https://orcid.org/0000-0001-6111-9927
Figures
Abstract
Patients after holmium laser lithotripsy have a certain probability of getting postoperative infection. An early and accurate diagnosis of postoperative infection allows a timely administration of appropriate antibiotic treatment. However, doctors can not accurately determine whether the patient has the infection. Here, a novel strategy is put forward to assist in predicting postoperative infection early by using machine learning methods. We retrospectively collected 1006 cases of patients with urinary stone after treatment of holmium laser lithotripsy from Zhejiang Provincial People’s Hospital. Feature engineering was added to filter the important characteristics and Miceforest multiple imputation method was applied to tackle the missing data problem. We used 5-fold cross-validation to train and validate the six machine learning methods. Besides, we could also find key variables important to postoperative infection by explaining the model. The hyperparameters were constantly adjusted to achieve the best performance of model. The result showed that LR had a best performance in independent datasets with AUC of 0.734. And the SHAP values indicated that preoperative urine leukocyte count was the most important variable to the prediction. Our study enables accurate predictions of infection in urology perioperative periods, the key variables can be interpreted better and more accurately to support clinical decision making.
Citation: Xia W, Zhang M, Zheng X, Wu Z, Xuan Z, Huang P, et al. (2025) Machine learning for early prediction of the infection in patients with urinary stone after treatment of holmium laser lithotripsy. PLoS One 20(5): e0317584. https://doi.org/10.1371/journal.pone.0317584
Editor: Bryan Kwun-Chung Cheng, Private Urologist, HONG KONG
Received: September 20, 2024; Accepted: January 1, 2025; Published: May 16, 2025
Copyright: © 2025 Xia et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Data Availability: Data was approved for limited use by the Institutional Review Boards of the respective hospitals, and not publicly available.
Funding: This work was financially supported by Research Program for Medicine and Health Science and Technology of Zhejiang Province (2022KY585 to X. Yang).
Competing interests: The authors have declared that no competing interests exist.
Introduction
Urinary tract stone is one of the most common diseases of urinary system, and the prevalent rate of the population is about 1%–15%, among which upper urinary tract stones is about 70 percent [1]. Holmium laser lithotripsy is used for the clinical treatment of upper urinary tract stones [2]. However, it still results in a high incidence of postoperative infection and has the risk of the progression to urosepsis in the later stage (about 12–20%) [3,4]. Stone location, stone size and so on could affect the surgery [5]. If urosepsis is not controlled timely and effectively, the condition may rapidly progress to septic shock [6], and eventually leading to multiple organ failure, with a fatality rate of up to 22% ~ 76%, which will not only bring great pain to the patients themselves, but also bring economic burden to the family and society. Therefore, early identification of high-risk patients complicated with infection after holmium laser lithotripsy and timely adoption of targeted intervention measures can not only reduce the probability of pyelonephritis and risk factors for septic shock, but also promote the rehabilitation of patients. Currently, the occurrence of postoperative infections is typically identified through the clinical experience of the doctors or by monitoring infection indicators. We hope to leverage machine learning methods to detect infection earlier, thereby avoiding the overuse of antibiotics.
Machine learning (ML), as a branch of artificial intelligence (AI), uses computer systems to realize prediction or decision-making tasks with algorithms and statistical models, which can provide great possibilities for the progress of medical science [7]. Machine learning is playing an increasingly important role in the medical field because of its methodological advantages, such as cardiovascular disease prediction [8–10], cancer prediction [11,12], adverse reaction prediction [13,14] and so on. Therefore, in the clinical field, the use of machine learning methods to mine medical big data, identify valuable features, and build prediction models adapted to complex data is a current research hotspot. Unlike traditional methods that depend on postoperative infection indicators to identify infections, machine learning methods can predict the likelihood of postoperative infections beforehand, which enables the appropriate and timely administration of antibiotics, thereby reducing the overall use of antibiotics and minimizing the risk of antibiotic resistance.
