Implementation of a robot-assisted thoracic surgery program at a German center: analysis of the first 100 consecutive cases

Abstract

Background

Robot-assisted thoracic surgery (RATS) is increasingly being implemented in Europe, but data on the early institutional adoption and integration of RATS programs in Germany remain limited. This study describes the implementation of a RATS program at a German thoracic surgery center and evaluates operative characteristics, procedural safety, and temporal changes during the first 100 consecutive cases.

Methods

This retrospective study included 100 consecutive RATS procedures performed between July 2024 and January 2025. Procedures included lobectomies, segmentectomies, bisegmentectomies, trisegmentectomies, wedge resections [diagnostic, lung-volume-reduction surgery (LVRS) and pneumothorax], and mediastinal tumor resections. Cumulative sum (CUSUM) analysis was used as an exploratory tool to assess temporal changes in total operative time, console time, and docking time during program implementation. Phase comparisons were performed using the Mann-Whitney U test.

Results

The 100 patients had a median age of 70 years (range, 39–87 years); 58% were male. Median total operative time was 100 min, median console time 80 min, and median docking time 12 min. The conversion rate was 5.0% and the intraoperative complication rate 3.0%. CUSUM analysis demonstrated a temporal inflection point around case 36 for total operative time and around case 31 for docking time. Docking time decreased significantly from Phase I to Phase II (median 15.5 vs. 11.0 min; P<0.001), and estimated blood loss also decreased significantly (median 50 vs. 30 mL; P=0.006). A significant downward trend was observed for docking time across all cases (Spearman rho =−0.559, P<0.001).

Conclusions

The first 100 consecutive RATS procedures at our institution demonstrate that implementation of a robotic thoracic surgery program in a German thoracic surgery center is feasible and can be performed with acceptable operative outcomes, a low conversion rate, and a low incidence of intraoperative complications. Temporal improvements were observed particularly in docking performance, suggesting progressive team-based adaptation to the robotic workflow. Because of procedural heterogeneity and the exploratory nature of the analysis, the present findings should be interpreted primarily as an evaluation of program implementation rather than as a definitive determination of a learning threshold.

Highlight box.

Key findings

• This study describes the early implementation of a robot-assisted thoracic surgery (RATS) program in a German thoracic surgery center and demonstrates progressive improvement in docking workflow during the first 100 consecutive cases.

What is known and what is new?

• RATS adoption in Germany remains limited, and German early implementation data are scarce.

• This study adds institutional implementation data and uses exploratory cumulative sum analysis to describe temporal changes during program adoption.

What is the implication, and what should change now?

• Docking time may be a useful marker of team-based workflow maturation during RATS implementation. Procedure-specific and risk-adjusted analyses are needed to define learning curves more precisely.

Introduction

Minimally invasive approaches have redefined thoracic surgery, shifting the focus from traditional open thoracotomy to techniques that reduce tissue trauma, accelerate recovery, and expand treatment options for lung and mediastinal diseases. Video-assisted thoracoscopic surgery (VATS) established minimally invasive thoracic surgery as a standard for many thoracic conditions, delivering less pain, shorter hospital stays, and fewer pulmonary complications than open surgery (1-3). Building on these gains, robot-assisted thoracic surgery (RATS) has emerged as a significant evolution within minimally invasive thoracic surgery, offering enhanced three-dimensional visualization, articulated instrumentation, and ergonomic advantages that may translate into improved precision in confined intrathoracic spaces and more thorough mediastinal lymphadenectomy (1,4,5).

In Germany and other European health systems, growing adoption of minimally invasive surgery has coincided with careful evaluation of the oncologic equivalence or superiority of RATS in selected settings, alongside important economic and organizational considerations for program implementation (2,6,7). However, compared with developments in the United States and other European countries, RATS has been slow to spread in Germany. As recently reported, only approximately 6.8% of all lobectomies in Germany were performed robotically, compared to 17.5% in the United States (6). This hesitance has been attributed to high investment and maintenance costs, inadequate reimbursement by German health insurers, and the well-established tradition of VATS at many German centers (6,8).

For a German thoracic surgery center, the integration of RATS entails more than acquiring a robotic platform. It demands deliberate planning around training curricula, credentialing, proctoring, operating room logistics, and alignment with enhanced recovery pathways. The emerging literature underscores that structured, team-based training—often incorporating simulation, dual-console systems, and staged skill progression—facilitates safe initiation and sustained performance of robotic lung resections after an initial learning period (1,9,10). Comparative real-world data from Germany and Europe suggest that, in experienced centers, RATS can shorten hospital stay and reduce certain postoperative complications relative to open or VATS approaches (6,11,12).

