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Purpose The primary objective of this study was to determine the surgical team’s learning curve for robotic-arm assisted TKA through assessments of operative times, surgical team comfort levels, accuracy of implant positioning, limb alignment, and postoperative complications. Secondary objectives were to compare accuracy of implant positioning and limb alignment in conventional jig-based TKA versus robotic-arm assisted TKA. Methods This prospective cohort study included 60 consecutive conventional jig-based TKAs followed by 60 consecutive robotic-arm assisted TKAs performed by a single surgeon. Independent observers recorded surrogate markers of the learning curve including operative times, stress levels amongst the surgical team using the state-trait anxiety inventory (STAI) questionnaire, accuracy of implant positioning, limb alignment, and complications within 30 days of surgery. Cumulative summation (CUSUM) analyses were used to assess learning curves for operative time and STAI scores in robotic TKA. Results Robotic-arm assisted TKA was associated with a learning curve of seven cases for operative times ( p  = 0.01) and surgical team anxiety levels ( p  = 0.02). Cumulative robotic experience did not affect accuracy of implant positioning (n.s.) limb alignment (n.s.) posterior condylar offset ratio (n.s.) posterior tibial slope (n.s.) and joint line restoration (n.s.). Robotic TKA improved accuracy of implant positioning ( p  < 0.001) and limb alignment ( p  < 0.001) with no additional risk of postoperative complications compared to conventional manual TKA. Conclusion Implementation of robotic-arm assisted TKA led to increased operative times and heightened levels of anxiety amongst the surgical team for the initial seven cases but there was no learning curve for achieving the planned implant positioning. Robotic-arm assisted TKA improved accuracy of implant positioning and limb alignment compared to conventional jig-based TKA. The findings of this study will enable clinicians and healthcare professionals to better understand the impact of implementing robotic TKA on the surgical workflow, assist the safe integration of this procedure into surgical practice, and facilitate theatre planning and scheduling of operative cases during the learning phase. Level of evidence II.

IntroductionThere has been increased interest in assessing the surgeon learning curve for new skill acquisition. While there is no consensus around the best methodology, one of the most frequently used learning curve assessments in the surgical literature is the cumulative sum curve (CUSUM) of operative time. To demonstrate the limitations of this methodology, we assessed the CUSUM of console time across cohorts of surgeons with differing case acquisition rates while varying the total number of cases used to calculate the CUSUM.MethodsWe compared the CUSUM curves of the average console times of surgeons who completed their first 20 robotic-assisted (RAS) cases in 13, 26, 39, and 52 weeks, respectively, for their first 50 and 100 cases, respectively. This analysis was performed for prostatectomy (1094 surgeons), malignant hysterectomy (737 surgeons), and inguinal hernia (1486 surgeons).ResultsIn all procedures, the CUSUM curve of the cohort of surgeons who completed their first 20 procedures in 13 weeks demonstrated a lower slope than cohorts of surgeons with slower case acquisition rates. The case number at which the peak of the CUSUM curve occurs uniformly increases when the total number of cases used in generation of the CUSUM chart changes from 50 to 100 cases.ConclusionThe CUSUM analyses of these three procedures suggests that surgeons with fast initial case acquisition rates have less variability in their operative times over the course of their learning curve. The peak of the CUSUM curve, which is often used in surgical learning curve literature to denote “proficiency” is predictably influenced by the total number of procedures evaluated, suggesting that defining the peak as the point at which a surgeon has overcome the learning curve is subject to routine bias. The CUSUM peak, by itself, is an insufficient measure of “conquering the learning curve.”