In this study, six machine learning prediction models were established to determine whether postoperative infection occurred in urology perioperative periods [15]. Firstly, 1006 cases of holmium laser lithotripsy in Zhejiang Provincial People’s Hospital from October 2018 to August 2021 were collected in this study. Preoperative and intraoperative risk factors of patients were collected, and Chi-square test [16] was used for feature selection. Since some variable factors contained missing values, we applied multiple imputation to make up for the missing data [17,18]. Secondly, the prediction models were constructed by using the six mainstream machine learning methods, and the hyperparameters were constantly adjusted to select the best model. Besides, we used the 5-fold cross-validation to ensure the generalization performance of the model. We also showed the importance of those characteristics of patients in medical records. The SHapley Additive exPlanation (SHAP) values [19] were used to explain these prediction models. Our study enables accurate predictions of infection in urology perioperative periods, the interpretation of key variables can be interpreted better and more accurately to support clinical decision making.
Materials and methods
Data collection
A total of 1006 patients with upper urinary calculi who received treatment in Zhejiang Provincial People’s Hospital from October 2018 to August 2021 were collected in this study. The dates from a retrospective study were accessed for research purposes on Mar27, 2023 by Ethics Committee of the Zhejiang Provincial People’s Hospital. The incidence of postoperative infection in 1006 patients after surgery was observed. Postoperative infections include postoperative urinary tract infection, systemic inflammatory response syndrome, and urosepsis, the diagnostic criteria were based on EAU Guidelines on Urological Infections (2020). Finally, 325 patients were infected in urology perioperative periods and 681 were not infected. The characteristics of the patients, such as age, gender, hydronephrosis, hypertension, diabetes, nephrological related diseases, preoperative infection, stone location, intraoperative abnormalities (such as turbid urine and ureter malformation during the operation), operation duration were recorded and analyzed for the following process of modeling and analysis.
Data preprocessing
In order to construct the prediction model, the above variables were converted into numerical values in this study. For the convenience of operation, each index was assigned as a binary variable. The details are in the S1 File.
Feature selection
In the process of machine learning model construction, feature engineering refers to the screening and conversion of features extracted from original data into suitable subset, and the subset of these characteristics is the input of the model [20]. In this study, the feature selection method based on Chi-square test [21] was used to filter the above features. Chi-square test is an independence test for discrete variables. The null hypothesis of Chi-square test is that two discrete variables are independent of each other. In feature selection, it is used to distinguish labels and features, so it can be used to judge whether a certain feature and label are independent. If so, it means that the feature is not helpful to the prediction of labels. So in many cases, the chi-square test is a very important way to eliminate irrelevant features. The formula for the chi-square test is as follows:
Imputation of missing data
Multiple Imputation is a method to deal with missing values based on repeated simulation [22], which can provide a more accurate estimate of missing data compared with single mean or mode imputation. Here, in this paper, we apply the multiple imputation method Miceforest to impute the missing data.
Model construction
SVM.
Support vector machine (SVM) [23] is a kind of classification model. Its principle is to find a hyperplane in the space which can divide the data into different parts. For the binary classification problem, SVM algorithm needs to find parameters and
to divide the positive and negative samples well.
LR.
Logistic regression (LR) [24] is a linear classifier. Through logistic function (Sigmoid function), the data features are mapped to a probability value (the possibility that the sample belongs to a positive example) in the interval of 0 ~ 1.
RF.
Random Forest (RF) is an ensemble algorithm [25], which integrates multiple decision tree sets, but there is no direct correlation among decision trees. Each decision tree in the random forest classifies randomly selected samples according to the characteristic attributes of the selected samples. Each decision tree has a classification result, and finally according to the prediction results of these trees, the RF votes to decide the final prediction category of the samples.
DT.
Decision Tree (DT) is a tree structure [26], in which each internal node represents a judgment on an attribute, each branch represents the output of a judgment result, and finally each leaf node represents a classification result. Essentially, DT classifies datasets through several conditional discriminant procedures and finally obtains the desired results.
XGBoost、LightGBM
eXtreme Gradient Boosting (XGBoost) and Light Gradient Boosting Machine (LightGBM) are all boosting algorithms [27,28]. Gradient Boosting Decision Tree (GBDT) is the basis of XGBoost and LightGBM. In each iteration, GBDT calculates the current negative gradient of the sample based on the empirical loss function, and then a new weak classifier is trained and the weight of the weak classifier is calculated to update the model. The difference between GBDT and XGBoost is that XGBoost adds regularization method. The formula is as 2. LightGBM is an improved version of XGBoost. Compared to XGBoost, it adds many new methods to improve the model, including parallel schemes, gradient-based unilateral detection, and so on. Technical details are given in the S1 File.
