Abstract
Objective
Segmentectomy has been shown to be comparable with lobectomy for small lung cancers; however, it is unclear the degree to which the expansion and adoption of robotics have affected the use of segmentectomy. We aim to compare the use of segmentectomies performed between adopters and nonadopters of robotic surgery.
Methods
A retrospective case-control analysis was conducted using the National Cancer Database from 2010 to 2021. Sustained robotic adoption was defined as facility performance of ≥90% of lung resections robotically for 2+ consecutive years from 2016 onwards. Time periods for facilities were categorized by pretransition (<50% usage), transition (50%-90%) and posttransition (≥90%). For all facilities, the proportion of segmentectomies performed for <2 cm lung nodules was quantified along with clinical outcomes.
Results
In total, 10,045 cases of lung resection across 57 facilities met inclusion criteria. National use of segmentectomy increased from 5% in 2011 to 12% in 2019-2021. The proportion of segmentectomies performed for nodules meeting criteria has increased more with sustained robotic adopters (5.5% to 12.0%) compared with control facilities (4.0% to 6.3%) (difference-in-difference = 4.3%; 95% CI, 2.6%-6.0%). There was an increase in mean lymph node harvest for segmentectomy with sustained adopters (5.1 to 11.4) compared with control facilities (6.02 vs 10.2).
Conclusions
Facilities with sustained robotic adoption were more likely to have increased uptake of segmentectomy compared with control facilities. There was also an associated increase in mean lymph node sampling. With continued uptake of robotics, we expect to see greater use of segmentectomy for lung resection for nodules meeting criteria.
Key Words: robotic surgery, non–small cell lung cancer, outcomes
Graphical Abstract
Segmentectomy proportions between robotic adopting facilities and matched controls.
Central Message.
Facilities with sustained adoption of robotics showed increased use of segmentectomy for NSCLC nodules <2 cm. Robotic adoption may be associated with improved oncologic benefit.
Perspective.
Uptake of the robotic platform in thoracic surgery continues, although there is little insight into the impact of robotics on use and outcomes for segmentectomy for NSCLC. Our findings prompt a concerted effort to analyze the temporal trends in anatomic lung resection and investigate surgeon- and institution-specific factors that lead to robotic adoption.
Over the last decade, the uptake of a robotic platform for thoracic surgery has increased markedly.1, 2, 3 The increased dexterity and visualization provided by the robotic approach is postulated to be the reason why surgeons are more apt to use it. Concurrently, segmentectomy has become a viable alternative to lobectomy for small, peripheral non–small cell lung cancer (NSCLC).4, 5, 6, 7, 8, 9 As expected, over the last decade, the use of segmentectomy for anatomic lung resection has increased nationally, although segmentectomy is often considered to be a more technically challenging operation.2,10 Given the advantages and disadvantages of the robotic approach, it is unknown the degree to which adoption of robotic lung resection may have influenced the increased use of segmentectomy. Past literature that has assessed the impact of adoption of robotic surgery has either lacked generalizability or failed to account for the surgeon's learning curve of robotic surgery.11 In a recent analysis, Madeka and colleagues12 examined clinical outcomes for lung resection for NSCLC across 3 periods of transition to the robotic platform at the facility level: preadoption, during adoption, and postadoption. Although they provided a foundation for our current investigation, this previous work did not provide a detailed assessment of the use of segmentectomy and the associated clinical and oncologic outcomes for NSCLC lesions <2 cm.
In this study, we set out to perform descriptive analyses of the frequency and distribution of segmentectomies at a national facility level between adopters and nonadopters of robotic surgery for NSCLC tumors of less than 2 cm. In addition, we aim to evaluate the associated trends in clinical outcomes over the pretransition period, transition period, and posttransition period to robotic surgery with the hypothesis that robotic adoption would be correlated with more use of segmentectomy.
Methods
A retrospective case-control analysis was conducted using the National Cancer Database (NCDB) from 2010 to 2021. The NCDB is a joint project of the Commission on Cancer (CoC) of the American College of Surgeons and the American Cancer Society. The database captures approximately 70% of newly diagnosed cancers in the United States annually from 1500 hospitals with CoC-accredited cancer programs.13 The American College of Surgeons and the CoC have not verified and are not responsible for the analysis or conclusions drawn from this study.
