Spontaneous Passage Of Ureteral Stones Research Articles

Applying urinary ultrasound to predict the risk of spontaneous ureteral stone passage: a retrospective cohort study

BackgroundTo assess the value of urological ultrasound in predicting the risk of spontaneous passage of ureteral stones.MethodsClinical and ultrasound data were collected consecutively from patients receiving conservative treatment for ureteral stones, and the outcome of spontaneous passage was followed up for 1 month. Ultrasound variables independently associated with the risk of spontaneous stone passage were screened. A logistic regression prediction model was constructed based on the independent risk factors, and the discriminative efficacy and clinical utility of the prediction model in inferring the risk of spontaneous passing were assessed by the receiver operating characteristic (ROC) curve, calibration curve and clinical decision curve.ResultsA total of 163 patients undergoing conservative treatment for ureteral stones were included in the study, with a mean age of 45.95 ± 13.01 years. Among them, 47 cases (28.83%) experienced failure of spontaneous stone passage. Multivariable analysis revealed that stone length (OR: 2.622, P = 0.027), distal stone location (OR: 0.219, P = 0.003), and ureteral jetting frequency (OR: 6.541, P < 0.001) were independent risk factors for spontaneous stone passage. A prediction model incorporating stone length, stone location, and affected ureteral jetting frequency was developed to assess the risk of spontaneous stone passage. The area under the ROC curve was 0.814 (95% CI: 0.747–0.882), indicating good discriminatory power. The prediction model also demonstrated favorable net clinical benefit.ConclusionA prediction model based on ultrasound-derived stone length, location, and ureteral jetting frequency can accurately evaluate the risk of spontaneous stone passage in patients with ureteral stones, providing a basis for optimizing the clinical decision-making on ureteral stones, and has reliable clinical application value.

Radiological and Hematological Parameters Predicting Success of Medical Expulsive Therapy in Patients With Ureteral Calculus.

Background Urolithiasis, characterized by the formation of stones in the urinary tract, is a common condition with significant morbidity. Medical expulsive therapy (MET) has emerged as a non-invasive treatment modality for facilitating the spontaneous passage of ureteral stones. However, MET success varies widely among individuals, and identifying predictive factors for treatment outcomes is crucial for optimizing patient care. Objective This study aimed to evaluate the predictive role of radiological and hematological parameters in the success of MET for ureteral stones. Methods A prospective observational cohort study was conducted at the Department of Urology, tertiary care center, Patna, involving consecutive patients who underwent MET for ureteral stones. Radiological parameters (stone size, location, and hydronephrosis) and hematological markers (C-reactive protein (CRP) and neutrophil-to-lymphocyte ratio (NLR)) were assessed as predictors of treatment success. Univariate and multivariate logistic regression analyses were performed to identify significant predictors of MET success. Results Among the 156 patients included in the study (mean age: 42 years; range: 18-50 years), radiological parameters, such as smaller stone size (<10 mm) and absence of hydronephrosis, were significantly associated with higher rates of MET success (p<0.05). Lower CRP levels (p<0.001) and NLR (p<0.05) were also predictive of treatment success. Logistic regression analysis confirmed the independent predictive value of these parameters after adjusting for potential confounders. Conclusion Radiological and hematological parameters are valuable predictors of the success of MET for ureteral stones. Incorporating these predictive factors into clinical decision-making can optimize treatment outcomes and minimize unnecessary interventions in patients with a higher likelihood of spontaneous stone passage.

CT-Based Predictors of Spontaneous Ureteral Stone Passage.

