PRE-FRAIL - Decoding and Preventing Frailty After Robotic-assisted Surgery - Investigating Biomarkers and Clinical Outcomes in Urinary Tract Cancer Patients: Findings from a retrospective cohort study
Translated title
PRE-FRAIL - Afdækning og Forebyggelse af Skrøbelighed Efter Robotassisteret Kirurgi - Undersøgelse af Biomarkører og Kliniske Udfald hos Patienter med Urinvejskræft: Fund fra et retrospektivt kohortestudie
Term
5. Term (Master thesis)
Education
Publication year
2025
Submitted on
2025-12-05
Pages
36
Abstract
Background and Objective: Bladder cancer primarily affects older, comorbid, and frail patients, making radical cystectomy a high-risk procedure with potential for major com- plications. Previous studies using 2D CT analyses have suggested correlations between body composition and postoperative outcomes, but readmissions, a secondary concern, may also place additional strain on healthcare resources. This study explores whether 3D AI-based analysis of preoperative CT scans can use body composition (CTBC) parameters to predict patients at higher risk of complications or readmission in patients undergoing robotic-assisted radical cystectomy at AaUH in 2020-2025. Methods: This study included 134 patients with bladder cancer (BC) undergoing robotic- assisted radical cystectomy. Logistic univariate regression was used to assess the asso- ciation between CTBC-parameters and major complications (defined by a Clavien–Dindo classification at or above grade 3), or readmission rate. Finally, LASSO regression and ROC curves were performed. Results: In relation to major complications, univariable-adjusted logistic regression showed no significant associations between CTBC parameters and major complications. Conse- quently, the LASSO regression and the corresponding ROC yielded an AUC of 0.5. In the univariable adjusted logistic regression for readmissions, intramuscular adipose tissue index (IMATI) was found to be associated with readmission [OR, 1.11; 95% CI, 1.03- 1.21; P = 0.01]. No association was found between readmission rate and the remaining measured CTBC-parameters. Additionally, the LASSO regression and the corresponding ROC curve yielded an AUC of 0.676. Conclusion: This study found no associations between CTBC-parameters and major com- plications. LASSO found an AUC of 0.5, which correlates to random chance. Furthermore, IMATI was statistically associated with readmission. However, LASSO regression and the corresponding ROC yielded an AUC of 0.676, which was deemed clinically insignificant. A larger sample size may be necessary to find a possible association and predictive vari- ables between 3D-measured CTBC-parameters and major complications or readmission in BC patients undergoing robotic-assisted cystectomy.
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