ArticleJournal of clinical medicine2021
A Machine Learning Predictive Model for Post-Ureteroscopy Urosepsis Needing Intensive Care Unit Admission: A Case-Control YAU Endourology Study from Nine European Centres.
Article in Journal of clinical medicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 36 papers, 7 of them syntheses that pooled it.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
36 citing papers in PubMed, 7 syntheses or guidelines pooled it, 63 citations in OpenAlex.
- Risk prediction models for postoperative complications of urosepsis in patients with upper urinary calculi: a systematic review and meta-analysis.BMC infectious diseases · 2026Pooled it
- Trends and methods in intensive care unit (ICU) research using machine learning: latent dirichlet allocation (LDA)-based thematic literature review.BMC medical informatics and decision making · 2025Pooled it
- Mortality from ureteroscopy for kidney stone disease: systematic review of literature from the section of EAU endourology.World journal of urology · 2025Pooled it
- High-power versus low-power laser settings during endoscopic stone disease management: a systematic review from the EAU endourology section.World journal of urology · 2024Pooled it
- Clinical significance of stone culture during endourological procedures in predicting post-operative urinary sepsis: should it be a standard of care-evidence from a systematic review and meta-analysis from EAU section of Urolithiasis (EULIS).World journal of urology · 2024Pooled it
- International Alliance of Urolithiasis guideline on retrograde intrarenal surgery.BJU international · 2023Guideline
- Use of artificial intelligence for sepsis risk prediction after flexible ureteroscopy: a systematic review.Revista do Colegio Brasileiro de Cirurgioes · 2023Pooled it
- Article
- Preoperative prediction of postoperative SIRS and sepsis after ureteroscopic lithotripsy: discriminative performance of large language models.Urolithiasis · 2026Article
- Explainable machine learning algorithms for preoperative risk stratification of urosepsis after retrograde intrarenal surgery.Urolithiasis · 2026Article
- Comment on "Clinical management implications from 33,579 urinary stones: novel patterns in composition, comorbidities, seasonal variation, and machine learning-based urosepsis prediction".International journal of surgery (London, England) · 2026Article
- The incremental value of a novel immuno-inflammatory index (SIICI) in predicting sepsis after ureteroscopic lithotripsy: development and validation of a nomogram.Frontiers in cellular and infection microbiology · 2026Article
- Preoperative machine learning algorithm for predicting urosepsis after percutaneous nephrolithotomy using EMR data.Urolithiasis · 2025Article
- Development and validation of an explainable machine learning model for predicting sepsis risk following flexible ureteroscopic lithotripsy.Urolithiasis · 2025Article
- Prediction of Sepsis after Endourologic Kidney Stone Surgery: A Machine Learning Approach.Journal of endourology · 2025Article
- 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
- Role of suction in revolutionising endourology: Is it the final frontier - an overview from EAU Endourology.Central European journal of urology · 2025Article
- A new scoring system to predict febrile urinary tract infection after retrograde intrarenal surgery.Urolithiasis · 2024Article
- 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.World journal of urology · 2024Article
Corrections and comments
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Authors and funding
16 authors at 9 institutions in 5 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
introductionWith the rise in the use of ureteroscopy and laser stone lithotripsy (URSL), a proportionate increase in the risk of post-procedural urosepsis has also been observed. The aims of our paper were to analyse the predictors for severe urosepsis using a machine learning model (ML) in patients that needed intensive care unit (ICU) admission and to make comparisons with a matched cohort.
methodsA retrospective study was conducted across nine high-volume endourology European centres for all patients who underwent URSL and subsequently needed ICU admission for urosepsis (Group A). This was matched by patients with URSL without urosepsis (Group B). Statistical analysis was performed with 'R statistical software' using the 'randomforests' package. The data were segregated at random into a 70% training set and a 30% test set using the 'sample' command. A random forests ML model was then built with
resultsA total of 114 patients were included (57 in each group) with a mean age of 60 ± 16 years and a male:female ratio of 1:1.19. The ML model correctly predicted risk of sepsis in 14/17 (82%) cases (Group A) and predicted those without urosepsis for 12/15 (80%) controls (Group B), whilst overall it also discriminated between the two groups predicting both those with and without sepsis. Our model accuracy was 81.3% (95%, CI: 63.7-92.8%), sensitivity = 0.80, specificity = 0.82 and area under the curve = 0.89. Predictive values most commonly accounting for nodal points in the trees were a large proximal stone location, long stent time, large stone size and long operative time.
conclusionUrosepsis after endourological procedures remains one of the main reasons for ICU admission. Risk factors for urosepsis are reasonably accurately predicted by our innovative ML model. Focusing on these risk factors can allow one to create predictive strategies to minimise post-operative morbidity.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.