To assess CT-scan based parameters, particularly ureteral wall thickness (UWT), in predicting spontaneous ureteral stone passage. Cross-sectional, analytical study. Place and Duration of the Study: Section of Urology, Department of Surgery, The Aga Khan University Hospital, Karachi, Pakistan, from June to November 2023. Patients with symptomatic, single, radio-opaque, unilateral ureteral stones having size ≤10 mm with normal kidney functions, diagnosed by non-contrast CT-scan KUB, and treated by conservative option for four weeks were enrolled. Clinical and radiological predictors for stone passage (SP), including stone size, area, laterality, location, density, degree of hydronephrosis, maximal UWT at the stone site, and ureteral diameter and density above and below the stone, were evaluated. Binary logistic regression analysis was employed to identify predictors of stone passage. Receiver operating characteristic (ROC) curve was used to find the optimal cut-off for UWT. Among 34 eligible patients, 22 (64.7%) passed their stones spontaneously. Patients who passed had smaller stone size and area and lesser UWT. Stone location, laterality, degree of hydronephrosis, stone density, ureteral wall diameter, and density above and below stones were not associated with SP. Multivariate analysis revealed maximum UWT as the independent predictor of SP, with a cut-off of 1.95 mm and an accuracy of 0.94. UWT was the single most convincing factor for the spontaneous passage of ureteral stone in this study. By applying UWT's optimal cut-off value, it might be an extremely significant tool when taking decisions in daily practice. Ureteral wall thickness, Medical expulsive therapy, Non-contrast computed tomography.

Neutrophil to lymphocyte ratio and serum procalcitonin level as a predictor of spontaneous ureteral stone passage: A prospective study.

Ureteric stone is responsible for around 20% of urinary tract stones and among them 70% of these are located in distal portion of the ureter. Stone causing ureter obstruction produce inflammatory changes in ureteric wall and prevent spontaneous passage of stone. The objective of the study is to compare the predictive role of procalcitonin and Neutrophil-to-lymphocyte ratio (NLR) for spontaneous passage of stone. Total 150 participants having ureteric stone of 4-8 mm, were included in prospective observational study. The patients were followed up for 4 weeks. Spontaneous Stone Passage (SSP) was confirmed with either the patient collecting the stone during urination or by Non-Contrast CT performed at 4 weeks. Blood samples of the patients were analysed and White blood cells, sedimentation, Neutrophile to Lymphocyte (NLR), procalcitonin level compared to analyse predictors of future SSP. The procalcitonin levels of the Spontaneous stone passing SSP (-ve) group (209.05 ± 78.45 pg/ml) were significantly higher than the not passing the SSP (+ve) group (130.76 ± 24.18) (p < 0.001). NLR is significantly higher in the SSP -ve (3.84 ± 0.41) than the SSP +ve (2.18 ± 0.38) group (p < 0.001). In single and multivariate analysis, significant activity was found for procalcitonin in SP +ve group. The findings of the study suggests that high level of procalcitonin, and high NLR have a negative effect on passage of stone. So early intervention can be planned to these patients to prevent complications.

Two novel deep-learning models to predict spontaneous ureteral calculi passage: Model development and validation

Abstract Objective The aim of this study was to develop and evaluate two deep-learning (DL) models for predicting spontaneous ureteral stone passage (SSP). Materials and methods A total of 1217 patients with thin-layer computed tomography–confirmed ureteral stones in our hospital from January 2019 to December 2022 were retrospectively examined. These patients were grouped into 3 data sets: the training set (n = 1000), the validation set (n = 100), and the test set (n = 117). Two DL models based on residual neural network (ResNet)—2-dimensional (2D) ResNet29 and 3-dimensional (3D) ResNet29—were separately developed, trained, and assessed. The predictive ability of a conventional approach using a stone diameter of &lt;5 mm on computed tomography was investigated, and the results were compared with those of the two DL models. Results Of the 1217 patients, SSP was reported in 446 (36.6%). The total accuracy, sensitivity, and specificity were 76.9%, 56.1%, and 90.8% for the stone diameter approach; 87.1%, 84.2%, and 92.7% for the 2D ResNet29 model; and 90.6%, 88.2%, and 95.1% for the 3D ResNet29 model, respectively. Both the 2D and 3D ResNet29 models showed significantly higher accuracy than the stone diameter approach. Receiver operating characteristic curve analysis showed that both DL models had a significantly higher area under the curve than the stone diameter–based classification. Conclusions The DL models, particularly the 3D model, are novel and effective methods for predicting SSP rates. Using such models may help determine whether a patient should receive surgical intervention or expect a long interval before stone passage.

