Phase Of Learning Curve Research Articles

Electromagnetic navigation bronchoscopy-guided preoperative lung nodule localization in video-assisted thoracic surgery (VATS): a learning curve analysis.

Electromagnetic navigation bronchoscopy (ENB) has been widely used to mark small peripheral pulmonary nodules (PPNs) in video-assisted thoracic surgery (VATS) resection. This technique offers the advantages of a high accuracy and fewer complications. However, few studies have analyzed the learning curve of ENB-guided preoperative localization. We aimed to describe the learning curve and factors influencing ENB-guided thoracoscopic pulmonary nodule resection. This study included 300 consecutive patients with PPNs who underwent ENB-guided localization by the same endoscopist in our department between November 2019 and December 2021. The cumulative sum (CUSUM) method was used to analyze the learning curve of ENB-guided localization and the learning curve in different lobes, while logistic regression was used to analyze the risk factors affecting ENB operative time (OT). In 184 patients with 300 nodules, three learning phases were identified through turning points of the learning curve: Phase I (the 16th nodule), Phase II (the 17th to the 107th nodule), and Phase III (the 107th to the 300th nodule). No significant difference was found in the success rate of ENB-guided localization in each phase of the learning curve (100%, 96.7%, and 97.9%, P=0.78). The distance from the localization to the pleura in Phase I was statistically significantly shorter than that in Phase II and Phase III (0.6±0.4 vs. 1.1±0.6 vs. 1.0±0.5 cm, P=0.001 and P=0.003). Furthermore, the learning curves for nodules in different lobes were different. The learning curve for the upper lobe nodules was divided into two phases; the learning curve for the middle lobe disclosed more negative values; and the learning curve for the lower lobe nodules displayed no obvious pattern. Significant differences were found in nodule location, distance from the localization to the pleura and learning curve phase (P=0.003, P<0.001, P=0.02). The independent factors for OT included gender, smoking history, nodule type, distance from localization to the pleura, and learning curve phase. ENB OT at the 107th nodule leveled off and showed a downward trend. Different lobes have different learning curves, the middle lobe is the easiest lobe to learn with ENB and can be used as the first lobe of choice for beginners. The learning curve can objectively evaluate the accuracy of ENB location and help endoscopists identify areas for improvement.

Learning curve stratified outcomes after robotic pancreatoduodenectomy: International multicenter experience

BackgroundRobotic pancreatoduodenectomy is increasingly being implemented worldwide, with good results reported from individual expert centers. However, it is unclear to what extent outcomes will continue to improve during the learning curve, as large international studies are lacking. MethodsAn international retrospective multicenter case series, including consecutive patients after robotic pancreatoduodenectomy from 18 centers in 8 countries in Europe, Asia, and South America until December 31, 2019, was conducted. A cumulative sum analysis was performed to determine the inflection points for the feasibility (operative time and blood loss) and proficiency (postoperative pancreatic fistula grade B/C and major morbidity) learning curves. Outcomes were compared in 3 groups on the basis of the learning curve inflection points. ResultsOverall, 2,186 patients after robotic pancreatoduodenectomy were included. The feasibility learning curve was reached after 30–45 robotic pancreatoduodenectomy procedures and the proficiency learning curve after 90 robotic pancreatoduodenectomy procedures. These inflection points created 3 phases, which were associated with major morbidity (24.7%, 23.4%, and 12.3%, P < .001) but not 30-day mortality (2.1%, 2.0%, and 1.5%, P = .670). Other outcomes mostly continued to improve, including median operative time 432, 390, and 300 minutes (P < .0001), conversion 6.0%, 4.7%, and 2.7% (P = .002), bile leakage 7.2%, 4.1%, and 2.4% (P < .001), postpancreatectomy hemorrhage 6.5%, 6.1%, and 1.8% (n = 21) but not R0 resection (pancreatic ductal adenocarcinoma only) 78.5%, 73.9%, and 82.8% (P = .35), and 90-day mortality rate 3.1%, 3.5%, and 2.1% (P = .191). Centers performing >20 robotic pancreatoduodenectomies annually had lower rates of conversion, reoperation, and shorter median operative time as compared with centers performing 10–20 robotic pancreatoduodenectomies annually. ConclusionThis international multicenter study demonstrates that most outcomes of robotic pancreatoduodenectomy continued to improve during 3 learning curve phases without a negative effect on 90-day mortality. Randomized studies are needed in high-volume centers that have surpassed the first learning curves, to compare these outcomes with the open approach.

