Donato Cannoletta, Elio Mazzone, Paolo Dell'Oglio, Greta Pettenuzzo, Matteo Pacini, Luca Lambertini, Antony Angelo Pellegrino, Ruben Calvo Sauer, Juan R Torres-Anguiano, Armando Stabile, Francesco Pellegrino, Giorgio Gandaglia, Riccardo Bartoletti, Andrea Minervini, Alessandro Antonelli, Francesco Montorsi, Alberto Briganti, Simone Crivellaro
{"title":"针对使用机器人平台治疗的前列腺癌患者的新型合并症评分的开发和验证及其对 DaVinci 单孔系统的影响。","authors":"Donato Cannoletta, Elio Mazzone, Paolo Dell'Oglio, Greta Pettenuzzo, Matteo Pacini, Luca Lambertini, Antony Angelo Pellegrino, Ruben Calvo Sauer, Juan R Torres-Anguiano, Armando Stabile, Francesco Pellegrino, Giorgio Gandaglia, Riccardo Bartoletti, Andrea Minervini, Alessandro Antonelli, Francesco Montorsi, Alberto Briganti, Simone Crivellaro","doi":"10.1007/s11701-024-02152-w","DOIUrl":null,"url":null,"abstract":"<p><p>To develop and validate a novel Comorbidity score for Robotic Surgery (CRS) in predicting severe complications after robot-assisted radical prostatectomy (RARP). Furthermore, we investigated the impact of the surgical platform (Multi-Port - MP vs Single-Port - SP) according to this score. We included 2085 (\"development cohort\") and 595 (\"validation cohort\") patients undergoing RARP at two tertiary referral centers between 2014 and March 2024 in a retrospective study. Statistical analyses included validation of the Charlson Comorbidity Index (CCI) to predict 30-day severe complications (Clavien-Dindo ≥ 3a), development and external validation of CRS using calibration plots and decision curve analysis. Lastly, locally weighted scatterplot smoothing (LOWESS) analysis was used to graphically explore the impact of the robotic platform according to novel CRS. CCI exhibited limited predictive ability for severe complications (60% in the validation cohort). In multivariable logistic regression analyses testing the correlation between each condition included in CCI and severe complications, diabetes and myocardial infarction resulted as independent predictors (OR 1.75 [95%CI 1.05-2.82]; OR 1.92 [95%CI 1.26-2.88]) and were subsequently fitted into a multivariable logistic model including age, previous abdominal surgery and obesity (BMI > 30). The resulting predictive model demonstrated superior discrimination and clinical net benefit in predicting severe complications compared to CCI (AUC 64 vs 60%). At LOWESS analysis, SP platform was associated with lower risk of severe complications as CRS increased compared to MP system. The validated CRS showed better accuracy compared to CCI in predicting severe complications after RARP. Additionally, the use of SP robotic platform may reduce the risk of severe complications in highly comorbid patients according to CRS.</p>","PeriodicalId":47616,"journal":{"name":"Journal of Robotic Surgery","volume":"18 1","pages":"400"},"PeriodicalIF":2.2000,"publicationDate":"2024-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Development and validation of a novel comorbidity score specific for prostate cancer patients treated with robotic platform and its implication on DaVinci single-port system.\",\"authors\":\"Donato Cannoletta, Elio Mazzone, Paolo Dell'Oglio, Greta Pettenuzzo, Matteo Pacini, Luca Lambertini, Antony Angelo Pellegrino, Ruben Calvo Sauer, Juan R Torres-Anguiano, Armando Stabile, Francesco Pellegrino, Giorgio Gandaglia, Riccardo Bartoletti, Andrea Minervini, Alessandro Antonelli, Francesco Montorsi, Alberto Briganti, Simone Crivellaro\",\"doi\":\"10.1007/s11701-024-02152-w\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>To develop and validate a novel Comorbidity score for Robotic Surgery (CRS) in predicting severe complications after robot-assisted radical prostatectomy (RARP). Furthermore, we investigated the impact of the surgical platform (Multi-Port - MP vs Single-Port - SP) according to this score. We included 2085 (\\\"development cohort\\\") and 595 (\\\"validation cohort\\\") patients undergoing RARP at two tertiary referral centers between 2014 and March 2024 in a retrospective study. Statistical analyses included validation of the Charlson Comorbidity Index (CCI) to predict 30-day severe