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The impact of facility characteristics on Merkel cell carcinoma outcomes: a retrospective cohort study
Journal Pre-proof The impact of facility characteristics on Merkel cell carcinoma outcomes: a retrospective cohort study Shayan Cheraghlou, BA, George O. Agogo, PhD, Michael Girardi, MD PII:
S0190-9622(19)32664-7
DOI:
https://doi.org/10.1016/j.jaad.2019.08.058
Reference:
YMJD 13780
To appear in:
Journal of the American Academy of Dermatology
Received Date: 2 June 2019 Revised Date:
19 August 2019
Accepted Date: 22 August 2019
Please cite this article as: Cheraghlou S, Agogo GO, Girardi M, The impact of facility characteristics on Merkel cell carcinoma outcomes: a retrospective cohort study, Journal of the American Academy of Dermatology (2019), doi: https://doi.org/10.1016/j.jaad.2019.08.058. This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. © 2019 Published by Elsevier on behalf of the American Academy of Dermatology, Inc.
1 Article type: Original article Title: The impact of facility characteristics on Merkel cell carcinoma outcomes: a retrospective cohort study Shayan Cheraghlou, BA1; George O. Agogo, PhD2; Michael Girardi, MD1 1 2
Department of Dermatology, Yale School of Medicine, New Haven, Connecticut Department of Internal Medicine, Yale School of Medicine, New Haven, Connecticut
Corresponding author: Michael Girardi, MD 333 Cedar Street., PO Box 208059, Department of Dermatology, Yale University School of Medicine New Haven, CT 06520 Telephone: (203) 785-4092 Fax: (203) 776-6188 Email: [email protected] Funding sources: None.
IRB approval status: Reviewed and exempted from institutional review by the Yale Human Investigation Committee (IRB #2000023704) Conflicts of interest: The authors have no conflicts of interest or financial disclosures, and all authors had access to the data and a role in writing the manuscript. Manuscript word count: 2,499 Abstract word count: 200 Capsule summary word count: 50 References: 67 Figures: 2 Supplementary figures: 0 Tables: 3 Supplementary tables: 0
Key Words: academic affiliation; case volume; survival; merkel cell carcinoma; regionalization; NCDB
2 CAPSULE •
Previous work has suggested that, for rare malignancies, large regional centers achieve improved patient outcomes. We found that treatment at academic/high-volume facilities is associated with improved MCC survival.
•
While further study evaluating disease-specific survival is required, our results suggest that care regionalization may provide an avenue to improve MCC survival.
3 ABSTRACT Background: Previous work has suggested that facility-level characteristics such as case volume and academic affiliation are associated with patient survival for rare malignancies. Merkel cell carcinoma (MCC) is a rare neuroendocrine skin cancer with high mortality and rising incidence. The impact of facility characteristics on MCC outcomes is not yet established. Objective: We aimed to investigate the association of facility academic affiliation and case volume with MCC patient survival. Methods: We conducted a retrospective cohort analysis of US adult MCC cases diagnosed from 2004-2014 in the NCDB. Results: Both facility academic affiliation (p<0.001) and case volume (p<0.001) were significantly associated with patient survival. For propensity score-matched cohorts of patients treated at academic versus non-academic facilities, five-year survival was 63.0% (SE: 1.7) and 53.4% (SE: 1.9) respectively. Five-year survival for propensity score-matched cohorts of patients treated at high versus low/intermediate case volume facilities was 67.4% (SE: 2.1) and 58.6% (SE: 2.0) respectively. Limitations: Disease-specific survival and local recurrence data were not available. Conclusions: Treatment of MCC at academic and high-volume centers is associated with significantly improved patient survival. Further study, taking into account comorbidities and evaluating disease-specific survival, is needed to establish whether experienced centers have improved outcomes in MCC treatment.
4 INTRODUCTION Merkel cell carcinoma (MCC) is a highly malignant neuroendocrine skin cancer, rapidly rising in incidence worldwide.1-4 The most recent US epidemiologic study of MCC reported 2,488 yearly incident cases and predicted 2,835 yearly cases by 2020 and 3,284 by 2025.5 Studies of Australian and French populations have also revealed increasing MCC incidence.6,7 This is an especially concerning trend given the high mortality rate for MCC compared to other cutaneous malignancies.8-10 Nonetheless, perhaps due to its relative rarity and its nonspecific clinical features mimicking benign cutaneous lesions such as pyogenic granuloma or keratoacanthoma, MCC presents a diagnostic and therapeutic challenge.11,12 Work on other rare malignancies, such as cholangiocarcinoma,13,14 pediatric head and neck sarcoma,15 and laryngeal cancer,16 as well as malignant melanoma,17 has revealed high treatment facility case volume and academic affiliation to be significantly associated with improved patient outcomes. The relationship between treatment facility characteristics and patient outcomes has spurred a movement toward cancer care regionalization, wherein cases are directed to regional, typically academic, high-volume "centers of excellence". Proponents of this shift in care delivery argue that these centers can leverage caseload experience and academic advantages to deliver higher quality care than smaller community centers as evidenced by their improved patient outcomes. A number of mechanisms have been proposed to underlie this phenomenon, including increased discussion of patients at multidisciplinary tumor boards, clinical trial participation, and increased diagnostic and technical expertise. Nevertheless, despite the increased recognition of the influence of facility-level characteristics, including case volume and academic affiliation, the impact of such factors on MCC outcomes is not yet
5 established. In the present study, we aim to evaluate the associations between MCC outcomes and facility-level characteristics, specifically facility academic affiliation and case volume.
6 METHODS Data Source Data originated from the National Cancer Database (NCDB) from 2004-2017 with diagnostic dates 2004-2014. The NCDB is a nationwide clinical surveillance resource data set that includes approximately 70% of all newly diagnosed malignancies in the United States from over 1,500 cancer programs as previously described.18 The NCDB has been used in a number of studies of system-level impacts on cancer outcomes.19-21 This study was determined to be exempt from institutional review by the Yale Human Investigation Committee.
Study Population Using a cohort of patients with histologically confirmed MCC using International Classification of Diseases for Oncology 3 code 8247, we identified 14,950 patients. We excluded patients who were under 18 at the time of diagnosis (n=2), had any other malignancies (n=5036), did not receive definitive surgical excision of the primary tumor (n=1426), had distant metastases (n=204), had unknown metastatic status (n=212), had unknown primaries (n=2905) or had missing/incomplete follow-up (n=625). For multivariate analysis and propensity-score matched analyses, we excluded patients if they were absent information on clinical staging (n=203), radiation administration status (n=27), or chemotherapy administration status (n=86), or if they received chemotherapy without radiotherapy (n=95).
Statistical Analysis Patients were defined as having government insurance if the patient's primary insurance carrier was Medicare, Medicaid, or another government-administered insurance carrier.
