Can the Abdominal Aortic Atherosclerotic Plaque Index Predict Functional Outcomes after Robot-Assisted Partial Nephrectomy?
Alessandro Veccia,
Emanuele Serafin,
Alessandro Tafuri,
Sarah Malandra,
Bogdan Maris,
Giulia Tomelleri,
Alessandro Spezia,
Enrico Checcucci,
Pietro Piazza,
Severin Rodler,
Loic Baekelandt,
Karl-Friedrich Kowalewski,
Ines Rivero Belenchon,
Mark Taratkin,
Stefano Puliatti,
Pieter De Backer,
Juan Gomez Rivas,
Giovanni Enrico Cacciamani,
Giulia Zamboni,
Paolo Fiorini,
Alessandro Antonelli
Affiliations
Alessandro Veccia
Department of Urology, Azienda Ospedaliera Universitaria Integrata Verona, 37126 Verona, Italy
Emanuele Serafin
Department of Urology, Azienda Ospedaliera Universitaria Integrata Verona, 37126 Verona, Italy
Alessandro Tafuri
Department of Urology, Azienda Ospedaliera Universitaria Integrata Verona, 37126 Verona, Italy
Sarah Malandra
Department of Surgery, Dentistry, Pediatrics and Gynecology, University of Verona, Azienda Ospedaliera Universitaria Integrata (AOUI) Verona, 37126 Verona, Italy
Bogdan Maris
Department of Computer Science, University of Verona, 37126 Verona, Italy
Giulia Tomelleri
Department of Radiology, Azienda Ospedaliera Universitaria Integrata Verona, 37126 Verona, Italy
Alessandro Spezia
Department of Radiology, Azienda Ospedaliera Universitaria Integrata Verona, 37126 Verona, Italy
Enrico Checcucci
Department of Surgery, Candiolo Cancer Institute, FPO-IRCCS, 10060 Turin, Italy
Pietro Piazza
Division of Urology, IRCCS Azienda Ospedaliero-Universitaria di Bologna, 40138 Bologna, Italy
Severin Rodler
Department of Urology, LMU University Hospital, LMU Munich, 81377 Munich, Germany
Loic Baekelandt
Department of Urology, University Hospitals Leuven, 3000 Leuven, Belgium
Karl-Friedrich Kowalewski
Department of Urology, University Medical Center Mannheim, University of Heidelberg, 69117 Mannheim, Germany
Ines Rivero Belenchon
Urology and Nephrology Department, Virgen del Rocío University Hospital, Manuel Siurot s/n, 41013 Seville, Spain
Mark Taratkin
Institute for Urology and Reproductive Health, Sechenov University, 119992 Moscow, Russia
Stefano Puliatti
Department of Urology, University of Modena and Reggio Emilia, 41126 Modena, Italy
Pieter De Backer
ORSI Academy, 9090 Melle, Belgium
Juan Gomez Rivas
Department of Urology, Hospital Clinico San Carlos, 28040 Madrid, Spain
Giovanni Enrico Cacciamani
USC Institute of Urology, University of Southern California, Los Angeles, CA 90007, USA
Giulia Zamboni
Department of Surgery, Dentistry, Pediatrics and Gynecology, University of Verona, Azienda Ospedaliera Universitaria Integrata (AOUI) Verona, 37126 Verona, Italy
Paolo Fiorini
Department of Computer Science, University of Verona, 37126 Verona, Italy
Alessandro Antonelli
Department of Urology, Azienda Ospedaliera Universitaria Integrata Verona, 37126 Verona, Italy
This study aims to evaluate the abdominal aortic atherosclerotic plaque index (API)’s predictive role in patients with pre-operatively or post-operatively developed chronic kidney disease (CKD) treated with robot-assisted partial nephrectomy (RAPN) for renal cell carcinoma (RCC). One hundred and eighty-three patients (134 with no pre- and post-operative CKD (no CKD) and 49 with persistent or post-operative CKD development (post-op CKD)) who underwent RAPN between January 2019 and January 2022 were deemed eligible for the analysis. The API was calculated using dedicated software by assessing the ratio between the CT scan atherosclerotic plaque volume and the abdominal aortic volume. The ROC regression model demonstrated the influence of API on CKD development, with an increasing effect according to its value (coefficient 0.13; 95% CI 0.04–0.23; p = 0.006). The Model 1 multivariable analysis of the predictors of post-op CKD found that the following are independently associated with post-op CKD: Charlson Comorbidity Index (OR 1.31; p = 0.01), last follow-up (FU) Δ%eGFR (OR 0.95; p p = 0.01). Model 2 showed API ≥ 10 as the only factor associated with CKD development (OR 25.2; p = 0.04). The median follow-up was 22 months. Our results demonstrate API to be a strong predictor of post-operative CKD, allowing the surgeon to tailor the best treatment for each patient, especially in those who might be at higher risk of CKD.