MP16-01 MACHINE LEARNING MODEL TO PREDICT LIKELIHOOD OF SPONTANEOUS URETERAL STONE PASSAGE

You have accessJournal of UrologyCME1 Apr 2023MP16-01 MACHINE LEARNING MODEL TO PREDICT LIKELIHOOD OF SPONTANEOUS URETERAL STONE PASSAGE Katherine Fischer, Abhay Singh, Joey Logan, Benjamin Schurhamer, Brent Cao, Roby Daniel, Ryan McGregor, Iqra Nadeem, Curran Uppaluri, Alice Xiang, Ester Choi, Yuemeng Li, Yong Fan, Justin Ziemba, and Gregory Tasian Katherine FischerKatherine Fischer More articles by this author , Abhay SinghAbhay Singh More articles by this author , Joey LoganJoey Logan More articles by this author , Benjamin SchurhamerBenjamin Schurhamer More articles by this author , Brent CaoBrent Cao More articles by this author , Roby DanielRoby Daniel More articles by this author , Ryan McGregorRyan McGregor More articles by this author , Iqra NadeemIqra Nadeem More articles by this author , Curran UppaluriCurran Uppaluri More articles by this author , Alice XiangAlice Xiang More articles by this author , Ester ChoiEster Choi More articles by this author , Yuemeng LiYuemeng Li More articles by this author , Yong FanYong Fan More articles by this author , Justin ZiembaJustin Ziemba More articles by this author , and Gregory TasianGregory Tasian More articles by this author View All Author Informationhttps://doi.org/10.1097/JU.0000000000003236.01AboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookLinked InTwitterEmail Abstract INTRODUCTION AND OBJECTIVE: Standard treatment options for patients with a ureteral stone are a trial of passage or surgical intervention. Prior studies have tried to identify factors that predict the likelihood of spontaneous passage, such as stone size and location, but this remains difficult. Successful prediction of spontaneous passage can help avoid the risks of unnecessary surgery or shorten the symptomatic period in patients that will ultimately require surgery. Our goal was to create a machine learning model incorporating patient clinical and imaging characteristics to accurately predict the likelihood of ureteral stone passage. METHODS: We performed a retrospective cohort study of pediatric and adult patients presenting with ureteral stones on CT scan. Chart review was performed to ascertain patient clinical and imaging characteristics and to determine the primary outcome of spontaneous stone passage. A random forest model was built using these characteristics to predict spontaneous ureteral stone passage. RESULTS: 103 pediatric (median age 14 years, 56% female) and 153 adult (median age 57 years, 35.9% female) patients with confirmed ureteral stones were identified. Spontaneous passage occurred in 54% of pediatric and 44.4% of adult patients. Separate models were created for pediatric and adult patients since different features were important for prediction in each cohort. The pediatric model had an accuracy of 70% (95% CI 67-74%) and the adult model 63% (95% CI 58-66%). Stone area was the most important feature in both models (Figure 2). CONCLUSIONS: We created a machine learning model that predicted ureteral stone passage based on clinical and imaging features with 63-70% accuracy. Our long-term aim is to create a deep learning model that incorporates clinical characteristics and automated segmentation of CT imaging to accurately predict stone passage without the need for human abstraction and input. Our hope is that this will allow for better individualized patient care through early identification of those who will have a successfully trial of passage versus those that require surgical intervention. Source of Funding: NIDDK P20 CHOP/ Penn Center for Machine Learning in Urology (P20DK127488)AUA Care Foundation and SPU Sushil Lacy Research Scholar Award (KMF) © 2023 by American Urological Association Education and Research, Inc.FiguresReferencesRelatedDetails Volume 209Issue Supplement 4April 2023Page: e201 Advertisement Copyright & Permissions© 2023 by American Urological Association Education and Research, Inc.MetricsAuthor Information Katherine Fischer More articles by this author Abhay Singh More articles by this author Joey Logan More articles by this author Benjamin Schurhamer More articles by this author Brent Cao More articles by this author Roby Daniel More articles by this author Ryan McGregor More articles by this author Iqra Nadeem More articles by this author Curran Uppaluri More articles by this author Alice Xiang More articles by this author Ester Choi More articles by this author Yuemeng Li More articles by this author Yong Fan More articles by this author Justin Ziemba More articles by this author Gregory Tasian More articles by this author Expand All Advertisement PDF downloadLoading ...