7 Patients were defined as having received chemotherapy if they received any chemotherapeutic agent. We considered patients to have received radiotherapy if they received external beam radiation to the primary site. Facilities were divided into low (first), intermediate (second and third), and high (fourth) volume groupings prior to case exclusions based on volume quartile such that ~25% of patients were treated at low-volume facilities, ~50% at intermediate-volume facilities, and ~25% of patients at high-volume facilities. The dividing points between these groups were: Q1=1 and Q3=4 cases/year. The primary outcome was overall survival. Administration of immunotherapy was not included in the final analysis since it was used in only 0.07% of the 4,340 analytic sample (n=3), with 0.76% of cases (n=33) with an unknown immunotherapy administration status. All results were unchanged with exclusion of these cases. Univariate Kaplan-Meier analyses stratified by facility yearly case-volume and facility academic status were performed. Multivariate survival analyses using Cox proportional hazards models were then conducted and repeated excluding either facility volume and facility type due to a possible confounding interaction between the two variables. Variables outlined in Table 1 were tested for appropriateness for inclusion in this model using Akaike information criterion minimization in order to decrease the likelihood of overfitting data.22 Propensity-score matching was conducted for cohorts of patients treated at academic and non-academic facilities as well as for cohorts of patients treated at low/intermediate and high-volume facilities. This methodology controls for potential differences in outcomes due to systemic differences between the two groups.17,23-25 Scores were calculated using a logit model with the factors outlined in Table 1 except for facility type and volume respectively. Groups were
8 generated using a one-to-one nearest neighbor match without replacement. Group differences were tested using the two-tailed student's t-test for continuous variables and the chi-squared test for categorical variables. Statistical significance was determined at the p<0.05 level. Data analysis was performed using STATA version 13 (StataCorp LP, College Station, TX).
9 RESULTS The characteristics of the analytic sample are outlined in Table 1. The majority of patients were White (94.5%) and male (59.0%), with a median age of 75. Most patients had a Charlson/Deyo score of 0 (74.7%), carried Medicare insurance (69.3%) and were treated at nonacademic centers (55.3%). Forty-five facilities were designated as "high-volume" centers which treated approximately 25% of patients, 386 as "intermediate-volume" centers which treated approximately 50% of patients, and 548 as "low-volume" centers which treated 25% of patients. The most commonly utilized treatment combination in our sample was surgery with adjuvant radiotherapy (45.7%), followed by surgery without adjuvant therapy (42.8%). Among cases with known adjuvant therapy, adjuvant radiotherapy was used for similar proportions of stage I, II, and III cases, 46.8%, 50.2%, and 51.7% respectively. In contrast, adjuvant chemoradiotherapy was used for 21.2% of stage III cases versus 2.7% and 7.4% of stage I and stage II cases respectively. Mean follow-up was 3.50 years with a maximum follow-up of 12.72 years. Univariate Kaplan-Meier analyses stratified by both facility academic affiliation and volume revealed significant associations with patient survival. Patients treated at academic facilities had significantly improved long-term outcomes compared to those treated at nonacademic centers (Figure 1A). Three-, 5-, and 10-year survival was 70.6% (SE: 1.1), 60.9% (SE: 1.3), and 41.9% (SE: 2.7) respectively at academic facilities and 61.0% (SE: 1.0), 51.0% (SE: 1.2), and 32.6% (SE: 1.9) respectively at non-academic facilities. Additionally, patients treated at higher volume centers experienced significantly improved long-term survival compared to those treated at lower volume centers (Figure 1B). At high-volume centers, 3-, 5-, and 10-year
10 survival was 73.7% (SE: 1.3), 64.2% (SE: 1.6), and 44.2% (SE: 3.6) respectively; while 3-, 5-, and 10-year survival was 63.0% (SE: 1.1), 53.0% (SE: 1.3), and 38.8% (SE: 2.0) respectively for intermediate-volume centers, and 59.6% (SE: 1.6), 49.5% (SE: 1.7), and 26.8% (SE: 2.8) respectively for low-volume centers. Multivariate Cox survival regressions revealed similar associations (Table 2). A regression analysis of cases with non-missing data revealed that patients treated at nonacademic facilities had significantly worse survival compared to those treated at academic centers (Hazard Ratio [HR], 1.219; 95% Confidence Interval [CI] 1.072-1.386). Given the likely confounding effects between facility volume and academic affiliation, regressions were repeated excluding either facility volume or academic affiliation. Excluding facility volume from the model revealed a similar worsened survival for patients treated at non-academic versus academic facilities (HR 1.317, 95% CI 1.189-1.458). Excluding facility academic affiliation from the model, facility volume was also significantly associated with survival with patients treated at both intermediate (HR 1.271, 95% CI 1.122-1.440) and low (HR 1.379, 95% CI 1.200-1.583) experiencing significantly poorer survival than those treated at high-volume centers. Subsequent propensity-score matched analyses also demonstrated these associations. Matched cohorts of patients treated at academic and non-academic facilities were generated, the characteristics of which are outlined in Table 3. Survival analysis of these cohorts revealed significantly improved survival for patients treated at academic versus non-academic facilities (Figure 2A). Three-, 5-, and 10-year survival was 72.0% (SE: 1.6), 62.4% (SE: 1.9), and 41.6% (SE: 3.9) respectively for patients treated at academic facilities, and 63.0% (SE: 1.7), 53.4% (SE: 1.9), and 33.9 (SE: 3.1) respectively for patients treated at non-academic facilities. The
11 characteristics of matched cohorts of patients treated at high and low/intermediate-volume facilities are also outlined in Table 3. Patients in the high-volume cohort exhibited significantly improved survival compared to those in the low/intermediate-volume cohort (Figure 2B). Three-, 5-, and 10-year survival was 77.0% (SE: 1.7), 67.4% (SE: 2.1), and 44.8% (SE: 4.8) respectively for patients treated at high-volume facilities, and 67.1% (SE: 1.8), 58.6% (SE: 2.0), and 36.5% (SE: 3.7) respectively for patients treated at low/intermediate-volume facilities. We also found that, prior to propensity score matching, both non-academic (p=0.010) and low/intermediate-volume (p=0.001) facilities had greater proportions of Medicare patients and lower proportions of patients carrying private insurance. Additionally, prior to propensity score matching, academic (p=0.002) and high-volume (p<0.001) facilities treated greater proportions of Stage I patients.
