ArticleWorld journal of urology2025
Enhancing surgical efficiency: predicting same-day cancellations in urologic procedures.
Article in World journal of urology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to 2 registered trials, which are not on this map. Cited by 1 paper.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Pilot Study of Patient Navigation Intervention to Improve Follow-up Care for Patients With Urinary Stone Disease
Reducing No-Shows in High-Risk Urology Patients Through Enhanced Preoperative Outreach
Who cites it
1 citing paper in PubMed.
- Predictive Utility of Epic's Health Composite Score for Same-Day Cancellation in Cataract Surgery.Clinical ophthalmology (Auckland, N.Z.) · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
Abstract
purposeDay-of-surgery (DOS) cancellations negatively impact healthcare efficiency and patient outcomes. Early identification of at-risk patients is essential for timely perioperative interventions. This study aims to develop and evaluate a predictive model for DOS cancellations in urologic surgeries using a comprehensive set of electronic health record (EHR) derived variables, including social determinants of health (SDOH).
methodsThis retrospective cohort study examined adult patients scheduled for non-emergent urologic procedures at Zuckerberg San Francisco General Hospital (ZSFGH), a major safety-net institution. EHR data from January 2021 to June 2023 included demographics, social determinants of health, clinical history, and healthcare utilization. A lasso-optimized logistic regression model was used to predict DOS cancellations, with performance evaluated via area under the curve (AUC) of the receiver operating characteristic curve (ROC), accuracy, predictive values, sensitivity, specificity, and kappa at a 0.4 threshold to prioritize high-risk identification. Internal validation was conducted using cross-validation.
resultsOf the 778 patients identified as undergoing elective urologic procedures, 136 (17.5%) had a DOS cancellation. Our prediction model consisted of 13 variables selected by optimizing the penalty (λ) of the lasso to achieve maximal accuracy. When evaluating the model on the validation data, it performed with an AUC of 0.80 [CI: 0.70-0.89], 83% accuracy, and a kappa of 0.20. The model also had 81% sensitivity and 55% specificity with a cut-off threshold of 0.4.
conclusionThis is the first study to integrate comprehensive SDOH data into a risk score model for predicting DOS cancellations. With an AUC of 0.80, the model offers a novel tool to help identify high-risk patients of DOS cancellation and guide preoperative interventions. However, further studies assessing the external validity of our model at other clinical sites are necessary.
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Registered trials
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