The use of tamsulosin in the treatment of 10–15 mm lower ureteral stones in adults: a double-blinded randomized controlled trial

BackgroundThe discovery of alpha-adrenergic receptors in the ureteral smooth muscle cells led to a thorough investigation of the therapeutic potential of alpha-blockers for ureteral calculi. Tamsulosin is a selective blocker of alpha-1A and alpha-1B adrenoceptors. It significantly improved the expulsion of distal ureteral stones measuring 3–10 mm in most randomized trials. To the best of our knowledge, tamsulosin was not tested before in the management of ureteral stones 10–15 mm. Hence, the present study aimed at estimation of the efficacy of tamsulosin in the expulsion of lower ureteral stones 10–15 mm in length compared to placebo in adult patients (primary goal) and the need for ureteral dilatation at scheduled ureteroscopy.MethodsBetween November 2017 and November 2019, 80 patients with distal ureteral stones 10–15 mm were divided into two equal groups. One group received tamsulosin 0.4 mg/day and the other received a placebo. Patients were followed-up for 8 weeks.ResultsSix patients of the tamsulosin group (15%) passed their stones spontaneously compared to none of the placebo group (p = 0.026). Two of the 6 patients who passed the ureteral stones developed urinary retention and required endoscopic treatment of urethral stones. So, the adjusted spontaneous ureteral stone passage ratios for the tamsulosin and the control groups were 10 and 0%, respectively (p = 0.12). Overall, 37.5% reported adverse effects in the tamsulosin group, and 30% in the placebo group (p = 0.7). The most common adverse effect reported in both groups was dizziness, which occurred more frequently with tamsulosin (25%) than placebo (22.5%) (p = 0.9). We noticed increased ejaculatory dysfunction among men in the tamsulosin group versus placebo group [17.9% vs. 3.5% (p = 0.1)]. Among patients who needed ureteroscopy, ureteral dilatation was always needed in the control group versus 85.3% of the tamsulosin group (p = 0.015). Although 23.5% of the treatment group didn’t need stents after ureteroscopy compared to only 12.5% of the control group, this difference was not statistically significant (p = 0.2).ConclusionsAlthough tamsulosin significantly increased spontaneous passage of 10–15 mm lower ureteral stones in adults, it did not decrease the need for operative intervention. Preoperative tamsulosin significantly facilitated ureteral dilations during ureteroscopic management.

The role of inflammatory serum markers and ureteral wall thickness on spontaneous passage of ureteral stone < 10 mm: A prospective cohort study

IntroductionUreteral stone is a worldwide disease and accounts for 20% of all urolithiasis. There is a widespread discussion on the preferred initial treatment method, whether medical or surgical, and each has its pros and cons. In this study, we aimed to assess the role of both ureteral wall thickness around the stone and inflammatory markers in guiding the decision-making process. MethodsIn this prospective study, 161 patients who presented with ureteric colic and were diagnosed with ureteral stone with NCCT were included. UWT around the stone was measured, and the NLR and PLR were calculated. The patients were given a single daily dose of tamsulosin 0.4 mg for 4 weeks with weekly follow-up to determine SSP or failure. ResultsOf the 161 patients with a mean age 40.12 ± 12.36 SD, 55.9% had a spontaneous stone passage. Receiver operating characteristics showed a cut off value of 2.45 mm UWT of non SSP patients with an 83% sensitivity and 86% specificity. Moreover, there was a significant correlation between higher NLR, PLR and increased UWT (Pearson correlation of 0.314 and 0.426 respectively). The combined higher NLR, PLR and increased UWT were associated with failure of SSP (p-value <0.001). ConclusionMany factors play a role in decision making for management of ureteral stones. Our study concludes that patients with high NLR, PLR, and UWT around the stone have lesser chance of SSP using MET. Their rise can be used as predictors to decide early intervention.

Spontaneous ureteral stone passage: a novel and comprehensive nomogram.