Evaluation
A common criterion for evaluating binary or multi-classification models is confusion matrix, which compares the predicted label with real label [29]. True Positive (TP) means that both the true label of the sample and the predicted label of model are positive. False Negative (FN) represents that the positive sample is predicted incorrectly, while False Positive (FP) refers to the negative sample is considered as positive. True Negative (TN) means that both the true label of the sample and the prediction of the model are negative. The accuracy, precision, recall can be obtained by calculating the prediction label and the real label of the model according to these four indexes. According to the precision and recall, we can calculate the F1. Area under the receiver operator characteristics curve (AUROC) is also used as the evaluation metric.
Results
Statistical analysis
The datasets had 14 characteristics and the targets were postoperative infection or no infection. Table 1 showed the all characteristics of the patient, and we used Chi-square test to analyze the feature set. When P-value is less than 0.05, it indicates that there is a significant difference between the two groups of data in this feature. Therefore, the features with P-value less than 0.05 were included in the following model training. It could be seen from Table 1 that the P-value of a total of 8 indicators were less than 0.05, including Age, Preoperative albumin, Duration of surgery, Asymptomatic bacteriuria, The control of preoperative infection, Intraoperative abnormalities, Preoperative urine leukocyte count and Urine occult blood.
Hyperparameters optimization
We used 5-fold cross-validation to train and test the model. The dataset was divided into five parts. One of the five sets was selected as test set, the rest four sets were selected as training set. To optimize each machine learning models, different hyperparameters were explored through grid search. In LR, we selected regularization method with L1, L2, penalty coefficient C with 0.001, 0.01, 0.1, 1, 10, 100, 1000. In SVM, different kernel function with linear, poly, rbf, penalty coefficient C with 1, 10, 100, 1000 were included. For DT, we considered the maximum depth with 30, 50, 60, 100, min_samples_leaf with 2, 3, 5, 10 and min_impurity_decrease with 0.1, 0.2, 0.5. The n_estimators of RF ranged from 10 to 100. For XGBoost, we considered the maximum depth from 1 to 20, min_child_weight from 1 to 20 and n_estimators from 100 to 200. And for LightGBM, we used the maximum depth with 5, 10, 15, 20, 30 and the learning rate with 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8. The best hyperparameters were selected according to the mean performance based on cross validation [30,31]. And the optimum values were displayed in Table 2.
Imputation method analysis
We compared Multiple Imputation method with Mean Imputation method by train six machine learning models. Table 3 was the AUC result of two different imputation methods. We could clearly find that the prediction ability of Multiple Imputation method was greater than Mean Imputation.
Performance evaluation after normalization
The purpose of normalization is to limit the feature in a certain range so that we can eliminate the adverse effects caused by the singular sample data. We normalized the continuous variables described above. SVM, RF, LR, DT, XGBoost and LightGBM were respectively used in this study. During the training, 5-fold cross-validation was adopted, and the minimum loss was taken as the training objective. Fig 1 showed the AUC results of SVM, LR, DT, RF, LightGBM and XGBoost models, where each line represented a validation in the 5-fold cross-validation. As can be seen from the figure, except DT, the average AUC of the model was greater than 0.70.
The above 5-fold cross-validation results indicated that the DT model had no classification ability at all in this dataset, so we only compared the performance of the other five models. Fig 2 displayed the performance of the independent test dataset based on the five classification models. In the figure, the horizontal axis represented AUC, Accuracy, F1 respectively, while the vertical axis represented the corresponding value. The green bar was SVM, the orange was LR, the grey bar was RF, the red was LightGBM, and the blue was XGBoost. It could be clearly seen that the performance of LR and RF on AUC was superior than other models. But in accuracy and F1, LightGBM performed the best. Positive Predictive Value (PPV) indicates the proportion of patients who are judged as positive (infected) by the test that are actually infected. It serves as a metric for assessing the test’s capability to avoid “false positive” misdiagnoses. The Positive Predictive Values (PPVs) of these five models are 0.687, 0.73, 0.712, 0.733, 0.697 respectively. LightGBM performed the best.
