The influence of previous robotic experience in the initial learning curve of laparoscopic radical prostatectomy

Neto, José Anastácio Dias; Dall’Oglio, Marcos F.; Colombo, João Roberto; Coelho, Rafael F.; Nahas, William Carlos

doi:10.1590/s1677-5538.ibju.2016.0526

Cited by 6 publications

(2 citation statements)

References 28 publications

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“…Moreover, Jirapinyo et al 38 showed that a log-linear regression model can describe surgical training learning curves reasonably well when all learning data are used. However, other studies contract this 19,39 . With respect to our study, even though we extracted very simple learning curve characteristics, we still found limitation in the use of log-linear models.…”

Section: Discussionmentioning

confidence: 99%

A machine learning approach to predict surgical learning curves

Gao

Krüger

Intes

et al. 2020

Surgery

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Background: Contemporary surgical training programs rely on the repetition of selected surgical motor tasks. Such methodology is inherently open-ended with no control on the time taken to attain a set level of proficiency, given the trainees' intrinsic differences in initial skill levels and learning abilities. Hence, an efficient training program should aim at tailoring the surgical training protocols to each trainee. In this regard, a predictive model utilizing information from the initial learning stage to predict learning curve characteristics should facilitate the whole surgical training process.Methods: This paper analyzes learning curve data to train a multivariate supervised machine learning model. One factor is extracted to define the trainees' learning ability. An unsupervised machine learning model is also utilized for trainee classification. When established, the model can predict robustly the learning curve characteristics based on the first few trials. Results:We show that the information present in the first ten trials of surgical tasks can be utilized to predict the number of trials required to achieve proficiency (R 2 = 0.72) and the final performance level (R 2 = 0.89). Furthermore, only a single factor, learning index, is required to describe the learning process and to classify learners with unique learning characteristics. Conclusions:Using machine learning models, we show, for the first time, that the first few trials contain sufficient information to predict learning curve characteristics and that a single factor can capture the complex learning behavior. Using such models holds the potential for personalization of training regimens, leading to greater efficiency and lower costs.

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Section: Discussionmentioning

confidence: 99%

A machine learning approach to predict surgical learning curves

Gao

Krüger

Intes

et al. 2020

Surgery

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“…Robotic-assisted radical prostatectomy is a highly technically demanding procedure. High-volume surgeons with great experience have better results than low-volume surgeons with less experience [ 32 ].…”

Section: Methodsmentioning

confidence: 99%

Can the prophylactic administration of tranexamic acid reduce the blood loss after robotic-assisted radical prostatectomy? Robotic Assisted Radical Prostatectomy with tranEXamic acid (RARPEX): study protocol for a randomized controlled trial

Balík

Košina

Hušek

et al. 2022

Trials

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Background The prophylactic administration of tranexamic acid reduces blood loss during procedures at high risk of perioperative bleeding. Several studies in cardiac surgery and orthopedics confirmed this finding. The aim of this prospective, double-blind, randomized study is to evaluate the effect of tranexamic acid on peri-and postoperative blood loss and on the incidence and severity of complications. Methods/design Based on the results of our pilot study, we decided to conduct this prospective, double-blind, randomized trial to confirm the preliminary data. The primary endpoint is to analyze the effect of tranexamic acid on perioperative and postoperative blood loss (decrease in hemoglobin levels) in robotic-assisted radical prostatectomy. The additional endpoint is to analyze the effect of tranexamic acid on postoperative complications and confirm the safety of tranexamic acid in robotic-assisted radical prostatectomy. Discussion No study to date has tested the prophylactic administration of tranexamic acid at the beginning of robotic-assisted radical prostatectomy. This study is designed to answer the question of whether the administration of tranexamic acid might lower the blood loss after the procedure or increase the rate and severity of complications. Trial registration ClinicalTrials.gov NCT04319614. Registered on 25 March 2020

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