We considered facilities that performed at least 75 lung resections for NSCLC from 2010 to 2021 as candidates for case and control facilities. We then categorized facilities on the basis of their use of robotic surgery for anatomic lung resection. Case facilities were defined as facilities that performed ≥90% of their lung resections as robotic for patients with NSCLC for at least 2 consecutive years from 2016 onwards. This was deemed sustained adoption of the robotic platform. Facilities with <80% robotic use in the most recent year were excluded as facilities that did not experience sustained adoption, whereas facilities with >50% adoption in the facility's first year with data in the study period were excluded as facilities that did not undergo an adoption period on record. A flow diagram of facility and case inclusion is provided in Figure E1. Time periods for case facilities were categorized as follows: preadoption (<50% usage in all years), adoption (50%-90% usage), and postadoption (the first year with at least 90% usage through the end of study period). We then created a 1:1 matched control facility cohort (<50% robotic use across all years) and evaluated outcomes across the previously defined time periods for each case facility. This allowed for comparison of control facility clinical outcomes independently of the shift to robotic surgery.
Figure E1.
Consolidated Standards of Reporting Trials flow diagram for case and facility inclusion. NSCLC, Non–small cell lung cancer; NCDB, National Cancer Database.
Once these 2 cohorts were created, we then subset the data to only include cases of lung resection for NSCLC of 2 cm or less for further analysis. We analyzed demographics and short-term clinical outcomes including proportion of segmentectomies among all lung resections, 30-day mortality, 90-day mortality, mean lymph node sampling, pathologic upstaging rate, margin positivity, readmission rate, and robotic-to-open conversion rate. R version 4.3.1 (R Core Team) statistical software was used for analysis and Prism 10.01 (GraphPad Software) was used for graphics. This study was deemed exempt from institutional review board approval. No informed patient consent was required.
Results
Among all facilities, there were 57 case facilities included as sustained adopters of the robotic surgical platform. Most facilities were comprehensive (n = 23, 40.4%), followed by integrated (n = 21, 36.8%), academic (n = 8, 14%), and community (n = 5, 8.8%). Most facilities were located in the South Atlantic and East North Central region (n = 15, 26.3% for both regions). This group of facilities accounted for a total of 10,045 cases of lung resection for NSCLC less than 2 cm. There were 4458 (44.4%) cases performed in the pretransition period, 3195 (31.8%) performed in the transition period, and 2392 (23.8%) performed posttransition to the robotics platform. Demographic and clinical outcomes of case facilities for any type of lung resection were analyzed and can be found in Table E1.
Control facilities (<50% usage in all years) were matched 1:1 with robotic adopting facilities and associated demographic and clinical outcomes were also analyzed. Demographic data over the study period were similar to the case cohort, although there were fewer overall cases in the control group. Among our 57 matched control facilities, case distribution was also similar. There were 8104 total cases of lung resection for NSCLC <2 cm; these were allocated to the same time periods as the associated matched case facilities with 3855 in the pretransition period, 2333 in the transition period, and 1916 in the posttransition period. Clinical outcomes and demographic data for this control group were analyzed and can be found in Table E2.
Among the 10,045 cases of lung resection at case facilities, there were 771 cases of segmentectomy across the study period. Within this segmentectomy cohort at case facilities, the median age was 69 years (interquartile range, 63-75) and mean Charlson-Deyo score was 0.89 (95% CI, 0.82-0.95). Most patients in this cohort were female, White, had Medicare for the primary insurance coverage, and had clinical stage I NSCLC. Clinical stage data were unknown for 16.0% of the population (n = 123), with similar distribution across the robotic adoption periods. Mean lymph node harvest increased from the pretransition period to the posttransition period (5.1 to 11.4) with an associated increase in pathologic upstaging rate (12.7% to 15.5%). We found that 30- and 90-day mortality decreased in the posttransition period compared with the pretransition period (30-day: 1.7% to 0.6%, 90-day: 2.9% to 0.6%). There was also an observed decrease in 30-day readmission rate (4.9% to 2.8%) and rate of resection margin positivity (1.6% to 0.6%) across the same periods. There was a marginal increase in rate of robotic-to-open conversion during surgery from the pre- to posttransition periods (0% to 0.7%); see Table 1 and Figure 1.
