Albumin-globulin ratio: a novel predictor of sepsis after flexible ureteroscopy in patients with solitary proximal ureteral stones

Sepsis prevalence and associated hospital admission and mortality after ureteroscopy in employed adults

OBJECTIVE

To determine 30-day inpatient mortality, intensive care unit (ICU) admissions, inpatient admissions/readmissions, and yearly trends in sepsis prevalence and inpatient mortality after ureteroscopy (URS) in employed adults.

MATERIALS AND METHODS

We performed a retrospective analysis of the IBM MarketScan Commercial Database to identify employed adults aged 18-64 years who underwent URS between 2015 and 2019. Patients were categorized as having no sepsis (controls), non-severe sepsis, or severe sepsis within 30 days of URS. The main outcomes included inpatient mortality, ICU admissions, inpatient admissions, readmissions, and annual rates of sepsis and associated inpatient mortality.

RESULTS

Among 109 496 patients undergoing URS, 5.6% developed sepsis (4.1% non-severe, 1.5% severe). The 30-day inpatient mortality rates were 0.03%, 0.3% and 2.5% for controls, non-severe sepsis and severe sepsis, respectively (P < 0.001). In a multivariable analysis, diagnosis of sepsis regardless of severity (hazard ratio [HR] 17.2, 95% confidence interval [CI] 10.5-28.1; P < 0.001) or severe sepsis (HR 49.5, 95% CI 28.9-84.7; P < 0.001) increased the risk of 30-day inpatient mortality compared to no sepsis (controls). ICU admissions on the day of procedure (1.5%, 19.8% and 52.4%), inpatient admission rates (18.3%, 74.9% and 76.9%) and readmission rates (7.1%, 12.0% and 15.9%) were higher with severe sepsis and non-severe sepsis vs controls (all P < 0.001). During the study period, the prevalence of sepsis after URS increased from 4.7% to 6.6% (P < 0.001), while the associated mortality rate decreased from 0.7% to 0.2% (P < 0.001).

CONCLUSION

Among working adults aged 18-64 years, sepsis after URS increases the risk of 30-day inpatient mortality, ICU and hospital admission, and hospital readmission. Although the prevalence of sepsis after URS is increasing over time, associated mortality rates are declining. Urologists should be aware of the potentially deadly consequences of sepsis after URS in younger patients.

Development and Validation of the Prediction Model of Sepsis in Patients After Percutaneous Nephrolithotomy and Sepsis Progresses to Septic Shock

Background: To study the predictors of sepsis and the progression of sepsis to septic shock in patients after percutaneous nephrolithotomy (PCNL) and to establish and validate predictive models. Methods: The patients were assigned to either the development cohort or the validation cohort depending on their hospital. In the development cohort, univariate and multivariate logistic regression analyses were used to screen independent risk factors for sepsis after PCNL and sepsis progression to septic shock. Nomogram prediction models were established according to the related independent risk factors. Areas under the receiver operating characteristic curves, calibration plots, and decision curve analysis (DCA) were used to estimate the discrimination, calibration, and clinical usefulness of the prediction models, respectively. The two sets of models were further validated on the validation cohort. Results: In the development cohort, the risk factors for sepsis after PCNL were diabetes, urine nitrite, staghorn calculi, HU value, albumin-globulin ratio, and high-sensitivity C-reactive protein/albumin ratio. The pre- and postoperative white blood cell counts were risk factors for the progression of sepsis to septic shock. The area under the ROC curve value for predicting sepsis risk was 0.891 and that for predicting septic shock risk was 0.981 in the development cohort; in the validation cohort, these values were 0.893 and 0.996, respectively. In the development cohort, the calibration test p values in the sepsis and septic shock cohorts were 0.946 and 0.634, respectively; in the validation cohort, these values were 0.739 and 0.208, respectively. DCA of the model in the sepsis and septic shock cohorts showed threshold probabilities of 10%-90% in the development cohort; in the validation cohort, these values were 10%-90%. Conclusion: The individualized nomogram prediction models can help improve the early identification of patients who are at higher risk of developing sepsis after PCNL and the progression of sepsis to septic shock to avoid further damage.