12 DISCUSSION In the present study, we demonstrate that both academic affiliation and high facility case volume are independently associated with improved survival for patients with MCC. While this has not been previously studied in MCC, this is consistent with recent findings of the impact of these factors on survival for melanoma17,26 and non-cutaneous malignancies.13-16 The National Comprehensive Cancer Network publishes yearly treatment guidelines for MCC.27 However, due to the lack of high-level evidence present for disease management, multiple options are offered at each step of the diagnostic/treatment algorithm with discretion left to the treating physician. Thus, the majority of the literature advocates for a multidisciplinary approach.28-32 Proponents of this approach argue that case discussion at multidisciplinary tumor boards allows for improved treatment decision-making and multispecialty care coordination. Indeed, centers with established multidisciplinary MCC care teams report superior stage-specific patient survival compared to national cohorts.33-35 While not extensively studied in the context of MCC, cases brought to multidisciplinary clinics for other malignancies have been shown to be staged more accurately, treated more promptly, and more often treated within evidence-based guidelines.36-40 Data from Swedish hospitals suggests that one of the drivers of this discrepancy in guideline adherence at such centers may be the increased likelihood of patient discussion at multidisciplinary tumor conferences, shown to be an independent predictor of the delivery of guideline-adherent care.38,41 Previous work has established that multidisciplinary care teams achieve improved outcomes for a variety of malignancies including: non-small cell lung cancer,42 breast cancer,43,44 esophageal carcinoma,45 malignant teratoma,46 ovarian cancer,47 soft tissue sarcoma,48 and metastatic testicular
13 cancer.49 Indeed, the increased likelihood of case discussion at multidisciplinary tumor boards at high-volume and academic centers may be a major reason for our findings of improved MCC outcomes at these centers.41 Patients treated at academic facilities and high-volume may also benefit from enrollment in the clinical trials that are conducted at such centers. While there is still some debate about this point,50-52 there has been evidence to suggest that cancer patients participating in clinical trials experience improved outcomes.53-56 Indeed, there is evidence from Germany that clinical trial participation on the hospital level is associated with improved outcomes for ovarian cancer patients treated at these centers.57 This may be especially relevant in the case of MCC, for which there is currently no evidence of a durable response to systemic chemotherapy.58 While not captured in our analytic sample, which included cases diagnosed between 2004 and 2014 inclusive, avelumab, an anti-PD-L1 antibody shown to be effective in MCC with distant metastasis,59 is now being trialed in MCC with nodal spread (NCT03271372), and may provide an effective therapy that is not available outside the context of the clinical trial. Additionally, the increased level of experience with treating MCC at high-volume facilities may lead to improved decision-making and more thorough surgical excisions. For other cancers treated with surgical excision, a growing body of work has demonstrated improved outcomes for cases treated both by individual high-volume surgeons and at high-volume facilities irrespective of individual surgeon volume.60-63 For MCC, provider experience may be particularly salient given the rarity and aggressiveness associated with the disease. Furthermore, although treatment at high-volume and academic centers was associated with
14 improved outcomes when controlling for factors such as disease stage, we found that these centers treated a significantly greater proportion of stage I disease, perhaps reflecting improved detection of a difficult-to-diagnose malignancy. These results suggest that the policy of cancer care regionalization – treatment of certain malignancies at high-volume, typically academic, regional centers – may result in improved outcomes for patients with MCC. While much of the literature supporting care regionalization has focused on high-risk cancer surgeries,64-66 this practice may be similarly applicable for the treatment of rare and deadly malignancies such as MCC. Some of the work studying cancer care regionalization has suggested referral of high-risk patients to regional care center,66 which, in the case of MCC would likely refer to cases with nodal or extra-nodal metastasis for which the relative 10-year survival rate is under 50%.10 Given the limited number of disease experts and relatively low incidence of disease, regionalization of care for rare malignancies such as MCC may also be more logistically practical than for more common malignancies, for which universal care regionalization may overload the resources of regional centers. Nevertheless, a number of barriers exist for policies of care regionalization, including the fact that some geographic areas lack the case density to support a regional center.67 Further work is required to study the potential feasibility of MCC care regionalization and the benefits and deficits of such a policy. Our study was limited by a number of factors. First, we did not have access to data on local recurrence or disease-specific survival due to their absence from the data source and were therefore unable to assess the impact of facility characteristics on these outcomes. We were also limited by the absence of additional variables such as Merkel cell polyomavirus status,
15 cytokeratin 20 expression, resection margin, and case discussion at a multidisciplinary tumor board, which would have provided additional control measures. Furthermore, due to the time frame of our study, with diagnostic dates 2004-2014, we were unable to assess the impact of immunotherapy, which is likely to be an integral component of future MCC therapy, on our observed results. Additionally, a component of the improved outcomes at experienced centers may be due to selection given that social issues and comorbidities, many of which were not controlled for in our analysis, can often preclude patients from travelling to experienced MCC centers. In some of these cases, experienced centers may consult with the patient and the local care team to direct the patient's care, and our study is also limited by the lack of data on this type of outside consultant input. Our study reveals the association of facility-level factors with survival for patients diagnosed with MCC. While further work assessing disease-specific survival is needed to confirm the improved survival noted for patients treated at experienced centers, these results suggest that MCC care regionalization, where feasible, may provide an opportunity to improve the outcomes of patients with this disease.
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Bilimoria KY, Bentrem DJ, Talamonti MS, Stewart AK, Winchester DP, Ko CY. Risk-based selective referral for cancer surgery: a potential strategy to improve perioperative outcomes. Ann Surg. 2010;251(4):708-716. Dimick JB, Finlayson SR, Birkmeyer JD. Regional availability of high-volume hospitals for major surgery. HEALTH AFFAIRS-MILLWOOD VA THEN BETHESDA MA-. 2004;23:VAR-45.
21 FIGURE LEGEND Figure 1. Kaplan-Meier univariate patient survival stratified by (A) facility academic affiliation, (B) facility case volume. Figure 2. Overall survival for propensity score-matched cohorts of patients treated at (A) academic versus other facilities and (B) high versus low/intermediate case volume facilities.
22 Table 1. Characteristics of the analytic sample. Variable Age, median (SD) Sex Female Male Race White Black Hispanic Asian/Pacific Islander Other/Unknown Charlson/Deyo Score 0 1 2 ≥3 Insurance Private Medicare Other Government None Unknown Facility Type Academic Other Facility Volume Low Intermediate High Clinical Stage 1 2 3 Missing Treatment Surgery Alone Surgery + RT Surgery + CRT Surgery + Unknown
Overall (n=4340) 75 (11.4)
No. (%) Cases with Non-Missing Data (n=3929) 75 (11.4)
1778 (41.0) 2562 (59.0)
1618 (41.2) 2311 (58.8)
4102(94.5) 55 (1.3) 92 (2.1) 29 (0.7) 62 (1.4)
3720 (94.7) 46 (1.2) 81 (2.1) 27 (0.7) 55 (1.4)
3242 (74.7) 807 (18.6) 211 (4.9) 80 (1.8)
2950 (75.1) 725 (18.4) 186 (4.7) 68 (1.7)
1122 (25.8) 3006 (69.3) 129 (3.0) 31 (0.7) 52 (1.2)
1014 (25.8) 2738 (69.7) 115 (2.9) 24 (0.6) 38 (1.0)
1941 (44.7) 2399 (55.3)
1782 (45.4) 2147 (54.6)
1067 (24.6) 2020 (46.5) 1253 (28.9)
944 (24.0) 1809 (46.0) 1176 (29.9)
2421 (55.8) 1046 (24.1) 670 (15.4) 203 (4.7)
2342 (59.6) 993 (25.3) 594 (15.1) -
1856 (42.8) 1983 (45.7) 281 (6.5) 220 (5.1)
1766 (45.0) 1901 (48.4) 262 (6.7) -
23 Table 2. Multivariate analysis of factors associated with survival. Overall Excluding Facility Volume from Model Variable Hazard Ratio (p-value) 95% CI Hazard Ratio (p-value) 95% CI 1.068 (<0.001) 1.061-1.075 1.068 (<0.001) 1.061-1.074 Age Sex Female 1[Reference] 1[Reference] Male 1.463 (<0.001) 1.320-1.622 1.461 (<0.001) 1.318-1.620 Charlson/Deyo Score 0 1[Reference] 1[Reference] 1 1.387 (<0.001) 1.228-1.567 1.392 (<0.001) 1.236-1.572 2.024 (<0.001) 1.660-2.466 2 1.997 (<0.001) 1.638-2.435 ≥3 2.340 (<0.001) 1.710-3.201 2.354 (<0.001) 1.721-3.218 Insurance Private 1[Reference] 1[Reference] Medicare 1.158 (0.060) 0.994-1.349 1.156 (0.062) 0.993-1.347 Other Government 1.602 (0.005) 1.151-2.231 1.581 (0.007) 1.136-2.200 None 1.837 (0.035) 1.044-3.231 1.904 (0.025) 1.084-3.344 Unknown 1.283 (0.352) 0.759-2.170 1.313 (0.309) 0.777-2.218 Facility Type Academic 1[Reference] 1[Reference] Other 1.219 (0.003) 1.072-1.386 1.317 (<0.001) 1.189-1.458 Facility Volume High 1[Reference] Intermediate 1.131 (0.100) 0.977-1.310 Low 1.177 (0.065) 0.990-1.398 Clinical Stage 1 1[Reference] 1[Reference] 2 1.620 (<0.001) 1.442-1.819 1.636 (<0.001) 1.459-1.836 3 2.762 (<0.001) 2.416-3.159 2.782 (<0.001) 2.433-3.181 Treatment Combination Surgery Alone 1[Reference] 1[Reference] Surgery + RT 0.828 (<0.001) 0.746-0.918 0.827 (<0.001) 0.746-0.918 Surgery + CRT 0.882 (0.231) 0.718-1.083 0.880 (0.225) 0.716-1.082 Note: Race removed as variable from multivariate model after Akaike information criterion minimization.