Table 1.
Case-control facility-level secondary outcomes for segmentectomy by robotic adoption era
| Facility type |
Case facilities |
Control facilities |
||||
|---|---|---|---|---|---|---|
| Period | Pretransition | Transition | Posttransition | Pretransition | Transition | Posttransition |
| Case count | 243 | 240 | 288 | 155 | 133 | 121 |
| Robotic cases | 26 | 212 | 280 | 10 | 32 | 42 |
| Nonrobotic cases | 217 | 28 | 8 | 145 | 101 | 79 |
| 30-d mortality | 1.7% | 0.0% | 0.6% | 1.3% | 3.0% | 0.0% |
| 90-d mortality | 2.9% | 0.0% | 0.6% | 1.3% | 3.0% | 0.0% |
| 30-d readmission rate | 5.0% | 7.5% | 2.8% | 5.2% | 2.3% | 0.8% |
| Upstaging rate | 12.7% | 13.0% | 15.5% | 16.7% | 9.6% | 10.8% |
| Rate of conversion to open surgery | 0.0% | 1.4% | 0.7% | 0.0% | 3.1% | 0.0% |
| Resection margin positivity rate | 1.6% | 0.4% | 0.7% | 0.7% | 2.3% | 0.8% |
| Mean lymph nodes sampled | 5.1 | 9.2 | 11.4 | 6.0 | 9.1 | 10.2 |
Figure 1.
Secondary outcomes at case and control facility by robotic adoption era.
Of 8104 cases of lung resection at control facilities, there were 409 cases of segmentectomy performed. In the control facility segmentectomy cohort, the median age was 69 years (interquartile range, 62-74 years) and mean Charlson-Deyo score was 0.80 (95% CI, 0.71-0.90). The majority of patients were female, White, had Medicare for the primary insurance coverage, and had clinical stage I NSCLC. Mean lymph node harvest increased from the pretransition period to the posttransition period (6.02 vs 10.2), although pathologic upstaging rate decreased (16.7% vs 10.8%). We observed a slight decrease in 30- and 90-day mortality in the posttransition period compared with the pretransition period (30-day: 1.3% to 0%; 90-day: 1.3% to 0%). In addition, there was an observed decrease in 30-day readmission rates (5.2% to 0.8%) with a similar rate of resection margin positivity (0.7% to 0.8%). There were no cases requiring conversion from robotic to open surgery in the control cohort of facilities; see Table 1 and Figure 1.
Among case facilities, the rate of segmentectomy for <2 cm NSCLC lesions was 5% in 2011 and increased to 12% by 2021. This corresponds to segmentectomy proportions of 5.5%, 7.5%, and 12.0% for the pretransition, transition, and posttransition time periods. Among matched control facilities, the proportion of segmentectomy performed started at 3.5% in 2010 and increased to 9.3% by 2021. This corresponds to segmentectomy proportions of 4.0%, 5.7%, and 6.3% for the pretransition, transition, and posttransition time periods. From pre- to postrobotic transition periods, we observed an absolute increase in segmentectomy rate of 6.6% in case facilities and 2.3% in control facilities (difference-in-difference = 4.3%; 95% CI, 2.6%-6.0%) (Figure 2). Demographic and clinical outcome data were analyzed for the segmentectomy cohorts in case and control facilities and can be found in Tables 2 and 3.
Figure 2.
Yearly rates of segmentectomy use at case and control facilities.
Table 2.