BackgroundUltrasound (US) guidance is not yet commonly used for cardiac device implantation, despite the first report of US-guided axillary access over twenty years ago and recent randomised trials demonstrating similar success rates to subclavian puncture or cephalic access. Changes to workflow and the learning curve represent barriers to more widespread adoption. There is limited real-world experience of the learning curve for ultrasound (US) guided axillary venous access for cardiac device implantation.PurposeWe described US-guided axillary venous access adapted to standard implant workflow, including application to device upgrade procedures, and using a standard vascular US probe. We investigated its learning curve, radiation exposure, safety, and efficacy.MethodsUS-guided access was performed by an experienced electrophysiologist with no prior application of the technique. Patients underwent standard skin preparation and draping. A standard vascular ultrasound probe was placed in the deltopectoral grove, medially and upwards towards the clavicle until the axillary vein and artery could be seen in the out-of-plane projection, with the vein in the middle of the imaging field. Lidocaine was injected and transdermal incision was made from below the midpoint of the probe inferiorly and parallel to the deltopectoral groove. The punctures were made after only the dermis had been incised which maintained optimal image quality. Access (needle to wire) and fluoroscopy times for US-guided access were compared to fluoroscopy guided access in ten control patients.Results147 US-guided punctures were performed in 74 patients for one (8%), two (71%) or three (17%) leads, or upgrades (4%). Access was successful in 97% (n=72). There were no access related peri-procedural complications.First US-guided access time was 30 seconds (interquartile range, IQR: 17,60), and was similar to fluoroscopy guided access time (43 seconds, IQR: 24,58; p=0.45). Access time stabilised after 45 procedures, decreasing from 81 (IQR: 61,90) to 16 (IQR: 10,20) seconds from the first to the last fifteen procedures (p<0.001).96% (n=69) did not require fluoroscopy. 4% (n=3) required 1 second fluoroscopy to confirm wire position after difficult passage. Radiation exposure saving estimated from controls was 29 (IQR: 17,56) seconds of fluoroscopy, resulting in 0.25 (IQR: 0,1.4) mGy cumulative skin dose, and 0.03 (IQR 0.02,0.5) Gy.cm2 effective dose area product.DiscussionThis study describes a novel technique for ultrasound guided axillary venous access for cardiac device implantation using standard equipment and minimal modification to the workflow. The technique demonstrated a high success rate in a cohort of 147 punctures in 74 patients, including device upgrade procedures. For an experienced operator, the learning curve stabilized after 45 procedures which is acceptable and safe even during training. Radiation dose-saving was approximately equivalent to one chest radiograph.ConclusionUS-guided axillary venous access for cardiac device implantation is a feasible alternative to fluoroscopy guided access. It can be performed safely by using a standard vascular ultrasound probe with a high success rate, even during the short learning curve.Abstract 95 Figure 1Ultrasound guided axillary vein anatomy. Ultrasound images are obtained by placing a vascular probe below and perpendicular to the clavicleAbstract 95 Figure 2Illustration of procedural steps (step 1: position of the ultrasound transducer in the deltopectoral grove, moved medially and upwards towards the clavicle until the axillary vein and artery could be seen in the out-of-plane projection, step 2: inject the lidocaine and make transdermal incision from below the midpoint of the probe inferiorly and parallel to the deltopectoral groove, step 3: puncture at superior end of incision, step 4: dissect down to pre-pectoral fascia, step 5: create pre-pectoral pocket)Abstract 95 Figure 3Access times for first puncture per patient decrease with experience. Statistically similar fluoroscopy guided access times for controls are shown in green for reference. One procedure requiring 506 seconds is not represented on the graphConflict of InterestNo

Background Robotic-assisted surgery is used with increasing frequency in general surgery for a variety of applications. In spite of this increase in usage, the learning curve is not yet defined. This study reviews the literature on the learning curve in robotic general surgery to inform adopters of the technology. Methods PubMed and EMBASE searches yielded 3690 abstracts published between July 1986 and March 2016. The abstracts were evaluated based on the following inclusion criteria: written in English, reporting original work, focus on general surgery operations, and with explicit statistical methods. Results Twenty-six full-length articles were included in final analysis. The articles described the learning curves in colorectal (9 articles, 35%), foregut/bariatric (8, 31%), biliary (5, 19%), and solid organ (4, 15%) surgery. Eighteen of 26 (69%) articles report single-surgeon experiences. Time was used as a measure of the learning curve in all studies (100%); outcomes were examined in 10 (38%). In 12 studies (46%), the authors identified three phases of the learning curve. Numbers of cases needed to achieve plateau performance were wide-ranging but overlapping for different kinds of operations: 19–128 cases for colorectal, 8–95 for foregut/bariatric, 20–48 for biliary, and 10–80 for solid organ surgery. Conclusion Although robotic surgery is increasingly utilized in general surgery, the literature provides few guidelines on the learning curve for adoption. In this heterogeneous sample of reviewed articles, the number of cases needed to achieve plateau performance varies by case type and the learning curve may have multiple phases as surgeons add more complex cases to their case mix with growing experience. Time is the most common determinant for the learning curve. The literature lacks a uniform assessment of outcomes and complications, which would arguably reflect expertise in a more meaningful way than time to perform the operation alone.