Excluding Facility Type from Model Hazard Ratio (p-value) 95% CI 1.067 (<0.001) 1.061-1.074 1[Reference] 1.457 (<0.001)
1.315-1.615
1[Reference] 1.388 (<0.001) 1.990 (<0.001) 2.336 (<0.001)
1.229-1.568 1.632-2.427 1.707-3.196
1[Reference] 1.168 (0.046) 1.625 (0.004) 1.745 (0.053) 1.230 (0.439)
1.003-1.361 1.167-2.261 0.993-3.068 0.728-2.079
1[Reference] 1.271 (<0.001) 1.379 (<0.001)
1.122-1.440 1.200-1.583
1[Reference] 1.609 (<0.001) 2.715 (<0.001)
1.433-1.807 2.376-3.102
1[Reference] 0.842 (0.001) 0.907 (0.353)
0.757-0.932 0.738-1.112
24
Table 3. Characteristics of patients by facility volume and academic affiliation. Before Propensity-Score After Propensity-Score Matching Matching for Academic Affiliation Variable Academic Non-Academic Academic Non-Academic p=1.000 p<0.001 Age, mean (SE) 75.7 (0.3) 75.7 (0.3) 72.7 (0.3) 74.0 (0.2) p=0.802 p=1.000 Sex Female 730 (41.0) 888 (41.4) 352 (39.6) 352 (39.6) Male 1052 (59.0) 1259 (58.6) 534 (60.3) 534 (60.3) p=0.939 p=1.000 Race White 1687 (94.7) 2033 (94.7) 886 (100.0) 886 (100.0) Black 18 (1.0) 28 (1.3) 0 (0.0) 0 (0.0) Hispanic 38 (2.1) 43 (2.0) 0 (0.0) 0 (0.0) Asian/Pacific Islander 14 (0.8) 13 (0.6) 0 (0.0) 0 (0.0) Other/Unknown 25 (1.4) 30 (1.4) 0 (0.0) 0 (0.0) p=0.039 p=1.000 p=1.000 Charlson/Deyo Score 0 1375 (77.2) 1575 (73.4) 761 (86.0) 761 (86.0) 1 305 (17.1) 420 (19.6) 107 (11.8) 107 (11.8) 2 72 (4.0) 114 (5.3) 15 (1.7) 15 (1.7) ≥3 30 (1.7) 38 (1.8) 3 (0.4) 3 (0.4) p=0.010 p=1.000 p=1.000 Insurance Private 500 (28.1) 514 (23.9) 169 (19.1) 169 (19.1) Medicare 1195 (67.1) 1543 (71.9) 712 (80.4) 712 (80.4) Other Government 53 (3.0) 62 (2.9) 4 (0.4) 4 (0.4) None 11 (0.6) 13 (0.6) 0 (0.0) 0 (0.0) Unknown 23 (1.3) 15 (0.7) 1 (0.1) 1 (0.1) p=1.000 p=1.000 p=0.002 Clinical Stage 1 1108 (61.2) 1234 (57.5) 619 (69.9) 619 (69.9) 2 404 (22.7) 589 (27.4) 183 (20.6) 183 (20.6) 3 270 (15.2) 324 (15.1) 84 (9.5) 84 (9.5) p<0.001 p=1.000 p=1.000 Treatment Combination Surgery Alone 888 (49.5) 878 (40.9) 432 (48.8) 432 (48.8) Surgery + RT 804 (45.2) 1097 (51.1) 431 (48.6) 431 (48.6) Surgery + CRT 90 (5.4) 172 (8.0) 23 (2.6) 23 (2.6)
Before Propensity-Score Matching High p<0.001 71.8 (0.3) =0.960 485 (41.2) 691 (58.8) p=0.403 1124 (95.6) 10 (0.8) 19 (1.6) 6 (0.5) 17 (1.4) p<0.001 937 (79.7) 189 (16.1) 32 (2.7) 18 (1.5) p=0.001 351 (29.8) 772(65.6) 40 (3.4) 5 (0.4) 8 (0.7) p<0.001 777 (66.1) 237 (20.2) 162 (13.8) p<0.001 603 (51.3) 519 (44.1) 54 (4.6)
Low/Intermediate 74.1 (0.2) 1133 (41.2) 1620 (58.8) 2596 (94.7) 36 (1.3) 62 (2.2) 21 (0.8) 38 (1.4) 2013 (73.1) 536 (19.5) 154 (5.6) 50 (1.8) 663 (24.1) 1966 (71.4) 75 (2.7) 19 (0.7) 30 (1.1) 1565 (56.8) 756 (27.5) 432 (15.7) 1163 (42.2) 1382 (50.2) 208 (7.6)
After Propensity-Score Matching for Facility Volume High Low/Intermediate p=1.000 74.1 (0.4) 74.1 (0.4) p=1.000 298 (40.5) 298 (40.5) 437 (59.5) 437 (59.5) p=1.000 734 (99.9) 734 (99.9) 0 (0.0) 0 (0.0) 0 (0.0) 0 (0.0) 0 (0.0) 0 (0.0) 1 (0.1) 1 (0.1) p=1.000 652 (88.7) 652 (88.7) 72 (9.8) 72 (9.8) 9 (1.2) 9 (1.2) 2 (0.3) 2 (0.3) p=1.000 168 (22.9) 168 (22.9) 565 (76.9) 565 (76.9) 2 (0.3) 2 (0.3) 0 (0.0) 0 (0.0) 0 (0.0) 0 (0.0) p=1.000 534 (72.6) 534 (72.6) 138 (18.8) 138 (18.8) 63(8.6) 63 (8.6) p=1.000 381 (51.8) 381 (51.8) 340 (46.3) 340 (46.3) 14 (1.9) 14 (1.9)
S0190-9622(19)32664-7
DOI:
https://doi.org/10.1016/j.jaad.2019.08.058
Reference:
YMJD 13780
To appear in:
Journal of the American Academy of Dermatology
Received Date: 2 June 2019 Revised Date:
19 August 2019
Accepted Date: 22 August 2019
Please cite this article as: Cheraghlou S, Agogo GO, Girardi M, The impact of facility characteristics on Merkel cell carcinoma outcomes: a retrospective cohort study, Journal of the American Academy of Dermatology (2019), doi: https://doi.org/10.1016/j.jaad.2019.08.058. This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. © 2019 Published by Elsevier on behalf of the American Academy of Dermatology, Inc.