complications (Clavien-Dindo ≥ 3a), development and external validation of CRS using calibration plots and decision curve analysis. Lastly, locally weighted scatterplot smoothing (LOWESS) analysis was used to graphically explore the impact of the robotic platform according to novel CRS. CCI exhibited limited predictive ability for severe complications (60% in the validation cohort). In multivariable logistic regression analyses testing the correlation between each condition included in CCI and severe complications, diabetes and myocardial infarction resulted as independent predictors (OR 1.75 [95%CI 1.05-2.82]; OR 1.92 [95%CI 1.26-2.88]) and were subsequently fitted into a multivariable logistic model including age, previous abdominal surgery and obesity (BMI > 30). The resulting predictive model demonstrated superior discrimination and clinical net benefit in predicting severe complications compared to CCI (AUC 64 vs 60%). At LOWESS analysis, SP platform was associated with lower risk of severe complications as CRS increased compared to MP system. The validated CRS showed better accuracy compared to CCI in predicting severe complications after RARP. 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Development and validation of a novel comorbidity score specific for prostate cancer patients treated with robotic platform and its implication on DaVinci single-port system.
To develop and validate a novel Comorbidity score for Robotic Surgery (CRS) in predicting severe complications after robot-assisted radical prostatectomy (RARP). Furthermore, we investigated the impact of the surgical platform (Multi-Port - MP vs Single-Port - SP) according to this score. We included 2085 ("development cohort") and 595 ("validation cohort") patients undergoing RARP at two tertiary referral centers between 2014 and March 2024 in a retrospective study. Statistical analyses included validation of the Charlson Comorbidity Index (CCI) to predict 30-day severe complications (Clavien-Dindo ≥ 3a), development and external validation of CRS using calibration plots and decision curve analysis. Lastly, locally weighted scatterplot smoothing (LOWESS) analysis was used to graphically explore the impact of the robotic platform according to novel CRS. CCI exhibited limited predictive ability for severe complications (60% in the validation cohort). In multivariable logistic regression analyses testing the correlation between each condition included in CCI and severe complications, diabetes and myocardial infarction resulted as independent predictors (OR 1.75 [95%CI 1.05-2.82]; OR 1.92 [95%CI 1.26-2.88]) and were subsequently fitted into a multivariable logistic model including age, previous abdominal surgery and obesity (BMI > 30). The resulting predictive model demonstrated superior discrimination and clinical net benefit in predicting severe complications compared to CCI (AUC 64 vs 60%). At LOWESS analysis, SP platform was associated with lower risk of severe complications as CRS increased compared to MP system. The validated CRS showed better accuracy compared to CCI in predicting severe complications after RARP. Additionally, the use of SP robotic platform may reduce the risk of severe complications in highly comorbid patients according to CRS.
期刊介绍：
The aim of the Journal of Robotic Surgery is to become the leading worldwide journal for publication of articles related to robotic surgery, encompassing surgical simulation and integrated imaging techniques. The journal provides a centralized, focused resource for physicians wishing to publish their experience or those wishing to avail themselves of the most up-to-date findings.The journal reports on advance in a wide range of surgical specialties including adult and pediatric urology, general surgery, cardiac surgery, gynecology, ENT, orthopedics and neurosurgery.The use of robotics in surgery is broad-based and will undoubtedly expand over the next decade as new technical innovations and techniques increase the applicability of its use. The journal intends to capture this trend as it develops.
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