Albumin Level is Associated with Short-Term and Long-Term Outcomes in Sepsis Patients Admitted in the ICU: A Large Public Database Retrospective Research

Objective

This study aimed to explore the relationship between albumin level with short- and long-term outcomes in sepsis patients admitted in the intensive care unit (ICU) based on a large public database to provide clinical evidence for physicians to make individualized plans of albumin supplementation.

Methods

Sepsis patients admitted in the ICU in MIMIC-IV were included. Different models were performed to investigate the relationships between albumin and mortalities of 28-day, 60-day, 180-day and 1-year. Smooth fitting curves were performed.

Results

A total of 5357 sepsis patients were included. Mortalities of 28-day, 60-day, 180-day and 1-year were 29.29% (n = 1569), 33.92% (n = 1817), 36.70% (n = 1966) and 37.71% (n = 2020). In the fully adjusted model (adjusted for all potential confounders), with each 1g/dL increment in albumin level, the risk of mortality in 28-day, 60-day, 180-day and 1-year decreased by 39% (OR = 0.61, 95% CI: 0.54-0.69), 34% (OR = 0.66, 95% CI: 0.59-0.73), 33% (OR = 0.67, 95% CI: 0.60-0.75), and 32% (OR = 0.68, 95% CI: 0.61-0.76), respectively. The non-linear negative relationships between albumin and clinical outcomes were confirmed by smooth fitting curves. The turning point of albumin level was 2.6g/dL for short- and long-term clinical outcomes. When albumin level ≤2.6g/dL, with each 1g/dL increment in albumin level, the risk of mortality in 28-day, 60-day, 180-day and 1-year decreased by 59% (OR = 0.41, 95% CI: 0.32-0.52), 62% (OR = 0.38, 95% CI: 0.30-0.48), 65% (OR = 0.35, 95% CI: 0.28-0.45), and 62% (OR = 0.38, 95% CI: 0.29-0.48), respectively.

Conclusion

Albumin level was associated with short- and long-term outcomes in sepsis. Albumin supplementation might be beneficial for septic patients with serum albumin<2.6g/dL.

Early predicting 30-day mortality in sepsis in MIMIC-III by an artificial neural networks model

OBJECTIVE

Early identifying sepsis patients who had higher risk of poor prognosis was extremely important. The aim of this study was to develop an artificial neural networks (ANN) model for early predicting clinical outcomes in sepsis.

METHODS

This study was a retrospective design. Sepsis patients from the Medical Information Mart for Intensive Care-III (MIMIC-III) database were enrolled. A predictive model for predicting 30-day morality in sepsis was performed based on the ANN approach.

RESULTS

A total of 2874 patients with sepsis were included and 30-day mortality was 29.8%. The study population was categorized into the training set (n = 1698) and validation set (n = 1176) based on the ratio of 6:4. 11 variables which showed significant differences between survivor group and nonsurvivor group in training set were selected for constructing the ANN model. In training set, the predictive performance based on the area under the receiver-operating characteristic curve (AUC) were 0.873 for ANN model, 0.720 for logistic regression, 0.629 for APACHEII score and 0.619 for SOFA score. In validation set, the AUCs of ANN, logistic regression, APAHCEII score, and SOFA score were 0.811, 0.752, 0.607, and 0.628, respectively.

CONCLUSION

An ANN model for predicting 30-day mortality in sepsis was performed. Our predictive model can be beneficial for early detection of patients with higher risk of poor prognosis.

