Predicting the effectiveness of extracorporeal shock wave lithotripsy on urinary tract stones. Risk groups for accurate retreatment

Hevia, M.; García, A.; Ancizu, F.J.; Merino, I.; Velis, J.M.; Tienza, Antonio; Algarra, R.; Doménech, P.; Diez-Caballero, F.; Rosell, D; Pascual, Juan I.; Robles, J.E.

doi:10.1016/j.acuroe.2016.12.012

Cited by 3 publications

(2 citation statements)

References 28 publications

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“…Several studies have proved that stone attenuation valuesmeasured in Hounsfield Units (HU) on computed tomography (CT) -can predict the outcome of SWL. Although the threshold values differ in various studies it has generally been accepted that higher CT attenuation values have poor outcomes in terms of success rates (2)(3)(4)(5). However, most of these studies address only the role of CT attenuation values from the success rate standpoint and there is a paucity of literature investigating the association between preoperative stone characteristics and the need for postoperative ancillary procedures.…”

Section: Original Papermentioning

confidence: 99%

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Can we predict the ancillary treatments after extracorporeal shockwave lithotripsy for renal and upper ureteral stones?

Ibrahim

Elatreisy

Khogeer

et al. 2022

Arch Ital Urol Androl

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Objective: To quantify the predictors for the ancillary treatments after extracorporeal shock wave lithotripsy (SWL) for renal and upper ureteral stones. Materials and methods: From January 2014 to January 2017, patients undergoing SWL using an electromagnetic lithotripter machine (Compact Delta; Dornier MedTech GmbH, Wessling, Germany) for renal and upper ureteral stones ≤ 20 mm were retrospectively reviewed. All patients underwent CT urography prior to SWL. The cohort was subdivided into three groups according to stone attenuation values in Hounsfield Units (HU). Group I; HU < 500 (n = 20), group II; HU 500-1000 (n = 51) and group III; HU ≥ 1000 (n = 180). The parameters included for multivariate analysis were stone size, location, multiplicity, stone attenuation value, number of shocks and stone clearance rate by 3 months. The ancillary treatments were ureteroscopy (URS), ureteral stenting and hospital readmission for pain or fever. Results: A total of 251 patients were included in the study. The overall SWL success rate was 92.4%. Mean stone size was 10.9 ± 2.1, 11.6 ± 3 and 11.4 ± 3.6 mm and mean stone attenuation values were 364 ± 125, 811 ± 154 and 1285 ± 171 HU for groups I, II and III respectively. The stone clearance rates by 3 months were 96%, 92% and 88.4% for groups I, II and III respectively. On subgroup analysis, group III required ancillary treatments in 70% of patients whereas group I, II, did not require any ancillary treatments. On multivariate analysis, stone multiplicity, stone location (lower calyceal stones) and HU were independent significant predictors for the need for ancillary treatments after SWL (p values < 0.05). Conclusions: Patients with stone attenuation value (HU) > 1000, multiple stones and/or lower calyceal stones have higher risk to necessitate ancillary treatments after SWL. These patients would likely benefit from upfront endoscopic lithotripsy for treating symptomatic renal or upper ureteral stones.

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Section: Original Papermentioning

confidence: 99%

“…For each stone, the mean attenuation value was calculated from a small, non-overlapping region of interest. The SWL procedures were all performed as previously described (3). The treatments were performed under conscious sedation using intravenous fentanyl as the primary anesthetic agent.…”

Section: Can We Predict the Ancillary Treatments After Extracorporeal...mentioning

confidence: 99%

Can we predict the ancillary treatments after extracorporeal shockwave lithotripsy for renal and upper ureteral stones?

Ibrahim

Elatreisy

Khogeer

et al. 2022

Arch Ital Urol Androl

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Machine learning analysis of CT images for the prediction of extracorporeal shock wave lithotripsy efficacy

Cao,

Yuan,

Qiao

et al. 2024

Preprint

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Objective The study aimed to evaluate the use decision support analysis for the prediction of extracorporeal shock wave lithotripsy (ESWL) efficacy and to analyze the factors influencing outcomes in patients who underwent ESWL using machine learning (ML) methods. Methods This retrospective study analyzed the clinical data, including preoperative CT images, of 302 patients who received a single ESWL session treatment for urinary tract stone (UTS) between May and October 2022 in the Department of Urology. The data was preprocessed and incorporated into an ML model, and the dataset was validated at a ratio of 4:1. The AUC and the confusion matrix were used to evaluate the predictive efficacy of the model. Results The CT image-based ML model predicting ESWL efficacy for UTS removal achieved an AUC of 0.86, precision of 88.33%, F1 score of 86.57%, sensitivity of 82.86%, and specificity of 88.89%. The model showed increased predictive accuracty for stones in different locations, with an AUC of 0.95 for kidney stones, 95.45% precision, 96% F1 score, 100% sensitivity, and 90% specificity. The AUC value for upper ureteral stones was 0.89, with 89.14% precision, 88.05% F1 score, 83.33% sensitivity, and 94.51% specificity, while that for mid-ureteral stones was 0.85, with 82.93% precision, 84.09% F1 score, 74% sensitivity, and 96.88% specificity, and the success rate of ESWL for lower ureteral stones was 100%, with an AUC of 1. Conclusions ML analysis was used to predict outcomes following ESWL treatment for UTS. The ML-based model was found to be approximately 86% accurate. The use of ML algorithms can provide matched insights to domain knowledge on effective and influential factors for the prediction of ESWL outcomes.

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Machine learning models for predicting the type and outcome of ureteral stones treatments

Langarizadeh,

Rashidi,

Sayadi

et al. 2023

Adv Biomed Res

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Predicting the effectiveness of extracorporeal shock wave lithotripsy on urinary tract stones. Risk groups for accurate retreatment

Cited by 3 publications

References 28 publications

Can we predict the ancillary treatments after extracorporeal shockwave lithotripsy for renal and upper ureteral stones?

Can we predict the ancillary treatments after extracorporeal shockwave lithotripsy for renal and upper ureteral stones?

Machine learning analysis of CT images for the prediction of extracorporeal shock wave lithotripsy efficacy

Machine learning models for predicting the type and outcome of ureteral stones treatments

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