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 profles. 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 identifcation and treatment.
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 / Castellani, Daniele; De Stefano, Virgilio; Brocca, Carlo; Mazzon, Giorgio; Celia, Antonio; Bosio, Andrea; Gozzo, Claudia; Alessandria, Eugenio; Cormio, Luigi; Ratnayake, Runeel; Vismara Fugini, Andrea; Morena, Tonino; Tanidir, Yiloren; Sener, Tarik Emre; Choong, Simon; Ferretti, Stefania; Pescuma, Andrea; Micali, Salvatore; Pavan, Nicola; Simonato, Alchiede; Miano, Roberto; Orecchia, Luca; Pirola, Giacomo Maria; Naselli, Angelo; Emiliani, Esteban; Hernandez-Peñalver, Pedro; Di Dio, Michele; Bisegna, Claudio; Campobasso, Davide; Serafin, Emauele; Antonelli, Alessandro; Rubilotta, Emanuele; Ragoori, Deepak; Balloni, Emanuele; Paolanti, Marina; Gauhar, Vineet; Galosi, Andrea Benedetto. - In: WORLD JOURNAL OF UROLOGY. - ISSN 1433-8726. - 42:(2024). [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
Castellani, Daniele
;De Stefano, Virgilio;Brocca, Carlo;Balloni, Emanuele;Galosi, Andrea Benedetto
2024-01-01
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 profles. 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 identifcation and treatment.File | Dimensione | Formato | |
---|---|---|---|
Castellani_Infection-post-flexible-UreteroreNoscopy-(I-FUN)-predictive-model_2024.pdf
Solo gestori archivio
Tipologia:
Versione editoriale (versione pubblicata con il layout dell'editore)
Licenza d'uso:
Tutti i diritti riservati
Dimensione
833.47 kB
Formato
Adobe PDF
|
833.47 kB | Adobe PDF | Visualizza/Apri Richiedi una copia |
I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.