A Novel, Simple, and Low-Cost Approach for Machine Learning Screening of Kidney Cancer: An Eight-Indicator Blood Test Panel with Predictive Value for Early Diagnosis

Clear cell renal cell carcinoma (ccRCC) accounts for more than 90% of all renal cancers. The five-year survival rate of early-stage (TNM 1) ccRCC reaches 96%, while the advanced-stage (TNM 4) is only 23%. Therefore, early screening of patients with renal cancer is essential for the treatment of renal cancer and the long-term survival of patients. In this study, blood samples of patients were collected and a pre-defined set of blood indicators were measured. A random forest (RF) model was established to predict based on each indicator in the blood, and was trained with all relevant indicators for comprehensive predictions. In our study, we found that there was a high statistical significance (p < 0.001) for all indicators of healthy individuals and early cancer patients, except for uric acid (UA). At the same time, ccRCC also presented great differences in most blood indicators between males and females. In addition, patients with ccRCC had a higher probability of developing a low ratio of albumin (ALB) to globulin (GLB) (AGR < 1.2). Eight key indicators were used to classify and predict renal cell carcinoma. The area under the receiver operating characteristic (ROC) curve (AUC) of the eight-indicator model was as high as 0.932, the sensitivity was 88.2%, and the specificity was 86.3%, which are acceptable in many applications, thus realising early screening for renal cancer by blood indicators in a simple blood-draw physical examination. Furthermore, the composite indicator prediction method described in our study can be applied to other clinical conditions or diseases, where multiple blood indicators may be key to enhancing the diagnostic potential of screening strategies.

A model for sepsis prediction after retrograde intrarenal surgery and the use of the preoperative/postoperative white blood cell ratio to predict progression from sepsis to septic shock

OBJECTIVES

To study the predictors of sepsis and progression to septic shock after RIRS; to establish and validate predictive models accordingly.

METHODS

In total, 1220 patients were included in the study during. Eight hundred forty-eight patients were assigned to the development cohort and 372 to the validation cohort according to medical record. Univariate and multivariate logistic regression analyses were used to screen independent risk factors for post-RIRS (Retrograde intrarenal surgery) sepsis and progression to septic shock. Nomogram prediction models were established according to the related independent risk factors. Areas under the receiver operating characteristic curves, calibration plots, and DCA (Decision curve analysis) were used to estimate the discrimination, calibration and clinical usefulness of the prediction model, respectively.

RESULTS

In the development cohort, sepsis occurred in 59 patients, 16 of whom developed septic shock. Multivariate logistic regression analyses showed that the independent risk factors for sepsis after RIRS were preoperative D-J stent implantation, hydronephrosis > 6.25 HU (Hounsfield units), AGR (Albumin/globulin ratio) < 1.95, hs-CRP/Alb (High-sensitivity C-reactive protein/albumin ratio) > 0.060, operating time > 67.5 min, and urinary nitrite positivity. The preoperative/postoperative WBC ratio > 1.5 was an independent risk factor for progression from sepsis to septic shock. In the development cohort, the AUC (Area under curve) for predicting sepsis risk was 0.845, and the AUC for predicting septic shock risk was 0.896; in the validation cohort, the corresponding values were 0.896 and 0.974, respectively. In the development cohort, the calibration test P values in the sepsis and septic shock cohorts, respectively, were 0.921 and 0.817; in the validation cohort, these values were 0.882 and 0.859. DCA of the model in the sepsis and septic shock cohorts showed threshold probabilities of 10-90% in the development cohort and 10-50% and 10-20% in the validation cohort.

CONCLUSION

These individualized nomogram prediction models can improve the early identification of patients at risk for developing sepsis after RIRS or progressing from sepsis to septic shock.

Construction and validation of an early warning model for predicting the acute kidney injury in elderly patients with sepsis

BACKGROUND

Sepsis-induced acute kidney injury (S-AKI) is a significant complication and is associated with an increased risk of mortality, especially in elderly patients with sepsis. However, there are no reliable and robust predictive models to identify high-risk patients likely to develop S-AKI. We aimed to develop a nomogram to predict S-AKI in elderly sepsis patients and help physicians make personalized management within 24 h of admission.

METHODS

A total of 849 elderly sepsis patients from the First Affiliated Hospital of Xi'an Jiaotong University were identified and randomly divided into a training set (75%, n = 637) and a validation set (25%, n = 212). Univariate and multivariate logistic regression analyses were performed to identify the independent predictors of S-AKI. The corresponding nomogram was constructed based on those predictors. The calibration curve, receiver operating characteristics (ROC)curve, and decision curve analysis were performed to evaluate the nomogram. The secondary outcome was 30-day mortality and major adverse kidney events within 30 days (MAKE30). MAKE30 were a composite of death, new renal replacement therapy (RRT), or persistent renal dysfunction (PRD).

