Credentialing for robotic lobectomy: what is the learning curve? A retrospective analysis of 272 consecutive cases by a single surgeon

National learning curves among robotic thoracic surgeons in the United States: Quantifying the impact of procedural experience on efficiency and productivity gains

OBJECTIVE

This study aims to characterize the aggregate learning curves of US surgeons for robotic thoracic procedures and to quantify the impact on productivity.

METHODS

National average console times relative to cumulative case number were extracted from the My Intuitive application (Version 1.7.0). Intuitive da Vinci robotic system data for 56,668 lung resections performed by 870 individual surgeons between 2021 and 2022 were reviewed. Console time and hourly productivity (work relative value units/hour) were analyzed using linear regression models.

RESULTS

Average console times improved for all robotic procedures with cumulative case experience (P = .003). Segmentectomy and thymectomy had the steepest initial learning curves with a 33% and 34% reduction of the average console time for proficient (51-100 cases) relative to novice surgeons (1-10 cases), respectively. The hourly productivity increase for proficient surgeons ranged from 11.4 work relative value units/hour (+26%) for lobectomy to 17.0 work relative value units/hour (+50%) for segmentectomy. At the expert level (101+ cases), average console times continued to decrease significantly for esophagectomy (-18%) and lobectomy (-23%), but only minimally for wedge resections (-1%) (P = .003). The work relative value units/hour increase at the expert level reached 50% for lobectomy and 40% for esophagectomy. Surgeon experience level, dual console use, system model, and robotic stapler use were factors independently associated with console time for robotic lobectomy.

CONCLUSIONS

The aggregate learning curve for robotic thoracic surgeons in the United States varies significantly by procedure type and demonstrate continued improvements in efficiency beyond 100 cases for lobectomy and esophagectomy. Improvements in efficiency with growing experiences translate to substantial productivity gains.

Assessment of Quality Outcomes and the Learning Curve for Robot-Assisted Anatomical Lung Resections

Background: To determine the perioperative quality assessment results and learning curves for robot-assisted anatomical lung resection. Methods: We analyzed the data of the initial 400 patients who underwent lobectomies or segmentectomies by 1 surgeon from January 2020 to November 2021. The learning curve was analyzed using cumulative sum analysis. Results: The surgical experience was divided into an initial phase (1st-40th procedures), a transition phase (41st-131st procedures), and a proficient phase (132nd procedure onward). The operative time showed a conspicuously continuous improvement over the 400 consecutive patients. After the 120th procedure, there were significant improvements in the rate of persistent air leakage (11.7% versus 3.9%; P = .003), chest tube duration (3.92 ± 1.91 versus 2.99 ± 1.31, P = .00), and postoperative hospital stay (6.22 ± 2.02 versus 4.93 ± 1.44, P = .00). Conclusions: In conclusion, 40 patients were necessary to pass the learning curve, and technical proficiency with favorable perioperative outcomes was achieved after 120-130 patients.

Single-center experience of transitioning from video-assisted laparoscopic to robotic Heller myotomy with Dor fundoplication for esophageal motility disorders

BACKGROUND

Video-assisted laparoscopic Heller myotomy (LHM) has become the standard treatment option for achalasia. While robotic surgery offering some specific advantages such as better three-dimensional (3D) stereoscopic vision, hand-eye consistency, and flexibility and stability with the endowrist is expected to be shorter in learning curve than that of LHM for surgeons who are proficient in LHM. The aim of this study was to describe a single surgeon's experience related to the transition from video-assisted laparoscopic to robotic Heller myotomy with Dor fundoplication.

METHODS

We conducted a retrospective observational study based on the recorded data of the first 66 Heller myotomy performed with laparoscopic Heller myotomy with Dor fundoplication (LHMD, 26 cases) and with the robotic Heller myotomy with Dor fundoplication (RHMD, 40 cases) by the same surgeon in Department of Thoracic Surgery of The First Affiliated Hospital of Nanchang University in China. The operation time and intraoperative blood loss were analyzed using the cumulative sum (CUSUM) method. Corresponding statistical tests were used to compare outcomes of both serials of cases.

