Frequency of Blood Transfusion in Percutaneous Nephrolithotomy

Ullah, Sami; Ali, Sikandar; Karimi, Sundas; Farooque, Umar; Hussain, Manzoor; Qureshi, Faisal; Shah, SM Ismail; Afzal, Anoshia; Tauseef, Abubakar; Khan, Muhammad Umair

doi:10.7759/cureus.11086

Cited by 8 publications

(6 citation statements)

References 19 publications

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“…However, transfusion remains the most common and serious complication. In our study, we identi ed 5.85% patients receiving PCNL need transfusion, which was higher with occurrence rates of UK (2.5%) [21] and USA (1.3%) [22], while lower than other Asian countries (18.3%, 23.1%) [23,24]. This may suggest that the relationship of the transfusion rate in PCNL varied between Asian and Western populations, and it is crucial to establish risk prediction models.…”

Section: Discussionmentioning

confidence: 70%

Application of Machine-Learning Models in Predicting Transfusion Among Complex Renal Stones Patients Receiving Percutaneous Nephrolithotomy：A Retrospective Study

Yang¹,

Zhi²,

Wang³

et al. 2022

Preprint

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To predict and compare machine-learning models with logistic regression (LR) for transfusion requirement among complex renal stones patients receiving PCNL. The data of 513 patients with complex renal stones receiving PCNL between January 2016 and December 2021 from 2 centers were analyzed retrospectively and reviewed the pre-, intra-, and post-operation factors. We used 80% training set and 20% test set for the predictions of transfusion by LR and machine-learning algorithms, random forest (RF), support vector machine (SVM), and extreme gradient boosting trees (XGBoost), and compared the prediction accuracies, precisions, and area under curve (AUC) values of receiving operating characteristic (ROC) curves in each model. In our study, the prediction accuracies of RF (Accuracy = 0.974), SVM (Accuracy = 0.968) and XGBoost (Accuracy = 0.968) models all outperformed LR (Accuracy = 0.948), and the precision of RF (Precision = 0.818) was higher than XGBoost (Precision = 0.8), SVM (Precision = 0.8) and LR (Precision = 0.8). Moreover, RF (AUC = 0.962), SVM (AUC = 0.786) and XGBoost (AUC = 0.867) offered higher AUC values than LR (AUC = 0.77). The RF confirmed size of tract, number of tracts, and puncture correctness as significant risk factors, which was the same as LR model, while the size of tract was the main factor in XGBoost. All machine-learning algorithms yielded better predictions than LR for transfusion among PCNL patients. The importance of machine learning algorithm can give matched insights to domain knowledge on effective and influential factors for transfusion requirement.

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Section: Discussionmentioning

confidence: 70%

Application of Machine-Learning Models in Predicting Transfusion Among Complex Renal Stones Patients Receiving Percutaneous Nephrolithotomy：A Retrospective Study

Yang¹,

Zhi²,

Wang³

et al. 2022

Preprint

View full text Add to dashboard Cite

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“…В таких случаях необходимо проведение Кт-ангиографии, с последующей эмболизацией кровоточащего сосуда [151]. Неудачная пункция, единственная оставшаяся почка, удвоенная почка, множественные камни, длительная операция, инфекция мочевыводящих путей, инфекционные камни и сахарный диабет считаются факторами риска послеоперационных геморрагических осложнений [152][153][154][155]. Несмотря на то, что анатомичная пункция почки является важным этапом, чрезмерное усилие на инструменте при нефроскопии может привести к разрыву почки, увеличивая риск кровотечения.…”

Section: осложненияunclassified

International alliance of Urolithiasis (IAU) guideline on percutaneous nephrolithotomy

et al. 2022

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The International Alliance of Urolithiasis (IAU) would like to release the latest guideline on percutaneous nephrolithotomy (PCNL) and to provide a clinical framework for surgeons performing PCNL. These recommendations were collected and appraised from a systematic review and assessment of the literature covering all aspects of PCNLs from the PubMed database between January 1, 1976, and July 31, 2021. Each generated recommendation was graded using a modified GRADE methodology. The quality of the evidence was graded using a classification system modified from the Oxford Center for Evidence-Based Medicine Levels of Evidence. Forty-seven recommendations were summarized and graded, which covered the following issues, indications and contraindications, stone complexity evaluation, preoperative imaging, antibiotic strategy, management of antithrombotic therapy, anesthesia, position, puncture, tracts, dilation, lithotripsy, intraoperative evaluation of residual stones, exit strategy, postoperative imaging and stone-free status evaluation, complications. The present guideline on PCNL was the first in the IAU series of urolithiasis management guidelines. The recommendations, tips and tricks across the PCNL procedures would provide adequate guidance for urologists performing PCNLs to ensure safety and efficiency in PCNLs.

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“…Kim et al found that correct renal puncture was a significant risk factor for severe hemorrhage requiring angioembolization (27). Ullah et al reported that operating time, female gender, and stone size were the main risk factors (28).…”

Section: Postoperative Large Hematomamentioning

confidence: 99%

Risk Factors for Intra-operative Bleeding in Percutaneous Nephrolithotomy in an Academic Center: A Retrospective Study

et al. 2022

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Background: Percutaneous nephrolithotomy (PNL) is the treatment of choice for renal stones as a safe, effective, and minimally invasive method. However, bleeding remains a major concern in the procedure. Objectives: This study aimed to investigate the risk factors of bleeding in PNL. Methods: This retrospective descriptive cross-sectional study was conducted in the Urology department of Razi hospital. The data of patients with urinary calculi staghorn type who underwent PNL in a prone position under general anesthesia were recorded. A checklist including patients' demographics, surgical characteristics, and outcomes was filled out for each patient. Results: The data from 151 complete files were gathered. The mean age of the cases was 47.89 ± 12.41 years. The mean hemoglobin (Hb) drop was 1.92 ± 1.56 mg/dL. At least 1 mg/dL Hb drop was observed in all cases. The highest Hb drop was 3 mg/dL.). There was no significant relationship between stone bulk, age, BMI, GFR, surgery duration, and the number of tracts, and Hb drop during PNL (P > 0.05). But there was a positive correlation between Urinary Tract Infection (UTI) history (P = 0.01) and transfusion (P = 0.0001) and Hb drop during PNL. Also, the history of open kidney surgery (P = 0.031), nephrostomy insertion (P = 0.003), and extracorporeal shock wave lithotripsy therapy (ESWL) (P = 0.041) were correlated with the increased risk of Hb drop. Conclusions: Urinary tract infection, history of open surgery, nephrostomy implantation, and ESWL were significantly associated with more bleeding in PNL.

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Frequency of Blood Transfusion in Percutaneous Nephrolithotomy

Cited by 8 publications

References 19 publications

Application of Machine-Learning Models in Predicting Transfusion Among Complex Renal Stones Patients Receiving Percutaneous Nephrolithotomy：A Retrospective Study

Application of Machine-Learning Models in Predicting Transfusion Among Complex Renal Stones Patients Receiving Percutaneous Nephrolithotomy：A Retrospective Study

International alliance of Urolithiasis (IAU) guideline on percutaneous nephrolithotomy

Risk Factors for Intra-operative Bleeding in Percutaneous Nephrolithotomy in an Academic Center: A Retrospective Study

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