ArticleWorld journal of urology2024
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.
Article in World journal of urology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 15 papers, 2 of them syntheses that pooled it.
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Who cites it
15 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Can AI and predictive models accurately predict stone-free status? a systematic review and meta-analysis.The Canadian journal of urology · 2026Pooled it
- Mortality from ureteroscopy for kidney stone disease: systematic review of literature from the section of EAU endourology.World journal of urology · 2025Pooled it
- Explainable machine learning algorithms for preoperative risk stratification of urosepsis after retrograde intrarenal surgery.Urolithiasis · 2026Article
- Development of a calibrated logistic regression clinical risk score for predicting postoperative sepsis following retrograde intrarenal surgery.World journal of urology · 2026Article
- A computational model for predicting shock in patients at risk of urosepsis after urolithiasis surgery.Urolithiasis · 2026Article
- Machine learning models to predict postoperative incontinence after endoscopic enucleation of the prostate for benign prostatic hyperplasia: An EAU-Endourology study.Prostate cancer and prostatic diseases · 2026Article
- Artificial intelligence-based prediction of postoperative sepsis in patients undergoing suction mini-percutaneous nephrolithotomy in real world practice: a multicenter analysis from the global Suction Lechnology Utility in Mini-PCNL Study (STUMPS) registry.World journal of urology · 2026Article
- Development and validation of an interpretable machine learning model for predicting postoperative fever after flexible ureteroscopic lithotripsy: a single-center retrospective cohort study.Frontiers in medicine · 2026Article
- Predicting postoperative fever in culture-negative patients undergoing mini-PCNL using MAP score-augmented machine learning: a retrospective cohort study.World journal of urology · 2025Article
- Article
- Use of Artificial Intelligence Methods for Improved Diagnosis of Urinary Tract Infections and Urinary Stone Disease.Journal of clinical medicine · 2025Review
- Development and external validation of a nomogram for predicting sepsis following flexible ureteroscopy.European journal of medical research · 2025Observational
- Predictors and associations of complications in ureteroscopy for stone disease using AI: outcomes from the FLEXOR registry.Urolithiasis · 2025Article
- A novel predictive method for URS and laser lithotripsy using machine learning and explainable AI: results from the FLEXOR international database.World journal of urology · 2025Article
- The utility of flexible and navigable suction access sheath (FANS) in patients undergoing same session flexible ureteroscopy for bilateral renal calculi: a global prospective multicenter analysis by EAU endourology.World journal of urology · 2025Article
Corrections and comments
- Erratum issued
Authors and funding
37 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purposeTo create a machine-learning model for estimating the likelihood of post-retrograde intrarenal surgery (RIRS) sepsis.
methodsAll consecutive patients with kidney stone(s) only undergoing RIRS in 16 centers were prospectively included (January 2022-August 2023). INCLUSION CRITERIA: adult, renal stone(s) only, CT scan (within three months), mid-stream urine culture (within 10 days). 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.
results1552 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/
conclusionsOur 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.
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