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Multicenter Study
. 2024 Nov 1;42(1):612.
doi: 10.1007/s00345-024-05314-5.

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  1 , Virgilio De Stefano  2 , Carlo Brocca  2 , Giorgio Mazzon  3 , Antonio Celia  3 , Andrea Bosio  4 , Claudia Gozzo  4 , Eugenio Alessandria  4 , Luigi Cormio  5   6 , Runeel Ratnayake  5   6 , Andrea Vismara Fugini  7 , Tonino Morena  7 , Yiloren Tanidir  8 , Tarik Emre Sener  8 , Simon Choong  9 , Stefania Ferretti  10 , Andrea Pescuma  10 , Salvatore Micali  10 , Nicola Pavan  11 , Alchiede Simonato  11 , Roberto Miano  12   13 , Luca Orecchia  12   13 , Giacomo Maria Pirola  14 , Angelo Naselli  14 , Esteban Emiliani  15 , Pedro Hernandez-Peñalver  15 , Michele Di Dio  16 , Claudio Bisegna  16 , Davide Campobasso  17 , Emanuele Serafin  18 , Alessandro Antonelli  18 , Emanuele Rubilotta  18 , Deepak Ragoori  19 , Emanuele Balloni  20 , Marina Paolanti  21 , Vineet Gauhar  22 , Andrea Benedetto Galosi  2
Affiliations
Multicenter Study

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 et al. World J Urol. .

Erratum in

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).

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.

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.

Keywords: Complications; Flexible ureteroscopy; Kidney stones; Machine learning; Retrograde intrarenal surgery; Sepsis.

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References

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