Purpose Total Knee Arthroplasty (TKA) procedures incorporate technology in an attempt to improve outcomes. The Active Robot (ARo) performs a TKA with automated resections of the tibia and femur in efforts to optimize bone cuts. Evaluating the Learning Curve (LC) is essential with a novel tool. The purpose of this study was to assess the associated LC of ARo for TKA. Methods A multi-center prospective FDA cohort study was conducted from 2017 to 2018 including 115 patients that underwent ARo. Surgical time of the ARo was defined as Operative time (OT), segmented as surgeon-dependent time (patient preparation and registration) and surgeon-independent time (autonomous bone resection by the ARo). An average LC for all surgeons was computed. Complication rates and patient-reported outcome (PRO) scores were recorded and examined to evaluate for any LC trends in these patient related factors. Results The OT for the cases 10–12 were significantly quicker than the OT time of cases 1–3 ( p  < 0.028), at 36.5 ± 7.4 down from 49.1 ± 17 min. CUSUM and confidence interval analysis of the surgeon-dependent time showed different LCs for each surgeon, ranging from 12 to 19 cases. There was no difference in device related complications or PRO scores over the study timeframe. Conclusion Active Robotic total knee arthroplasty is associated with a short learning curve of 10–20 cases. The learning curve was associated with the surgical time dedicated to the robotic specific portion of the case. There was no learning curve-associated device-related complications, three-dimensional component position, or patient-reported outcome scores. Level of evidence Level II.

Objective The purpose of the study was to evaluate the effect of an interactive training program on the learning curve of radiology residents for bladder MRI interpretation using the VI-RADS score. Methods Three radiology residents with minimal experience in bladder MRI served as readers. They blindly evaluated 200 studies divided into 4 subsets of 50 cases over a 3-month period. After 2 months, the first subset was reassessed, resulting in a total of 250 evaluations. An interactive training program was provided and included educational lessons and case-based practice. The learning curve was constructed by plotting mean agreement as the ratio of correct evaluations per batch. Inter-reader agreement and diagnostic performance analysis were performed with kappa statistics and ROC analysis. Results As for the VI-RADS scoring agreement, the kappa differences between pre-training and post-training evaluation of the same group of cases were 0.555 to 0.852 for reader 1, 0.522 to 0.695 for reader 2, and 0.481 to 0.794 for reader 3. Using VI-RADS ≥ 3 as cut-off for muscle invasion, sensitivity ranged from 84 to 89% and specificity from 91 to 94%, while the AUCs from 0.89 (95% CI:0.84, 0.94) to 0.90 (95% CI:0.86, 0.95). Mean evaluation time decreased from 5.21 ± 1.12 to 3.52 ± 0.69 min in subsets 1 and 5. Mean grade of confidence improved from 3.31 ± 0.93 to 4.21 ± 0.69, in subsets 1 and 5. Conclusion An interactive dedicated education program on bladder MRI and the VI-RADS score led to a significant increase in readers’ diagnostic performance over time, with a general improvement observed after 100–150 cases. Key Points • After the first educational lesson and 100 cases were interpreted, the concordance on VI-RADS scoring between the residents and the experienced radiologist was significantly higher. • An increase in the grade of confidence was experienced after 100 cases. • We found a decrease in the evaluation time after 150 cases.

