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Related Experiment Video

Updated: Jan 12, 2026

Retzius-Sparing Robot-Assisted Radical Prostatectomy
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Prediction Model for Same-Day Discharge in Robotic-Assisted Radical Laparoscopic Prostatectomy.

Luca Alfredo Morgantini1, Puja Saha2, Antony Pellegrino1

  • 1Department of Urology, University of Illinois at Chicago, Chicago, Illinois, USA.

Journal of Endourology
|October 30, 2025
PubMed
Summary

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Development of an Artificial Intelligence-Driven Three-Dimensional Reconstruction Model of Renal Vasculature for Surgical Planning in Robot-Assisted Partial Nephrectomy.

The international journal of medical robotics + computer assisted surgery : MRCAS·2026
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Single-port robot-assisted adrenalectomy: technical considerations and a single-center experience.

European journal of surgical oncology : the journal of the European Society of Surgical Oncology and the British Association of Surgical Oncology·2026
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External validation of the preoperative risk evaluation for partial nephrectomy (PREP) score.

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Impact of prostate mpMRI quality on diagnostic accuracy in prostate cancer: a prospective study.

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Safety and efficacy of different surgical approaches in single-port robot-assisted radical prostatectomy based on propensity score matching analysis.

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Feasibility and Safety of Same-say Discharge after Robot-assisted Radical Prostatectomy: A Systematic Review and Meta-analysis.

European urology focus·2026

A new predictive model helps identify patients likely to have a prolonged hospital stay after robotic prostatectomy. This aids in better postoperative care and selecting candidates for same-day discharge.

Area of Science:

  • Urology
  • Surgical Oncology
  • Health Informatics

Background:

  • Robotic-assisted radical prostatectomy (RARP) is a common procedure for prostate cancer.
  • Predicting hospital stay duration post-RARP is crucial for optimizing patient care and resource allocation.
  • Current methods for identifying patients at risk for prolonged hospitalization are limited.

Purpose of the Study:

  • To develop and validate a predictive model for prolonged hospital stay after RARP.
  • To identify key preoperative factors influencing hospital stay duration.
  • To assist in selecting suitable candidates for same-day discharge after RARP.

Main Methods:

  • Retrospective analysis of patients undergoing RARP (January 2013 - December 2022).
  • Inclusion of preoperative variables: age, BMI, comorbidities, PSA, Gleason score, surgical approach.
Keywords:
nomogramspatient dischargepostoperative careprostatectomyrobotics

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  • Logistic regression model developed to predict hospital stay > 24 hours; performance assessed via ROC AUC and cross-validation.

    • Main Results:

    • Significant predictors of prolonged stay: BMI, PSA, Gleason score, surgical approach, and comorbidities.
    • Transperitoneal approach associated with increased odds of prolonged stay (OR 4.23, p < 0.00001).
    • Cross-validated accuracy of the predictive model was 73.7%.

    Conclusions:

    • The developed nomogram accurately predicts prolonged hospital stay risk post-RARP.
    • Informed surgical planning, patient counseling, and postoperative management can be enhanced.
    • The model can aid in identifying same-day discharge candidates and optimizing hospital resource use.