Ureteral stones pose a high economic and medical burden among Emergency Department (ED) admissions. Management strategies vary from expectant therapy to surgical interventions. Since predictors of ureteral spontaneous stone passage (SSP) are still not well understood, we sought to create a novel nomogram to guide management decisions. Charts were retrospectively reviewed for patients who presented to our institution's ED with non-febrile renal colic and received a radiological diagnosis of ureteral stone ≤10 mm. Demographic, clinical, laboratory, and non-contrast CT data were collected. This novel nomogram incorporates the serum neutrophil-to-lymphocyte ratio (NLR) as a potential predictor of SSP. The model was derived from a multivariate logistic regression and was validated on a different cohort. A receiver operator characteristic (ROC) curve was constructed and the area under the curve (AUC) was computed. A total of 1186 patients presented to our ED between January 2010 and October 2018. We randomly divided our population into a derivation and validation cohort in one to five ratio. A stone size ≥7 mm was the strongest predictor of SSP failure; OR=9.47; 95% CI: 6.03-14.88. Similarly, a NLR≥3.14 had 2.17; (1.58-2.98) the odds of retained stone. SSP failure was also correlated with proximal position, severe hydronephrosis, and leukocyte esterase ≥75, P=0.02, P=0.05, and P=0.006, respectively. The model had an AUC of 0.804 (0.776-0.832). The nomogram was also used to compute the risk of SSP failure (AUC 0.769 [0.709-0.829]). Our novel nomogram can be used as a predictor for SSP and can be used clinically in decision making.

What is the role of inflammatory markers in predicting spontaneous ureteral stone passage?

To investigate the role of inflammatory markers in predicting the spontaneous passage of ureteral stones. We retrospectively reviewed 279 patients with ureteral stones sized 4-10 mm that were managed conservatively. The patients were divided into two groups: Group 1 consisted of 137 patients who passed the stone spontaneously; Group 2 comprised 142 patients without spontaneous stone passage. The groups were compared using the Mann-Whitney U and chi-square tests. In addition, univariate and multivariate analyses were performed to identify the significance of the parameters. The mean age of the patients was 41.2 years. The patients in Group 1 had a significantly lower mean stone size, white blood cell count and neutrophil count. In addition, stone location, presence of hydronephrosis and history of urolithiasis were significantly different between the groups. Neutrophil percentage, neutrophil-to-lymphocyte ratio and platelet-to-lymphocyte ratio were insignificantly lower in Group 1. In a multivariate analysis, stone size, distal location and hydronephrosis status significantly predicted the spontaneous stone passage. However, inflammatory markers including white blood cell count, neutrophil count and neutrophil-to-lymphocyte ratio could not determine the likelihood of spontaneous stone passage. Our results suggest that inflammatory markers are no meaningful parameters for the prediction of spontaneous stone passage.

Development of prediction models of spontaneous ureteral stone passage through machine learning: Comparison with conventional statistical analysis.

ObjectivesTo develop a prediction model of spontaneous ureteral stone passage (SSP) using machine learning and logistic regression and compare the performance of the two models. Indications for management of ureteral stones are unclear, and the clinician determines whether to wait for SSP or perform active treatment, especially in well-controlled patients, to avoid unwanted complications. Therefore, suggesting the possibility of SSP would help make a clinical decision regarding ureteral stones.MethodsPatients diagnosed with unilateral ureteral stones at our emergency department between August 2014 and September 2018 were included and underwent non-contrast-enhanced computed tomography 4 weeks from the first stone episode. Predictors of SSP were applied to build and validate the prediction model using multilayer perceptron (MLP) with the Keras framework.ResultsOf 833 patients, SSP was observed in 606 (72.7%). SSP rates were 68.2% and 75.6% for stone sizes 5–10 mm and <5 mm, respectively. Stone opacity, location, and whether it was the first ureteral stone episode were significant predictors of SSP. Areas under the curve (AUCs) for receiver operating characteristic (ROC) curves for MLP, and logistic regression were 0.859 and 0.847, respectively, for stones <5 mm, and 0.881 and 0.817, respectively, for 5–10 mm stones.ConclusionSSP prediction models were developed in patients with well-controlled unilateral ureteral stones; the performance of the models was good, especially in identifying SSP for 5–10-mm ureteral stones without definite treatment guidelines. To further improve the performance of these models, future studies should focus on using machine learning techniques in image analysis.

