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.

IF 2.8 2区 医学 Q2 UROLOGY & NEPHROLOGY
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, Emauele Serafin, Alessandro Antonelli, Emanuele Rubilotta, Deepak Ragoori, Emanuele Balloni, Marina Paolanti, Vineet Gauhar, Andrea Benedetto Galosi
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引用次数: 0

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.

基于机器学习的柔性输尿管镜检查后感染(I-FUN)预测模型:评估肾结石逆行肾内手术后脓毒症风险的新临床工具。
目的:建立一个机器学习模型,用于估计逆行肾内手术(RIRS)后发生败血症的可能性:纳入标准:成人、仅肾结石、CT扫描(3个月内)、中段尿培养(10天内);排除标准:合并输尿管结石、双侧手术。如果出现无症状感染/无症状菌尿症,患者将根据药敏谱接受为期六天的抗生素治疗。所有患者均接受了抗生素预防治疗。模型所选变量:年龄、性别、年龄调整后的夏尔森综合指数、结石体积、术前留置的膀胱导尿管、尿培养、单个/多个结石、术前留置的支架/肾造瘘术、输尿管通道鞘、手术时间。分析使用 Python 编程语言和 Pandas 库进行,机器学习模型使用 Scikit-learn 库实现。测试的机器学习算法包括决策树、随机森林、梯度提升。总体性能通过 K-Fold 交叉验证进行了精确估算:共纳入 1552 名患者。共有 20 例(1.3%)败血症病例,16 例(1.0%)脓毒性休克病例,另有 3 例(0.2%)败血症相关死亡病例。随机森林模型表现最佳(精确度=1.00;召回率=0.86;F1得分=0.92;准确度=0.92)。该预测模型的网络界面已建成,可在 https://emabal.pythonanywhere.com/ 网站上查阅:我们的模型可以高度准确地预测 RIRS 后脓毒症,有助于选择日间手术患者,并识别脓毒症风险较高的患者,这些患者值得高度重视,以便及时发现和治疗。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
World Journal of Urology
World Journal of Urology 医学-泌尿学与肾脏学
CiteScore
6.80
自引率
8.80%
发文量
317
审稿时长
4-8 weeks
期刊介绍: The WORLD JOURNAL OF UROLOGY conveys regularly the essential results of urological research and their practical and clinical relevance to a broad audience of urologists in research and clinical practice. In order to guarantee a balanced program, articles are published to reflect the developments in all fields of urology on an internationally advanced level. Each issue treats a main topic in review articles of invited international experts. Free papers are unrelated articles to the main topic.
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