optimal combination has previously been postulated to rule out UTI with a high reliability [8, 12]. Similarly to urine parameters, elevated CRP levels have been reported to be with an 18-fold increase of UTI in case of obstructive pyelonephritis compared to patients without UTI and dilated renal pelvis [7, 17]. To the best of our knowledge, our model and the corresponding nomogram are the first to incorporate all three parameters to allow accurate prediction of UTI and guide clinical decision-making.

While an experienced urologist will not always require a nomogram to identify patients with concomitant UTI, our findings are still clinically important, as we were able to demonstrate that patients who do not exhibit the findings shown in our nomogram are indeed very unlikely to develop UTI. Hence, with an NPV of 98.1%, our model offers a very reliable method for physicians unfamiliar with obstructive urolithiasis to rule out urinary tract infection. Even though the threshold for early renal decompression should remain low, safe exclusion of UTI could help to reduce the rate of empirical antibiotic therapy, especially in the era of increasing antibiotic resistances. Validated decision-making tools are necessary, as we have found that even in our specialized urological department, 12% of all patients received an antibiotic therapy, which in fact was not necessarily due to negative urine culture. Considering the overall high incidence of symptomatic urolithiasis [23‐25], this amounts to a significant amount of unnecessary antibiotic therapy that could be omitted. As genuine clinical applicability of a nomogram has previously been proposed for validated models/nomograms who exhibit AUC/C-indices > 0.75, we feel that our nomogram offers the potential to guide clinical decision-making and could be used by non-urologists for early decision-making and triage [26].

As calibration and validation of nomograms are paramount before the implementation in clinical practice, we performed a statistically rigorous evaluation of the proposed model [27]. Indeed, our model showed nearly perfect calibration properties. Furthermore, the nomogram demonstrates a wide range of predicted probabilities. Finally, the inclusion of only three readily available variables offers a very low level of complexity for our nomogram, suggesting that it is easily reproducible. To allow a realistic model performance evaluation, we aimed to imitate external validation by splitting our cohort into two different cohorts of patients. While true external validation with separate cohorts remains the best assessment of a models accuracy and a crucial step before transferring the models into clinical practice [27], we found that, encouragingly, all results from the training cohort could be reproduced in the testing cohort.

Although our current study uses a statistically rigorous validation and calibration process, several limitations exist. First are the limitations inherent to the retrospective study design. Thus, it is impossible to determine whether laboratory parameters appear to be affected by existing comorbidities and whether a patient has already received an unreported antibiotic treatment prior to evaluation. Second is the single center approach and the limited sample size, as reflected by high odds ratios and wide confidence interval in our multivariable logistic regression model. Third, our endpoint was positive urine culture, which, however, consisted of a single urine collection at the patient admission, giving the potential for missing a positive urine culture during the further clinical course. Additionally, patients with atypical infections or organisms that are hard to culture might have been falsely excluded from our analysis. It should also be considered that a midstream urine culture could be negative, while the urine proximal to an obstructing ureteral stone may be infected. This has been shown in previous studies where urine cultures taken from the renal pelvis were significantly more often positive compared to midstream urine cultures [28]. Furthermore, patients with an infected stone could also have a negative urine culture and might initiate a urinary tract infection during the further clinical course (e.g., by manipulation intraoperatively) [29]. It is therefore important to note that our nomogram predicts a positive midstream urine culture but not a urinary tract infection. Fourth, our results are limited by the failure to control for additional potential predictive parameters such as levels of cytokines or procalcitonin. Finally, radiological findings concerning pyelonephritis or fornix rupture in mostly unenhanced computed tomography are of limited utility. External validation in a larger patient population is needed to verify our findings and help identify patients who require early renal decompression and antibiotic treatment.

We developed and internally validated a highly accurate, easy-to-use nomogram for prediction of concomitant positive midstream urine culture in patients presenting with symptomatic ureterolithiasis. External validation in a larger patient population is needed to verify our findings and help identify patients who require antibiotic treatment and immediate renal decompression.