Demographics, segmentectomy cohort, and case facilities
| Category | Count | Proportion | 95% CI |
|---|---|---|---|
| Sex | |||
| Male | 259 | 33.59% | (0.303-0.371) |
| Female | 512 | 66.41% | (0.629-0.697) |
| Race | |||
| While | 707 | 91.70% | (0.895-0.935) |
| Black | 40 | 5.19% | (0.038-0.071) |
| Asian | 15 | 1.95% | (0.011-0.033) |
| Other race | 6 | 0.78% | (0.003-0.018) |
| Unknown race | 3 | 0.39% | (0.001-0.012) |
| Insurance type | |||
| Uninsured | 5 | 0.65% | (0.002-0.016) |
| Private | 198 | 25.68% | (0.227-0.289) |
| Medicaid | 36 | 4.67% | (0.033-0.065) |
| Medicare | 525 | 68.09% | (0.647-0.713) |
| Other insurance | 5 | 0.65% | (0.002-0.016) |
| Unknown insurance | 2 | 0.26% | (0-0.01) |
| Staging | |||
| Clinical stage 0 | 3 | 0.39% | (0.001-0.012) |
| Clinical stage 1 | 614 | 79.64% | (0.766-0.824) |
| Clinical stage 2 | 9 | 1.17% | (0.006-0.023) |
| Clinical stage 3 | 17 | 2.21% | (0.013-0.036) |
| Clinical stage 4 | 5 | 0.65% | (0.002-0.016) |
| Unknown clinical stage | 123 | 15.95% | (0.135-0.188) |
| Pathologic stage 0 | 14 | 1.82% | (0.01-0.031) |
| Pathologic stage 1 | 626 | 81.19% | (0.782-0.839) |
| Pathologic stage 2 | 36 | 4.67% | (0.033-0.065) |
| Pathologic stage 3 | 19 | 2.46% | (0.015-0.039) |
| Pathologic stage 4 | 5 | 0.65% | (0.002-0.016) |
| Unknown pathologic stage | 71 | 9.21% | (0.073-0.115) |
| Upstaging rate | 81 | 13.80% | (0.112-0.169) |
| Procedure type | |||
| Lobectomy | 0 | 0% | (0-0.006) |
| Wedge resection | 0 | 0% | (0-0.006) |
| Segmentectomy | 771 | 100% | (0.994-1) |
| Pneumonectomy | 0 | 0% | (0-0.006) |
| Other/unknown procedure | 0 | 0% | (0-0.006) |
Table 3.
Demographics, segmentectomy cohort, and control facilities
| Category | Count | Proportion | 95% CI |
|---|---|---|---|
| Sex | |||
| Male | 155 | 37.90% | (0.332-0.428) |
| Female | 254 | 62.10% | (0.572-0.668) |
| Race | |||
| While | 329 | 80.44% | (0.762-0.841) |
| Black | 37 | 9.05% | (0.065-0.124) |
| Asian | 30 | 7.34% | (0.051-0.104) |
| Other race | 5 | 1.22% | (0.005-0.03) |
| Unknown race | 8 | 1.96% | (0.009-0.04) |
| Insurance type | |||
| Uninsured | 4 | 0.98% | (0.003-0.027) |
| Private | 119 | 29.10% | (0.248-0.338) |
| Medicaid | 13 | 3.18% | (0.018-0.055) |
| Medicare | 267 | 65.28% | (0.604-0.699) |
| Other insurance | 4 | 0.98% | (0.003-0.027) |
| Unknown insurance | 2 | 0.49% | (0.001-0.02) |
| Staging | |||
| Clinical stage 0 | 2 | 0.49% | (0.001-0.02) |
| Clinical stage 1 | 341 | 83.37% | (0.793-0.868) |
| Clinical stage 2 | 11 | 2.69% | (0.014-0.049) |
| Clinical stage 3 | 6 | 1.47% | (0.006-0.033) |
| Clinical stage 4 | 5 | 1.22% | (0.005-0.03) |
| Unknown clinical stage | 44 | 10.76% | (0.08-0.143) |
| Pathologic stage 0 | 4 | 0.98% | (0.003-0.027) |
| Pathologic stage 1 | 338 | 82.64% | (0.785-0.861) |
| Pathologic stage 2 | 16 | 3.91% | (0.023-0.064) |
| Pathologic stage 3 | 16 | 3.91% | (0.023-0.064) |
| Pathologic stage 4 | 4 | 0.98% | (0.003-0.027) |
| Unknown pathologic stage | 31 | 7.58% | (0.053-0.107) |
| Upstaging rate | 43 | 12.57% | (0.093-0.167) |
| Procedure type | |||
| Lobectomy | 0 | 0% | (0-0.012) |
| Wedge resection | 0 | 0% | (0-0.012) |
| Segmentectomy | 409 | 100% | (0.988-1) |
| Pneumonectomy | 0 | 0% | (0-0.012) |
| Other/unknown procedure | 0 | 0% | (0-0.012) |
Comment
In this study, we conducted a facility-level analysis evaluating differences in rates of use of segmentectomy for anatomic lung resection for NSCLC tumors less than 2 cm in the pre- and postadoption periods of the robotic platform. This analysis shows that robotic adopters had a more substantial increase in use of segmentectomy for NSCLC lesions <2 cm. This correlates with the 2024 Society of Thoracic Surgeons Update on Outcomes and Research, which reported continued rising rates of robotic thoracic surgery and segmentectomy,2 although we observed a more substantial uptake of segmentectomy for lesions meeting criteria at sustained robotic adopting facilities compared with control facilities. We also found that sustained robotic adopters had greater mean lymph node harvest and greater pathologic upstaging rates compared with matched control facilities who did not adopt robotic surgery, which is likely attributable to the use of a robotic platform. We observed a decrease in 30- and 90-day mortality, 30-day readmission rate, and rate of resection margin positivity across all facilities.