RESULTS

The median operation time was shorter in the RHMD group compared to the LHMD group (130 [IQR 123-141] minutes vs. 163 [IQR 153-169]) minutes, p < 0.001). In the RHMD group, one patient (2.5%) experienced mucosal perforation, whereas, in the LHMD group, the incidence of this complication was significantly higher at 19.2% (5 patients) (p = 0.031). Based on cumulative sum analyses, operation time decreased starting with case 20 in the LHMD group and with case 18 in the RHMD group. Intraoperative blood loss tended to decline starting with case 19 in the LHMD group and with case 16 in the RHMD group.

CONCLUSIONS

Both RHMD and LHMD are effective surgical procedures for symptom relief of achalasia patients. RHMD demonstrates superior outcomes in terms of operation time and mucosal perforation during surgery compared to LHMD. Proficiency with RHMD can be achieved after approximately 16-18 cases, while that of LHMD can be obtained after around 19-20 cases.

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

It is not established which 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 first cases of 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 and included in the analysis. Surgeries performed were different: Surgeon I (SI) performed 54 lobectomies (93.11%), whereas Surgeon II (SII) had a varied mix of cases. SI was proctored in his first 10 cases (17.24%), while SII in his first 41 cases (70.68%). The mean interval between surgeries was 8 days for SI and 16 days for SII. There were differences in the LC phases of the two surgeons, mainly regarding complications and conversions. There was shorter CT by 30% in the presence of a proctor, 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 proctor and use of bolder technologies reduced console time. We wonder if in phase 3 it is necessary to keep a proctor on complex cases to avoid serious complications. More studies are necessary to understand which factors impact the LC.

Analysis of trends in perioperative outcomes in over 1000 robotic-assisted anatomic lung resections

Robotic-assisted surgery is gaining popularity as a minimally invasive approach for anatomic lung resection. We investigated the temporal changes in case volume, costs, and postoperative outcomes for robotic-assisted anatomic lung resection in over 1000 cases. We reviewed our institutional STS database for patients who had undergone robotic-assisted lobectomy, bi-lobectomy, or segmentectomy as the primary procedure between years 2009-2021. The patients were divided into two groups: first 500 cases (n = 501) and second 500 cases (n = 500). Temporal trends of case volume, surgical indications, hospital length of stay, costs, and perioperative outcomes were analyzed. A total of 1001 patients were analyzed, of which 968 (96.7%) patients underwent robotic-assisted lobectomy, 21 (2.1%) patients underwent bi-lobectomy, 10 (1.0%) patients underwent segmentectomy, and 3 (0.3%) patients underwent sleeve lobectomy. Primary lung cancer was the most common indication (87.7%), followed by metastatic lung tumors (7.1%), and benign diagnosis (5.2%). The overall postoperative complication rate decreased from 46.1% for the first 500 cases compared to 29.6% for the second 500 cases (p < 0.0001). The median hospital length of stay was down trending, which was 4 days [IQR: 3-7] for the first 500 cases and 3 days [IQR: 3-5] (p = 0.0001) for the second. The inflation-adjusted direct and indirect hospital costs were significantly lower in the second 500 cases (p < 0.0001). The complications rates, hospital costs, and hospital length of stay for robotic-assisted anatomic pulmonary resection decreased significantly over time at a single institution. Continuous improvement in perioperative outcomes may be observed with increasing institutional experience.

Robotic and video-assisted lobectomy/segmentectomy for non-small cell lung cancer have similar perioperative outcomes: a systematic review and meta-analysis

Background

At present, the clinical conclusion that robotic-assisted thoracic surgery (RATS) and video-assisted thoracic surgery (VATS), which is better for patients with non-small cell lung cancer (NSCLC) is not clear. Therefore, this meta-analysis aimed to compare the perioperative outcomes between RATS and VATS for NSCLC.