RESULTS

The independent predictors for nomogram construction were mean arterial pressure (MAP), serum procalcitonin (PCT), and platelet (PLT), prothrombin time activity (PTA), albumin globulin ratio (AGR), and creatinine (Cr). The predictive model had satisfactory discrimination with an area under the curve (AUC) of 0.852-0.858 in the training and validation cohorts, respectively. The nomogram showed good calibration and clinical application according to the calibration curve and decision curve analysis. Furthermore, the prediction model had perfect predictive power for predicting 30-day mortality (AUC = 0.813) and MAKE30 (AUC = 0.823) in elderly sepsis patients.

CONCLUSION

The proposed nomogram can quickly and effectively predict S-AKI risk in elderly sepsis patients within 24 h after admission, providing information for clinicians to make personalized interventions.

The Predictive Value of Preoperative Albumin–Globulin Ratio for Systemic Inflammatory Response Syndrome After Percutaneous Nephrolithotomy

Purpose

Methods

Results

Conclusion

Predictors and Healthcare Utilization of Sepsis Post-Ureteroscopy in a US-Based Population : Results from the Endourological Society TOWER Collaborative

Purpose To investigate the incidence, predictive factors, and healthcare utilization of sepsis post-ureteroscopy (URS) in patients enrolled in commercial insurance plans. Materials and Methods A retrospective claims analysis was conducted using the IBM® MarketScan® commercial database. Patients ≥18 years were included if they had URS between January 2015-October 2019 and developed sepsis within 30 days of URS. Multivariate logistic regression was used to identify various clinical and demographic predictors of sepsis post-URS. All-cause healthcare utilization (i.e., inpatient admissions and intensive care unit stays) and all-cause healthcare costs up to 1 month post-septic event were measured. Results Among the 104,100 URS patients meeting the inclusion criteria, 5.5% developed sepsis. Patients with diabetes (OR=1.52; p<0.0001), older age (age 55-64 versus 18-34; OR=1.35; p<0.0001), baseline sepsis (OR=3.51; p<0.0001), baseline inpatient visits (OR=1.17; p=0.0012), and higher Elixhauser comorbidity scores (OR=1.09; p<0.0001) had a significantly higher likelihood of developing sepsis post-URS. In septic patients, 94.8% required inpatient care and 35% were admitted to the ICU. Mean hospital stay for septic patients was 6.86 days. Average all-cause healthcare cost per patient at 1 month in the septic cohort was $49,625 versus $17,782 in the non-septic cohort indicating an incremental all-cause cost of $31,843 (p<0.0001). Conclusions A total of 5.5% of commercially insured patients undergoing URS developed sepsis post-URS. Diabetes, older age, baseline sepsis, baseline inpatient visit, and higher comorbidity score were all found to be independent predictors of post-URS sepsis. Patients with sepsis post-URS had higher healthcare utilization and costs indicating that sepsis is both a significant clinical and economic event.

Ureteral calculi lithotripsy for single ureteral calculi: can DNN-assisted model help preoperatively predict risk factors for sepsis?

OBJECTIVES

To explore the utility of radiomics and deep learning model in assessing the risk factors for sepsis after flexible ureteroscopy lithotripsy (FURL) or percutaneous nephrolithotomy (PCNL) in patients with ureteral calculi.

METHODS

This retrospective analysis included 847 patients with treatment-naive proximal ureteral calculi who received FURL or PCNL. All participants were preoperatively conducted non-contrast computed tomography scans, and relevant clinical information was meanwhile collected. After propensity score matching, the radiomics model was established to predict the onset of sepsis. A deep learning model was also adapted to further improve the prediction accuracy. Performance of these trained models was verified in another independent external validation set including 40 cases of ureteral calculi patients.