Past studies have evaluated the clinical outcomes for thoracic surgery with the robotic platform. Most current literature has investigated oncologic outcomes, mortality, and overall survival on the basis of the type of surgical approach with mixed results. Some groups14, 15, 16, 17 showed no difference in upstaging rates, but these did not incorporate sustained adoption of the robotic platform at the facility level. In a matched, retrospective cohort of 7452 patients with clinical stage I NSCLC, Tang and colleagues15 showed an increased mean lymph node harvest with robotic compared to open surgery. In another study, Kalata and colleagues18 looked at lymph node sampling and pathologic upstaging rates across 16 centers in Michigan across 5 years and found that robotic lobectomy was associated with sampling from greater numbers of lymph node stations but did not show any differences in pathologic upstaging rates or overall mean lymph node harvest. These studies both evaluated lymph node sampling for all cases of lung resection, not only segmentectomies. Through our analysis, we observed an increased mean lymph node harvest in facilities adopting robotic segmentectomy with an associated increase in pathologic upstaging rates. These markers function as surrogate oncologic outcomes, and prompt further investigation of long-term oncologic measures such as disease-free survival and overall survival.
Previous work has demonstrated rising rates of minimally invasive lung resection19 and increased use segmentectomy at the population level.20 Our analysis is unique because it provides an evaluation of clinical outcomes in robotic surgery on the basis of the phase of adoption to robotic surgery and shows that the cohort of robotic adopters contributes more to national segmentectomy uptake. A recent investigation performed by Madeka and colleagues12 served as a foundation for our study. In their study, they found improved mortality rates, increased mean lymph node sampling, and greater pathologic upstaging rates in sustained adopters of the robotic platform. Although this provided a general assessment of all lung resections for any NSCLC lesion, it does not assess the use of segmentectomy for appropriate lesions. However, our work used similar definitions of robotic adoption periods from this study. Otherwise, previous investigation on the impact of robotic adoption on outcomes in thoracic surgery is limited. Dyas and colleagues11 evaluated implementation of the robotic platform at the Veterans Affairs Medical Center from 2015 to 2021. However, this study may not be generalizable to a broader population. In addition, the period for postrobotic implementation was from the time of the first case, and this does not account for adequate time for the surgeon's learning curve to flatten.
Some of these observed associations from this study are due to improved technical mastery and comfort with the robotic platform, allowing for the advantages of robotic-assisted surgery (ie, improved range of motion, precision, and visualization) to be fully realized.21 Dissecting further into the arborization of the lung may be easier with more dexterity and visualization, hence the associated willingness to perform segmentectomy on the robotic platform. However, although some of the observed effects may be attributed to technical mastery, there are other factors to consider. Facilities adopting robotic surgery may have supplied an environment that allowed for effective transition including access to the robotic platform and financial backing to support this platform. Patients may also seek out minimally invasive techniques over traditional open techniques, resulting in skewed patient selection. In addition, there is speculation that surgeons at robotic adopting facilities may have had an inherent willingness to learn and perform robotic surgery compared to control facilities. Our inclusion criteria likely include facilities categorized as extreme adopters of the robotic platform, which may skew the observe trends. Regardless, these questions prompt additional future investigation into the variables responsible for a facility's adoption of the robotic platform.