How many cases are required to achieving early proficiency in purely off-clamp robot-assisted partial nephrectomy?

Off-clamp robot-assisted partial nephrectomy (Offc-RAPN) is a technically challenging procedure that can effectively avoid renal ischemia owing to the absence of hilar vessel preparation and clamping. However, data on the learning curve (LC) for this technique are limited. The purpose of this study was to assess the LC of Offc-RAPN and compare the perioperative outcomes between different learning phases. This retrospective study included 50 consecutive patients who underwent purely Offc-RAPN between January 2022 and April 2023. Multidimensional cumulative sum (CUSUM) analysis method was used to assess LC. Spearman's correlation and LOWESS analysis were performed to analyze the continuous variables of perioperative outcomes. Baseline characteristics and perioperative outcomes were compared using χ2-test, t-test and U-test. CUSUM analysis identified two LC phases: phase I (the first 24 cases) and phase II (the subsequent 26 cases). Phase II showed significant reductions in mean operative time (133.5 vs. 115.31 min; p = 0.04), mean console time (103.21 vs. 81.27 min; p = 0.01), and mean postoperative length of stay (5.33 vs. 4.30 days; p = 0.04) compared to phase I. However, no significant differences were observed in other perioperative outcomes or baseline characteristics between the two LC phases. Offc-RAPN performed by a surgeon with experience in laparoscopic and robotic surgeries achieved early proficiency in 24 cases. Moreover, Offc-RAPN alone is safe and feasible even in the initial phase of the LC for an experienced surgeon.

Artificial intelligence-based analysis of associations between learning curve and clinical outcomes in endoscopic and microsurgical lumbar decompression surgery.

A common spine surgery procedure involves decompression of the lumbar spine. The impact of the surgeon's learning curve on relevant clinical outcomes is currently not well examined in the literature. A variety of machine learning algorithms have been investigated in this study to determine how a surgeon's learning curve and other clinical parameters will influence prolonged lengths of stay (LOS), extended operating times (OT), and complications, as well as whether these clinical parameters can be reliably predicted. A retrospective monocentric cohort study of patients with lumbar spinal stenosis treated with microsurgical (MSD) and full-endoscopic (FED) decompression was conducted. The study included 206 patients with lumbar spinal stenosis who underwent FED (63; 30.6%) and MSD (118; 57.3%). Prolonged LOS and OT were defined as those exceeding the 75th percentile of the cohort. Furthermore, complications were assessed as a dependent variable. Using unsupervised learning, clusters were identified in the data, which helped distinguish between the early learning curve (ELC) and the late learning curve (LLC). From 15 algorithms, the top five algorithms that best fit the data were selected for each prediction task. We calculated the accuracy of prediction (Acc) and the area under the curve (AUC). The most significant predictors were determined using a feature importance analysis. For the FED group, the median number of surgeries with case surgery type at the time of surgery was 72 in the ELC group and 274 in the LLC group. FED patients did not significantly differ in outcome variables (LOS, OT, complication rate) between the ELC and LLC group. The random forest model demonstrated the highest mean accuracy and AUC across all folds for each classification task. For OT, it achieved an accuracy of 76.08% and an AUC of 0.89. For LOS, the model reached an accuracy of 83.83% and an AUC of 0.91. Lastly, in predicting complications, the random forest model attained the highest accuracy of 89.90% and an AUC of 0.94. Feature importance analysis indicated that LOS, OT, and complications were more significantly affected by patient characteristics than the surgical technique (FED versus MSD) or the surgeon's learning curve. A median of 72 cases of FED surgeries led to comparable clinical outcomes in the early learning curve phase compared to experienced surgeons. These outcomes seem to be more significantly affected by patient characteristics than the learning curve or the surgical technique. Several study variables, including the learning curve, can be used to predict whether lumbar decompression surgery will result in an increased LOS, OT, or complications. To introduce the provided prediction tools into clinics, the algorithms need to be implemented into open-source software and externally validated through large-scale randomized controlled trials.

