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Survival implications of time to surgical treatment of endometrial cancers

Published:December 08, 2016DOI:https://doi.org/10.1016/j.ajog.2016.11.1050

      Background

      Optimal care for women with endometrial cancers often involves transfer of care from diagnosing physicians (eg, obstetrician-gynecologists) to treating physicians (eg, gynecologic oncologists.) It is critical to determine the effect of time to treatment on cancer outcomes to set best practices guidelines for referral processes.

      Objective

      We sought to determine the impact of time from diagnosis of endometrial cancer to surgical treatment on mortality and to characterize those patients who may be at highest risk for worsened survival related to surgical timing.

      Study Design

      The National Cancer Database was queried for incident endometrial cancers in adults from 2003 through 2012. Cancers were classified as low risk (grade 1 or 2 endometrioid histologies) or high risk (nonendometrioid and grade 3 endometrioid histologies) and analyzed separately. Demographic, clinicopathologic, and health system factors were collected. Unadjusted and adjusted hazard ratios for mortality were calculated by interval between diagnosis and surgery. Linear regression of patient and health care system characteristics was performed on diagnosis-to-surgery interval.

      Results

      For low-risk cancers (N = 140,078), surgery in the first and second weeks after diagnosis was independently associated with mortality risk (hazard ratio, 1.4; 95% confidence interval, 1.3–1.5; and hazard ratio, 1.1; 95% confidence interval, 1.0–1.2, respectively). The 30-day postoperative mortality was significantly higher among patients undergoing surgery in the first or second week postdiagnosis, compared to patients treated in the third or fourth week postdiagnosis (0.7% vs 0.4%; P < .001). Mortality risk was also significantly higher than baseline when time between diagnosis and surgery was >8 weeks. Independent associations with added time to surgery of at least 1 week were seen with black race (1.1 weeks; 95% confidence interval, 0.9–1.4), uninsurance (1.3 weeks; 95% confidence interval, 1.1–1.5), Medicaid insurance (1.7 weeks; 95% confidence interval, 1.5–1.9), and Charlson-Deyo comorbidity score >1 (1.0 weeks; 95% confidence interval, 0.8–1.2). For high-risk cancers (N = 68,360), surgery in the first and second weeks after diagnosis was independently associated with mortality risk (hazard ratio, 1.5; 95% confidence interval, 1.3–1.6; and hazard ratio, 1.2; 95% confidence interval, 1.1–1.2, respectively). The 30-day postoperative mortality was significantly higher among patients undergoing surgery in the first or second week postdiagnosis, compared to patients treated in the third or fourth week postdiagnosis (2.5% vs 1.0%; P < .001). Surgery after the third week postdiagnosis was not associated with a statistically significant increase in the adjusted risk of mortality. Independent associations with added time to surgery of at least 1 week were seen with uninsurance (1.4 weeks; 95% confidence interval, 0.9–1.9) and Medicaid insurance (1.4 weeks; 95% confidence interval, 1.1–1.7).

      Conclusion

      Surgery in the first 2 weeks after diagnosis of endometrial cancer was associated with worsened survival associated with elevated perioperative mortality and treatment in low-volume hospitals. Delay in surgical treatment was a risk factor for mortality in low-risk cancers only and was likely associated with poor access to specialty care. We suggest that the target interval between diagnosis and treatment of endometrial cancers be ≤8 weeks; however, referral to an experienced surgeon and adequate preoperative optimization should be prioritized over expedited surgery.