RESULTS

The overall incidence of sepsis after FURL or PCNL was 5.9%. The least absolute shrinkage and selection operator (LASSO) regression analysis revealed 26 predictive variables, with an overall AUC of 0.881 (95% CI, 0.813-0.931) and an AUC of 0.783 (95% CI, 0.766-0.801) in external validation cohort. Judicious adaption of a deep neural network (DNN) model to our dataset improved the AUC to 0.920 (95% CI, 0.906-0.933) in the internal validation. To eliminate the overfitting, external validation was carried out for DNN model (AUC = 0.874 (95% CI, 0.858-0.891)).

CONCLUSIONS

The DNN was more effective than the LASSO model in revealing risk factors for sepsis after FURL or PCNL in single ureteral calculi patients, and females are more susceptible to sepsis than males. Deep learning models have the potential to act as gatekeepers to facilitate patient stratification.

KEY POINTS

• Both the least absolute shrinkage and selection operator (LASSO) and deep neural network (DNN) models were shown to be effective in sepsis prediction. • The DNN model achieved superior prediction capability, with an AUC of 0.920 (95% CI, 0.906-0.933). • DNN-assisted model has potential to serve as a gatekeeper to facilitate patient stratification.

Identifying the Risk of Sepsis in Patients With Cancer Using Digital Health Care Records: Machine Learning-Based Approach

BACKGROUND

Sepsis is diagnosed in millions of people every year, resulting in a high mortality rate. Although patients with sepsis present multimorbid conditions, including cancer, sepsis predictions have mainly focused on patients with severe injuries.

OBJECTIVE

In this paper, we present a machine learning-based approach to identify the risk of sepsis in patients with cancer using electronic health records (EHRs).

METHODS

We utilized deidentified anonymized EHRs of 8580 patients with cancer from the Samsung Medical Center in Korea in a longitudinal manner between 2014 and 2019. To build a prediction model based on physical status that would differ between sepsis and nonsepsis patients, we analyzed 2462 laboratory test results and 2266 medication prescriptions using graph network and statistical analyses. The medication relationships and lab test results from each analysis were used as additional learning features to train our predictive model.

RESULTS

Patients with sepsis showed differential medication trajectories and physical status. For example, in the network-based analysis, narcotic analgesics were prescribed more often in the sepsis group, along with other drugs. Likewise, 35 types of lab tests, including albumin, globulin, and prothrombin time, showed significantly different distributions between sepsis and nonsepsis patients (P<.001). Our model outperformed the model trained using only common EHRs, showing an improved accuracy, area under the receiver operating characteristic (AUROC), and F1 score by 11.9%, 11.3%, and 13.6%, respectively. For the random forest-based model, the accuracy, AUROC, and F1 score were 0.692, 0.753, and 0.602, respectively.

CONCLUSIONS

We showed that lab tests and medication relationships can be used as efficient features for predicting sepsis in patients with cancer. Consequently, identifying the risk of sepsis in patients with cancer using EHRs and machine learning is feasible.

A Novel Nomogram for Predicting Post-Operative Sepsis for Patients With Solitary, Unilateral and Proximal Ureteral Stones After Treatment Using Percutaneous Nephrolithotomy or Flexible Ureteroscopy

Background

The postoperative sepsis is a latent fatal complication for both flexible ureteroscopy (fURS) and percutaneous nephrolithotomy (PNL). An effective predictive model constructed by readily available clinical markers is urgently needed to reduce postoperative adverse events caused by infection. This study aims to determine the pre-operative predictors of sepsis in patients with unilateral, solitary, and proximal ureteral stones after fURS and PNL.

Methods

We retrospectively enrolled 910 patients with solitary proximal ureteral stone with stone size 10–20 mm who underwent fURS or PNL from Tongji Hospital's database, including 412 fURS cases and 498 PNL cases. We used the least absolute shrinkage and selection operator (LASSO) regression and multivariate logistic regression analysis to identify the risk factors for sepsis. Finally, a nomogram was assembled utilizing these risk factors.