Endoscopic port access resection of left atrial myxoma: Clinical outcomes and a single surgeon’s learning curve experience

ObjectivesTo evaluate the safety and efficacy of the port access approach for left atrial (LA) myxoma resection and to analyze the learning curve for this procedure. MethodsThirty-six consecutive patients with LA myxoma who underwent port access surgery between April 2018 and March 2023 were enrolled in this retrospective study. The procedure included (1) unilateral or bilateral femoral artery cannulation; (2) the use of three 5-mm trocars and a 20- to 30-mm port; (3) a transseptal, biatrial, or LA approach depending on the location of the tumor base; and (4) complete or subendocardial tumor resection. CUSUM analysis was used to evaluate the aortic cross-clamp (ACC) time and cardiopulmonary bypass (CPB) time learning curves. Variables among the learning curve phases were compared. ResultsThe average ACC and CPB times were 49 (range, 45-79) minutes and 127 (range, 120-164) minutes, respectively. There was 1 case of conversion to sternotomy due to aortic root bleeding and 1 case of unilateral pulmonary edema. CUSUMACCtime analysis included 3 phases: phase I, the initial learning period (cases 1-11); phase II, the technical competence period (cases 12-23); and phase III, the challenging period (cases 24-36). ConclusionsThe port access approach is safe and feasible for LA myxoma resection. According to the learning curve analysis, 11 cases are required to achieve technical competence, and 23 cases are required to address highly challenging cases.

Transition from laparoscopic to robotic approach in rectal cancer: a single-center short-term analysis based on the learning curve.

As a novel procedure becomes more and more used, knowledge about its learning curve and its impact on outcomes is useful for future implementations. Our aim is (i) to identify the phases of the robotic rectal surgery learning process and assess the safety and oncological outcomes during that period, (ii) to compare the robotic rectal surgery learning phases outcomes with laparoscopic rectal resections performed before the implementation of the robotic surgery program. We performed a retrospective study, based on a prospectively maintained database, with methodological quality assessment by STROBE checklist. All the procedures were performed by the same two surgeons. A total of 157 robotic rectal resections from June 2018 to January 2022 and 97 laparoscopic rectal resections from January 2018 to July 2019 were included. The learning phase was completed at case 26 for surgeon A, 36 for surgeon B, and 60 for the center (both A & B). There were no differences in histopathological results or postoperative complications between phases, achieving the same ratio of mesorectal quality, circumferential and distal resection margins as the laparoscopic approach. A transitory increase of major complications and anastomotic leakage could occur once overcoming the learning phase, secondary to the progressive complexity of cases. Robotic rectal cancer surgery learning curve phases in experienced laparoscopic surgeons was completed after 25-35 cases. Implementation of a robotic rectal surgery program is safe in oncologic terms, morbidity, mortality and length of stay.

Learning curve for robotic-assisted total mesorectal excision: a multicentre, prospective study.

Robotic-assisted surgery (RAS) is becoming increasingly important in colorectal surgery. Recognition of the short, safe learning curve (LC) could potentially improve implementation. We evaluated the extent and safety of the LC in robotic resection for rectal cancer. Consecutive rectal cancer resections (January 2018 to February 2021) were prospectively included from three French centres, involving nine surgeons. LC analyses only included surgeons who had performed more than 25 robotic rectal cancer surgeries. The primary endpoint was operating time LC and the secondary endpoint conversion rate LC. Interphase comparisons included demographic and intraoperative data, operating time, conversion rate, pathological specimen features and postoperative morbidity. In 174 patients (69% men; mean age 62.6 years) the mean operating time was 334.5 ± 92.1 min. Operative procedures included low anterior resection (n = 143) and intersphincteric resection (n = 31). For operating time, there were two or three (centre-dependent) LC phases. After 12-21 cases (learning phase), there was a significant decrease in total operating time (all centres) and an increase in the number of harvested lymph nodes (two centres). For conversion rate, there were two or four LC phases. After 9-14 cases (learning phase), the conversion rate decreased significantly in two centres; in one centre, there was a nonsignificant decrease despite the treatment of significantly more obese patients and patients with previous abdominal surgery. There were no significant differences in interphase comparisons. The LC for RAS in rectal cancer was achieved after 12-21 cases for the operating time and 9-14 cases for the conversion rate. RAS for rectal cancer was safe during this time, with no interphase differences in postoperative complications and circumferential resection margin.

