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

Nomogram and scoring system for preoperative prediction of the risk of systemic inflammatory response syndrome in one-stage flexible ureteroscopy lithotripsy

Background

Flexible ureteroscopy lithotripsy (FURL) is a prevalent intervention for the management of upper urinary tract stones (UUTS). Assessing the onset of systemic inflammatory response syndrome (SIRS) in patients during and postoperatively is a critical determinant in the decision-making process regarding the necessity of preoperative ureteral stenting prior to FURL.

Materials and methods

A total of 340 patients with UUTS who underwent one-stage FURL were analyzed retrospectively. Least absolute shrinkage and selection operator and multivariate logistic regression analysis were used to screen out independent risk factors, subsequently developing a nomogram. The predictive performance was internally assessed using the concordance index (C-index), receiver operating characteristic curve, and calibration curve. Additionally, we evaluated the risk of SIRS in the context of one-stage FURL, considering the impact of various available variables.

Results

Age, urinary white blood cells, urine bacterial culture, and systemic immune-inflammation index (SII) were integrated to establish a nomogram for prediction of the risk of SIRS in patients undergoing one-stage FURL. The SII exhibited the highest odds ratio (OR = 30.356) for SIRS. The nomogram demonstrated a favorable predictive performance with a C-index of 0.964 (95% CI = 0.932-0.996), an area under the curve of 0.935, and a calibration curve validating its accuracy. We further developed a scoring system and classified the risk of SIRS into four grades.

Conclusion

The developed nomogram and risk scoring system demonstrate favorable predictive ability and clinical serviceability for the personalized estimation of SIRS risk in UUTS patients undergoing one-stage FURL. It is advisable to place a ureteral stent prior to FURL in individuals with an SII exceeding 1,300 and meeting one of the following criteria: age > 60 years, urinary white blood cell levels of 1+/2+/3+, or positive urine bacterial culture. The insights provided may assist clinicians in selecting safer therapeutic approaches for UUTS patients.

predictive model of nosocomial infection in patients with upper urinary tract stones after flexible ureterorenoscopy with laser lithotripsy: A retrospective study

Objectives

To construct a predictive model of nosocomial infection in patients with upper urinary tract (UUT) stones after flexible ureterorenoscopy with laser lithotripsy (FURSLL).

Methods

Medical records of 196 patients with UUT stones who underwent FURSLL in Suzhou Hospital of Integrated Traditional Chinese and Western Medicine from December 2019 to December 2022 were retrospectively analyzed. Patients were divided into infected group or uninfected group based on the presence of infection during postoperative hospitalization. Univariate and multivariate logistic regressions were used to identify risk factors of postoperative nosocomial infections. A nomogram prediction model was constructed using R software. The predictive ability of the model was assessed using the receiver operating characteristic (ROC) curve.

Results

A total of 54 patients (27.6%) developed nosocomial infections after FURSLL. Logistic regression analysis showed that older age, diabetes, preoperative urinary system infection, ureteral stricture, hydronephrosis, double J-stent retention time, and stone diameter were risk factors of nosocomial infection. The nomogram model was constructed based on these risk factors. The ROC showed that the area under the curve (AUC) of the model was 0.930 (95% CI: 0.890-0.970), and the sensitivity and specificity were 92.6% and 81.7%, respectively, indicating that the prediction model was effective.

Conclusions

Risk of nosocomial infection in patients with UUT stones after FURSLL is affected by older age, diabetes, preoperative urinary system infection, ureteral stenosis, hydronephrosis, double J-stent retention time, and stone diameter. The nomogram prediction model, constructed based on the above factors, has good predictive value.

Identification of two robust subclasses of sepsis with both prognostic and therapeutic values based on machine learning analysis

Background

Sepsis is a heterogeneous syndrome with high morbidity and mortality. Optimal and effective classifications are in urgent need and to be developed.

Methods and results

A total of 1,936 patients (sepsis samples, n=1,692; normal samples, n=244) in 7 discovery datasets were included to conduct weighted gene co-expression network analysis (WGCNA) to filter out candidate genes related to sepsis. Then, two subtypes of sepsis were classified in the training sepsis set (n=1,692), the Adaptive and Inflammatory, using K-means clustering analysis on 90 sepsis-related features. We validated these subtypes using 617 samples in 5 independent datasets and the merged 5 sets. Cibersort method revealed the Adaptive subtype was related to high infiltration levels of T cells and natural killer (NK) cells and a better clinical outcome. Immune features were validated by single-cell RNA sequencing (scRNA-seq) analysis. The Inflammatory subtype was associated with high infiltration of macrophages and a disadvantageous prognosis. Based on functional analysis, upregulation of the Toll-like receptor signaling pathway was obtained in Inflammatory subtype and NK cell-mediated cytotoxicity and T cell receptor signaling pathway were upregulated in Adaptive group. To quantify the cluster findings, a scoring system, called, risk score, was established using four datasets (n=980) in the discovery cohorts based on least absolute shrinkage and selection operator (LASSO) and logistic regression and validated in external sets (n=760). Multivariate logistic regression analysis revealed the risk score was an independent predictor of outcomes of sepsis patients (OR [odds ratio], 2.752, 95% confidence interval [CI], 2.234-3.389, P<0.001), when adjusted by age and gender. In addition, the validation sets confirmed the performance (OR, 1.638, 95% CI, 1.309-2.048, P<0.001). Finally, nomograms demonstrated great discriminatory potential than that of risk score, age and gender (training set: AUC=0.682, 95% CI, 0.643-0.719; validation set: AUC=0.624, 95% CI, 0.576-0.664). Decision curve analysis (DCA) demonstrated that the nomograms were clinically useful and had better discriminative performance to recognize patients at high risk than the age, gender and risk score, respectively.

Conclusions

In-depth analysis of a comprehensive landscape of the transcriptome characteristics of sepsis might contribute to personalized treatments and prediction of clinical outcomes.