Can Machine Learning Revolutionize Post-Retrograde Intrarenal Surgery Urosepsis Prediction? A Single-Center Study
DOI:
https://doi.org/10.21649/akemu.v31iSpl2.6082Keywords:
Machine Learning, Post-RIRS, Urosepsis, PredictionAbstract
Background:
One of the main surgical methods for upper urinary stones is retrograde intrarenal surgery (RIRS). Urosepsis is a serious complication of RIRS that threatens patients and confronts clinicians. To construct a valid predictive model for post-RIRS urosepsis, a dataset including demographic and pre-operative factors from 260 patients who underwent RIRS was used.
Objective:
The aim of this research was to create a machine learning (ML) model as a novel solution to predict high-risk patient populations for urosepsis after retrograde intrarenal surgery (RIRS).
Method:
This retrospective analysis involved 260 patients who were treated with retrograde intrarenal surgery (RIRS) without pre-stenting at Pakistan Kidney and Liver Institute & Research Center from September 2018 to August 2024. Demographic, clinical, and preoperative data were retrieved to construct a predictive model for post-RIRS urosepsis. Supervised machine learning algorithms, i.e., Support Vector Machine, Gaussian Naïve Bayes, Logistic Regression, Decision Tree, and k-nearest Neighbors, were utilized. Model performance was assessed by accuracy, precision, recall, and Area Under the Receiver Operating Characteristic Curve.
Results:
The machine learning models were able to predict post-RIRS urosepsis based on preoperative demographic and clinical features. Of the algorithms used, Support Vector Machine (SVM), Logistic Regression, and k-Nearest Neighbors (KNN) classifiers performed best in terms of predictive accuracy, and SVM had the best overall accuracy. The findings prove that ML-based methods are capable of predicting high-risk patients before surgery effectively.
Conclusion:
This algorithm encompasses the potential to detect and prevent the development of urosepsis in RIRS patients and creating proper care plans through machine learning models.
References
1. Sorokin I, Mamoulakis C, Miyazawa K, Rodgers A, Talati J, Lotan Y. Epidemiology of stone disease across the world. World J Urol. 2017;35(9):1301–20. doi:10.1007/s00345-017-2008-6
2. Liu Y, Chen Y, Liao B, Luo D, Wang K, Li H, et al. Epidemiology of urolithiasis in Asia. Asian J Urol. 2018;5(4):205–14. doi:10.1016/j.ajur.2018.08.007
3. Alatab S, Pourmand G, El Howairis MF, Buchholz N, Najafi I, Pourmand MR, et al. National profiles of urinary calculi: a comparison between developing and developed worlds. Iran J Kidney Dis. 2016;10(2):51–61.
4. Ganpule AP, Vijayakumar M, Malpani A, Desai MR. Percutaneous nephrolithotomy (PCNL): a critical review. Int J Surg. 2016;36:660–4. doi:10.1016/j.ijsu.2016.11.028
5. National Institute for Health and Care Excellence (NICE). Renal and ureteric stones: assessment and management. BJU Int. 2019;123(2):220–32. doi:10.1111/bju.14654
6. Bhaskar KS, Ploumidis A, Pappas A, Doizi S, Babawale O, Dragos L, et al. Pictorial review of tips and tricks for ureteroscopy and stone treatment: an essential guide for urologists from PETRA research consortium. Transl Androl Urol. 2019;8(4):S371–80. doi:10.21037/tau.2019.06.04
7. Rouprêt M, Babjuk M, Burger M, Capoun O, Cohen D, Compérat EM, et al. European Association of Urology Guidelines on Upper Urinary Tract Urothelial Carcinoma: 2020 Update. Eur Urol. 2021;79(1):62–79. doi:10.1016/j.eururo.2020.05.042
8. Türk C, Petřík A, Sarica K, Seitz C, Skolarikos A, Straub M, et al. EAU Guidelines on interventional treatment for urolithiasis. Eur Urol. 2016;69(3):475–82. doi:10.1016/j.eururo.2015.07.041
9. Guliciuc M, Maier AC, Maier IM, Kraft A, Cucuruzac RR, Marinescu M, et al. The Urosepsis – a literature review. Medicina (Kaunas). 2021;57(9):872. doi:10.3390/medicina57090872
10. Font MD, Thyagarajan B, Khanna AK. Sepsis and septic shock: basics of diagnosis, pathophysiology and clinical decision making. Med Clin North Am. 2020;104(4):573–85. doi:10.1016/j.mcna.2020.02.011