When implementing new surgical techniques, the concept of the learning curve—the process by which surgical proficiency improves with accumulated experience—is of particular clinical relevance. Understanding the learning curve is essential for ensuring patient safety during the transition period, for establishing training protocols, and for planning resource allocation. The cumulative sum (CUSUM) method is a well-established statistical tool originally developed for industrial quality control that has been widely applied to the evaluation of surgical learning curves (13,14). Several studies from Japan, Norway, China, and the United States have utilized CUSUM to analyze the learning curve of RATS lobectomy, reporting values ranging from 5–6 cases for individual experienced surgeons to 28–117 cases for institutional teams (15-18). A systematic review of 22 studies found that technical proficiency with robotic lobectomy was achieved at a mean of 25.3±12.6 cases (7).

Despite the growing body of international evidence, data regarding the implementation of RATS programs in German thoracic surgery centers remain limited. While learning-curve analyses have been reported from several international institutions, comparable data from Germany are scarce.

The aim of this study was to describe the implementation of a RATS program at a German thoracic surgery center and to evaluate operative characteristics, procedural safety, and temporal changes during the first 100 consecutive RATS procedures. CUSUM analysis was performed as an exploratory tool to assess changes in operative performance during program adoption. We present this article in accordance with the STROBE reporting checklist (available at https://jtd.amegroups.com/article/view/10.21037/jtd-2026-1065/rc).

Methods

Patients

This retrospective cohort study analyzed the first 100 consecutive RATS procedures performed at the Department of Thoracic Surgery, Knappschaft Kliniken Luenen, between July 5, 2024 and January 9, 2025. All consecutive patients undergoing RATS lobectomy, segmentectomy, bisegmentectomy, trisegmentectomy, wedge resection, and mediastinal tumor resection were included, provided the procedures were performed within the first 100 robotic cases by two surgeons who were experienced in VATS prior to adopting robotic surgery. Cases converted to open thoracotomy for non-technical reasons before robotic docking, as well as cases with incomplete intraoperative data, were excluded. The study was approved by the Ethics Committee of the Ärztekammer Westfalen-Lippe (No. 2025-1004-f-S) and conducted in accordance with the Declaration of Helsinki and its subsequent amendments. Written informed consent was obtained from all participants prior to inclusion in the study.

Surgical procedure

All procedures were performed using the da Vinci Xi robotic surgical system (Intuitive Surgical Inc., Sunnyvale, CA, USA). For anatomical resections, a five-port configuration was used in all anatomical resections, consisting of four robotic ports and one assistant port. During the initial phase of the program, a four-port approach was used in selected cases. In two particularly simple wedge resections, a reduced three-port setup was applied. Prior to implementation of the robotic program, both operating surgeons had extensive experience in minimally invasive thoracic surgery and routinely performed VATS anatomical lung resections. The introduction of RATS was preceded by structured robotic training, including simulation-based training and proctored procedures. During the implementation phase, case selection was performed according to institutional standards and progressively expanded as experience with the robotic platform increased. During this period, the robotic platform was available 24/7, which allowed progressive integration of RATS across the full spectrum of thoracic indications at our institution rather than restricting robotic use to anatomical resections only. For mediastinal tumor resections, the robotic approach was selected according to lesion location and laterality, using either a right-sided or left-sided thoracic approach; a subxiphoid approach was not routinely used in this cohort. Docking and operating room setup were performed within a dedicated robotic team consisting of two robotic surgeons and two experienced robotic operating room nurses. In parallel, the remaining nursing staff underwent structured stepwise training under the supervision of two experienced and certified operating room nurses. With increasing experience, standardized port positioning, fixed team roles, and repeated use of a consistent docking sequence contributed to shorter docking times.

Data collection

Data were obtained from operative reports, anesthesia records, and intraoperative nursing logs. For each procedure, the following variables were recorded: patient age, sex, date of surgery, laterality, type of operation, docking time, console time, total operative time, conversion rate, estimated blood loss, and intraoperative complications. Docking time was defined as the interval from skin incision to successful docking of the robotic system and establishment of robotic instrumentation, recorded in minutes. Console time was defined as the duration of surgeon activity at the robotic console. Total operative time was defined as the time from skin incision to wound closure. Conversion was defined as conversion to multiport VATS or open thoracotomy. Cases converted to open thoracotomy for non-technical reasons before robotic docking and/or cases with incomplete intraoperative data were excluded. Estimated blood loss was recorded in milliliters based on the surgeon’s operative report prepared immediately after surgery. The estimate was based on the surgeon’s assessment, the number of peanut sponges used, and the volume of aspirated fluid. Intraoperative complications were defined as adverse events occurring during the procedure, including vascular injury, significant air leaks requiring intervention, hemodynamic instability, or events necessitating conversion. No missing data were identified for the analyzed intraoperative variables.