Short-term Outcomes of Robotic Versus Open Pancreatoduodenectomy: Propensity Score-matched Analysis.

The goal of the current study was to investigate the perioperative outcomes of robotic pancreaticoduodenectomy (RPD) and open pancreaticoduodenectomy (OPD) in a high-volume center. Despite RPDs prospective advantages over OPD, current evidence comparing the 2 has been limited and has prompted further investigation. The aim of this study was to compare both approaches while including the learning curve phase for RPD. A 1:1 propensity score-matched analysis of a prospective database of RPD with OPD (2017-2022) at a high-volume center was performed. The main outcomes were overall- and pancreas-specific complications. Of 375 patients who underwent PD (OPD n=276; RPD n=99), 180 were included in propensity score-matched analysis (90 per group). RPD was associated with less blood loss [500 (300-800) vs 750 (400-1000) mL; P =0.006] and more patients without a complication (50% vs 19%; P <0.001). Operative time was longer [453 (408-529) vs 306 (247-362) min; P <0.001]; in patients with ductal adenocarcinoma, fewer lymph nodes were harvested [24 (18-27) vs 33 (27-39); P <0.001] with RPD versus OPD. There were no significant differences for major complications (38% vs 47%; P =0.291), reoperation rate (14% vs 10%; P =0.495), postoperative pancreatic fistula (21% vs 23%; P =0.858), and patients with the textbook outcome (62% vs 55%; P =0.452). Including the learning phase, RPD can be safely implemented in high-volume settings and shows potential for improved perioperative outcomes versus OPD. Pancreas-specific morbidity was unaffected by the robotic approach. Randomized trials with specifically trained pancreatic surgeons and expanded indications for the robotic approach are needed.

The adoption of oncoplastic surgery: Is there a learning curve?

Oncoplastic surgery (OPS) is a form of breast conservation surgery involving partial mastectomy followed by volume displacement or replacement surgery. As the field of OPS is growing, we sought to determine if there was a learning curve to this surgery. A retrospective chart review was conducted of all patients who underwent OPS over a 6-year period with a single surgeon formally trained in both Plastic Surgery and Breast Oncology. Cumulative summation analysis (CUSUM) was performed on mean operative time to generate the learning curve and learning curve phases. Outcomes were compared between phases to determine significance. Mean operative time decreased significantly across the 6-year period, generating three distinct learning curve phases: Learner phase (cases 1-23), Competence phase (24-73), and Mastery phase (74 and greater). The overall positive margin rate was 10.9% and there was no significant difference in rates between phases (p = 0.49). Overall complication rates, reoperation rates, and locoregional recurrence remained the same across all phases (p = 0.16; p = 0.65; p = 0.41). The rate of partial nipple loss decreased between phases (p = 0.02). As with many complex operations, there does appear to be a learning curve with OPS, as the operative time and the rates of partial nipple loss decreased over time.

Characteristics of the learning curve in robotic thoracic surgery in an emerging country.

It is notestablishedwhich factors impact the learning curve (LC) in robotic thoracic surgery (RTS), especially in emerging countries. The aim of this study is to analyze LC in RTS in Brazil and identify factors that can accelerate LC. We selected the firstcasesof two Brazilian surgeons who started their LC. We used CUSUM and the Lowess technique to measure LC for each surgeon and Poisson regression to assess factors associated with shorter console time (CT). 58 patients were operated by each surgeon andincluded in the analysis. Surgeries performed weredifferent: Surgeon I (SI) performed 54 lobectomies (93.11%), whereas Surgeon II (SII) had a varied mix of cases. SI was proctored in hisfirst 10 cases (17.24%), while SIIin hisfirst 41 cases (70.68%). The mean interval between surgeries was 8days for SI and 16days for SII.There were differences in the LC phases of the two surgeons, mainlyregarding complications and conversions. There wasshorterCT by 30% in the presence of aproctor, and by 20% with the Da Vinci Xi. Mix of cases did not seem to contribute to faster LC. Higher frequency between surgeries seems to be associated with a faster curve. Presence of proctorand use of bolder technologiesreduced console time. We wonder ifin phase 3 itisnecessary to keep a proctoron complex cases to avoid serious complications. More studies are necessaryto understandwhich factors impact theLC.