Results

In this study, 49 patients (5.4%) developed sepsis after fURS or PNL surgery. Lasso regression showed postoperative sepsis was associated with gender (female), pre-operative fever, serum albumin (<35 g/L), positive urine culture, serum WBC (≥10,000 cells/ml), serum neutrophil, positive urine nitrite and operation type (fURS). The multivariate logistic analysis indicated that positive urine culture (odds ratio [OR] = 5.9092, 95% CI [2.6425–13.2140], p < 0.0001) and fURS (OR = 1.9348, 95% CI [1.0219–3.6631], p = 0.0427) were independent risk factors of sepsis and albumin ≥ 35g/L (OR = 0.4321, 95% CI [0.2054–0.9089], p = 0.0270) was independent protective factor of sepsis. A nomogram was constructed and exhibited favorable discrimination (area under receiver operating characteristic curve was 0.78), calibration [Hosmer–Lemeshow (HL) test p = 0.904], and net benefits displayed by decision curve analysis (DCA).

Conclusions

Patients who underwent fURS compared to PNL or have certain pre-operative characteristics, such as albumin <35 g/L and positive urine culture, are more likely to develop postoperative sepsis. Cautious preoperative evaluation and appropriate operation type are crucial to reducing serious infectious events after surgery, especially for patients with solitary, unilateral, and proximal ureteral stones sized 10–20 mm.

Predictive Value of Preoperative High-Sensitive C-reactive Protein (hs-CRP)/Albumin Ratio in Systemic Inflammatory Response Syndrome (SIRS) After Semi-rigid Ureteroscopy

Objective: To determine the predictive value of high-sensitive C-reactive protein (hs-CRP)/albumin ratio in systemic inflammatory response syndrome (SIRS) after semi-rigid ureteroscopy (URS).

Material and Methods: Between April 2021 and October 2021, 148 patients who had ureteral stone treatment with a ureteroscope in our hospital were included. Preoperative hs-CRP/albumin ratio was obtained by dividing the hs-CRP level by the albumin level. High-sensitivity modified Glasgow prognostic score (hs-mGPS) was obtained according to hs-CRP and albumin values. Two groups were identified as post-URS SIRS positive and negative. Inflammation biomarkers were evaluated in groups.

Results: There was a statistically significant difference between groups in terms of preoperative hs-CRP, albumin, and hs-CRP/albumin ratio (p < 0.001, p = 0.003, and p < 0.001, respectively). The optimal cutoff value for the hs-CRP/albumin ratio was 0.04651. While the risk of developing SIRS after surgery was 72.73% in patients with a hs-CRP/albumin ratio higher than 0.04651, the chance of not developing SIRS was 87.5% in patients below this value. The probability of developing SIRS was found to be significantly different in hs-mGPS (p < 0.001).

Conclusion: Our study indicated that hs-CRP/albumin ratio can predict post-URS SIRS. Larger-scale, multicentric prospective studies should certainly be done to validate the predictive value of hs-CRP/albumin ratio in post-URS SIRS.

Clinical Significance of Serum Albumin/Globulin Ratio in Patients With Pyogenic Liver Abscess

Pyogenic liver abscess (PLA) remains a significant challenge for modern clinicians. Serum albumin/globulin ratio (AGR) can reflect the progress of many diseases. However, the clinical significance of AGR in PLA has not been evaluated. The aim of this study was to explore the effect of AGR on the clinical characteristic and prognosis in PLA patients. This retrospective study included 392 PLA patients who admitted to the First Affiliated Hospital of Xi'an Jiaotong University from January, 2007 to December, 2016. The medical records on admission were collected. Compared with the healthy controls and the patients with extraperitoneal infection or non-infectious liver disease, PLA patients had lower levels of AGR. The mean level of AGR in PLA patients was 1.02 ± 0.25. There were 179 (45.4%) patients with AGR > 1.02 and 213 (54.6%) patients with AGR ≤ 1.02. The baseline data and treatment plans of PLA patients with high or low AGR were comparative. However, PLA patients with a low AGR had higher body temperature, leukocytes and neutrophils, lower hemoglobin, poorer liver and coagulation function, larger abscess diameter, higher positive rate of pus culture and proportion of Escherichia coli, and were more susceptible to multiple bacteria. Moreover, PLA patients with a low AGR had more complications, including systemic inflammatory response syndrome (SIRS), peritoneal effusion and pleural effusion. And it also needs longer time for temperature normalization and hospital stay. In conclusion, PLA patients have lower AGR and lower AGR is associated with worse clinical manifestations, more complications and poorer prognosis. Thus, monitoring of AGR is of great clinical significance for evaluating the progress of PLA patients.