11. Stoller J, Halpin L, Weis M, Aplin B, Qu W, Georgescu C, et al. Epidemiology of severe sepsis: 2008–2012. J Crit Care. 2016;31(1):58–62. doi:10.1016/j.jcrc.2015.09.034
12. Khwannimit B, Bhurayanontachai R. The direct costs of intensive care management and risk factors for financial burden of patients with severe sepsis and septic shock. J Crit Care. 2015;30(5):929–34. doi:10.1016/j.jcrc.2015.05.011
13. Cindolo L, Castellan P, Scoffone CM, Cracco CM, Celia A, Paccaduscio A, et al. Mortality and flexible ureteroscopy: analysis of six cases. World J Urol. 2016;34(3):305–10. doi:10.1007/s00345-015-1642-0
14. Castellani D, De Stefano V, Brocca C, Mazzon G, Celia A, Bosio A, et al. 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. World J Urol. 2024;42(1):612. doi:10.1007/s00345-024-05314-5
15. Corrales M, Sierra A, Doizi S, Traxer O. Risk of sepsis in retrograde intrarenal surgery: a systematic review of the literature. Eur Urol Open Sci. 2022;44:84–91. doi:10.1016/j.euros.2022.08.008
16. Yang M, Li Y, Huang F. A nomogram for predicting postoperative urosepsis following retrograde intrarenal surgery in upper urinary calculi patients with negative preoperative urine culture. Sci Rep. 2023;13:2123. doi:10.1038/s41598-023-29352-y
17. Senel C, Erkan A, Keten T, Aykanat IC, Ozercan AY, Tatlici K, et al. A new scoring system to predict febrile urinary tract infection after retrograde intrarenal surgery. Urolithiasis. 2024;53(1):15. doi:10.1007/s00240-024-01685-x
18. Mwamba D, Joe I. A deep-learned embedding technique for categorical features encoding. IEEE Access. 2021;9:114381-91. doi:10.1109/ACCESS.2021.3104357
19. Huang L, Qin J, Zhou Y, Zhu F, Liu L, Shao L. Normalization techniques in training DNNs: methodology, analysis and application. IEEE Trans Pattern Anal Mach Intell. 2023; 45(8):10173-96. doi:10.1109/TPAMI.2023.3250241
20. Dhal P, Azad C. A comprehensive survey on feature selection in the various fields of machine learning. Appl Intell. 2022;52:1–22. doi:10.1007/s10489-021-02550-9
21. Shafiee S, Lied L, Burud I, Dieseth JA, Alsheikh M, Lillemo M. Sequential forward selection and support vector regression in comparison to LASSO regression for spring wheat yield prediction based on UAV imagery. Comput Electron Agric. 2021;183:106036. doi:10.1016/j.compag.2021.106036
22. Bansal M, Goyal A, Choudhary A. A comparative analysis of K-nearest neighbor, genetic, support vector machine, decision tree, and long short term memory algorithms in machine learning. Decis Anal J. 2022;3:100071. doi:10.1016/j.dajour.2022.100071
23. Pietropaolo A, Geraghty RM, Veeratterapillay R, Rogers A, Kallidonis P, Villa L, et al. A machine learning predictive model for post-ureteroscopy urosepsis needing intensive care unit admission: a case-control YAU Endourology study from nine European centres. J Clin Med. 2021;10(17):3888. doi:10.3390/jcm10173888
24. Bunn C, Kulshrestha S, Boyda J, Balasubramanian N, Birch S, Karabayir I, et al. Application of machine learning to the prediction of postoperative sepsis after appendectomy. Surgery. 2021;169(3):671–7. doi:10.1016/j.surg.2020.07.045.
25. Su M, Guo J, Chen H, Huang J. Developing a machine learning prediction algorithm for early differentiation of urosepsis from urinary tract infection. Clin Chem Lab Med. 2022;61(3):521–9. doi:10.1515/cclm-2022-1006. PMID: 36383696.
26. Hong X, Liu G, Chi Z, Yang T, Zhang Y. Predictive model for urosepsis in patients with upper urinary tract calculi based on ultrasonography and urinalysis using artificial intelligence learning. Int Braz J Urol. 2023;49(2):221–32. doi:10.1590/S1677-5538.
27. Chen M, Yang J, Lu J, Zhou Z, Huang K, Zhang S, et al. Ureteral calculi lithotripsy for single ureteral calculi: can DNN-assisted model help preoperatively predict risk factors for sepsis? Eur Radiol. 2022;32(12):8540–9. doi:10.1007/s00330-022-08882-5.
28. Chen T, Zhang Y, Dou Q, Zheng X, Wang F, Zou J, et al. Machine learning-assisted preoperative diagnosis of infection stones in urolithiasis patients. J Endourol. 2022 Aug;36(8):1091–8. doi:10.1089/end.2021.0783
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