Purpose The application of robotics in the operating theatre for total knee arthroplasty (TKA) remains controversial. As with all new technology, the introduction of new systems is associated with a learning curve and potentially associated with extra complications. Therefore, the aim of this study is to identify and predict the learning curve of robot-assisted (RA) TKA. Methods A RA TKA system (MAKO) was introduced in April 2018 in our service. A retrospective analysis was performed of all patients receiving a TKA with this system by six surgeons. Operative times, implant and limb alignment, intraoperative joint balance and robot-related complications were evaluated. Cumulative summation (CUSUM) analyses were used to assess learning curves for operative time, implant alignment and joint balance in RA TKA. Linear regression was performed to predict the learning curve of each surgeon. Results RA TKA was associated with a learning curve of 11–43 cases for operative time ( p  < 0.001). This learning curve was significantly affected by the surgical profile (high vs. medium vs. low volume). A complete normalisation of operative times was seen in four out of five surgeons. The precision of implant positioning and gap balancing showed no learning curve. An average deviation of 0.2° (SD 1.4), 0.7° (SD 1.1), 1.2 (SD 2.1), 0.2° (SD 2.9) and 0.3 (SD 2.4) for the mLDFA, MPTA, HKA, PDFA and PPTA from the preoperative plan was observed. Limb alignment showed a mean deviation of 1.2° (SD 2.1) towards valgus postoperatively compared to the intraoperative plan. One tibial stress fracture was seen as a complication due to suboptimal positioning of the registration pins. Conclusion RA TKA is associated with a learning curve for surgical time, which might be longer than reported in current literature and dependent on the profile of the surgeon. There is no learning curve for component alignment, limb alignment and gap balancing. Level of evidence IV.

Purpose The purpose of this study was to investigate the learning curve associated with robotic assisted knee arthroplasty (RAS KA). Therefore, the evaluation of the influence of an experienced surgeon on the overall team performance of three surgeons regarding the learning curve in RAS KA was investigated. It was hypothesized that the presence of an experienced surgeon flattens the learning curve and that there was no inflection point for the learning curve of the surgical team. Methods Fifty-five cases consisting of 31 total knee arthroplasties (TKA) and 24 unicompartmental arthroplasties (UKA) performed by three surgeons during 2021 were prospectively investigated. Single surgeon and team performance for operation time learning curve and inflection points were investigated using cumulative sum analysis (CUSUM). Results A downward trend line for individual surgeons and the team performance regarding the operation time learning curve was observed. No inflexion point was observed for the overall team performance regarding TKA and UKA. The surgeon that performed all cases with the assistance of the experienced surgeon had significantly shorter surgical times than the surgeon that only occasionally received assistance from the experienced surgeon ( p  = 0.004 TKA; p  = 0.002 UKA). Conclusion The presence of an experienced surgeon in robotically assisted knee arthroplasty can flatten the learning curve of the surgical team formerly unexperienced in robotic assisted systems. Manufacturers should provide expanded support during initial cases in centres without previous experience to robotic assisted knee arthroplasty. Level of evidence III.

Background Laparoscopic pancreaticoduodenectomy (LPD), an advanced minimally invasive technique, has demonstrated advantages to open pancreaticoduodenectomy (OPD). However, this complex procedure requires a relatively long training period to ensure technical proficiency. This study was therefore designed to analyze the learning curve for LPD. Methods From October 2010 to September 2015, 63 standard pancreaticoduodenectomy procedures were to be performed laparoscopically by a single surgeon at the Department of Pancreatic Surgery, West China Hospital, Sichuan University, China. After applying the inclusion and exclusion criteria, a total of 57 patients were included in the study. Data for all the patients, including preoperative, intraoperative, and postoperative variables, were prospectively collected and analyzed. The learning curve for LPD was evaluated using both cumulative sum (CUSUM) and risk-adjusted CUSUM (RA-CUSUM) methods. All of the variables among the learning curve phases were compared. Results Based on the CUSUM and the RA-CUSUM analyses, the learning curve for LPD was grouped into three phases: phase I was the initial learning period (cases 1–11), phase II represented the technical competence period (cases 12–38), and phase III was regarded as the challenging period (cases 39–57). The operative time, intraoperative blood loss, and postoperative ICU demand significantly decreased with the learning curve. More lymph nodes were collected after the initial learning period. There were no significant differences in terms of postoperative complications or the 30-day mortality among the three phases. More challenging cases were encountered in phase III. Conclusions According to this study, the learning curve for LPD consisted of three phases. Conservatively, to attain technical competence for performing LPD, a minimum of 40 cases are required for laparoscopic surgeons with a degree of laparoscopic experience.

