Introduction: The aim of this series was to evaluate predictors of Proficiency score (PS) achievement on a multicentric series of robot-assisted radical prostatectomies (RARP) performed by trainee surgeons with two different surgical techniques at four tertiary-care centers.
Material And Methods: Four institutional datasets were merged and queried for RARPs performed by surgeons during their learning curve (LC) between 2010 and 2020 using two different approaches (Group A, Retzius-sparing RARP, n = 164; Group B, standard anterograde RARP, n = 79). Logistic regression analysis was performed to identify predictors of PS achievement for the overall trainee cohort. For all analyses, a two-sided p <0.05 was considered significant.
Results: Group B showed significantly increased median operative time, positive surgical margins (PSM) status, increased number of nerve-sparing procedures, shorter LC time (each p <0.04). PS, continence status, potency, biochemical recurrence and 1-year trifecta rates were comparable between groups (each p >0.3). On multivariable analysis, time from LC starting ≥12 months (OR = 2.79; 95%IC [1.15-6.76]; p = 0.02) and a nerve-sparing intent (OR = 3.18; 95%IC [1.15-8.77]; p = 0.02) were independent predictors of PS score achievement (Table 3).
Conclusions: Higher PS rates for RARP trainees may be expected after 12 months from LC beginning. Short-term training courses are unlikely to confer proper surgical training, while long-term structured training programs seem to be beneficial on perioperative outcomes.
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http://dx.doi.org/10.5173/ceju.2023.260 | DOI Listing |
Diagn Progn Res
January 2025
Department of Applied Health Sciences, College of Medicine and Health, University of Birmingham, Edgbaston, Birmingham, UK.
Background: Pressure injuries (PIs) place a substantial burden on healthcare systems worldwide. Risk stratification of those who are at risk of developing PIs allows preventive interventions to be focused on patients who are at the highest risk. The considerable number of risk assessment scales and prediction models available underscores the need for a thorough evaluation of their development, validation, and clinical utility.
View Article and Find Full Text PDFKnee Surg Relat Res
January 2025
Bioengineering Laboratory, Department of Orthopedic Surgery, Massachusetts General Hospital, Harvard Medical School, 55 Fruit Street, Boston, MA, 02114, USA.
Background: Unplanned readmission, a measure of surgical quality, occurs after 4.8% of primary total knee arthroplasties (TKA). Although the prediction of individualized readmission risk may inform appropriate preoperative interventions, current predictive models, such as the American College of Surgeons National Surgical Quality Improvement Program (ACS-NSQIP) surgical risk calculator (SRC), have limited utility.
View Article and Find Full Text PDFJ Transl Med
January 2025
Department of Critical Care Medicine, Peking University Third Hospital, Beijing, 100191, China.
Background: Acute respiratory distress syndrome (ARDS) is a prevalent complication among critically ill patients, constituting around 10% of intensive care unit (ICU) admissions and mortality rates ranging from 35 to 46%. Hence, early recognition and prediction of ARDS are crucial for the timely administration of targeted treatment. However, ARDS is frequently underdiagnosed or delayed, and its heterogeneity diminishes the clinical utility of ARDS biomarkers.
View Article and Find Full Text PDFBMC Med Inform Decis Mak
January 2025
Institute of Mathematical Sciences Centre for Health Analytics and Modelling (CHaM), Strathmore University, Nairobi, Kenya.
Background: Measures of diagnostic test accuracy provide evidence of how well a test correctly identifies or rules-out disease. Commonly used diagnostic accuracy measures (DAMs) include sensitivity and specificity, predictive values, likelihood ratios, area under the receiver operator characteristic curve (AUROC), area under precision-recall curves (AUPRC), diagnostic effectiveness (accuracy), disease prevalence, and diagnostic odds ratio (DOR) etc. Most available analysis tools perform accuracy testing for a single diagnostic test using summarized data.
View Article and Find Full Text PDFNPJ Digit Med
January 2025
Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, 02129, USA.
Remote, digital cognitive testing on an individual's own device provides the opportunity to deploy previously understudied but promising cognitive paradigms in preclinical Alzheimer's disease (AD). The Boston Remote Assessment for NeuroCognitive Health (BRANCH) captures a personalized learning curve for the same information presented over seven consecutive days. Here, we examined BRANCH multi-day learning curves (MDLCs) in 167 cognitively unimpaired older adults (age = 74.
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