Robotic Rives-Stoppa ventral hernia repair (rRS-VHR) is a minimally invasive technique that incorporates extraperitoneal mesh placement, using either transabdominal or totally extraperitoneal access. An understanding of its learning curve and technical challenges may guide and encourage its adoption. We aim at evaluating the rRS-VHR learning curve based on operative times while accounting for adverse outcomes. We conducted a retrospective analysis of patients undergoing rRS repair for centrally located ventral and incisional hernias. A single surgeon operative time-based cumulative sum (CUSUM) analysis learning curve was created, and a composite outcome was used for risk-adjusted CUSUM (RA-CUSUM). Eighty-one patients undergoing rRS-VHR were included. A learning curve was created by using skin-to-skin times. Accordingly, patients were grouped into three phases. The mean skin-to-skin time was 72.2 minutes, and there was a significant decrease in skin-to-skin times throughout the learning curve (Phase-I: 86.4 minutes versus Phase-III: 63.8 minutes; = .001), with a gradual decrease after 29 cases. Eleven patients experienced adverse composite outcomes, which were used to create a RA-CUSUM graph. Results showed the highest adverse outcome rates in Phase-II, with a gradual decrease in risk-adjusted operative times after 51 cases. Consistently decreasing operative times and adverse outcome rates in rRS-VHR was observed after the completion of 29 and 51 cases, respectively. Future studies that provide group learning curves for this procedure can deliver more generalizable results in terms of its performance rates.
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http://dx.doi.org/10.1089/lap.2020.0624 | DOI Listing |
Stat Methods Med Res
January 2025
CITMAga and Department of Statistics and Operations Research, Universidade de Vigo, Vigo, Galicia, Spain.
The study of the predictive ability of a marker is mainly based on the accuracy measures provided by the so-called confusion matrix. Besides, the area under the receiver operating characteristic curve has become a popular index for summarizing the overall accuracy of a marker. However, the nature of the relationship between the marker and the outcome, and the role that potential confounders play in this relationship could be fundamental in order to extrapolate the observed results.
View Article and Find Full Text PDFEur Heart J Digit Health
January 2025
Department of Cardiovascular Medicine, Mayo Clinic, 200 First Street SW, Rochester, MN 55905, USA.
Aims: Aortic stenosis (AS) is a common and progressive disease, which, if left untreated, results in increased morbidity and mortality. Monitoring and follow-up care can be challenging due to significant variability in disease progression. This study aimed to develop machine learning models to predict the risks of disease progression and mortality in patients with mild AS.
View Article and Find Full Text PDFEur Heart J Digit Health
January 2025
School of Life Course & Population Sciences, King's College London, SE1 1UL London, UK.
Cardiovascular disease (CVD) remains a major cause of mortality in the UK, prompting the need for improved risk predictive models for primary prevention. Machine learning (ML) models utilizing electronic health records (EHRs) offer potential enhancements over traditional risk scores like QRISK3 and ASCVD. To systematically evaluate and compare the efficacy of ML models against conventional CVD risk prediction algorithms using EHR data for medium to long-term (5-10 years) CVD risk prediction.
View Article and Find Full Text PDFFront Neurol
January 2025
Department of Neurology, The Third People's Hospital of Yibin, Yibin, China.
Objective: To evaluate the clinical utility of improved machine learning models in predicting poor prognosis following endovascular intervention for intracranial aneurysms and to develop a corresponding visualization system.
Methods: A total of 303 patients with intracranial aneurysms treated with endovascular intervention at four hospitals (FuShun County Zigong City People's Hospital, Nanchong Central Hospital, The Third People's Hospital of Yibin, The Sixth People's Hospital of Yibin) from January 2022 to September 2023 were selected. These patients were divided into a good prognosis group ( = 207) and a poor prognosis group ( = 96).
J Exp Orthop
January 2025
Clinica Ortopedica E Traumatologica II IRCCS Istituto Ortopedico Rizzoli Bologna Italy.
Purpose: The learning curve of a single surgeon performing hip arthroscopy is reported to be steep, but, to date, the inflection point after which procedures are more successful is still unknown. The aim of this study was to design a learning curve focused on clinical outcomes, complications and revision/conversion rates.
Methods: Seventy-one hip arthroscopies performed for femoroacetabular impingement (FAI) by a single surgeon, with a minimum follow-up of 5 years, were considered.
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