Importance of clinical variables
In order to facilitate the interpretation of model, the SHAP value has been introduced to judge the positive or negative relationship of clinical variable with prediction [32]. A dot is created for each feature attribution value for the model of each patient, and thus one patient is allocated one dot on the line for each feature. Red represents higher feature values, and blue represents lower feature values. The ranking of the features is in descending order according to the average absolute value of SHAP. RF model was chosen to analyze the importance of characteristics. According to the importance and impacts of variables on model prediction, a bee swarm plot was formed for each feature. As shown in Fig 3, In RF, we found that patients with high preoperative urine leukocyte count (red) had a higher risk of developing postoperative infection than patients with low preoperative urine leukocyte count (blue). Other characteristics were also found that a high value had positive effect on developing postoperative infection.
Discussion
Perioperative postoperative infection is an infection that occurs within 1 month after surgical operation or surgical intervention [33,34], which is one of the common complications after surgical operation. Individuals suffering from ureteral stones often experience persistent, low-grade inflammation within the ureteral lining as a result of the prolonged mechanical irritation caused by the stone. These patients are prone to getting perioperative postoperative infection which can lead to prolonged time in hospital, increased medical costs, increased mortality and a variety of adverse outcomes. Therefore, for subsequent antibiotic administration and patient’s life safety, it is vital to predict whether patients have a risk of infection [35]. If we can detect patients with infection as early as possible, then the clinician can make a more rational medication.
Several models have been developed for the prediction of infection after urolithiasis surgery [36,37]. Nonetheless, the postoperative infection risk factors considered in previous studies are not comprehensive. Here, we developed an early prediction of the infection in patients with urinary stone after treatment of holmium laser lithotripsy by applying machine learning methods to help physicians accurately identify the tendency of patients to get infected after surgery. By selecting the best dataset imputation method and the best number of clinical variables (eight of fourteen variables were chosen in our risk prediction model), and constantly adjusting the model parameters, we constructed the model with a reliable ability to predict postoperative infection. We used six mainstream machine learning methods and the grid seach method to find the best parameters, and the LR model performed best in the AUC value.
The results of this study demonstrate that preoperative urine leukocyte count and asymptomatic bacteriuria are significantly associated with the occurrence of postoperative infection. Numerous other studies have also shown that preoperative elevated urine leukocyte counts, positive urine cultures, and positive nitrite tests in urine are significant risk factors for postoperative infections, with the degree of bacteriuria potentially correlating to the severity of postoperative urinary tract infection [38,39]. Besides, Preoperative albumin, Duration of surgery, the presence of urinary tract anomalies discovered during surgery are associated with an increased risk of postoperative infections [36]. Of course, there are some limitations in this study. This is a single-center study, which limits the generalizability of our findings. Compared to single-center studies, multi-center studies involve a more geographically and demographically diverse patient population, providing a broader representation of the target population. Thus, in the future work, our model should be further optimized in multi-centric real world clinical data and other public clinical database. In the process of machine learning model construction, the feature selection method is not very comprehensive. In the future research, more feature selection methods should be tried. Besides, we should consider more variables vital to the prediction of postoperative infection in order to further improve the performance of model.
Conclusion
At present, clinicians frequently administer antibiotics without assessing the actual infection status of the patients, resulting in the overuse of antibiotics. However, determining the likelihood of postoperative infections places an additional burden on clinicians and typically requires the expertise of experienced clinicians. To address these challenges, this study employs machine learning methods to develop a postoperative infection prediction tool which can assist clinicians in predicting infections in urology perioperative periods, thereby significantly reducing the unnecessary use of antibiotics and improving overall patient care. The findings may help doctors guide patient treatment, and prevent unnecessary pain and cost. To enhance the usability of this tool, we plan to develop it into a mobile application. This app will enable doctors to input various characteristics directly, and the app will instantly provide a prediction of the likelihood of postoperative infection. This user-friendly approach will facilitate more accurate and timely decision-making, ultimately improving patient care and reducing the overuse of antibiotics.