Atlas of scoring systems, grading tools and nomograms in Endourology: A comprehensive overview from The TOWER Endourological Society research group

INTRODUCTION

With an increase in the prevalence of kidney stone disease (KSD), there has been a universal drive to develop reliable and user-friendly tools such as grading systems and predictive nomograms. An atlas of scoring systems, grading tools and nomograms in Endourology is provided in this paper.

METHODS

A comprehensive search of world literature was performed to identify nomograms, grading systems and classification tools in endourology related to KSD. Each of these were reviewed by the authors and have been evaluated in a narrative format with details on those which are externally validated and their respective citation count on google scholar.

RESULTS

A total of 54 endourological tools have been described in our atlas of endourological scoring systems, grading tools and nomograms. Of the tools, 23 (43%) are published in the last 3 years showing an increasing interest in this area. This includes 5 for percutaneous nephrolithotomy (PCNL), 6 for flexible ureteroscopy (fURS), 3 for semi-rigid URS (sURS), 9 for shockwave lithotripsy (SWL), 2 for stent encrustations, 3 for intra-operative appearance at the time of URS and 3 to classify intra-operative ureteric injury. There were 3 tools for renal colic assessment, one each for prediction of future stone event, stone classification and stone impaction and 2 for need of emergency intervention in ureteric stone. While 2 tools are related to stone recurrence, 6 are related to post-procedural complications. There are now 2 tools for simulation in endourology and 5 for patient reported outcome measures (PROMS).

CONCLUSIONS

A number of reliable and established tools exist currently in endourology. Each of these offers their own respective advantages and disadvantages. While nomograms and scoring systems can help in the decision making, these must be tailored to individual patients based on their specific clinical scenarios, expectations and informed consent.

Percutaneous Nephrolithotomy Can Reduce the Incidence of Sepsis Compared with Flexible Ureteroscopy in Treating Solitary Proximal Ureteral Stone Patients with Positive Urine Culture

Background

Sepsis is a potentially lethal complication for both flexible ureteroscopy (fURS) and percutaneous nephrolithotomy (PCNL). This study is aimed at comparing the sepsis rate after fURS and PCNL and the risk factors for sepsis in patients with solitary proximal ureteral stone.

Methods

We reviewed the data of patients with calculi between 10 mm to 20 mm who underwent fURS or PCNL surgery from Tongji Hospital's database. A total of 910 patients were eligible with 412 fURS cases and 498 PCNL cases. We used univariate analysis and multivariate logistic regression analysis to identify the risk factors for sepsis. Subgroup analysis was performed using logistic regression analysis.

Results

In the cohort, 27 (6.6%) and 19 (3.8%) patients developed sepsis after fURS and PCNL, respectively. Multivariate analysis shows that the risk factors for sepsis are fURS (OR = 3.160, P = 0.004), serum WBC ≥ 10,000 cells/μL (OR = 3.490, P = 0.008), albumin − globulin ratio < 1.2 (OR = 2.192, P = 0.029), positive urine culture (OR = 6.145, P < 0.001), and prolonged operation time (OR = 1.010, P = 0.046). Subgroup analysis was conducted using potential risk factors: stone size, serum WBC, urine culture, and albumin-globulin ratio (AGR). In subgroup of positive urine culture, patients were more likely to develop sepsis after fURS than PCNL.

Conclusions

PCNL may be a better choice than fURS to reduce postoperative sepsis, especially for patients with positive urine culture.