The Role of Procalcitonin and Other Markers of Inflammation in Predicting Spontaneous Passage of Ureteral Stones

The Role of Procalcitonin and Other Markers of Inflammation in Predicting Spontaneous Passage of Ureteral Stones

Prediction of the ureteric stone outcome by CT scan

Background and objective: Urolithiasis is prevalent in both developed and developing countries, which is attributed to changes in lifestyles. Management of ureteric stones is highly dependent on their characteristics such as volume, size, and density which can be determined based on the images obtained from CT scan. The present study was carried out to examine the association between the final outcomes of the stones and different stone parameters specified by CT scan. Methods: The present prospective cross-sectional study consisted of 100 patients who had been referred to the Radiology Department of Rizgary and Erbil Teaching Hospitals in Erbil, Kurdistan Region, Iraq, over six months in 2019 to undergo abdominal CT scan with clinical suspicion of ureteric stones. All patients underwent non-contrast MDCT scan, and the CT images were evaluated for the presence of ureteric stone, site of the stone, side, diameter in three planes, volume, and density. The outcome of the stone was correlated with each above variables. Results: Ureteric stones were more prevalent among the young and middle age group (25-64 years) with an approximate male-to-female ratio of 3.34:1. There were significant associations between stone outcomes (i.e. spontaneous passage or need for intervention) and the stone characteristics including volume (P &lt;0.001), density (P &lt;0.001)site (upper, mid and lower ureter) (P = 0.02), anteroposterior diameter (P &lt;0.001), transverse diameter (P = 0.006), and superior inferior diameter (P &lt;0.001). Conclusion: Upon the significant correlation between the spontaneous passage of ureteric stones and their characteristics (i.e., volume, density, site, anteroposterior diameter, transverse diameter, and superior inferior diameter), it is highly recommended that all patients with urolithiasis undergo CT scan examination in order to make the proper decision regarding stone management. Keywords: Ureteric stones; CT scan; Stone characteristics; Spontaneous passage.

Radiological Study on the Anatomical Site and Size of Ureteric Calculi: A Retrospective Study

ntroduction: Ureteric stones are said to lodge most commonly at the narrowest anatomical areas of the ureter. The site of pain depends on the location of the calculi. Spontaneous passage of ureteric stones is influenced by size, location and laterality of the stones. Aim: To investigate the size and location of ureteric stones. Materials and Methods: This was a retrospective study of 72 patients in whom urinary stones were diagnosed by computed tomography between January 2018 and July 2019. The size of the ureteric stones was measured by its maximal width on Computed Tomography (CT) and location of the stones was also determined. The diameter of both the ureters were ascertained at the widest portion. Statistical analysis was performed using SPSS version 23. Descriptive data was presented as percentage and mean while comparative analysis was done using Student’s t-test. Results: The average patient age was 41±12.4 years (range, 17- 70 years) affecting 42 (58.33%) males and 30 females (41.77%). The ureteric stones were localized at the VUJ in 30 cases (41%), at the distal ureter in 11 (15%), at the level of external iliac vessel in 3 cases (5%), at the proximal part in 19 (27%) and at the PUJ in 9 cases (12%). Calculi were identified more often on the right ureter (44 cases, 61.2%) than the left (28 cases, 38.8%). The average diameter of the ureter on the right and left was 3.46 mm (range, 1.8-4.7 mm), and 3.33 mm (range, 2.1- 5.5 mm) respectively. There was no significant difference in the mean diameter of the ureteric stones of both the sides at the corresponding levels of the ureter. The smaller the stones, the closer to the VUJ were they located. Conclusion: This study provides an insight into the anatomy of the ureter and would be essential for selection of appropriate line of intervention and treatment modality.