Supporting information
S1 File. The S1 File includes the detail of Data preprocessing and Technical Details for the Statistical Analysis.
https://doi.org/10.1371/journal.pone.0317584.s001
(DOCX)
Acknowledgments
This work was financially supported by Research Program for Medicine and Health Science and Technology of Zhejiang Province (2022KY585 to X. Yang)
References
- 1. Shahmoradi L, Azizpour A, Bejani M, Shadpour P, Rezayi S. Prevention and control of urinary tract stones using a smartphone-based self-care application: design and evaluation. BMC Med Inform Decis Mak. 2021;21(1):299. pmid:34724936
- 2. Li X, Qiao J, Xiong S, Wang J, Wang Q, Li Z, et al. The surgical outcomes of reconstruction for the treatment of ureteral stricture after holmium laser lithotripsy: The comprehensive experiences. Asian J Surg. 2022;45(12):2713–8. pmid:35346585
- 3. Dreger NM, et al. Urosepsis--etiology, diagnosis, and treatment. Dtsch Arztebl Int 2015;112(49):837–47; quiz 848
- 4. Chugh S, et al. Predictors of urinary infections and urosepsis after ureteroscopy for stone disease: a systematic review from EAU Section of Urolithiasis (EULIS) Curr Urol Rep 2020;21(4):16.
- 5. Kaczmarek K, Jankowska M, Kalembkiewicz J, Kienitz J, Chukwu O, Lemiński A, et al. Assessment of the incidence and risk factors of postoperative urosepsis in patients undergoing ureteroscopic lithotripsy. Cent European J Urol. 2024;77(1):122–8. pmid:38645806
- 6. Yamamichi F, Shigemura K, Kitagawa K, Fujisawa M. Comparison between non-septic and septic cases in stone-related obstructive acute pyelonephritis and risk factors for septic shock: A multi-center retrospective study. J Infect Chemother. 2018;24(11):902–6. pmid:30174285
- 7. Adlung L, Cohen Y, Mor U, Elinav E. Machine learning in clinical decision making. Med. 2021;2(6):642–65. pmid:35590138
- 8. Tiwari A, Chugh A, Sharma A. Ensemble framework for cardiovascular disease prediction. Comput Biol Med. 2022;146:105624. pmid:35598355
- 9. Mohd Faizal AS, Thevarajah TM, Khor SM, Chang S-W. A review of risk prediction models in cardiovascular disease: conventional approach vs. artificial intelligent approach. Comput Methods Programs Biomed. 2021;207:106190. pmid:34077865
- 10. Elkenawy E.M., Alhussan AA, Khafaga DS, Tarek Z, Elshewey AM. Greylag goose optimization and multilayer perceptron for enhancing lung cancer classification. Sci Rep. 2024;14(1):23784. pmid:39390014
- 11. Kalafi EY, et al. Machine learning and deep learning approaches in breast cancer survival prediction using clinical data. Folia Biol (Praha) 2019;65(5-6):212–20.
- 12. Adeoye J, Tan JY, Choi S-W, Thomson P. Prediction models applying machine learning to oral cavity cancer outcomes: A systematic review. Int J Med Inform. 2021;154:104557. pmid:34455119
- 13. Galletti C, Aguirre-Plans J, Oliva B, Fernandez-Fuentes N. Prediction of adverse drug reaction linked to protein targets using network-based information and machine learning. Front Bioinform. 2022;2:906644. pmid:36304303
- 14. Mantripragada AS, Teja SP, Katasani RR, Joshi P, V M, Ramesh R. Prediction of adverse drug reactions using drug convolutional neural networks. J Bioinform Comput Biol. 2021;19(1):2050046. pmid:33472571
- 15. Yang SW, Hyon YK, Na HS, Jin L, Lee JG, Park JM, et al. Machine learning prediction of stone-free success in patients with urinary stone after treatment of shock wave lithotripsy. BMC Urol. 2020;20(1):88. pmid:32620102
- 16. Ratul I.J., Wani UH, Nishat MM, Al-Monsur A, Ar-Rafi AM, Faisal F, et al. Survival prediction of children undergoing hematopoietic stem cell transplantation using different machine learning classifiers by performing chi-square test and hyperparameter optimization: a retrospective analysis. Comput Math Methods Med. 2022;2022:9391136. pmid:36199778
- 17. Austin PC, et al. Missing data in clinical research: a tutorial on multiple imputation. Can J Cardiol 2021;37(9):1322–31.
- 18. Zhao X. Shen W. Wang G. Early prediction of sepsis based on machine learning algorithm. Comput Intell Neurosci, 2021;2021:6522633.
- 19. Mu F, et al. Analysis of a machine learning-based risk stratification scheme for acute kidney injury in vancomycin. Front Pharmacol 2022;13:1027230.