Methods

The Population, Interventions, Comparators, Outcomes, and Study design (PICOS) framework was employed to develop the search strategy, and the findings was reported in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement. We searched EMbase, The Cochrane Library, PubMed, Web of Science, CNKI, and Wan Fang Data to collect clinical studies about RATS vs. VATS for patients with NSCLC from inception to October 2019. The following outcomes were measured: rate of conversion to thoracotomy, postoperative complications, postoperative hospital mortality, lymph node dissection, hospitalization time, operating time, and postoperative drainage days. Estimation of potential publication bias was conducted by Begg’s test and Egger’s test. The Standardized Mean Difference (SMD) and Odds Ratio (OR) with 95% confidence intervals (CI) were pooled using Stata 15.0 software.

Results

A total of 18 studies involving 60,349 patients were included. Among them, 8,726 cases were in the RATS group, and 51,623 were in the VATS group. The results of meta-analysis showed that the operation time of RATS group was longer than that of VATS group (SMD=0.532, 95% CI: 0.391–0.674, P=0.000). And the further meta-analysis suggested that the incidence of postoperative complications was lower in patients who underwent RATS after 2015 (OR=0.848, 95% CI: 0.748–0.962, P=0.010). Meanwhile, there was no significant difference between both groups in postoperative hospitalization time (SMD=0.003, 95% CI: −0.104–0.110, P=0.957). In addition, more lymph nodes were retrieved in RATS group than VATS (SMD=0.308, 95% CI: 0.131–0.486, P=0.001). However, the conversion rate, retrieved lymph node station, days to tube removal and in-hospital mortality rate have no significant differences between both groups.

Discussion

The current meta-analysis indicates that the perioperative outcomes of RATS and VATS for NSCLC are equivalence. Due to the limited quantity and quality of included studies, the above conclusions still need to be verified by more high-quality studies.

Beyond the learning curve: a review of complex cases in robotic thoracic surgery

The number of thoracic surgery cases performed on the robotic platform has increased steadily over the last two decades. An increasing number of surgeons are training on the robotic system, which like any new technique or technology, has a progressive learning curve. Central to establishing a successful robotic program is the development of a dedicated thoracic robotic team that involves anesthesiologists, nurses, and bed-side assistants. With an additional surgeon console, the robot is an excellent platform for teaching. Compared to current methods of video-assisted thoracoscopic surgery (VATS), the robot offers improved wristed motion, a magnified, high definition three-dimensional vision, and greater surgeon control of the operation. These advantages are paired with integrated adjunctive technology such as infrared imaging. For pulmonary resection, these advantages of the robotic platform have translated into several clinical benefits, such as fewer overall complications, reduced pain, shorter length of stay, better postoperative pulmonary function, lower operative blood loss, and a lower 30-day mortality rate compared to open thoracotomy. With increased experience, cases of greater complexity are being performed. This review article details the process of becoming an experienced robotic thoracic surgeon and discusses a series of challenging cases in robotic thoracic surgery that a surgeon may encounter “beyond the learning curve”. Nearly all thoracic surgery can now be approached robotically, including sleeve lobectomy, pneumonectomy, resection of large pulmonary and mediastinal masses, decortication, thoracic duct ligation, rib resection, and pulmonary resection after prior chest surgery and/or chemoradiation.

Transference of skills in robotic vs. laparoscopic simulation: a randomized controlled trial

BACKGROUND

Elucidating how robotic skills are best obtained will enable surgeons to best develop future robotic training programs. We perform a randomized controlled trial to assess the performance of robotic compared to laparoscopic surgery, transference of pre-existing skills between the two modalities, and to assess the learning curve between the two using novice medical students.