Statistical analysis

CUSUM analysis was adopted as an exploratory descriptive method to evaluate temporal changes during implementation of the RATS program. CUSUM was calculated by sequentially accumulating the difference between each individual operative time and the mean operative time for all cases.

$$CUSU{M}_n={\displaystyle \sum i=}\ln \left(t_i-\overline{t}\right)$$ [1]

where t_i indicates each individual time, and $\overline{t}$ indicates the mean time of all cases. The CUSUM curve was interpreted descriptively to identify temporal inflection points during program adoption. Because the analysis was not risk-adjusted and the cohort included heterogeneous procedure types, inflection points were not interpreted as definitive thresholds of operative proficiency.

CUSUM analyses were performed separately for total operative time, console time, and docking time, both for all 100 consecutive cases and for the subgroup of anatomical resections. Continuous variables were expressed as median (range) or mean ± standard deviation (SD). Categorical variables were expressed as numbers and percentages. Comparisons between exploratory phases were performed using the Mann-Whitney U test. Trend analysis was performed using the Spearman rank correlation coefficient. A P value of <0.05 was considered statistically significant. Statistical analyses were performed using Python (SciPy library, version 1.11). The CUSUM analyses were based on the combined institutional consecutive-case series of two surgeons and were intended to reflect program implementation rather than surgeon-specific learning curves.

Results

Patient characteristics

A total of 100 consecutive RATS procedures were performed during the study period of 7 months (July 2024 to January 2025). Detailed patient characteristics are shown in Table 1. The median age was 70 years (range, 39–87 years), and 58 patients (58%) were male. Right-sided procedures accounted for 54% and left-sided for 46%. The most frequent procedures were segmentectomies (34%), followed by lobectomies (27%), wedge resections for pneumothorax (12%), wedge resections for diagnostic purposes (9%), bisegmentectomies (6%), wedge resections for lung-volume-reduction surgery (LVRS) (6%), mediastinal tumor resections (5%), and trisegmentectomies (1%). In total, 68 (68%) of all procedures were anatomical resections.

Table 1. Patient characteristics and operative parameters (N=100).

Parameter	Value
Patient characteristics
Age, years	70 [39–87]; 69.0±9.8
Sex
Male	58 (58.0)
Female	42 (42.0)
Side of operation
Right	54 (54.0)
Left	46 (46.0)
Type of operation
Segmentectomy	34 (34.0)
Lobectomy	27 (27.0)
Wedge resection (pneumothorax)	12 (12.0)
Wedge resection (diagnostic)	9 (9.0)
Bisegmentectomy	6 (6.0)
Wedge resection (LVRS)	6 (6.0)
Mediastinal tumor resection	5 (5.0)
Trisegmentectomy	1 (1.0)
Anatomical resections total	68 (68.0)
Operative parameters
Total operative time, min	100 [45–189]
Total operative time, min	102.6±32.0
Console time, min	80 [25–150]
Console time, min	75.3±28.7
Docking time, min	12 [6–35]
Docking time, min	13.0±5.4
Estimated blood loss, mL	40 [5–500]
Estimated blood loss, mL	62.4±71.3
Safety outcomes
Conversion to thoracotomy	5 (5.0)
Intraoperative complications	3 (3.0)

Data are expressed as n (%), mean ± standard deviation or median [range]. LVRS, lung-volume-reduction surgery.

Data are expressed as number or median [range]. Comparisons were performed using the Mann-Whitney U test.

Operative parameters

The median total operative time for all cases was 100 min (range, 45–189 min), with a mean of 102.6±32.0 min. The median console time was 80 min (range, 25–150 min), and the median docking time was 12 min (range, 6–35 min). The median estimated blood loss was 40 mL (range, 5–500 mL).

Conversions and complications

Conversion to open thoracotomy occurred in 5 cases (5.0%). The reasons for conversion were fibrothorax (case 19), morbid obesity (case 36), intraoperative bleeding from the A6 artery (case 53), intraoperative bleeding from another artery (case 67), and thick tissue impeding robotic manipulation (case 92). Intraoperative complications were recorded in 3 cases (3.0%), two of which were bleeding events leading to conversion and one case of hemodynamic instability (Table 2).