- 20. Wen H, Zheng W, Li M, Li Q, Liu Q, Zhou J, et al. Multiparametric quantitative US examination of liver fibrosis: a feature-engineering and machine-learning based analysis. IEEE J Biomed Health Inform. 2022;26(2):715–26. pmid:34329172
- 21. Onesime M, Yang Z, Dai Q. Genomic island prediction via chi-square test and random forest algorithm. Comput Math Methods Med. 2021;2021:9969751. pmid:34122622
- 22. Blazek K, van Zwieten A, Saglimbene V, Teixeira-Pinto A. A practical guide to multiple imputation of missing data in nephrology. Kidney Int. 2021;99(1):68–74. pmid:32822702
- 23. Meng C, Ju Y, Shi H. TMPpred: A support vector machine-based thermophilic protein identifier. Anal Biochem. 2022;645:114625. pmid:35218736
- 24. Schober P, Vetter TR. Logistic regression in medical research. Anesth Analg. 2021;132(2):365–6.
- 25. Hanko M, Grendár M, Snopko P, Opšenák R, Šutovský J, Benčo M, et al. Random forest-based prediction of outcome and mortality in patients with traumatic brain injury undergoing primary decompressive craniectomy. World Neurosurg. 2021;148:e450–8. pmid:33444843
- 26. Flayer C.H., Perner C., Sokol C.L. A decision tree model for neuroimmune guidance of allergic immunity. Immunol Cell Biol. 2021;99(9):936–48. pmid:34115905
- 27. Song X, et al. XGBoost-based feature learning method for mining COVID-19 novel diagnostic markers. Front Public Health 2022;10:926069
- 28. Liao H, Zhang X, Zhao C, Chen Y, Zeng X, Li H. LightGBM: an efficient and accurate method for predicting pregnancy diseases. J Obstet Gynaecol. 2022;42(4):620–9. pmid:34392771
- 29. Hu J, Zhou L, Li B, Zhang X, Chen N. Improve hot region prediction by analyzing different machine learning algorithms. BMC Bioinformatics. 2021;22(Suppl 3):522. pmid:34696728
- 30. Abdollahzadeh B, et al. Puma optimizer (PO): a novel metaheuristic optimization algorithm and its application in machine learning. Cluster Computing 2024;27(4):5235–83.
- 31. El-Kenawy E-S.M., Ibrahim A. Football Optimization Algorithm (FbOA): a novel metaheuristic inspired by team strategy dynamics. J. Artif. Intell. Metaheuristics. 2024;8(1):21–38.
- 32. Tseng P-Y, Chen Y-T, Wang C-H, Chiu K-M, Peng Y-S, Hsu S-P, et al. Prediction of the development of acute kidney injury following cardiac surgery by machine learning. Crit Care. 2020;24(1):478. pmid:32736589
- 33. Uehara T, Takahashi S, Ichihara K, Hiyama Y, Hashimoto J, Kurimura Y, et al. Surgical site infection of scrotal and inguinal lesions after urologic surgery. J Infect Chemother. 2014;20(3):186–9. pmid:24462435
- 34. Tzelves L, et al. Using machine learning techniques to predict antimicrobial resistance in stone disease patients. World J Urol. 2022;40(7):1731–6. pmid:35616713
- 35. Bavare A, Rissmiller B, Devaraj S, Guffey D, Rajapakshe D, Weiner H, et al. Perioperative procalcitonin in predicting infection in children undergoing surgical procedures. J Surg Res. 2021;258:332–8. pmid:33131765
- 36. Southern JB, Higgins AM, Young AJ, Kost KA, Schreiter BR, Clifton M, et al. Risk factors for postoperative fever and systemic inflammatory response syndrome after ureteroscopy for stone disease. J Endourol. 2019;33(7):516–22. pmid:30569755
- 37. Senocak C, et al. Risk factors of infectious complications after flexible uretero-renoscopy with laser lithotripsy. Urol J 2018;15(4):158–63.
- 38.
Lu ZH, et al. Preoperative urine analysis is an effective tool to predict fever after miniaturized percutaneous nephrolithotomy on large renal stones 2021;18(6):600.
- 39. Ruan S, Chen Z, Zhu Z, Zeng H, Chen J, Chen H. Value of preoperative urine white blood cell and nitrite in predicting postoperative infection following percutaneous nephrolithotomy: a meta-analysis. Transl Androl Urol. 2021;10(1):195–203. pmid:33532309