New nomenclature for robotic-assisted thoracic surgery also gets rid of RATS

Defining the learning curve of robotic portal segmentectomy in small pulmonary lesions: a prospective observational study

Although robotic segmentectomy has been applied for the treatment of small pulmonary lesions for many years, studies on the learning curve of robotic segmentectomy are quite limited. Thus, we aim to investigate the learning curve of robotic portal segmentectomy with 4 arms (RPS-4) using prospectively collected data in patients with small pulmonary lesions. One hundred consecutive patients with small pulmonary lesions who underwent RPS-4 between June 2018 and April 2021 were included in the study. Da Vinci Si/Xi systems were used to perform RPS-4. The mean operative time, console time, and docking time for the entire cohort were 119.2 ± 41.6, 85.0 ± 39.6, and 6.6 ± 2.8 min, respectively. The learning curve of RPS-4 can be divided into three different phases: 1-37 cases (learning phase), 38-78 cases (plateau phase), and > 78 cases (mastery phase). Moreover, 64 cases were required to ensure acceptable surgical outcomes. The total operative time (P < 0.001), console time (P < 0.001), blood loss (P < 0.001), and chest tube duration (P = 0.014) were reduced as experience increased. In conclusion, the learning curve of RPS-4 could be divided into three phases. 37 cases were required to pass the learning phase, and 78 cases were needed to truly master this technique.

Cost-effectiveness Analysis of Robotic-assisted Lobectomy for Non-small Cell Lung Cancer

BACKGROUND

Robot-assisted thoracic surgery has emerged as an alternative to video-assisted thoracic surgery (VATS) for treating patients with resectable non-small cell lung cancer (NSCLC). The objective of this study was to evaluate the cost-effectiveness of robotic-assisted lobectomy (RAL) compared to VATS and open lobectomy for adults with NSCLC.

METHODS

A decision analysis model was employed to compare the cost-effectiveness of RAL, VATS, and open lobectomy with 1-year time horizon from both healthcare and societal perspectives. Healthcare costs (2020$) and quality-adjusted life-years (QALYs) were compared between the approaches. Incremental cost-effectiveness ratios (ICERs) were calculated in terms of cost per QALY gained. Sensitivity analyses were performed to identify variables driving cost-effectiveness across several willingness-to-pay (WTP) thresholds.

RESULTS

Open thoracotomy was not cost-effective compared to both RAL and VATS lobectomy. From the healthcare sector perspective, RAL was $394.97 more expensive per case than VATS resulting in an ICER of $180,755.10 per QALY. From the societal perspective, RAL was $247.77 more expensive per case than VATS, resulting in an ICER of $113,388.80 per QALY. RAL becomes cost-effective with marginally lower robotic instrument costs, shorter operating room times, lower conversion rates, shorter lengths of stay, higher hospital volumes, and improved quality of life. RAL is also cost-effective if surgeons can increase the proportion of minimally invasive lobectomies using robotic technology.

CONCLUSIONS

Compared to VATS, RAL is not cost-effective for lung cancer lobectomy at lower WTP thresholds. However, several factors may drive RAL to emerge as the more cost-effective approach for minimally invasive lung cancer resection.

Learning curve of robotic portal lobectomy for pulmonary neoplasms: A prospective observational study

BACKGROUND

We aim to assess the learning curve of robotic portal lobectomy with four arms (RPL-4) in patients with pulmonary neoplasms using prospectively collected data.

METHODS

Data from 100 consecutive cases with lung neoplasms undergoing RPL-4 were prospectively accumulated into a database between June 2018 and August 2019. The Da Vinci Si system was used to perform RPL-4. Regression curves of cumulative sum analysis (CUSUM) and risk-adjusted CUSUM (RA-CUSUM) were fit to identify different phases of the learning curve. Clinical indicators and patient characteristics were compared between different phases.

RESULTS

The mean operative time, console time, and docking time for the entire cohort were 130.6 ± 53.8, 95.5 ± 52.3, and 6.4 ± 3.0 min, respectively. Based on CUSUM analysis of console time, the surgical experience can be divided into three different phases: 1-10 cases (learning phase), 11-51 cases (plateau phase), and >51 cases (mastery phase). RA-CUSUM analysis revealed that experience based on 56 cases was required to truly master this technique. Total operative time (p < 0.001), console time (p < 0.001), and docking time (p = 0.026) were reduced as experience increased. However, other indicators were not significantly different among these three phases.