Table 2. Structured summary of conversions to thoracotomy (n=5).

Case number	Phase	Reason for conversion	Outcome
19	Phase I	Fibrothorax	Conversion to thoracotomy; managed intraoperatively
36	Phase I	Morbid obesity	Conversion to thoracotomy; managed intraoperatively
53	Phase II	Bleeding from A6 artery	Conversion to thoracotomy; managed intraoperatively
67	Phase II	Bleeding from another artery	Conversion to thoracotomy; managed intraoperatively
92	Phase II	Thick tissue impeding robotic manipulation	Conversion to thoracotomy; managed intraoperatively

Implementation of RATS: exploratory CUSUM analysis

The exploratory CUSUM analysis for total operative time across all 100 consecutive cases is shown in Figure 1A. The mean total operative time was 102.6 min. The CUSUM curve reached its peak around case 36, suggesting a temporal inflection point during program implementation. Because operative time is influenced by procedure type and case complexity, this finding should not be interpreted as a definitive learning threshold.

Figure 1.

The CUSUM plot for RATS procedures. (A) The plot regarding total operative time. The curve shows a temporal inflection point around case 36. (B) The plot regarding console time. The curve shows a continued increase throughout the series, with the peak at case 97, possibly reflecting increasing case complexity during program implementation. CUSUM, cumulative sum; RATS, robot-assisted thoracic surgery.

The CUSUM analysis for console time is shown in Figure 1B. The mean console time was 75.3 min. The console-time CUSUM did not show a clear early peak but rather a gradual and continued increase, with the peak at case 97. This pattern may reflect increasing case complexity and progressive expansion of robotic indications during the implementation phase rather than a simple measure of technical maturation.

The CUSUM analysis for docking time is shown in Figure 2. The mean docking time was 13.0 min. The CUSUM curve peaked around case 31, indicating progressive improvement in docking workflow. Because docking is a relatively standardized and repetitive process, docking time may represent a meaningful indicator of team-based adaptation during implementation of the robotic program.

Figure 2.

The CUSUM plot for docking time. The curve shows a temporal inflection point around case 31, suggesting progressive improvement in standardized docking workflow. CUSUM, cumulative sum.

A separate exploratory CUSUM analysis restricted to the 68 anatomical resections (lobectomies, segmentectomies, bisegmentectomies, and trisegmentectomies) showed a peak at the 14th anatomical case (corresponding to overall case 25), providing additional subgroup information for a more clinically coherent cohort (Figure 3). This subgroup result was interpreted descriptively and not as a definitive proficiency threshold.

Figure 3.

The CUSUM plot for anatomical resections only (total operative time). The peak was reached at the 14th anatomical case (corresponding to overall case 25). CUSUM, cumulative sum.

Phase comparisons

Patients were stratified into exploratory Phase I (cases 1–36) and Phase II (cases 37–100) based on the CUSUM peak for total operative time. The results are shown in Table 3. Because the cohort was heterogeneous and the CUSUM analysis was not risk-adjusted, this phase division was used for descriptive comparison only.

Table 3. Interphase comparison of patient characteristics and operative parameters.

Parameter	Phase I (N=36)	Phase II (N=64)	P value
Age, years	66 [56–84]	72 [39–87]	–
Sex, male/female	17/19	41/23	–
Total operative time, min	105 [50–189]	100 [45–175]	0.31
Console time, min	75 [25–128]	80 [25–150]	0.29
Docking time, min	15.5 [8–35]	11 [6–18]	<0.001
Estimated blood loss, mL	50 [5–300]	30 [10–500]	0.006

Total operative time and console time did not differ significantly between the two phases (P=0.31 and P=0.29, respectively). Docking time was significantly shorter in Phase II (median 11 vs. 15.5 min, P<0.001). Estimated blood loss was also significantly lower in Phase II (median 30 vs. 50 mL, P=0.006). These findings suggest improvement in standardized team workflow, particularly docking performance, while overall operative-time improvement could not be definitively demonstrated.

Trend analysis revealed a significant downward trend for docking time across all consecutive cases (Spearman rho =−0.559, P<0.001), a significant upward trend for console time (rho =0.277, P=0.005), and no significant trend for total operative time (rho =0.097, P=0.34) (Figure 4).

Figure 4.

Scatter plots with linear regression for operative times across consecutive cases. (A) Total operative time; (B) console time; (C) docking time.

Discussion

This study describes the implementation of a RATS program during the first 100 consecutive robotic thoracic procedures at our institution. Rather than establishing a definitive learning threshold, the present analysis focuses on early adoption and integration of robotic surgery within a German thoracic surgery department. The observed temporal changes in operative metrics should therefore be interpreted as exploratory indicators of program maturation and workflow adaptation.