How Long Does It Take to Acquire Mastery of Performance in Laparoscopic Living Donor Nephrectomy? A Center-Based and Surgeon-Based Operative Time CUSUM Analysis

The safety of laparoscopic donor nephrectomy (LDN) has been widely documented, but its challenging learning curve (LC) requires an insightful assessment to expand its application. The aim of this study was to evaluate LC of LDN in a high-volume transplant center. Three hundred forty-three LDNs performed from 2001 to 2018 were evaluated. CUSUM analysis based on the operative time was used to assess the number of cases required to reach mastery in the technique for both the entire surgical team and for the 3 main surgeons considered separately. Analysis of association between demographics, perioperative characteristics, and complications within the different LC phases was conducted. Mean operative time was 228.9 minutes. Mean length of stay was 3.8 days and mean warm ischemia time (WIT) was 170.8 seconds. Surgical and medical complication rates were 7.3% and 6.4%, respectively. The CUSUM-LC showed a requirement of 157 cases (for surgical team) and 75 cases (for single surgeons) to reach competence in the procedure. Patient baseline characteristic showed no differences among the LC phases. Compared with the initial LC phase, hospital stay was significantly lower at the end of the LC whereas WIT results were longer in the LC descendent phase. This study confirms the safety and efficacy of LDN, with a low rate of complications. This analysis suggests that about 75 procedures are required to reach competence and 93 cases to achieve mastery level of skill for a single surgeon. It can be hypothesized that, in a high-volume transplant enter, the time to guarantee training in LDN is compatible with the duration of a clinical fellowship.

Supervision by an experienced surgeon can reduce the learning curve of uniportal thoracoscopic lobectomy.

This retrospective study was performed to investigate the learning curve of uniportal thoracoscopic lobectomy with ND2a-1 or greater lymphadenectomy for two senior surgeons, and to evaluate how supervision affected the learning curve. Between February 2019 and January 2022, 140 patients with primary lung cancer underwent uniportal thoracoscopic lobectomy with ND2a-1 or greater lymphadenectomy in our department. Two senior surgeons (HI and NM) performed most of the operations, with junior surgeons performing the rest. HI initiated this surgical method in our department and supervised all operations performed by other surgeons. Patient characteristics and perioperative outcomes were reviewed, and the learning curve was evaluated based on operative time and the cumulative sum method (CUSUMOT). No significant differences were observed in patient characteristics or perioperative outcomes between groups. Three distinct learning curve phases were identified for each senior surgeon: HI, cases 1-21, cases 22-40, cases 41-71; NM cases 1-16, cases 17-30, cases 31-49. For HI, the rate of conversion to thoracotomy was significantly higher in the initial phase (14.3%, P=0.04) although other perioperative outcomes were equivalent between phases. For NM, while the duration of postoperative drainage was significantly shorter in phase 2 and phase 3 (P=0.026), other perioperative outcomes, including conversion rate (5.3-7.1%), were equivalent between phases. Supervision by an experienced surgeon was important for avoiding conversion to thoracotomy during the initial period, and facilitated the surgeon rapidly gaining proficiency with the surgical method.

Prospective analysis of impact of learning curve in robotic-assisted rectal surgery in the high-volume Indian tertiary care centre.