1 Article type: Original article Title: The impact of facility characteristics on Merkel cell carcinoma outcomes: a retrospective cohort study Shayan Cheraghlou, BA1; George O. Agogo, PhD2; Michael Girardi, MD1 1 2
Department of Dermatology, Yale School of Medicine, New Haven, Connecticut Department of Internal Medicine, Yale School of Medicine, New Haven, Connecticut
Corresponding author: Michael Girardi, MD 333 Cedar Street., PO Box 208059, Department of Dermatology, Yale University School of Medicine New Haven, CT 06520 Telephone: (203) 785-4092 Fax: (203) 776-6188 Email: [email protected] Funding sources: None.
IRB approval status: Reviewed and exempted from institutional review by the Yale Human Investigation Committee (IRB #2000023704) Conflicts of interest: The authors have no conflicts of interest or financial disclosures, and all authors had access to the data and a role in writing the manuscript. Manuscript word count: 2,499 Abstract word count: 200 Capsule summary word count: 50 References: 67 Figures: 2 Supplementary figures: 0 Tables: 3 Supplementary tables: 0
Key Words: academic affiliation; case volume; survival; merkel cell carcinoma; regionalization; NCDB
2 CAPSULE •
Previous work has suggested that, for rare malignancies, large regional centers achieve improved patient outcomes. We found that treatment at academic/high-volume facilities is associated with improved MCC survival.
•
While further study evaluating disease-specific survival is required, our results suggest that care regionalization may provide an avenue to improve MCC survival.
3 ABSTRACT Background: Previous work has suggested that facility-level characteristics such as case volume and academic affiliation are associated with patient survival for rare malignancies. Merkel cell carcinoma (MCC) is a rare neuroendocrine skin cancer with high mortality and rising incidence. The impact of facility characteristics on MCC outcomes is not yet established. Objective: We aimed to investigate the association of facility academic affiliation and case volume with MCC patient survival. Methods: We conducted a retrospective cohort analysis of US adult MCC cases diagnosed from 2004-2014 in the NCDB. Results: Both facility academic affiliation (p<0.001) and case volume (p<0.001) were significantly associated with patient survival. For propensity score-matched cohorts of patients treated at academic versus non-academic facilities, five-year survival was 63.0% (SE: 1.7) and 53.4% (SE: 1.9) respectively. Five-year survival for propensity score-matched cohorts of patients treated at high versus low/intermediate case volume facilities was 67.4% (SE: 2.1) and 58.6% (SE: 2.0) respectively. Limitations: Disease-specific survival and local recurrence data were not available. Conclusions: Treatment of MCC at academic and high-volume centers is associated with significantly improved patient survival. Further study, taking into account comorbidities and evaluating disease-specific survival, is needed to establish whether experienced centers have improved outcomes in MCC treatment.
4 INTRODUCTION Merkel cell carcinoma (MCC) is a highly malignant neuroendocrine skin cancer, rapidly rising in incidence worldwide.1-4 The most recent US epidemiologic study of MCC reported 2,488 yearly incident cases and predicted 2,835 yearly cases by 2020 and 3,284 by 2025.5 Studies of Australian and French populations have also revealed increasing MCC incidence.6,7 This is an especially concerning trend given the high mortality rate for MCC compared to other cutaneous malignancies.8-10 Nonetheless, perhaps due to its relative rarity and its nonspecific clinical features mimicking benign cutaneous lesions such as pyogenic granuloma or keratoacanthoma, MCC presents a diagnostic and therapeutic challenge.11,12 Work on other rare malignancies, such as cholangiocarcinoma,13,14 pediatric head and neck sarcoma,15 and laryngeal cancer,16 as well as malignant melanoma,17 has revealed high treatment facility case volume and academic affiliation to be significantly associated with improved patient outcomes. The relationship between treatment facility characteristics and patient outcomes has spurred a movement toward cancer care regionalization, wherein cases are directed to regional, typically academic, high-volume "centers of excellence". Proponents of this shift in care delivery argue that these centers can leverage caseload experience and academic advantages to deliver higher quality care than smaller community centers as evidenced by their improved patient outcomes. A number of mechanisms have been proposed to underlie this phenomenon, including increased discussion of patients at multidisciplinary tumor boards, clinical trial participation, and increased diagnostic and technical expertise. Nevertheless, despite the increased recognition of the influence of facility-level characteristics, including case volume and academic affiliation, the impact of such factors on MCC outcomes is not yet
5 established. In the present study, we aim to evaluate the associations between MCC outcomes and facility-level characteristics, specifically facility academic affiliation and case volume.
6 METHODS Data Source Data originated from the National Cancer Database (NCDB) from 2004-2017 with diagnostic dates 2004-2014. The NCDB is a nationwide clinical surveillance resource data set that includes approximately 70% of all newly diagnosed malignancies in the United States from over 1,500 cancer programs as previously described.18 The NCDB has been used in a number of studies of system-level impacts on cancer outcomes.19-21 This study was determined to be exempt from institutional review by the Yale Human Investigation Committee.
Study Population Using a cohort of patients with histologically confirmed MCC using International Classification of Diseases for Oncology 3 code 8247, we identified 14,950 patients. We excluded patients who were under 18 at the time of diagnosis (n=2), had any other malignancies (n=5036), did not receive definitive surgical excision of the primary tumor (n=1426), had distant metastases (n=204), had unknown metastatic status (n=212), had unknown primaries (n=2905) or had missing/incomplete follow-up (n=625). For multivariate analysis and propensity-score matched analyses, we excluded patients if they were absent information on clinical staging (n=203), radiation administration status (n=27), or chemotherapy administration status (n=86), or if they received chemotherapy without radiotherapy (n=95).
Statistical Analysis Patients were defined as having government insurance if the patient's primary insurance carrier was Medicare, Medicaid, or another government-administered insurance carrier.