METHODS

Forty students were randomized into either Group A or B. Students practiced and were tested on a peg transfer task in either a laparoscopic simulator (LS) and robotic simulator (RS) in a pre-defined order. Performance, transference of skills and learning curve were assessed for each modality. Additionally, a fatigue questionnaire was issued.

RESULTS

There was no significant difference between overall laparoscopic scores (219 ± 19) and robotic scores (227 ± 23) (p = 0.065). Prior laparoscopic skills performed significantly better on robotic testing (236 ± 12) than without laparoscopic skills (216 ± 28) (p = 0.008). There was no significant difference in scores between students with prior robotic skills (223 ± 16) than without robotic skills (215 ± 22) (p = 0.162). Students reported no difference in fatigue between RS and LS. The learning curve plateaus at similar times between both modalities.

CONCLUSION

Novice medical students with laparoscopic skills performed better on a RS test than students without laparoscopic training, suggesting a transference of skills from laparoscopic to robotic surgery. These results suggest laparoscopic training may be sufficient in general surgery residencies as the skills transfer to robotic if used post-residency.

Effects of robot- and video-assisted thoracoscopic lobectomy experiences on the learning curve of lobectomy

BACKGROUND

This study aims to investigate the effects of robot- and videoassisted thoracoscopic lobectomy on the learning curve of lobectomy.

METHODS

Between September 2013 and February 2020, the first 68 consecutive patients (28 males, 40 females; median age: 71 years; range, 33 to 86 years) who were operated for lung malignancies and scheduled for robot-assisted thoracoscopic lobectomy were retrospectively analyzed. The characteristics of the patients and operative data were analyzed, and the operation times of the first 51 cases of video-assisted thoracoscopic lobectomy were compared with those of robot-assisted thoracoscopic lobectomy performed by a single surgeon.

RESULTS

Of the patients, 62 had primary lung cancer and six had metastatic lung tumors. The majority of primary lung cancer patients (87.1%) had an adenocarcinoma. The most common clinical stage was IA1 (30.9%). There was no emergent conversion to thoracotomy in any of the patients. The median operation time was 223.5 min, and console time was 151 min. The most common complication was an air leak. All patients were alive. Compared to video-assisted thoracoscopic lobectomy, the median operation time was significantly longer in the robot-assisted thoracoscopic lobectomy group (p=0.0002). Similar to the operation time learning curve of the video-assisted thoracoscopic surgery group, the operation time learning curve of the robotassisted thoracoscopic surgery group increased from the first to ninth case (Phase 1), plateaued from the 10th t o 14th c ase ( Phase 2 ), and decreased from the 15th case (Phase 3). There was a statistically significant decrease in the operation time between Phase 1 and Phase 3 (p=0.0063).

CONCLUSION

The results of robot-assisted thoracoscopic lobectomy by a single surgeon show that this surgery has a longer operation time, but the perioperative outcomes are satisfactory. The learning curve of this surgery may be gradual for experienced video-assisted thoracoscopic surgeons.

The robotic surgery learning curve of a surgeon experienced in video-assisted thoracoscopic surgery compared with his own video-assisted thoracoscopic surgery learning curve for anatomical lung resections

OBJECTIVES

Robotic surgery, although it shares some technical features with video-assisted thoracoscopic surgery (VATS), offers some advantages, such as ergonomic design and a 3-dimensional view. Thus, the learning curve for robotic lung resection could be expected to be shorter than that of VATS for surgeons who are proficient in VATS. The goal of this study was to analyse the robotic learning curve of a VATS experienced surgeon and to compare it to his own VATS learning curve for anatomical lung resections.

METHODS

We conducted a retrospective observational study based on the prospectively recorded data of the first 150 anatomical lung resections performed with VATS (75 cases) and with the robotic (75 cases) approach by the same surgeon in our centre. Learning curves were analysed using the cumulative sum method to assess the trends for total operating time and surgical failure (intraoperative complications, conversion, technical postoperative complications and reintervention) across case sequences. Subsequently, using adequate statistical tests, we compared the postoperative outcomes in both groups.