Minimally invasive surgery in rectal cancer has gained prominence owing to its various advantages in surgical outcomes. Due to rapid adoption of robotics in rectal surgery, we intended to assess the pace in which surgeons gain proficiency using cumulative summation (CUSUM) technique in learning curve. This was a prospective study of 262 rectal cancer cases who underwent robotic-assisted low anterior resection and abdominoperineal resection (RA-LAR and RA-APR). Parameters considered for the study were console time, docking time, lymph nodal yield, total operative time and post-operative outcomes. We used Manipal technique of port placements and modified centroside docking for the procedure. The mean age of our study was 46.62 ± 5.7 years, the mean body mass index (BMI) was 31.51 ± 3.2 kg/m2. 215 (82.06%) underwent RA-LAR and 47 (17.93%) underwent RA-APR. 2.67% of cases required to open during our initial period. We had three phases of learning curve, initial phase (11th case), plateau phase (29th case) and then phases of mastery (30th case onwards). Our mean total operative time reduced from 5.5 to 3.5 h (210 ± 8.2 min), console time from 4.5 to 2.9 h (174 ± 4.5 min) and docking time from 15 to 9 ± 1 min from 30th case onwards. RA surgeries for rectal cancer have got good oncological and functional outcomes in high BMI, male pelvis and low rectal cancers. Learning curve can be shortened with constant self-auditing of the surgeon and team with each surgeries performed, reviewing the steps and by improving techniques.

Learning Curve for Laparoscopic Sleeve Gastrectomy: a Cumulative Summation (CUSUM) Analysis

Bariatric surgery is currently considered the most effective and durable treatment option for morbid obesity. Laparoscopic sleeve gastrectomy (LSG) has become a popular technique and may currently be the most frequently practiced surgical operation to treat obesity. However, no objective analyses of its learning curve have been reported. to analyze the learning curve for LSG. We included all LSGs performed in our hospital (University Hospital, Spain; Public Practice) from April 2013 to February 2016. The learning curve for LSG was evaluated using cumulative sum (CUSUM) analysis. All variables among the learning curve phases were compared. According to the CUSUM analysis, the learning curve was divided into three unique phases: early learning (the initial 26 patients), acquisition of skills (the middle 30 patients), and mastery of technique (the final 56 patients). The operative time and gastric stenosis significantly decreased with progression of the learning curve without differences in the 30-day postoperative complication rate, postoperative stay, or weight loss. According to this study, the learning curve for LSG can be divided into 3 distinct phases, and about 25 patients are needed to demonstrate an improvement in surgical skill.

Learning Curves in Open, Laparoscopic, and Robotic Pancreatic Surgery: A Systematic Review and Proposal of a Standardization.

To depict and analyze learning curves for open, laparoscopic, and robotic pancreatoduodenectomy (PD) and distal pancreatectomy (DP). Formal training is recommended for safe introduction of pancreatic surgery but definitions of learning curves vary and have not been standardized. A systematic search on PubMed, Web of Science, and CENTRAL databases identified studies on learning curves in pancreatic surgery. Primary outcome was the number needed to reach the learning curve as defined by the included studies. Secondary outcomes included endpoints defining learning curves, methods of analysis (statistical/arbitrary), and classification of learning phases. Out of 1115 articles, 66 studies with 14,206 patients were included. Thirty-five studies (53%) based the learning curve analysis on statistical calculations. Most often used parameters to define learning curves were operative time (n = 51), blood loss (n = 17), and complications (n = 10). The number of procedures to surpass a first phase of learning curve was 30 (20-50) for open PD, 39 (11-60) for laparoscopic PD, 25 (8-100) for robotic PD (P = 0.521), 16 (3-17) for laparoscopic DP, and 15 (5-37) for robotic DP (P = 0.914). In a three-phase model, intraoperative parameters improved earlier (first to second phase: operating time -15%, blood loss -29%) whereas postoperative parameters improved later (second to third phase: complications -46%, postoperative pancreatic fistula -48%). Studies with higher sample sizes showed higher numbers of procedures needed to overcome the learning curve (rho = 0.64, P < 0.001). This study summarizes learning curves for open-, laparoscopic-, and robotic pancreatic surgery with different definitions, analysis methods, and confounding factors. A standardized reporting of learning curves and definition of phases (competency, proficiency, mastery) is desirable and proposed.

Bleeding during Learning Curve of Thoracoscopic Lobectomy: CUSUM Analysis Results.

