Journal of Robotic Surgery最新文献

Urology has been a leading field in the adoption of robotic surgery, which offers technical advantages and low complication rates, including notably reduced intraoperative blood loss. In this study, we aimed to examine the relationship between formula-based estimated blood loss and visually estimated intraoperative blood loss in robotic urologic procedures. In this retrospective study, 111 robot-assisted urologic surgery were included. The agreement between the visually estimated intraoperative blood loss and the estimated values calculated using different formulas (Hb dilution method formula, Hb mass method, Gross Formula, López-Picado Formula). To determine how consistent each estimation was with the visually estimated intraoperative blood loss and with each other, Bland-Altman analysis, Concordance Correlation Coefficient (CCC) and Intraclass Correlation Coefficient (ICC) were applied. Intraoperative blood loss (visual estimation) indicated a mean blood loss of 220.72 ± 212.61 mL, whereas formula-based calculations consistently yielded higher estimates: López-Picado, 721.64 ± 532 mL; Hb mass method, 667.79 ± 429 mL; Gross formula, 726.97 ± 540 mL; and Hb dilution method, 737.99 ± 545 mL. The analyses revealed that all formulas differed statistically significantly from the visually estimated intraoperative blood loss. Evaluation of agreement and consistency demonstrated that the formulas showed poor agreement both with estimated blood loss and with one another. The strongest concordance was observed between López-Picado and Gross formula. There was a large discrepancy between visually estimated intraoperative blood loss and formula-based estimations. While formula-based methods show strong internal consistency, they differ substantially from the subjective estimates commonly used.

The transition from laparoscopic to robotic surgery for left-sided colorectal cancer raises safety concerns during the learning curve, particularly when complex cases are preferentially selected for the robotic platform. We evaluated a machine learning-based framework for risk-adjusted safety monitoring of robotic implementation, using outcomes from an established laparoscopic program as the reference. We retrospectively analyzed adult patients who underwent minimally invasive left-sided colorectal resection for malignancy between May 2023 and September 2025. A penalized logistic regression model predicting a composite adverse endpoint (anastomotic leak, reoperation, major complication, unplanned intensive care admission, or mortality) was developed in a laparoscopic training cohort (n = 211) using four preoperative variables (age, body mass index, American Society of Anesthesiologists physical status, and tumor location). Model-derived expected risks were applied to a robotic cohort (n = 93) to construct a risk-adjusted cumulative sum (RA-CUSUM) chart. The robotic cohort included a higher proportion of rectal tumors and more frequent neoadjuvant therapy than the laparoscopic cohort and had longer operative times, whereas the composite adverse event rate was similar (12.9% vs. 13.3%). The RA-CUSUM curve for the robotic series fluctuated around the expected risk baseline derived from the laparoscopic benchmark without a sustained upward drift. These findings suggest that, in this single-center experience, early robotic adoption did not show a clear signal of excess risk-adjusted short-term adverse events despite increased case complexity and demonstrate the feasibility of embedding a laparoscopic-derived risk model into RA-CUSUM analysis as a pragmatic tool for learning curve assessment.

The Deep Pelvic Endometriosis Index (dPEI) is a preoperative MRI-based score initially validated to predict surgical outcomes in patients undergoing laparoscopic treatment for deep pelvic endometriosis (DPE). Its applicability in robotic-assisted laparoscopy (RAL) has not yet been established. This study aimed to evaluate whether the dPEI can predict surgical outcomes following RAL for DPE. From February 2019 to December 2024, a retrospective analysis from a prospective database including patients undergoing RAL for DPE at Tenon Hospital, Paris, was performed. Preoperative staging was based on MRI and the dPEI scoring system, which evaluates the involvement of different anatomical compartments by deep endometriosis. Patients were classified into three categories: mild endometriosis (dPEI ≤ 2), moderate endometriosis (dPEI 3-4), and severe endometriosis (dPEI ≥ 5). Surgical outcomes including operative time, hospital stay, postoperative complications using the Clavien-Dindo classification and voiding dysfunction were assessed. A hundred and seventy patients were included. Overall complication rate was 24.7%, including 7.7% Clavien-Dindo grade > II. De novo voiding dysfunction occurred in 10.6% of patients, lasting > 1 month in 4.1%. dPEI categories showed a positive correlation with longer operative time (Spearman's ρ = 0.40, p < 0.001) and increased hospital stay (Spearman's ρ = 0.43, p < 0.001) and were also significantly associated with higher rates of grade > II complications (OR = 13.1; 95% CI [1.54-111.3], p = 0.02) and high incidence of voiding dysfunction (OR = 5.9; 95% CI [1.48-23.5], p = 0.01). Involvement of lateral compartments was associated with high operative time, hospital stay, and de novo voiding dysfunction. Our results support the dPEI as a useful preoperative tool for predicting surgical outcomes after RAL for DPE. Its use can improve patient counseling, and shared decision-making, particularly in cases of severe disease (dPEI ≥ 5).

