Salvatore Micali1, Maria Chiara Sighinolfi2, Andrea Iseppi1, Elena Morini1, Tommaso Calcagnile1, Mattia Benedetti1, Marco Ticonosco1, Shaniko Kaleci3, Luigi Bevilacqua1, Stefano Puliatti1, Cosimo De Nunzio4, Raphael Arada5, Francesco Chiancone6, Davide Campobasso7, Ahmed Eissa8, Giulia Bonfante1, Elisa Simonetti9, Michele Cotugno10, Riccardo Galli11, Pierpaolo Curti12, Luigi Schips13, Pasquale Ditonno14, Luca Villa15, Stefania Ferretti9, Franco Bergamaschi16, Giorgio Bozzini17, Ahmed Zoeir8, Ahmed El Sherbiny8, Antonio Frattini7, Paolo Fedelini6, Zhamshid Okhunov5, Andrea Tubaro4, Jaime Landman5, Giampaolo Bianchi1, Bernardo Rocco1. 1. Department of Urology, Azienda Ospedaliero-Universitaria, Urological Residency School Network, University of Modena & Reggio Emilia, Modena, Italy. 2. Department of Urology, Azienda Ospedaliero-Universitaria, Urological Residency School Network, University of Modena & Reggio Emilia, Modena, Italy. Electronic address: sighinolfic@gmail.com. 3. Clinical and experimental medicine (CEM), Department of Surgical, Medical, Dental and Morphological Sciences with Interest in Transplant, Oncology and Regenerative Medicine, University of Modena & Reggio Emilia, Modena, Italy. 4. Department of Urology, Ospedale Sant'Andrea, "La Sapienza" University, Rome, Italy. 5. Department of Urology, University of California, Irvine, Orange, CA, USA. 6. Department of Urology, AORN Antonio Cardarelli, Naples, Italy. 7. Department of Urology, Ospedale Civile di Guastalla, Urological Residency School Network, University of Modena & Reggio Emilia, Guastalla, Italy. 8. Department of Urology, Tanta University, Tanta, Egypt. 9. Department of Urology, Ospedale Maggiore, Urological Residency School Network, University of Modena & Reggio Emilia, Parma, Italy. 10. Department of Urology, Ospedale di Vaio, Urological Residency School Network, University of Modena & Reggio Emilia, Fidenza, Italy. 11. Department of Urology, Policlinico San Pietro, Ponte San Pietro, Italy. 12. Department of Urology, Ospedale Mater Salutis, Legnago, Italy. 13. Department of Urology, Ospedale SS. Annunziata, Chieti, Italy. 14. Department of Urology, IRCCS Giovanni Paolo II, Bari, Italy. 15. Department of Urology, IRCCS Ospedale San Raffaele, Milan, Italy. 16. Department of Urology, Arcispedale S. Maria Nuova, Urological Residency School Network, University of Modena & Reggio Emilia, Reggio Emilia, Italy. 17. Department of Urology, ASST Valle Olona, Urological Residency School Network, University of Modena & Reggio Emilia, Busto Arsizio, Varese, Italy.
Abstract
BACKGROUND: The gold standard treatment for solitary medium-sized (1-2 cm) renal stones is not defined by recent guidelines, since management modalities including shockwave lithotripsy (SWL), retrograde intrarenal surgery (RIRS), and percutaneous nephrolithotomy (PNL) are recommended. Improved ability to predict patient outcomes would aid in patients' counseling and decision-making. OBJECTIVE: To develop a nomogram predicting treatment failure, based on preoperative clinical variables, to be used in the preplanning setting. DESIGN, SETTING, AND PARTICIPANTS: We recruited 2605 patients from 14 centers and carried out a multicenter retrospective analysis of 699 SWL, 1290 RIRS, and 616 PN L procedures performed as first-line treatment for 1-2-cm kidney stones. The variables evaluated included age, gender, previous renal surgery, body mass index, stone size, location, stone density, skin-to-stone distance, presence of urinary tract infections (UTIs), and hydronephrosis. OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS: Multivariate logistic regression was fitted to predict treatment failure, defined as the presence of residual fragments >4 mm. A nomogram was developed based on the coefficients of the logit function. RESULTS AND LIMITATIONS: A total of 2431 (93.3%) patients were stone free; 174 (6.7%) treatment failures were recorded and considered the event to be predicted. On univariate analysis, type of procedure, preoperative hydronephrosis, stone density, stone location, and laterality turned out to be statistically significant. Skin-to-stone distance, UTIs, and previous renal surgery were predictors of failure on multivariate analysis. Each variable was given a score based on statistical relevance. The main limitation of the current study is its retrospective nature. CONCLUSIONS: This nomogram provides a prediction of treatment failure and need of reintervention for medium-sized kidney stones. External validation is needed to determine its reproducibility and validity. PATIENT SUMMARY: We developed a preoperative model of treatment outcomes for 1-2-cm kidney stones. Its application may assist urologists to counsel patients with regard to stone management modality.
BACKGROUND: The gold standard treatment for solitary medium-sized (1-2 cm) renal stones is not defined by recent guidelines, since management modalities including shockwave lithotripsy (SWL), retrograde intrarenal surgery (RIRS), and percutaneous nephrolithotomy (PNL) are recommended. Improved ability to predict patient outcomes would aid in patients' counseling and decision-making. OBJECTIVE: To develop a nomogram predicting treatment failure, based on preoperative clinical variables, to be used in the preplanning setting. DESIGN, SETTING, AND PARTICIPANTS: We recruited 2605 patients from 14 centers and carried out a multicenter retrospective analysis of 699 SWL, 1290 RIRS, and 616 PN L procedures performed as first-line treatment for 1-2-cm kidney stones. The variables evaluated included age, gender, previous renal surgery, body mass index, stone size, location, stone density, skin-to-stone distance, presence of urinary tract infections (UTIs), and hydronephrosis. OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS: Multivariate logistic regression was fitted to predict treatment failure, defined as the presence of residual fragments >4 mm. A nomogram was developed based on the coefficients of the logit function. RESULTS AND LIMITATIONS: A total of 2431 (93.3%) patients were stone free; 174 (6.7%) treatment failures were recorded and considered the event to be predicted. On univariate analysis, type of procedure, preoperative hydronephrosis, stone density, stone location, and laterality turned out to be statistically significant. Skin-to-stone distance, UTIs, and previous renal surgery were predictors of failure on multivariate analysis. Each variable was given a score based on statistical relevance. The main limitation of the current study is its retrospective nature. CONCLUSIONS: This nomogram provides a prediction of treatment failure and need of reintervention for medium-sized kidney stones. External validation is needed to determine its reproducibility and validity. PATIENT SUMMARY: We developed a preoperative model of treatment outcomes for 1-2-cm kidney stones. Its application may assist urologists to counsel patients with regard to stone management modality.
Authors: Marco Amato; Pietro Piazza; Yves Deruyver; Lina Del Favero; Thomas Van den Broeck; Luca Sarchi; Simone Scarcella; Carlo Andrea Bravi; Stefano Puliatti; Salvatore Micali; Carl Van Haute; Ben Van Cleynenbreugel Journal: CEN Case Rep Date: 2022-01-04