The infection post flexible UreteroreNoscopy (I-FUN) predictive model based on machine learning: a new clinical tool to assess the risk of sepsis post retrograde intrarenal surgery for kidney stone disease.
Daniele Castellani, Virgilio De Stefano, Carlo Brocca, Giorgio Mazzon, Antonio Celia, Andrea Bosio, Claudia Gozzo, Eugenio Alessandria, Luigi Cormio, Runeel Ratnayake, Andrea Vismara Fugini, Tonino Morena, Yiloren Tanidir, Tarik Emre Sener, Simon Choong, Stefania Ferretti, Andrea Pescuma, Salvatore Micali, Nicola Pavan, Alchiede Simonato, Roberto Miano, Luca Orecchia, Giacomo Maria Pirola, Angelo Naselli, Esteban Emiliani, Pedro Hernandez-Peñalver, Michele Di Dio, Claudio Bisegna, Davide Campobasso, Emauele Serafin, Alessandro Antonelli, Emanuele Rubilotta, Deepak Ragoori, Emanuele Balloni, Marina Paolanti, Vineet Gauhar, Andrea Benedetto Galosi
{"title":"The infection post flexible UreteroreNoscopy (I-FUN) predictive model based on machine learning: a new clinical tool to assess the risk of sepsis post retrograde intrarenal surgery for kidney stone disease.","authors":"Daniele Castellani, Virgilio De Stefano, Carlo Brocca, Giorgio Mazzon, Antonio Celia, Andrea Bosio, Claudia Gozzo, Eugenio Alessandria, Luigi Cormio, Runeel Ratnayake, Andrea Vismara Fugini, Tonino Morena, Yiloren Tanidir, Tarik Emre Sener, Simon Choong, Stefania Ferretti, Andrea Pescuma, Salvatore Micali, Nicola Pavan, Alchiede Simonato, Roberto Miano, Luca Orecchia, Giacomo Maria Pirola, Angelo Naselli, Esteban Emiliani, Pedro Hernandez-Peñalver, Michele Di Dio, Claudio Bisegna, Davide Campobasso, Emauele Serafin, Alessandro Antonelli, Emanuele Rubilotta, Deepak Ragoori, Emanuele Balloni, Marina Paolanti, Vineet Gauhar, Andrea Benedetto Galosi","doi":"10.1007/s00345-024-05314-5","DOIUrl":null,"url":null,"abstract":"<p><strong>Purpose: </strong>To create a machine-learning model for estimating the likelihood of post-retrograde intrarenal surgery (RIRS) sepsis.</p><p><strong>Methods: </strong>All consecutive patients with kidney stone(s) only undergoing RIRS in 16 centers were prospectively included (January 2022-August 2023).</p><p><strong>Inclusion criteria: </strong>adult, renal stone(s) only, CT scan (within three months), mid-stream urine culture (within 10 days).</p><p><strong>Exclusion criteria: </strong>concomitant ureteral stone, bilateral procedures. In case of symptomatic infection/asymptomatic bacteriuria, patients were given six days of antibiotics according to susceptibility profiles. All patients had antibiotics prophylaxis. Variables selected for the model: age, gender, age-adjusted Charlson Comorbidity Index, stone volume, indwelling preoperative bladder catheter, urine culture, single/multiple stones, indwelling preoperative stent/nephrostomy, ureteric access sheath, surgical time. Analysis was conducted using Python programming language, with Pandas library and machine learning models implemented using the Scikit-learn library. Machine learning algorithms tested: Decision Tree, Random Forest, Gradient Boosting. Overall performance was accurately estimated by K-Fold cross-validation with three folds.</p><p><strong>Results: </strong>1552 patients were included. There were 20 (1.3%) sepsis cases, 16 (1.0%) septic shock cases, and three more cases (0.2%) of sepsis-related deaths. Random Forest model showed the best performance (precision = 1.00; recall = 0.86; F1 score = 0.92; accuracy = 0.92). A web-based interface of the predictive model was built and is available at https://emabal.pythonanywhere.com/ CONCLUSIONS: Our model can predict post-RIRS sepsis with high accuracy and might facilitate patient selection for day-surgery procedures and identify patients at higher risk of sepsis who deserve extreme attention for prompt identification and treatment.</p>","PeriodicalId":23954,"journal":{"name":"World Journal of Urology","volume":null,"pages":null},"PeriodicalIF":2.8000,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"World Journal of Urology","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1007/s00345-024-05314-5","RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"UROLOGY & NEPHROLOGY","Score":null,"Total":0}
引用次数: 0
Abstract
Purpose: To create a machine-learning model for estimating the likelihood of post-retrograde intrarenal surgery (RIRS) sepsis.
Methods: All consecutive patients with kidney stone(s) only undergoing RIRS in 16 centers were prospectively included (January 2022-August 2023).
Exclusion criteria: concomitant ureteral stone, bilateral procedures. In case of symptomatic infection/asymptomatic bacteriuria, patients were given six days of antibiotics according to susceptibility profiles. All patients had antibiotics prophylaxis. Variables selected for the model: age, gender, age-adjusted Charlson Comorbidity Index, stone volume, indwelling preoperative bladder catheter, urine culture, single/multiple stones, indwelling preoperative stent/nephrostomy, ureteric access sheath, surgical time. Analysis was conducted using Python programming language, with Pandas library and machine learning models implemented using the Scikit-learn library. Machine learning algorithms tested: Decision Tree, Random Forest, Gradient Boosting. Overall performance was accurately estimated by K-Fold cross-validation with three folds.
Results: 1552 patients were included. There were 20 (1.3%) sepsis cases, 16 (1.0%) septic shock cases, and three more cases (0.2%) of sepsis-related deaths. Random Forest model showed the best performance (precision = 1.00; recall = 0.86; F1 score = 0.92; accuracy = 0.92). A web-based interface of the predictive model was built and is available at https://emabal.pythonanywhere.com/ CONCLUSIONS: Our model can predict post-RIRS sepsis with high accuracy and might facilitate patient selection for day-surgery procedures and identify patients at higher risk of sepsis who deserve extreme attention for prompt identification and treatment.
期刊介绍:
The WORLD JOURNAL OF UROLOGY conveys regularly the essential results of urological research and their practical and clinical relevance to a broad audience of urologists in research and clinical practice. In order to guarantee a balanced program, articles are published to reflect the developments in all fields of urology on an internationally advanced level. Each issue treats a main topic in review articles of invited international experts. Free papers are unrelated articles to the main topic.