RESULTS

The median operating time was similar for both approaches (P = 0.401). Surgical failure rate was higher for the robotic cases (21.3% vs 12%; P = 0.125). Based on cumulative sum analyses, operating time decreased starting with case 34 in the VATS group and with case 32 in the robotic cohort. Surgical failure tended to decline starting with case 28 in the VATS group and with case 32 in the robotic group. Perioperative results were similar in both groups.

CONCLUSIONS

When we compared robotic and VATS learning curves for anatomical lung resection, we did not find any differences. Postoperative outcomes were also similar with both approaches.

Intrathoracic gastric volvulus complicating a robotic left upper lobectomy: A case report and review of literature

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Intrathoracic gastric volvulus.

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Robotic Lobectomy unusual complication.

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Para esophageal hernias a cause for concern during robotic thoracic procedures.

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Gastric Ischemia.

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Etiology of gastric volvulus.

Unrecognized intrathoracic gastric volvulus can be a life-threatening condition, especially in elderly individuals undergoing major surgical procedures. We herein report the first case of a gastric volvulus after a robot-assisted left upper lobectomy for non-small-cell lung cancer in a patient with a known paraesophageal hernia. The operative procedure was performed by Dr Jacques Fontaine a senior thoracic surgeon at Moffitt Cancer Center in Tampa Florida a major academic institution. This operation was complicated by a large type-III hiatal hernia, with most of the stomach having herniated into the left pleural cavity and demonstrating organo-axial torsion one day after the indexed operation for the lung cancer. The patient required emergency surgery due to gastric ischemia. The patient underwent exploratory laparotomy with reduction of the volvulus and closure of the esophageal hiatus at that time. The patient was taken back to the operating room for a planned relook 24 h after the exploratory laparotomy to assess viability of the stomach. Unfortunately, the second look revealed necrotic areas of the stomach, which required to be resected. Given her age and poor nutritional status, we elected to place a feeding jejunostomy tube. Her postoperative course was marred by an abdominal wound infection treated with a wound vacuum-assisted closure device. Ultimately she was discharged home on POD#19 tolerating a regular diet. This case report highlights that in the elderly patients undergoing left lung resection with a known large hiatal hernia, the index of suspicion for herniation must be high and prompt recognition can avert mortality or morbidity.

Perioperative outcomes of robot-assisted vs video-assisted and traditional open thoracic surgery for lung cancer: A systematic review and network meta-analysis

BACKGROUND

The superiority of robot-assisted thoracic surgery (RATS) over video-assisted thoracic surgery (VATS) and thoracotomy remains controversial for lung cancer.

METHODS

A network meta-analysis (NMA) and pairwise meta-analysis (PMA) were performed to evaluate the perioperative outcomes using five databases.

RESULTS

Thirty-two studies involving 6593 patients were included for analysis. The NMA showed that RATS had similar operative time, conversion rate to thoracotomy, number of lymph node, postoperative morbidity, and length of hospital stay with VATS, except for lower 30-day mortality. Compared with thoracotomy, longer operative time and shorter hospital stay were observed in RATS, but no significant difference was observed in number of lymph node, postoperative morbidity, and 30-day mortality in both NMA and PMA. In lobectomy/segmentectomy subgroup, all outcomes, except for operative time of RATS vs VATS and number of lymph node, were similar with overall analyses.

CONCLUSIONS

RATS had comparable perioperative outcomes with VATS and open surgery.

[Minimally invasive thoracoscopic and robot-assisted lobectomy]

OBJECTIVE

To compare the early outcomes of video-assisted and robot-assisted lobectomy.