7 Patients were defined as having received chemotherapy if they received any chemotherapeutic agent. We considered patients to have received radiotherapy if they received external beam radiation to the primary site. Facilities were divided into low (first), intermediate (second and third), and high (fourth) volume groupings prior to case exclusions based on volume quartile such that ~25% of patients were treated at low-volume facilities, ~50% at intermediate-volume facilities, and ~25% of patients at high-volume facilities. The dividing points between these groups were: Q1=1 and Q3=4 cases/year. The primary outcome was overall survival. Administration of immunotherapy was not included in the final analysis since it was used in only 0.07% of the 4,340 analytic sample (n=3), with 0.76% of cases (n=33) with an unknown immunotherapy administration status. All results were unchanged with exclusion of these cases. Univariate Kaplan-Meier analyses stratified by facility yearly case-volume and facility academic status were performed. Multivariate survival analyses using Cox proportional hazards models were then conducted and repeated excluding either facility volume and facility type due to a possible confounding interaction between the two variables. Variables outlined in Table 1 were tested for appropriateness for inclusion in this model using Akaike information criterion minimization in order to decrease the likelihood of overfitting data.22 Propensity-score matching was conducted for cohorts of patients treated at academic and non-academic facilities as well as for cohorts of patients treated at low/intermediate and high-volume facilities. This methodology controls for potential differences in outcomes due to systemic differences between the two groups.17,23-25 Scores were calculated using a logit model with the factors outlined in Table 1 except for facility type and volume respectively. Groups were
8 generated using a one-to-one nearest neighbor match without replacement. Group differences were tested using the two-tailed student's t-test for continuous variables and the chi-squared test for categorical variables. Statistical significance was determined at the p<0.05 level. Data analysis was performed using STATA version 13 (StataCorp LP, College Station, TX).
9 RESULTS The characteristics of the analytic sample are outlined in Table 1. The majority of patients were White (94.5%) and male (59.0%), with a median age of 75. Most patients had a Charlson/Deyo score of 0 (74.7%), carried Medicare insurance (69.3%) and were treated at nonacademic centers (55.3%). Forty-five facilities were designated as "high-volume" centers which treated approximately 25% of patients, 386 as "intermediate-volume" centers which treated approximately 50% of patients, and 548 as "low-volume" centers which treated 25% of patients. The most commonly utilized treatment combination in our sample was surgery with adjuvant radiotherapy (45.7%), followed by surgery without adjuvant therapy (42.8%). Among cases with known adjuvant therapy, adjuvant radiotherapy was used for similar proportions of stage I, II, and III cases, 46.8%, 50.2%, and 51.7% respectively. In contrast, adjuvant chemoradiotherapy was used for 21.2% of stage III cases versus 2.7% and 7.4% of stage I and stage II cases respectively. Mean follow-up was 3.50 years with a maximum follow-up of 12.72 years. Univariate Kaplan-Meier analyses stratified by both facility academic affiliation and volume revealed significant associations with patient survival. Patients treated at academic facilities had significantly improved long-term outcomes compared to those treated at nonacademic centers (Figure 1A). Three-, 5-, and 10-year survival was 70.6% (SE: 1.1), 60.9% (SE: 1.3), and 41.9% (SE: 2.7) respectively at academic facilities and 61.0% (SE: 1.0), 51.0% (SE: 1.2), and 32.6% (SE: 1.9) respectively at non-academic facilities. Additionally, patients treated at higher volume centers experienced significantly improved long-term survival compared to those treated at lower volume centers (Figure 1B). At high-volume centers, 3-, 5-, and 10-year
10 survival was 73.7% (SE: 1.3), 64.2% (SE: 1.6), and 44.2% (SE: 3.6) respectively; while 3-, 5-, and 10-year survival was 63.0% (SE: 1.1), 53.0% (SE: 1.3), and 38.8% (SE: 2.0) respectively for intermediate-volume centers, and 59.6% (SE: 1.6), 49.5% (SE: 1.7), and 26.8% (SE: 2.8) respectively for low-volume centers. Multivariate Cox survival regressions revealed similar associations (Table 2). A regression analysis of cases with non-missing data revealed that patients treated at nonacademic facilities had significantly worse survival compared to those treated at academic centers (Hazard Ratio [HR], 1.219; 95% Confidence Interval [CI] 1.072-1.386). Given the likely confounding effects between facility volume and academic affiliation, regressions were repeated excluding either facility volume or academic affiliation. Excluding facility volume from the model revealed a similar worsened survival for patients treated at non-academic versus academic facilities (HR 1.317, 95% CI 1.189-1.458). Excluding facility academic affiliation from the model, facility volume was also significantly associated with survival with patients treated at both intermediate (HR 1.271, 95% CI 1.122-1.440) and low (HR 1.379, 95% CI 1.200-1.583) experiencing significantly poorer survival than those treated at high-volume centers. Subsequent propensity-score matched analyses also demonstrated these associations. Matched cohorts of patients treated at academic and non-academic facilities were generated, the characteristics of which are outlined in Table 3. Survival analysis of these cohorts revealed significantly improved survival for patients treated at academic versus non-academic facilities (Figure 2A). Three-, 5-, and 10-year survival was 72.0% (SE: 1.6), 62.4% (SE: 1.9), and 41.6% (SE: 3.9) respectively for patients treated at academic facilities, and 63.0% (SE: 1.7), 53.4% (SE: 1.9), and 33.9 (SE: 3.1) respectively for patients treated at non-academic facilities. The
11 characteristics of matched cohorts of patients treated at high and low/intermediate-volume facilities are also outlined in Table 3. Patients in the high-volume cohort exhibited significantly improved survival compared to those in the low/intermediate-volume cohort (Figure 2B). Three-, 5-, and 10-year survival was 77.0% (SE: 1.7), 67.4% (SE: 2.1), and 44.8% (SE: 4.8) respectively for patients treated at high-volume facilities, and 67.1% (SE: 1.8), 58.6% (SE: 2.0), and 36.5% (SE: 3.7) respectively for patients treated at low/intermediate-volume facilities. We also found that, prior to propensity score matching, both non-academic (p=0.010) and low/intermediate-volume (p=0.001) facilities had greater proportions of Medicare patients and lower proportions of patients carrying private insurance. Additionally, prior to propensity score matching, academic (p=0.002) and high-volume (p<0.001) facilities treated greater proportions of Stage I patients.