The exploratory CUSUM analysis for total operative time suggested a temporal inflection point around case 36. However, we agree that this finding must be interpreted cautiously because operative time is strongly influenced by procedure type, case complexity, adhesions, obesity, conversion, and other patient- and procedure-specific factors. Therefore, the revised manuscript does not claim that operative proficiency was definitively achieved after 36 cases.

The heterogeneous case mix is particularly relevant in the present cohort, which included lobectomies, segmentectomies, bisegmentectomies, trisegmentectomies, wedge resections, LVRS procedures, pneumothorax operations, and mediastinal tumor resections. Pooling these procedures in a single unadjusted CUSUM analysis may reflect changes in case selection as well as learning effects. For this reason, the anatomical resection subgroup analysis is presented as additional exploratory information for a more clinically coherent cohort rather than as a primary determinant of a definitive threshold.

Docking time showed the most consistent improvement, with a CUSUM inflection point around case 31 and a highly significant downward trend (rho =−0.559, P<0.001). Because docking is a standardized, repetitive, team-dependent process and is less influenced by intrathoracic case complexity, it may be a useful indicator of workflow maturation during RATS program implementation. The significant reduction from a median of 15.5 min in Phase I to 11.0 min in Phase II supports progressive team-based adaptation to the robotic platform.

Console time showed a slight upward trend over the series (rho =0.277, P=0.005). This finding should not be interpreted as a lack of feasibility but rather as evidence that case complexity may have increased over time as the surgical team gained confidence and progressively expanded robotic indications. This observation further supports the need for cautious interpretation of unadjusted operative-time-based learning curves in heterogeneous early implementation cohorts.

The overall conversion rate of 5.0% and intraoperative complication rate of 3.0% were acceptable for an early implementation cohort. However, we have toned down our safety claims because postoperative complications, length of stay, readmission, reoperation, mortality, pathological outcomes, lymph node yield, and other oncologic quality indicators were not available for the present analysis. Therefore, the present study supports the feasibility of intraoperative implementation but cannot provide a comprehensive assessment of postoperative safety or oncologic quality.

RATS has been slow to spread in Germany compared to other European countries and the United States (8). The main barriers include high investment and maintenance costs that are not adequately covered by the German DRG system, a strong existing VATS tradition, and a conservative healthcare culture in some regions. Our findings add German early implementation data and suggest that integration of a RATS program can be achieved with acceptable intraoperative outcomes when performed by surgeons experienced in minimally invasive thoracic surgery after structured simulation-based and proctored training (19).

This study has several limitations. First, it is a single-center retrospective analysis, which limits generalizability. Second, the cohort was heterogeneous and included multiple procedure types with different levels of technical complexity, limiting interpretation of a single operative-time-based CUSUM analysis. Third, the CUSUM analysis was descriptive and not risk-adjusted; therefore, procedure type, case complexity, surgeon-specific factors, and other determinants of operative time were not formally accounted for. Fourth, the identification of inflection points was exploratory and based on visual interpretation of CUSUM curves rather than formal change-point validation. Fifth, postoperative outcomes and oncologic outcomes were not available for this analysis. Finally, the present findings primarily reflect early institutional implementation and workflow adaptation rather than a definitive determination of a learning threshold.

Conclusions

The first 100 consecutive RATS procedures at our institution demonstrate that implementation of a robotic thoracic surgery program in a German thoracic surgery center is feasible and can be performed with acceptable intraoperative outcomes, a low conversion rate, and a low incidence of intraoperative complications. Temporal improvements were observed particularly in docking performance, suggesting progressive team-based adaptation to the robotic workflow. Because of procedural heterogeneity and the exploratory nature of the analysis, the present findings should be interpreted primarily as an evaluation of program implementation rather than as a definitive determination of a learning threshold. This study should be interpreted as an early institutional implementation report rather than as proof of completion of a surgical learning curve. Further procedure-specific and multicenter studies are warranted.

Supplementary

The article’s supplementary files as

Ethical Statement: The authors are accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved. The study was approved by the Ethics Committee of the Ärztekammer Westfalen-Lippe (No. 2025-1004-f-S) and conducted in accordance with the Declaration of Helsinki and its subsequent amendments. Written informed consent was obtained from all participants prior to inclusion in the study.

Data Sharing Statement

Available at https://jtd.amegroups.com/article/view/10.21037/jtd-2026-1065/dss

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DOI: 10.21037/jtd-2026-1065