CONCLUSIONS

The RPL-4 learning curve can be divided into three phases. Ten cases were required to pass the learning curve, but the mastery of RPL-4 for satisfactory surgical outcomes requires experience with at least 56 cases.

Why comprehensive adoption of robotic assisted thoracic surgery is ideal for both simple and complex lung resections

Minimally invasive thoracoscopic surgical techniques have grown increasingly popular due to improved outcome measures compared to conventional rib-spreading thoracotomy. However, video-assisted thoracoscopic surgery (VATS) presents with unique technical challenges that have limited its role in certain cases. Here, we discuss our perspectives on the implementation of a successful robotic thoracic program. We will then present the case for how the adoption of robotic assisted thoracic surgery (RATS) provides the benefits of minimally invasive VATS while still retaining the technical finesse of bimanual articulating instruments and 3-dimensional imaging that is a universal component of any open surgery. We will also discuss how to overcome some of the perceived disadvantages to RATS in regard to the higher cost, lack of tactile feedback and potential safety concerns.

Outcomes of major complications after robotic anatomic pulmonary resection

BACKGROUND

There is a paucity of robust clinical data on major postoperative complications following robotic-assisted resection for primary lung cancer. This study assessed the incidence and outcomes of patients who experienced major complications after robotic anatomic pulmonary resection.

METHODS

This was a multicenter, retrospective review of patients who underwent robotic anatomic pulmonary resection between 2002 and 2018. Major complications were defined as grade III or higher complications according to the Clavien-Dindo classification. Statistical analysis was performed based on patient-, surgeon-, and treatment-related factors.

RESULTS

During the study period, 1264 patients underwent robotic anatomic pulmonary resections, and 64 major complications occurred in 54 patients (4.3%). Univariate analysis identified male sex, forced expiratory volume in 1 second, diffusion capacity of the lung for carbon monoxide, neoadjuvant therapy, and extent of resection as associated with increased likelihood of a major postoperative complication. Patient age, performance status, body mass index, reoperation status, and surgeon experience did not have a significant impact on major complications. Patients who experienced at least 1 major complication were at higher risk for an intensive care unit stay of >24 hours (17.0% vs 1.4%; P < .001) and prolonged hospitalization (8.5 days vs 4 days; P < .001). Patients who experienced a major postoperative complication had a 14.8% risk of postoperative death.

CONCLUSIONS

In this series, the major complication rate during the postoperative period was 4.3%. A number of identified patient- and treatment-related factors were associated with an increased risk of major complications. Major complications had a significant impact on mortality and duration of stay.

Surgical Management of Lung Cancer: History, Evolution, and Modern Advances

PURPOSE OF REVIEW

Although surgery for lung cancer was not common before the early twentieth century, it has enjoyed remarkable progress since then both in type of resection and technical approach. This has been coupled with significant technological advances. Here, we will review the history and evolution of this relatively new field of surgery.

RECENT FINDINGS

The gold standard of the extent of resection for lung cancer evolved from pneumonectomy to lobectomy to even sublobar resection for select situations. In addition, major advances have occurred in the technical aspect of the surgical procedure. The incisional approach has evolved from rib spreading thoracotomy to thoracoscopic surgery with the latter showing significant improvement in short-term outcomes over open thoracotomy. However, standard video-assisted thoracoscopic surgery or VATS is associated with visual and mechanical limitations, including lack of depth perception and rigid straight instruments. This makes it appropriate only for early-stage peripheral and small tumors. Most of the limitations of VATS can be overcome with the more recently introduced robotic-assisted thoracic surgery (RATS). RATS utilizes wristed instruments that are introduced in the chest through 8-mm ports and can mimic the movements of the human hand. In addition, magnified, three-dimensional and high definition imaging gives the surgeon an image of the lung unlike any other modality. This has allowed surgeons to perform advanced resections such as pneumonectomy or sleeve resection in a minimally invasive fashion. In addition, RATS has become a platform for the addition of other technical enhancements such as incorporating a near infra-red light source into the camera allowing identification of autoflourescent agents, such as indocyanin green. This has allowed localization of small nodules for resection and identification of tissue planes for sublobar resection. However, new technologies also require investments in time and money. Thoracic surgery for lung cancer has evolved to include advanced minimally invasive techniques including video-assisted and robotic-assisted thoracoscopy. RATS in particular may enable surgeons to perform more advanced procedures in a minimally invasive fashion. It is hoped that the higher costs of new surgical technology may be offset by the potential for improved patient outcomes and resultant socioeconomic benefits.