Robot-assisted orthopedic surgery has garnered significant attention, yet comprehensive bibliometric and visualization analyses in this field remain scarce. This study aims to systematically map and visualize the global research landscape of robot-assisted orthopedic surgery. Employing bibliometric analysis methods and a suite of visualization tools-including CiteSpace, VOSviewer, and Scimago Graphica-this study systematically examined literature on orthopedic robotic surgery published in the Science Citation Index Expanded (SCIE) core collection from 2005 to 2024, analyzing global research trends across multiple dimensions. These dimensions encompass annual publication volume, collaborative networks among countries/regions and institutions, journal co-occurrence, keyword co-occurrence and clustering, research evolution pathways, and emerging keywords. A total of 820 articles on orthopedic robotics were included from 2005 to 2024. Analysis indicates exponential growth in global orthopedic robotic surgery research, with annual publications increasing from 5 in 2005 to 185 in 2024. The United States (338 articles), China (152 articles), and the United Kingdom (94 articles) emerged as core publishing nations. Institutional collaborations formed four major clusters: North America, Europe, Asia-Pacific, and industry-academia-research partnerships. Key influential journals in orthopedic robotic surgery include the Journal of Arthroplasty, Journal of Knee Surgery & Sports Traumatology & Arthroscopy, International Journal of Computer-Assisted Radiology and Surgery, and International Journal of Orthopaedic Knee Surgery. Research hotspots are highly concentrated in three key areas: prosthesis stability and long-term survival in robot-assisted total hip arthroplasty (THA); precise alignment and soft tissue balance in robot-assisted total knee arthroplasty (TKA); and accurate navigation and safe placement of pedicle screws in robot-assisted spinal surgery. Emerging keywords indicate recent research emphasis on "lumbar spine," "national joint registries," "patient matching," and "total hip." Orthopedic robotic surgery research is currently undergoing rapid development, with technology integration, precision, and personalization emerging as primary future directions. This study provides a reference framework for researchers to track field trajectories and optimize research planning, while also offering theoretical support for clinical practice and technological innovation.

Robotic-assisted bariatric surgery has shown sustained growth in recent years. However, evidence on its performance in the context of medical tourism is limited. This study describes the implementation of a robotic bariatric surgery program in a highly specialized center, analyzing the evolution of operative times and the learning curve of the surgical team. Observational, retrospective, and descriptive study that included all patients undergoing robotic-assisted bariatric surgery between January 2023 and May 2025. Demographic, clinical, and surgical variables were recorded, including total surgery time, docking time, and console time. Comparisons between years were made using ANOVA and Tukey's test, considering p < 0.05 as statistically significant. Ninetyfour cases were analyzed with a mean age of 42.8 (SD: 11.3) years and body mass index of 41.8 (SD: 7.7) kg/m²; 84% were women. Sleeve gastrectomy was the most frequent procedure (56.4%). The average docking, surgery and console times were 7.6 (SD: 3.0), 111.6 (SD: 51.0) and 69.6 (SD: 49.3) minutes, respectively. A significant decrease in docking time was observed over the years (p < 0.001), with no significant differences in surgical or console times. The progressive implementation of bariatric robotic surgery in a context of medical tourism is feasible. During the initial years of experience, a significant reduction in docking time was observed, while console and total operative times did not show statistically significant changes, similar to other international centers.