Background Increased uptake of robotic surgery has led to interest in learning curves for robot‐assisted procedures. Learning curves, however, are often poorly defined. This systematic review was conducted to identify the available evidence investigating surgeon learning curves in robot‐assisted surgery. Methods MEDLINE, Embase and the Cochrane Library were searched in February 2018, in accordance with PRISMA guidelines, alongside hand searches of key congresses and existing reviews. Eligible articles were those assessing learning curves associated with robot‐assisted surgery in patients. Results Searches identified 2316 records, of which 68 met the eligibility criteria, reporting on 68 unique studies. Of these, 49 assessed learning curves based on patient data across ten surgical specialties. All 49 were observational, largely single‐arm (35 of 49, 71 per cent) and included few surgeons. Learning curves exhibited substantial heterogeneity, varying between procedures, studies and metrics. Standards of reporting were generally poor, with only 17 of 49 (35 per cent) quantifying previous experience. Methods used to assess the learning curve were heterogeneous, often lacking statistical validation and using ambiguous terminology. Conclusion Learning curve estimates were subject to considerable uncertainty. Robust evidence was lacking, owing to limitations in study design, frequent reporting gaps and substantial heterogeneity in the methods used to assess learning curves. The opportunity remains for the establishment of optimal quantitative methods for the assessment of learning curves, to inform surgical training programmes and improve patient outcomes. Antecedentes La aceptación creciente de la cirugía robótica ha generado interés en las curvas de aprendizaje para los procedimientos asistidos por robot. Sin embargo, las curvas de aprendizaje a menudo están mal definidas. Esta revisión sistemática se realizó para identificar la evidencia disponible en relación a las curvas de aprendizaje del cirujano en la cirugía asistida por robot. Métodos En Febrero de 2018, se realizaron búsquedas en MEDLINE, Embase y Cochrane Library, de acuerdo con las recomendaciones PRISMA, junto con búsquedas manuales de congresos clave y de revisiones ya existentes. Los artículos elegibles fueron aquellos que evaluaron las curvas de aprendizaje asociadas con la cirugía asistida por robot efectuada en pacientes. Resultados Las búsquedas bibliográficas identificaron 2.316 registros de los cuales 68 cumplían los criterios de elegibilidad y correspondían a 68 estudios primarios. De estos 68 estudios, 49 evaluaron las curvas de aprendizaje basadas en datos de pacientes de 10 especialidades quirúrgicas. Los 49 estudios eran todos estudios observacionales, en su mayoría de un solo brazo (35/49 (71%)) e incluían pocos cirujanos. Las curvas de aprendizaje mostraban una notable heterogeneidad, variando entre procedimientos, estudios y parámetros analizados. Los estándares de presentación de informes fueron generalmente deficientes, con solo 17/49 (35%) cuantificando la experiencia previa. Los métodos utilizados para evaluar la curva de aprendizaje fueron heterogéneos, a menudo carecían de validación estadística y usaban terminología ambigua. Conclusión Las estimaciones de la curva de aprendizaje estaban sujetas a una considerable incertidumbre, careciendo de evidencia robusta por las limitaciones en el diseño del estudio, lagunas de información en los artículos y heterogeneidad sustancial en los métodos utilizados para evaluar las curvas de aprendizaje. Queda pendiente establecer métodos cuantitativos óptimos para evaluar las curvas de aprendizaje, informar de los programas de formación quirúrgica y mejorar los resultados del paciente. A broad systematic literature review was performed to characterize the current evidence base and appraise the methods used to measure and define learning curves for surgeons performing robot‐assisted surgery, taking a holistic, panspecialty view. The learning curve estimates identified are subject to considerable uncertainty, and robust evidence was often lacking due to limitations in study design and frequent reporting gaps. Thus, the opportunity remains for the establishment of optimal quantitative methods for the assessment of learning curves, which may inform surgical training programmes and improve patient outcomes. Little consistency between studies