Spontaneous ureteral stone passage: a novel and comprehensive nomogram

Background Ureteral stones pose a high economic and medical burden among Emergency Department (ED) admissions. Management strategies vary from expectant therapy to surgical interventions. Since predictors of ureteral spontaneous stone passage (SSP) are still not well understood, we sought to create a novel nomogram to guide management decisions. Methods Charts were retrospectively reviewed for patients who presented to our institution's ED with non-febrile renal colic and received a radiological diagnosis of ureteral stone ≤10 mm. Demographic, clinical, laboratory, and non-contrast CT data were collected. This novel nomogram incorporates the serum neutrophil-to-lymphocyte ratio (NLR) as a potential predictor of SSP. The model was derived from a multivariate logistic regression and was validated on a different cohort. A receiver operator characteristic (ROC) curve was constructed and the area under the curve (AUC) was computed. Results A total of 1186 patients presented to our ED between January 2010 and October 2018. We randomly divided our population into a derivation and validation cohort in one to five ratio. A stone size ≥ 7 mm was the strongest predictor of SSP failure; OR=9.47; 95%CI (6.03-14.88). Similarly, a NLR ≥ 3.14 had 2.17; (1.58-2.98) the odds of retained stone. SSP failure was also correlated with proximal position, severe hydronephrosis, and leukocyte esterase ≥ 75, p=0.02, p=0.05, and p=0.006, respectively. The model had an AUC of 0.804 (0.776-0.832). The nomogram was also used to compute the risk of SSP failure (AUC 0.769 (0.709 - 0.829). Conclusions Our novel nomogram can be used as a predictor for SSP and can be used clinically in decision making.

CT-related parameters and Framingham score as predictors of spontaneous passage of ureteral stones ≤ 10 mm: results from a prospective, observational, multicenter study

To investigate the reliability of newly defined CT-related parameters and cardiovascular risk factors in groups adjusted for stone size and location to predict spontaneous stone passage (SP) of uncomplicated ureteral stones ≤ 10mm. The data of 280 adult patients with solitary unilateral ureteral stones ≤ 10mm in diameter in non-contrast computed tomography were prospectively recorded. All patients undergoing a four-week observation protocol with medical expulsive therapy using tamsulosin were divided into two groups according to SP or no SP. Demographic, clinical and radiological findings of these groups were recorded. Spontaneous stone passage was observed in 176 (62.9%) of the patients, whereas the SP rate was 57.6% for 118 upper ureteral stones and 66.7% for 162 lower ureteral stones. The SP rate was 13.3 times greater with ureteral wall thickness < 1.88mm, 4.4 times greater with a ratio of ureter to stone diameter of < 1.24, 3.4 times greater with Framingham score of < 11.5%, 2 times greater with neutrophil lymphocyte ratio < 1.96, 1.9 times greater with ureteral diameter < 6.33mm and 1.5 times greater with stone volume < 38.54 mm3. Lower levels of ureteral wall thickness, ratio of ureter to stone diameter, Framingham score, neutrophil lymphocyte ratio, ureteral diameter, stone volume and absence of hydronephrosis were found to be more successful predictors. We consider that the success rate can be increased by selection of the proper option (observation or active treatment) according to these predictors.

Non-Enhanced Computed Tomography as a Predictor for Spontaneous Passage of Ureteral Stone

Non-Enhanced Computed Tomography as a Predictor for Spontaneous Passage ofUreteral StoneMagdy Mohamcd FI.Fawal,'1’ Ayman Fa thy Ahmed Amcr,(l> Mohammad Abd Allihalik Basha and ® |Q Ahmed Aeiad Hmad Abohakcr'Department of Radio-diagnosis, Faculty of Medicine-Zaga/ig University, Egypt.^'Department of Radio-diagnosis, Faculty of Medicine - Benghazi University- LibyaAbstract: QBackground: renal stone is one of the commonest causes of loin pain and leads to multiple visits to the urology emergency department with increasing costs for health systems. Objectives: This study aims to assess the accuracy of the NECT technique in the prediction of whether ureteral stones will pass spontaneously or not. Patients and Methods: In a prospective cohort study including 36 participants (age range, 18-70 years) having a ureteral stone, referred for NECT examination at the Radio-Diagnosis Department of Zagazig University Hospitals. Results: our study revealed a significant relationship between the two study groups according to the location (p 0.05). Our study demonstrated a method based on stone size and location can predict the probability of stone passage within a short time (8 wks.).Conclusions: That NECT is a good predictor for spontaneous passage of ureteral stones.

Can serum procalcitonin levels be useful in predicting spontaneous ureteral stone passage?