MATERIAL AND METHODS

There were 74 robot-assisted lung resections for the period 2017-2018. Sixty-six patients underwent lobectomy and 8 - segmentectomy. Patients were divided into 2 groups to compare thoracoscopic procedures. The main group consisted of 66 patients after robot-assisted lobectomy. The control group included 247 patients after video-assisted lobectomy. Mean age of patients was 60.5 years (range 35-70). There 72% men and 28% women.

CONCLUSION

Both thoracoscopic procedures are characterized by high efficiency and safety. However, certain disadvantages are peculiar to both methods.

Defining the learning curve of robotic thoracic surgery: what does it take?

BACKGROUND

Controversy exists as to what constitutes a learning curve to achieve competency, and how the initial learning period of robotic thoracic surgery should be approached.

METHODS

We conducted a systematic review of the literature published prior to December 2018 using PubMed/MEDLINE for studies of surgeons adopting the robotic approach for anatomic lung resection or thymectomy. Changes in operating room time and outcomes based on number of cases performed, type of procedure, and existing proficiency with video-assisted thoracoscopic surgery (VATS) were examined.

RESULTS

Twelve observational studies were analyzed, including nine studies on robotic lung resection and three studies on thymectomy. All studies showed a reduction in operative time with an increasing number of cases performed. A steep learning curve was described for thymectomy, with a decrease in operating room time in the first 15 cases and a plateau after 15-20 cases. For anatomic lung resection, the number of cases to achieve a plateau in operative time ranged between 15-20 cases and 40-60 cases. All but two studies had at least some VATS experience. Six studies reported on experience of over one hundred cases and showed continued gradual improvements in operating room time.

CONCLUSION

The learning curve for robotic thoracic surgery appears to be rapid with most studies indicating the steepest improvement in operating time occurring in the initial 15-20 cases for thymectomy and 20-40 cases for anatomic lung resection. Existing data can guide a standardized robotic curriculum for rapid adaptation, and aid credentialing and quality monitoring for robotic thoracic surgery programs.

Learning curve for robot-assisted lobectomy of lung cancer

Background

Robotic lobectomy is widely used for lung cancer treatment. So far, few studies have been performed to systematically analyze the learning curve. Our purpose is to define the learning curve to provide a training guideline of this technique.

Methods

A total of 208 consecutive patients with primary lung cancer who underwent robotic-assisted lobectomy by our surgical team were enrolled in this study. Baseline information and postoperative outcomes were collected. Learning curves were then analyzed using the cumulative sum (CUSUM) method. Patients were divided into three groups according to the cut-off points of the learning curve. Intraoperative characteristics and short-term outcomes were compared among the three groups.

Results

CUSUM plots revealed that the docking time, console time and total surgical time in patients were 20, 34 and 32 cases, respectively. Comparison of the surgical time among the 3 phases revealed that the total surgical time (197.03±27.67, 152.61±21.07, 141.35±29.11 min, P<0.001), console time (150.97±26.13, 103.89±18.04, 97.49±24.80 min, P<0.001) and docking time (13.53±2.08, 11.95±1.10, 11.89±1.49 min, P<0.001) were decreased significantly. Estimated blood loss differed among groups (90.63±45.41, 87.63±59.84, 60.29±28.59 mL, P=0.001) and was associated with shorter operative time. There was no conversion or 30-day mortality. No significant differences were observed among other clinic-pathological characteristics among the groups.

Conclusions

For a surgeon, the learning time of robotic lobectomy was in the 32th operation. For a bedside assistant, at least 20 cases were required to achieve the level of optimal docking.

Robotic thoracic surgery: a support team to replace the bedside surgeon

Establishing a new robotics program presents a unique set of challenges that differ from routine operative procedures. These include training robotic staff, operating room logistics, and surgeon training. As the surgeon moves away from the patient bedside, the responsibilities of the bedside team and circulating staff must increase to fill that void. Therefore, a critical element of robotic thoracic surgery is the training of the robotic team to facilitate fluid movement of the patient through the surgical process. We report our process in establishing a thoracic robotics program with an emphasis on the training personnel.