Importantly, our investigation period precedes the findings from Cancer and Leukemia Group B (CALGB) 140503 and Japan Clinical Oncology Group (JCOG)0802 (released in 2023 and 2022, respectively), which advocate for consideration of sublobar resection for appropriate tumors.4,9 After these key landmark trials, we hypothesize an even more dramatic increase in segmentectomy use, though future study would be needed to quantify this. In addition, of note, we did not exclude patients with clinical stage II-IV or unknown stage disease because this investigation was aimed to describe use of segmentectomy, regardless of stage. Most patients (79.6%) were clinical stage I with most of the remaining patients (16.0%) being unknown clinical stage. We expect some degree of coding error and failure of guideline adherence may account for some of the percentage of patients who were not stage I and underwent segmentectomy.
There are some additional limitations to this study. Our definitions of pretransition, transition, and posttransition to the robotic platform are modeled after recent previous work12 but are otherwise arbitrary. These groups were created using reasonable percentiles to define periods of robotic adoption and excluded any facilities that failed to sustain robotic adoption. We also excluded early adopters of the robotic platform to avoid skewed results. In addition, as previously mentioned, case facilities were self-selected and likely maintained an environment that allowed for effective transition to the robotic platform (ie, financial backing, surgeon willingness and access to pursue robotics). Although historically access to robotics may have been a limiting factor for robotic adoption, recent updates from the Society of Thoracic Surgeons have demonstrated that anatomic lung resection was performed robotically in just over 10% of cases in 2013 and has increased to nearly 50% by 2020. With around half of anatomic lung resections occurring robotically, we hope that this reduces the consideration of limited access as a confounder to this study.2 Furthermore, the use of the NCDB is inherent to biases (eg, selection bias) as it is a retrospective database. Our investigation is limited by variables supplied by this database. For the NCDB, there are no data captured for postoperative complications or lymph node stations, and this database does not include preoperative information such as pulmonary function. The NCDB also does not provide surgeon-specific details, which limits the ability to control for facilities who may have numerous surgeons with varying use of operative techniques. This may result in confounding of categorization of a facility's degree of robotic adoption. Reliability of readmission data may also be limited, because patients who present to outside hospitals are not captured. Lastly, the generalizability of this study is limited by the NCDB only including hospitals with CoC accreditation.
Sustained uptake of the robotic platform is associated with a more substantial increase in utilization of segmentectomy for NSCLC lesions meeting criteria. Moreover, our findings bolster the current views on the safety, mortality, and oncologic benefit of the robotic platform for this type of resection (Figure 3). These results prompt further monitoring of this trend over the course of the next decade as well as investigation of characteristics of the facilities and surgeons who successfully sustain robotic adoption.
Figure 3.
Overview of the uptake of segmentectomy for non–small cell lung cancer in adopters of robotic surgery.
Conflict of Interest Statement
O.T.O. reported honoraria from Intuitive Surgical, Johnson & Johnson, AtriCure, and AstraZeneca. N.R.E. III reported speaker and consultant honoraria from Intuitive, Merck, Bristol Myers Squibb, and AstraZeneca. All other authors reported no conflicts of interest.
The Journal policy requires editors and reviewers to disclose conflicts of interest and to decline handling or reviewing manuscripts for which they may have a conflict of interest. The editors and reviewers of this article have no conflicts of interest.
Appendix E1
Table E1.