BackgroundMedical expulsive therapy (MET) is recommended for ureteral stones when there is no indication for interventional treatment. Spontaneous passage (SP) may not always be perceived in patients undergoing MET. We aimed to demonstrate the effects of inflammatory factors on spontaneous ureteral stone passage in patients undergoing MET.MethodsOur study was conducted between August and November, 2016, in healthy volunteers and patients with a single distal ureteral stone between 5 and 10 mm in diameter and no indications for interventional therapy. Blood and urine samples from all patients and healthy volunteers were tested. The patients were followed up every 2 weeks for 1 month unless emergency situations appeared. Patients with stone-free status at follow-up were concluded to have achieved complete stone passage [SP(+)], and failure [SP(−)] was concluded if the patient had not passed the stone by the end of the study. Blood samples of the patients and the control group were analyzed, recording WBC (white blood cell), CRP (c-reactive protein), SED (sedimentation), MPV (mean platelet volume), NLR (neutrophil-to-lymphocyte ratio), and serum procalcitonin levels. Abnormalities in urine samples were recorded. All patients received diclofenac sodium 75 mg/day, tamsulosin 0.4 mg/day, and at least 3 l/day fluid intake. Patients were followed for a month with kidney, ureter, bladder (KUB) plain films, ultrasonography (USG), and unenhanced abdominal CT scans while undergoing MET. Comparative statistical analyses were performed between the SP(+) and SP(−) groups.ResultsThe procalcitonin levels of the SP(−) group were significantly higher (207 ± 145.1 pg/ml) than in the SP(+) group (132.7 ± 28.1 pg/ml) (p = 0.000). The leucocyturia rate of the SP(−) group was significantly higher than in the SP(+) group (p = 0.004). Based on the ROC curve analysis, 160 pg/ml (86.7% sensitivity, 70.8% specificity, p < 0.001; AUC: 0.788 95% CI (0.658–0.917) was identified as the optimal cut-off value for procalcitonin. In logistic regression analysis, a significant efficacy of procalcitonin and leucocyturia was observed in the univariate analysis on spontaneous passage. In the multivariate analysis, significant independent activity was observed with procalcitonin. (p < 0.05).ConclusionOur findings suggest that high procalcitonin levels and the presence of leucocyturia have a strong negative effect on SP of ureteral stones between 5 and 10 mm in diameter. This relationship can be explained by stone impaction, possibly caused by increased mucosal inflammation.

Patient Reported Outcomes Predicting Spontaneous Stone Passage May Not Have Acceptable Accuracy.

We assessed the accuracy of patient reported outcomes for predicting spontaneous ureteral stone passage. Patients with new unilateral ureteral calculi were prospectively assessed regarding current symptoms and whether they believed their stone had passed. The primary outcome was successful spontaneous stone passage as confirmed by ultrasound, and kidney, ureter and bladder x-ray. Spontaneous stone passage was compared to patient reported outcome responses to assess accuracy. Of the 212 patients 105 (49.5%) had successful spontaneous stone passage at a mean followup of 17.6 days. Compared to the unsuccessful spontaneous stone passage group, those with successful spontaneous stone passage had significantly smaller (mean 5.4 vs 7.6 mm), more distal (71.4% vs 34.6%) stones with slightly longer average time to followup at first visit (19.2 vs 16.0 days). Additionally, there was more patient reported cessation of pain (77.1% vs 44.9%) and perceived stone passage (55.2% vs 13.1%) in this group. Cessation of pain was 79.7% (95% CI 67.1-89.0) sensitive and 55.8% (95% CI 44.0-67.1) specific for successful spontaneous stone passage. Likewise, patient reported stone passage was 59.3% (95% CI 45.7-71.9) sensitive and 87.0% (95% CI 77.4-93.5%) specific. In the multivariable logistic regression analysis cessation of pain (OR 4.02, 95% CI 1.91-8.47, p <0.01) and reported stone passage (OR 3.79, 95% CI 1.73-8.28, p <0.01) were independent predictors of successful spontaneous stone passage. Cessation of pain and patient reported stone passage are independent predictors of successful spontaneous stone passage. However, both assessments may incorrectly gauge spontaneous stone passage, which raises concern for their validity as a sole clinical end point.

PD14-03 DEVELOPMENT OF PREDICTION MODELS OF SPONTANEOUS URETERAL STONE PASSAGE THROUGH MACHINE LEARNING: COMPARISON WITH CONVENTIONAL STATISTICAL ANALYSIS

INTRODUCTION AND OBJECTIVE:Indications for management of ureteral stones are unclear, and clinician determines whether to wait for spontaneous ureteral stone passage (SSP) or perform active treatme...