Emergent Percutaneous Nephrostomy for the Diagnosis and Management of Pyonephrosis

Li, Albert C.; Regalado, Sidney

doi:10.1055/s-0032-1326932

Cited by 36 publications

(5 citation statements)

References 20 publications

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“…Yuruk et al [ 32 ] showed that the CT values of pus in pyonephrosis patients were significantly higher than those of patients with hydronephrosis; the difference was statistically significant. Because pus often contains infectious substances, cell debris, and large numbers of micro-organisms, the CT value of the pus is higher than hydronephrosis, and application of CT values based on CT thin-layer scanning to identify hydronephrosis and pyonephrosis yields satisfactory results [ 33 ]. In clinical practice, a significant increase in CT attenuation value of hydronephrosis should raise the possibility of pyonephrosis, and an inadequately prepared endoscopic procedure can easily induce urosepsis.…”

Section: Discussionmentioning

confidence: 99%

Development and validation of a risk-prediction nomogram for patients with ureteral calculi associated with urosepsis: A retrospective analysis

Zhong

Cui

et al. 2018

PLoS ONE

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ObjectivesTo develop and validate an individualized nomogram to predict probability of patients with ureteral calculi developing into urosepsis.MethodsThe clinical data of 747 patients with ureteral calculi who were admitted from June 2013 to December 2015 in Affiliated Nanhai Hospital of Southern Medical University were selected and included in the development group, while 317 ureteral calculi patients who were admitted from January 2016 to December 2016 were included in the validation group. The independent risk factors of ureteral calculi associated with urosepsis were screened using univariate and multivariate logistic regression analyses. The corresponding nomogram prediction model was drawn according to the regression coefficients. The area under the receiver operating characteristic curves and the GiViTI calibration belts were used to estimate the discrimination and calibration of the prediction model, respectively.ResultsMultivariate logistic regression analysis showed that the five risk factors of gender, mean computed tomography(CT) attenuation value of hydronephrosis, functional solitary kidney, urine white blood cell(WBC) count and urine nitrite were independent risk factors of ureteral calculi associated with urosepsis. The areas under the receiver operating characteristic curve of the development group and validation group were 0.913 and 0.874 respectively, suggesting that the new prediction model had good discrimination capacity. P-values of the GiViTI calibration test of the two groups were 0.247 and 0.176 respectively, and the 95% CIs of GiViTI calibration belt in both groups did not cross the diagonal bisector line. Therefore the predicted probability of the model was consistent with the actual probability which suggested that the calibration of the prediction model in both groups were perfect and prediction model had strong concordance performance.ConclusionThe individualized prediction model for patients with ureteral calculi can facilitate improved screening and early identification of patients having higher risk of urosepsis.

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Section: Discussionmentioning

confidence: 99%

Development and validation of a risk-prediction nomogram for patients with ureteral calculi associated with urosepsis: A retrospective analysis

Zhong

Cui

et al. 2018

PLoS ONE

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“…Some clinical issues can be developed by urinary calculi, such as pyonephrosis, UTI with sepsis or renal deterioration (3,4). The persistence of upper urinary tract lithiasis can lead to hydronephrosis, which promotes local kidney complications such as total parenchyma destruction, including infections (5).…”

Section: Introductionmentioning

confidence: 99%

Bacteriological evaluation of the non-struvite nephrolithiasis and its association with urinary tract infections

Maier

Man

Chibelean

et al. 2015

Revista Romana De Medicina De Laborator

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“…Bacteria enters into the bloodstream as a consequence of acute renal empyema, leading to serious infectious disorders such sepsis that may harm the body functions [30].…”

Section: Discussionmentioning

confidence: 99%

Machine learning constructs a diagnostic prediction model for calculous pyonephrosis

Yang,

Zhong,

Yang

et al. 2024

Preprint

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In order to provide decision-making support for the auxiliary diagnosis and individualized treatment of calculous pyonephrosis, the study aims to analyze the clinical features of the condition, investigate its risk factors, and develop a prediction model of the condition using machine learning techniques. A retrospective analysis was conducted on the clinical data of 268 patients with calculous renal pelvic effusion who underwent ultrasonography-guided percutaneous renal puncture and drainage in our hospital during January 2018 to December 2022. The patients were included into two groups, one for pyonephrosis and the other for hydronephrosis. At a random ratio of 7:3, the research cohort was split into training and testing data sets. Single factor analysis was utilized to examine the 43 characteristics of the hydronephrosis group and the pyonephrosis group using the T test, Spearman rank correlation test and chi-square test. Disparities in the characteristic distributions between the two groups in the training and test sets were noted. The features were filtered using the minimal absolute value shrinkage and selection operator on the training set of data. Auxiliary diagnostic prediction models were established using the following five machine learning (ML) algorithms: random forest (RF), xtreme gradient boosting (XGBoost), support vector machines (SVM), gradient boosting decision trees (GBDT) and logistic regression (LR). The area under the curve (AUC) was used to compare the performance, and the best model was chosen. The decision curve was used to evaluate the clinical practicability of the models. The models with the greatest AUC in the training dataset were RF (1.000), followed by XGBoost (0.999), GBDT (0.977), and SVM (0.971). The lowest AUC was obtained by LR (0.938). With the greatest AUC in the test dataset going to GBDT (0.967), followed by LR (0.957), XGBoost (0.950), SVM (0.939) and RF (0.924). LR, GBDT and RF models had the highest accuracy were 0.873, followed by SVM, and the lowest was XGBoost. Out of the five models, the LR model had the best sensitivity and specificity is 0.923 and 0.887. The GBDT model had the highest AUC among the five models of calculous pyonephrosis developed using the ML, followed by the LR model. The LR model was considered be the best prediction model when combined with clinical operability. As it comes to diagnosing pyonephrosis, the LR model was more credible and had better prediction accuracy than common analysis approaches. Its nomogram can be used as an additional non-invasive diagnostic technique.

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Emergent Percutaneous Nephrostomy for the Diagnosis and Management of Pyonephrosis

Cited by 36 publications

References 20 publications

Development and validation of a risk-prediction nomogram for patients with ureteral calculi associated with urosepsis: A retrospective analysis

Development and validation of a risk-prediction nomogram for patients with ureteral calculi associated with urosepsis: A retrospective analysis

Bacteriological evaluation of the non-struvite nephrolithiasis and its association with urinary tract infections

Machine learning constructs a diagnostic prediction model for calculous pyonephrosis

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