Demographics, all lung resections, case facilities
| Category | Count | Proportion | 95% CI |
|---|---|---|---|
| Sex | |||
| Male | 3755 | 37.38% | (0.364-0.383) |
| Female | 6290 | 62.62% | (0.617-0.636) |
| Race | |||
| While | 9063 | 90.22% | (0.896-0.908) |
| Black | 729 | 7.26% | (0.068-0.078) |
| Asian | 149 | 1.48% | (0.013-0.017) |
| Other race | 63 | 0.63% | (0.005-0.008) |
| Unknown race | 41 | 0.41% | (0.003-0.006) |
| Insurance type | |||
| Uninsured | 112 | 1.12% | (0.009-0.013) |
| Private | 2790 | 27.78% | (0.269-0.287) |
| Medicaid | 467 | 4.65% | (0.042-0.051) |
| Medicare | 6480 | 64.51% | (0.636-0.654) |
| Other insurance | 136 | 1.35% | (0.011-0.016) |
| Unknown insurance | 60 | 0.60% | (0.005-0.008) |
| Staging | |||
| Clinical stage 0 | 101 | 1.01% | (0.008-0.012) |
| Clinical stage 1 | 7748 | 77.13% | (0.763-0.779) |
| Clinical stage 2 | 339 | 3.38% | (0.03-0.038) |
| Clinical stage 3 | 301 | 3.00% | (0.027-0.034) |
| Clinical stage 4 | 166 | 1.65% | (0.014-0.019) |
| Unknown clinical stage | 1390 | 13.84% | (0.132-0.145) |
| Pathologic stage 0 | 172 | 1.71% | (0.015-0.02) |
| Pathologic stage 1 | 7683 | 76.49% | (0.756-0.773) |
| Pathologic stage 2 | 722 | 7.19% | (0.067-0.077) |
| Pathologic stage 3 | 474 | 4.72% | (0.043-0.052) |
| Pathologic stage 4 | 165 | 1.64% | (0.014-0.019) |
| Unknown pathologic stage | 829 | 8.25% | (0.077-0.088) |
| Upstaging rate | 1474 | 18.53% | (0.177-0.194) |
| Procedure type | |||
| Lobectomy | 6732 | 67.02% | (0.661-0.679) |
| Wedge resection | 2332 | 23.22% | (0.224-0.241) |
| Segmentectomy | 771 | 7.68% | (0.072-0.082) |
| Pneumonectomy | 58 | 0.58% | (0.004-0.008) |
| Other/unknown procedure | 152 | 1.51% | (0.013-0.018) |
Table E2.
Demographics, all lung resections, control facilities
| Category | Count | Proportion | 95% CI |
|---|---|---|---|
| Sex | |||
| Male | 3183 | 39.28% | (0.382-0.404) |
| Female | 4921 | 60.72% | (0.596-0.618) |
| Race | |||
| While | 6880 | 84.90% | (0.841-0.857) |
| Black | 714 | 8.81% | (0.082-0.095) |
| Asian | 372 | 4.59% | (0.041-0.051) |
| Other race | 73 | 0.90% | (0.007-0.011) |
| Unknown race | 65 | 0.80% | (0.006-0.01) |
| Insurance type | |||
| Uninsured | 102 | 1.26% | (0.01-0.015) |
| Private | 2242 | 27.67% | (0.267-0.287) |
| Medicaid | 578 | 7.13% | (0.066-0.077) |
| Medicare | 5001 | 61.71% | (0.606-0.628) |
| Other insurance | 95 | 1.17% | (0.01-0.014) |
| Unknown insurance | 86 | 1.06% | (0.009-0.013) |
| Staging | |||
| Clinical stage 0 | 70 | 0.86% | (0.007-0.011) |
| Clinical stage 1 | 6097 | 75.23% | (0.743-0.762) |
| Clinical stage 2 | 280 | 3.46% | (0.031-0.039) |
| Clinical stage 3 | 265 | 3.27% | (0.029-0.037) |
| Clinical stage 4 | 123 | 1.52% | (0.013-0.018) |
| Unknown clinical stage | 1269 | 15.66% | (0.149-0.165) |
| Pathologic stage 0 | 109 | 1.35% | (0.011-0.016) |
| Pathologic stage 1 | 6054 | 74.70% | (0.737-0.756) |
| Pathologic stage 2 | 537 | 6.63% | (0.061-0.072) |
| Pathologic stage 3 | 382 | 4.71% | (0.043-0.052) |
| Pathologic stage 4 | 104 | 1.28% | (0.011-0.016) |
| Unknown pathologic stage | 918 | 11.33% | (0.106-0.12) |
| Upstaging rate | 877 | 14.53% | (0.137-0.154) |
| Procedure type | |||
| Lobectomy | 4881 | 60.23% | (0.592-0.613) |
| Wedge resection | 2498 | 30.82% | (0.298-0.318) |
| Segmentectomy | 409 | 5.05% | (0.046-0.056) |
| Pneumonectomy | 45 | 0.56% | (0.004-0.007) |
| Other/unknown procedure | 271 | 3.34% | (0.03-0.038) |
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