The management of intraoperative bleeding during thoracoscopic lobectomy is challenging, especially for non-experienced surgeons. We evaluated intraoperative bleeding in relation to learning curve of thoracoscopic lobectomy, the strategies to face it, the outcomes, and the target case number for gaining the technical proficiency. This was a retrospective single center study including consecutive patients undergoing thoracoscopic lobectomy for lung cancer. Based on cumulative sum analysis, patients were divided into early and late experience groups, and the differences on surgical outcomes, with particular focus on vascular injury, were statistically compared. Eight-three patients were evaluated. Cumulative sum charts showed a decreasing of operative time, blood loss, and hospital stay after the 49th, the 43th, and the 39th case, respectively. Early (n = 49) compared with late experience group (n = 34) was associated with higher conversion rate (p = 0.08), longer operative time (p <0.0001), greater blood loss (p <0.0001), higher transfusion rate (p = 0.01), higher postoperative air leak rate (p = 0.02), longer chest tube stay (p <0.0001), and hospitalization (p <0.0001). Six patients (7%) had intraoperative bleeding during early phase of learning curve, successfully treated by thoracoscopy in four cases. Patients with vascular injury (n = 6) compared with control group (n = 77) presented a longer operative time (p = 0.003), greater blood loss (p = 0.0001), and higher transfusion rate (p = 0.001); no significant differences were found regarding postoperative morbidity (p = 0.57), length of chest tube stay (p = 0.07), and hospitalization (p = 0.07). Technical proficiency was achieved after 50 procedures. All vascular injuries occurred in the early phase of learning curve; they were safely managed, without affecting surgical outcomes.

Assessing the Outcomes of Robotic Pancreatoduodenectomy According to the Benchmark Cutoffs for the Open Approach: International Multicenter Study

Introduction: Several studies from high-volume centers have reported on the safety and feasibility of robotic pancreatoduodenectomy (RPD). Since no randomized trials have compared RPD versus open pancreatoduodenectomy (OPD) it is unclear how outcomes after these two approaches compare. Recently, outcome benchmarks for OPD were established to allow for unbiased outcome comparisons. Methods: An international multicenter retrospective cohort study, including consecutive patients after RPD in high-volume centers (first case – 31 December 2019) was conducted. Outcomes of the benchmark cases (low-risk patients) and patients with ASA≥3 were compared to the OPD benchmark cutoffs, reported by Sánchez-Velázquez et al. Lastly, clinical outcomes were stratified based on learning curve phases and annual volume. Results: Overall, 2186 patients from 18 centers in 8 countries were included, the percentage of benchmark cases was 53.5% (n=1169). Median annual volume of RPD per center was 24. Operative outcomes of the RPD-benchmark cases were compared to the OPD-benchmark cutoffs: operative time (5.5 vs ≤7.5 hours), POPF grade B/C rate (13.6% vs ≤19%), Clavien-Dindo ≥3 rate (14.4% vs ≤30%), in-hospital or 30-day mortality (1.1% vs ≤1.6%) and R1 rate for PDAC (12.4% vs ≤39%). As expected, patients with ASA≥3 had worse outcomes (7.1 hours, 16.7%, 22.4%, 3.5%, and 32%, respectively). Conclusions: Outcomes of RPD in this international cohort meet the established OPD-benchmark cutoffs. Moreover, 11 out of 14 available OPD-benchmark cutoffs are also met in patients with ASA≥3. These findings suggest that RPD can be performed safely in both low-risk and high-risk patients in high-volume centers.

A propensity score-matching study on retzius-sparing robotic-assisted radical prostatectomy: Evidence of continence advantage on the early learning curve