12 DISCUSSION In the present study, we demonstrate that both academic affiliation and high facility case volume are independently associated with improved survival for patients with MCC. While this has not been previously studied in MCC, this is consistent with recent findings of the impact of these factors on survival for melanoma17,26 and non-cutaneous malignancies.13-16 The National Comprehensive Cancer Network publishes yearly treatment guidelines for MCC.27 However, due to the lack of high-level evidence present for disease management, multiple options are offered at each step of the diagnostic/treatment algorithm with discretion left to the treating physician. Thus, the majority of the literature advocates for a multidisciplinary approach.28-32 Proponents of this approach argue that case discussion at multidisciplinary tumor boards allows for improved treatment decision-making and multispecialty care coordination. Indeed, centers with established multidisciplinary MCC care teams report superior stage-specific patient survival compared to national cohorts.33-35 While not extensively studied in the context of MCC, cases brought to multidisciplinary clinics for other malignancies have been shown to be staged more accurately, treated more promptly, and more often treated within evidence-based guidelines.36-40 Data from Swedish hospitals suggests that one of the drivers of this discrepancy in guideline adherence at such centers may be the increased likelihood of patient discussion at multidisciplinary tumor conferences, shown to be an independent predictor of the delivery of guideline-adherent care.38,41 Previous work has established that multidisciplinary care teams achieve improved outcomes for a variety of malignancies including: non-small cell lung cancer,42 breast cancer,43,44 esophageal carcinoma,45 malignant teratoma,46 ovarian cancer,47 soft tissue sarcoma,48 and metastatic testicular
13 cancer.49 Indeed, the increased likelihood of case discussion at multidisciplinary tumor boards at high-volume and academic centers may be a major reason for our findings of improved MCC outcomes at these centers.41 Patients treated at academic facilities and high-volume may also benefit from enrollment in the clinical trials that are conducted at such centers. While there is still some debate about this point,50-52 there has been evidence to suggest that cancer patients participating in clinical trials experience improved outcomes.53-56 Indeed, there is evidence from Germany that clinical trial participation on the hospital level is associated with improved outcomes for ovarian cancer patients treated at these centers.57 This may be especially relevant in the case of MCC, for which there is currently no evidence of a durable response to systemic chemotherapy.58 While not captured in our analytic sample, which included cases diagnosed between 2004 and 2014 inclusive, avelumab, an anti-PD-L1 antibody shown to be effective in MCC with distant metastasis,59 is now being trialed in MCC with nodal spread (NCT03271372), and may provide an effective therapy that is not available outside the context of the clinical trial. Additionally, the increased level of experience with treating MCC at high-volume facilities may lead to improved decision-making and more thorough surgical excisions. For other cancers treated with surgical excision, a growing body of work has demonstrated improved outcomes for cases treated both by individual high-volume surgeons and at high-volume facilities irrespective of individual surgeon volume.60-63 For MCC, provider experience may be particularly salient given the rarity and aggressiveness associated with the disease. Furthermore, although treatment at high-volume and academic centers was associated with
14 improved outcomes when controlling for factors such as disease stage, we found that these centers treated a significantly greater proportion of stage I disease, perhaps reflecting improved detection of a difficult-to-diagnose malignancy. These results suggest that the policy of cancer care regionalization – treatment of certain malignancies at high-volume, typically academic, regional centers – may result in improved outcomes for patients with MCC. While much of the literature supporting care regionalization has focused on high-risk cancer surgeries,64-66 this practice may be similarly applicable for the treatment of rare and deadly malignancies such as MCC. Some of the work studying cancer care regionalization has suggested referral of high-risk patients to regional care center,66 which, in the case of MCC would likely refer to cases with nodal or extra-nodal metastasis for which the relative 10-year survival rate is under 50%.10 Given the limited number of disease experts and relatively low incidence of disease, regionalization of care for rare malignancies such as MCC may also be more logistically practical than for more common malignancies, for which universal care regionalization may overload the resources of regional centers. Nevertheless, a number of barriers exist for policies of care regionalization, including the fact that some geographic areas lack the case density to support a regional center.67 Further work is required to study the potential feasibility of MCC care regionalization and the benefits and deficits of such a policy. Our study was limited by a number of factors. First, we did not have access to data on local recurrence or disease-specific survival due to their absence from the data source and were therefore unable to assess the impact of facility characteristics on these outcomes. We were also limited by the absence of additional variables such as Merkel cell polyomavirus status,
15 cytokeratin 20 expression, resection margin, and case discussion at a multidisciplinary tumor board, which would have provided additional control measures. Furthermore, due to the time frame of our study, with diagnostic dates 2004-2014, we were unable to assess the impact of immunotherapy, which is likely to be an integral component of future MCC therapy, on our observed results. Additionally, a component of the improved outcomes at experienced centers may be due to selection given that social issues and comorbidities, many of which were not controlled for in our analysis, can often preclude patients from travelling to experienced MCC centers. In some of these cases, experienced centers may consult with the patient and the local care team to direct the patient's care, and our study is also limited by the lack of data on this type of outside consultant input. Our study reveals the association of facility-level factors with survival for patients diagnosed with MCC. While further work assessing disease-specific survival is needed to confirm the improved survival noted for patients treated at experienced centers, these results suggest that MCC care regionalization, where feasible, may provide an opportunity to improve the outcomes of patients with this disease.
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21 FIGURE LEGEND Figure 1. Kaplan-Meier univariate patient survival stratified by (A) facility academic affiliation, (B) facility case volume. Figure 2. Overall survival for propensity score-matched cohorts of patients treated at (A) academic versus other facilities and (B) high versus low/intermediate case volume facilities.
22 Table 1. Characteristics of the analytic sample. Variable Age, median (SD) Sex Female Male Race White Black Hispanic Asian/Pacific Islander Other/Unknown Charlson/Deyo Score 0 1 2 ≥3 Insurance Private Medicare Other Government None Unknown Facility Type Academic Other Facility Volume Low Intermediate High Clinical Stage 1 2 3 Missing Treatment Surgery Alone Surgery + RT Surgery + CRT Surgery + Unknown
Overall (n=4340) 75 (11.4)
No. (%) Cases with Non-Missing Data (n=3929) 75 (11.4)
1778 (41.0) 2562 (59.0)
1618 (41.2) 2311 (58.8)
4102(94.5) 55 (1.3) 92 (2.1) 29 (0.7) 62 (1.4)
3720 (94.7) 46 (1.2) 81 (2.1) 27 (0.7) 55 (1.4)
3242 (74.7) 807 (18.6) 211 (4.9) 80 (1.8)
2950 (75.1) 725 (18.4) 186 (4.7) 68 (1.7)
1122 (25.8) 3006 (69.3) 129 (3.0) 31 (0.7) 52 (1.2)
1014 (25.8) 2738 (69.7) 115 (2.9) 24 (0.6) 38 (1.0)
1941 (44.7) 2399 (55.3)
1782 (45.4) 2147 (54.6)
1067 (24.6) 2020 (46.5) 1253 (28.9)
944 (24.0) 1809 (46.0) 1176 (29.9)
2421 (55.8) 1046 (24.1) 670 (15.4) 203 (4.7)
2342 (59.6) 993 (25.3) 594 (15.1) -
1856 (42.8) 1983 (45.7) 281 (6.5) 220 (5.1)
1766 (45.0) 1901 (48.4) 262 (6.7) -
23 Table 2. Multivariate analysis of factors associated with survival. Overall Excluding Facility Volume from Model Variable Hazard Ratio (p-value) 95% CI Hazard Ratio (p-value) 95% CI 1.068 (<0.001) 1.061-1.075 1.068 (<0.001) 1.061-1.074 Age Sex Female 1[Reference] 1[Reference] Male 1.463 (<0.001) 1.320-1.622 1.461 (<0.001) 1.318-1.620 Charlson/Deyo Score 0 1[Reference] 1[Reference] 1 1.387 (<0.001) 1.228-1.567 1.392 (<0.001) 1.236-1.572 2.024 (<0.001) 1.660-2.466 2 1.997 (<0.001) 1.638-2.435 ≥3 2.340 (<0.001) 1.710-3.201 2.354 (<0.001) 1.721-3.218 Insurance Private 1[Reference] 1[Reference] Medicare 1.158 (0.060) 0.994-1.349 1.156 (0.062) 0.993-1.347 Other Government 1.602 (0.005) 1.151-2.231 1.581 (0.007) 1.136-2.200 None 1.837 (0.035) 1.044-3.231 1.904 (0.025) 1.084-3.344 Unknown 1.283 (0.352) 0.759-2.170 1.313 (0.309) 0.777-2.218 Facility Type Academic 1[Reference] 1[Reference] Other 1.219 (0.003) 1.072-1.386 1.317 (<0.001) 1.189-1.458 Facility Volume High 1[Reference] Intermediate 1.131 (0.100) 0.977-1.310 Low 1.177 (0.065) 0.990-1.398 Clinical Stage 1 1[Reference] 1[Reference] 2 1.620 (<0.001) 1.442-1.819 1.636 (<0.001) 1.459-1.836 3 2.762 (<0.001) 2.416-3.159 2.782 (<0.001) 2.433-3.181 Treatment Combination Surgery Alone 1[Reference] 1[Reference] Surgery + RT 0.828 (<0.001) 0.746-0.918 0.827 (<0.001) 0.746-0.918 Surgery + CRT 0.882 (0.231) 0.718-1.083 0.880 (0.225) 0.716-1.082 Note: Race removed as variable from multivariate model after Akaike information criterion minimization.
