International urology and nephrology · 2025
Development of a nomogram for prediction of postoperative bleeding after transurethral resection of bladder tumors.
СтатьяНазвание и аннотация: Перевод в очереди
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Pellegrino Paloma, Scherer Thomas Paul, Wettstein Marian Severin, Baumgartner Melanie, Eberli Daniel, Poyet Cédric, Bieri Uwe
Аннотация
PURPOSE: Postoperative bleeding is a known complication following transurethral resection of bladder tumors (TURBT). Although factors like tumor size and anticoagulant use are associated with increased bleeding risk, no validated prediction tool currently exists. This study aimed to determine predictors and develop a clinical tool for preoperative bleeding risk assessment.
METHODS: We conducted a retrospective analysis of patients undergoing TURBT at the University Hospital of Zurich between January 2016 and November 2022. Bleeding events were defined by four criteria: hemoglobin drop of ≥ 4 g/dl, need for transfusion, gross hematuria requiring prolonged irrigation, or surgical revision for bleeding during hospitalization or within 2 weeks after dischargement.
RESULTS: During this time period, 447 TURBTs were performed, of which 54 (12.1%) were associated with a bleeding event. Univariable logistic regression identified five significant predictors (p value < 0.05): HAS-BLED score, high-risk alcohol consumption, use of oral anticoagulant and/or platelet aggregation inhibitors, and bridging therapy. In the multivariable logistic regression model, only high-risk alcohol consumption and platelet aggregation inhibitors remained statistically significant (p value < 0.05). All predictors had positive coefficients, indicating increased bleeding risk. A nomogram was developed based on these variables to assist in preoperative risk assessment. The model showed good calibration (Brier score = 0.097) and strong overall fit (Likelihood Ratio Test: χ2 = 31.08, df = 5, p < 0.0001).
CONCLUSION: We developed a predictive model for bleeding risk after TURBT. This tool may support perioperative management, by raising awareness of risk factors for bleeding complications and enabling tailored interventions for at-risk patients.
METHODS: We conducted a retrospective analysis of patients undergoing TURBT at the University Hospital of Zurich between January 2016 and November 2022. Bleeding events were defined by four criteria: hemoglobin drop of ≥ 4 g/dl, need for transfusion, gross hematuria requiring prolonged irrigation, or surgical revision for bleeding during hospitalization or within 2 weeks after dischargement.
RESULTS: During this time period, 447 TURBTs were performed, of which 54 (12.1%) were associated with a bleeding event. Univariable logistic regression identified five significant predictors (p value < 0.05): HAS-BLED score, high-risk alcohol consumption, use of oral anticoagulant and/or platelet aggregation inhibitors, and bridging therapy. In the multivariable logistic regression model, only high-risk alcohol consumption and platelet aggregation inhibitors remained statistically significant (p value < 0.05). All predictors had positive coefficients, indicating increased bleeding risk. A nomogram was developed based on these variables to assist in preoperative risk assessment. The model showed good calibration (Brier score = 0.097) and strong overall fit (Likelihood Ratio Test: χ2 = 31.08, df = 5, p < 0.0001).
CONCLUSION: We developed a predictive model for bleeding risk after TURBT. This tool may support perioperative management, by raising awareness of risk factors for bleeding complications and enabling tailored interventions for at-risk patients.