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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

  • 期刊论文

  • 发表日期：

    2024年11月1日

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  • 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,
  • Emanuele Serafin,
  • Alessandro Antonelli,
  • Emanuele Rubilotta,
  • Deepak Ragoori,
  • Emanuele Balloni,
  • Marina Paolanti,
  • Vineet Gauhar,
  • Andrea Benedetto Galosi

摘要

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.