To investigate the outcome of retzius-sparing robotic-assisted radical prostatectomy (RS-RARP) compared with conventional RARP in the early learning curve. Consecutive patients with prostate cancer who underwent RS-RARP were included to compare against conventional RARP of the same period. Propensity-score matching was performed based on age, prostate size, nerve-sparing approach, and final pathological risk categories. All patients were re-admitted to undergo trial without Foley catheter from post-operative day 7-10. Clinical follow-up was performed with monitoring of continence (day 0, 3 months, and the latest continence during the study period) and surveillance of PSA level. Between July 2017 and August 2019, 24 consecutive patients received RS-RARP in our centre. Propensity score matching was performed with the best matched 24 controls receiving conventional RARP. Overall median follow-up duration was 15.5 months. A majority of the patients belonged to the intermediate-risk group, with most of them harbouring pT2 disease (RS-RARP: 87.5%; conventional RARP: 79.2%). More patients in RS-RARP group achieved day-0 continence (33.3% vs 0%, p=0.002) and 3-month continence (66.7% vs 12.5%, p=0.001). During the whole study period, more RS-RARP achieved continence with 0 pad (91.7% vs 66.7%, p=0.033). The mean months to continence is shorter in RS-RARP group (4.0 months vs 13.6 months, p=0.002). No statistically significant differences between the two groups with respect to surgical margins, post-operative PSA detection, and the use of adjuvant radiotherapy. RS-RARP showed better continence rates when compared to conventional RARP even during the learning curve phase.

Learning Curve in Laparoscopic Pancreaticoduodenectomy: Using Risk-Adjusted Cumulative Summation Methods.

Background: Laparoscopic pancreaticoduodenectomy (LPD) is one of the most technically challenging operations of minimally invasive surgery. We aimed to analyze the learning curve of a single surgeon who conducted 115 LPDs at a single center. Materials and Methods: From August 2015 to August 2020, 115 patients underwent LPD. Patient characteristics and perioperative variables were retrospectively collected and analyzed. Cumulative summation (CUSUM) and risk-adjusted cumulative summation (RA-CUSUM) analyses were used to evaluate the LPD learning curve. All variables were compared after dividing the learning curve phases. Results: After 74 cases, operative time improved based on the CUSUM analysis of the operation time. From the RA-CUSUM analysis, three distinct phases of the learning curve were identified (phase I: 1-42 cases, phase II: 43-73 cases, and phase III: 74-115 cases). The mean operative time was significantly lower in phase III compared with that in phases I and II (348.5 minutes versus 444.6 minutes and 439.9 minutes, P < .001 and P < .001, respectively). The rate of estimated blood loss >500 mL was significantly decreased among the three phases (P = .017). The conversion rate significantly decreased from 11.9% in phase I to 6.5% in phase II to 0% in phase III (P = .023). The rates of overall complication (Clavien-Dindo >IIIA), postoperative pancreatic fistula, and postpancreatectomy hemorrhage were significantly decreased as phases progressed. Postoperative hospital stay, 30-day mortality, and 30-day readmission did not significantly differ among phases. Conclusions: According to learning curve analyses, the LPD failure rate plateaued after 42 cases and stabilized after 73 cases.

Electromagnetic navigation-guided preoperative localization: the learning curve analysis.

BackgroundThe electromagnetic navigation bronchoscopy (ENB) was increasingly used to mark small pulmonary nodules (PNs) for video-assisted thoracic surgery (VATS) resection due to high effectiveness and low risk. However, no study reports the learning curve of ENB-guided preoperative localization. In the study, we aimed to describe the learning curve of ENB-guided preoperative PNs localization initially.MethodsConsecutive PNs cases that underwent ENB localizations between October 2018 and October 2019 by the same surgeon in our center were included in the study. The cumulative sum (CUSUM) method was used to analyze the learning curve of ENB localization.ResultsA total of 89 ENB localization from 64 patients were included in this study. The learning curve was divided into 3 phases: Phase I (the initial 11 cases), Phase II (the 12th to the 47th cases), and Phase III (the 47th to the 89th cases). The success rate of ENB localization has increased with the accumulation of operational experience in 3 phases (72.73%, 91.67%, and 97.62%, P=0.049). The distance from the ENB guide wire tip to the center of the lesion in Phase I was significantly longer than those in Phase II and Phase III (2.46±1.76 vs. 1.36±0.94 and 1.47±0.97 cm, P=0.014 and 0.027, respectively). Sex, bronchus sign, and learning curve phase were independent risk factors influencing operative time (OT) (OR =8.187, 18.847, and 13.920, respectively).ConclusionsThe technical competency, which is indicated by higher success rate, localization accuracy, and shorter OT, for ENB-guided preoperative PNs localization was achieved at the 47th operation.