Excluding Facility Type from Model Hazard Ratio (p-value) 95% CI 1.067 (<0.001) 1.061-1.074 1[Reference] 1.457 (<0.001)
1.315-1.615
1[Reference] 1.388 (<0.001) 1.990 (<0.001) 2.336 (<0.001)
1.229-1.568 1.632-2.427 1.707-3.196
1[Reference] 1.168 (0.046) 1.625 (0.004) 1.745 (0.053) 1.230 (0.439)
1.003-1.361 1.167-2.261 0.993-3.068 0.728-2.079
1[Reference] 1.271 (<0.001) 1.379 (<0.001)
1.122-1.440 1.200-1.583
1[Reference] 1.609 (<0.001) 2.715 (<0.001)
1.433-1.807 2.376-3.102
1[Reference] 0.842 (0.001) 0.907 (0.353)
0.757-0.932 0.738-1.112
24
Table 3. Characteristics of patients by facility volume and academic affiliation. Before Propensity-Score After Propensity-Score Matching Matching for Academic Affiliation Variable Academic Non-Academic Academic Non-Academic p=1.000 p<0.001 Age, mean (SE) 75.7 (0.3) 75.7 (0.3) 72.7 (0.3) 74.0 (0.2) p=0.802 p=1.000 Sex Female 730 (41.0) 888 (41.4) 352 (39.6) 352 (39.6) Male 1052 (59.0) 1259 (58.6) 534 (60.3) 534 (60.3) p=0.939 p=1.000 Race White 1687 (94.7) 2033 (94.7) 886 (100.0) 886 (100.0) Black 18 (1.0) 28 (1.3) 0 (0.0) 0 (0.0) Hispanic 38 (2.1) 43 (2.0) 0 (0.0) 0 (0.0) Asian/Pacific Islander 14 (0.8) 13 (0.6) 0 (0.0) 0 (0.0) Other/Unknown 25 (1.4) 30 (1.4) 0 (0.0) 0 (0.0) p=0.039 p=1.000 p=1.000 Charlson/Deyo Score 0 1375 (77.2) 1575 (73.4) 761 (86.0) 761 (86.0) 1 305 (17.1) 420 (19.6) 107 (11.8) 107 (11.8) 2 72 (4.0) 114 (5.3) 15 (1.7) 15 (1.7) ≥3 30 (1.7) 38 (1.8) 3 (0.4) 3 (0.4) p=0.010 p=1.000 p=1.000 Insurance Private 500 (28.1) 514 (23.9) 169 (19.1) 169 (19.1) Medicare 1195 (67.1) 1543 (71.9) 712 (80.4) 712 (80.4) Other Government 53 (3.0) 62 (2.9) 4 (0.4) 4 (0.4) None 11 (0.6) 13 (0.6) 0 (0.0) 0 (0.0) Unknown 23 (1.3) 15 (0.7) 1 (0.1) 1 (0.1) p=1.000 p=1.000 p=0.002 Clinical Stage 1 1108 (61.2) 1234 (57.5) 619 (69.9) 619 (69.9) 2 404 (22.7) 589 (27.4) 183 (20.6) 183 (20.6) 3 270 (15.2) 324 (15.1) 84 (9.5) 84 (9.5) p<0.001 p=1.000 p=1.000 Treatment Combination Surgery Alone 888 (49.5) 878 (40.9) 432 (48.8) 432 (48.8) Surgery + RT 804 (45.2) 1097 (51.1) 431 (48.6) 431 (48.6) Surgery + CRT 90 (5.4) 172 (8.0) 23 (2.6) 23 (2.6)
Before Propensity-Score Matching High p<0.001 71.8 (0.3) =0.960 485 (41.2) 691 (58.8) p=0.403 1124 (95.6) 10 (0.8) 19 (1.6) 6 (0.5) 17 (1.4) p<0.001 937 (79.7) 189 (16.1) 32 (2.7) 18 (1.5) p=0.001 351 (29.8) 772(65.6) 40 (3.4) 5 (0.4) 8 (0.7) p<0.001 777 (66.1) 237 (20.2) 162 (13.8) p<0.001 603 (51.3) 519 (44.1) 54 (4.6)
Low/Intermediate 74.1 (0.2) 1133 (41.2) 1620 (58.8) 2596 (94.7) 36 (1.3) 62 (2.2) 21 (0.8) 38 (1.4) 2013 (73.1) 536 (19.5) 154 (5.6) 50 (1.8) 663 (24.1) 1966 (71.4) 75 (2.7) 19 (0.7) 30 (1.1) 1565 (56.8) 756 (27.5) 432 (15.7) 1163 (42.2) 1382 (50.2) 208 (7.6)
After Propensity-Score Matching for Facility Volume High Low/Intermediate p=1.000 74.1 (0.4) 74.1 (0.4) p=1.000 298 (40.5) 298 (40.5) 437 (59.5) 437 (59.5) p=1.000 734 (99.9) 734 (99.9) 0 (0.0) 0 (0.0) 0 (0.0) 0 (0.0) 0 (0.0) 0 (0.0) 1 (0.1) 1 (0.1) p=1.000 652 (88.7) 652 (88.7) 72 (9.8) 72 (9.8) 9 (1.2) 9 (1.2) 2 (0.3) 2 (0.3) p=1.000 168 (22.9) 168 (22.9) 565 (76.9) 565 (76.9) 2 (0.3) 2 (0.3) 0 (0.0) 0 (0.0) 0 (0.0) 0 (0.0) p=1.000 534 (72.6) 534 (72.6) 138 (18.8) 138 (18.8) 63(8.6) 63 (8.6) p=1.000 381 (51.8) 381 (51.8) 340 (46.3) 340 (46.3) 14 (1.9) 14 (1.9)