      Key words

      Introduction

      Delay between diagnosis and surgical treatment of endometrial cancer may result in worsened overall survival, potentially as a consequence of disease progression or difficulty accessing care.
      • Dolly D.
      • Mihai A.
      • Rimel B.J.
      • et al.
      A delay from diagnosis to treatment is associated with a decreased overall survival for patients with endometrial cancer.
      A relationship between surgical delay and survival disadvantage has been demonstrated in breast,
      • Richards M.A.
      • Westcombe A.M.
      • Love S.B.
      • Littlejohns P.
      • Ramirez A.J.
      Influence of delay on survival in patients with breast cancer: a systematic review.
      • Bleicher R.J.
      • Ruth K.
      • Sigurdson E.R.
      • et al.
      Time to surgery and breast cancer survival in the United States.
      rectal,
      • Yun Y.H.
      • Kim Y.A.
      • Min Y.H.
      • et al.
      The influence of hospital volume and surgical treatment delay on long-term survival after cancer surgery.
      and bladder
      • Lee C.T.
      • Madii R.
      • Daignault S.
      • et al.
      Cystectomy delay more than 3 months from initial bladder cancer diagnosis results in decreased disease specific and overall survival.
      cancers; this relationship does not clearly exist for esophageal,
      • Kötz B.S.
      • Croft S.
      • Ferry D.R.
      Do delays between diagnosis and surgery in resectable esophageal cancer affect survival? A study based on West Midlands cancer registration data.
      gastric,
      • Yun Y.H.
      • Kim Y.A.
      • Min Y.H.
      • et al.
      The influence of hospital volume and surgical treatment delay on long-term survival after cancer surgery.
      renal cell,
      • Stec A.A.
      • Coons B.J.
      • Chang S.S.
      • et al.
      Waiting time from initial urological consultation to nephrectomy for renal cell carcinoma–does it affect survival?.
      or cervical
      • Umezu T.
      • Shibata K.
      • Kajiyama H.
      • Yamamoto E.
      • Mizuno M.
      • Kikkawa F.
      Prognostic factors in stage IA-IIA cervical cancer patients treated surgically: does the waiting time to the operation affect survival?.
      cancers. For endometrial cancer, findings to date have been mixed. Early work suggested that time to definitive treatment did not correlate with disease stage
      • Pirog E.C.
      • Heller D.S.
      • Westhoff C.
      Endometrial adenocarcinoma–lack of correlation between treatment delay and tumor stage.
      or survival
      • Menczer J.
      • Krissi H.
      • Chetrit A.
      • et al.
      The effect of diagnosis and treatment delay on prognostic factors and survival in endometrial carcinoma.
      • Levy T.
      • Golan A.
      • Menczer J.
      Endometrial endometrioid carcinoma: a glimpse at the natural course.
      ; however, these studies were limited by small sample sizes, mixed tumor histologies, and a focus on time from onset of abnormal uterine bleeding rather than from definite diagnosis of malignancy.
      Recently, 3 studies readdressed this issue with larger sample populations. A 2013 report of >9000 patients in Canada associated longer wait times with lower overall survival at 5 years.
      • Elit L.M.
      • O’Leary E.M.
      • Pond G.R.
      • Seow H.-Y.
      Impact of wait times on survival for women with uterine cancer.
      Although this study was criticized for including high-risk histologies, a subsequent subset analysis of >3000 patients included only endometrioid cancers undergoing simple hysterectomy and excluded patients receiving chemotherapy or radiation. A survival disadvantage was confirmed for women undergoing surgery <2 weeks after diagnosis or waiting >12 weeks for hysterectomy.
      • Elit L.M.
      • Pond G.
      • Seow H.-Y.
      Treatment delay in endometrial cancer. Reply to J. Menczer.
      In contrast, a study of 435 patients in California with grade 1-2 endometrioid-type endometrial cancer did not show an impact of wait time on overall survival, but was criticized for being underpowered to do so adequately.
      • Matsuo K.
      • Opper N.R.
      • Ciccone M.A.
      • et al.
      Time interval between endometrial biopsy and surgical staging for type I endometrial cancer: association between tumor characteristics and survival outcome.
      • Elit L.M.
      • Pond G.
      • Seow H.
      Time interval between endometrial biopsy and surgical staging for type I endometrial cancer: association between tumor characteristics and survival outcome.
      A third study using the National Cancer Database (NCDB) associated a diagnosis-to-surgery interval of ≥6 weeks with worsened outcomes; however this study analyzed a single time point and combined high- and low-risk histologies.
      • Strohl A.E.
      • Feinglass J.M.
      • Shahabi S.
      • Simon M.A.
      Surgical wait time: a new health indicator in women with endometrial cancer.
      Based on available data, we hypothesized that delayed or early surgical intervention may be associated with poor outcomes. Furthermore, the relationship between surgical interval and outcomes is likely to be different for low- and high-risk cancers. We therefore analyzed a large patient sample drawn from the NCDB to determine whether and when time from diagnosis of endometrial cancer to surgical treatment affects mortality and to characterize those patients who may be at highest risk for worsened survival related to timing of surgery.

      Materials and Methods

      The NCDB, maintained by the American College of Surgeons and the American Cancer Society, captures approximately 70% of cancer cases in the United States from 1500 Commission on Cancer (CoC)-accredited institutions nationwide.

      American College of Surgeons. National Cancer Database. Available at: https://www.facs.org/quality programs/cancer/ncdb. Accessed Feb. 4, 2016.

      The NCDB was queried for cases of endometrial cancer from 2003 through 2012. Cases included in the uterine corpus database with epithelial histologies were considered to be of endometrial origin. Low-risk (grade 1 and grade 2 endometrioid histologies) and high-risk (grade 3 endometrioid and all other epithelial histologies) tumors were analyzed separately. Uterine carcinosarcoma was included in the high-risk epithelial group, as this tumor likely originates from a dedifferentiated carcinoma.
      • Cantrell L.A.
      • Blank S.V.
      • Duska L.R.
      Uterine carcinosarcoma: a review of the literature.
      There were 420,445 patients in the initial sample. We limited our analysis to cases for which there was evidence that surgery was the only modality pursued, or occurred prior to any hormonal therapy, radiation, or chemotherapy. We excluded those for whom time between diagnosis and surgery was unavailable or diagnosis was made at the time of surgery. In all, 222,323 cases met initial inclusion criteria. We then excluded cases for which the tumor was coded as nonmalignant (N = 1396) or stage 0 (N = 1031), or for which diagnostic confirmation (N = 5) or hospital identifier (N = 208) were invalid. For analyses not including survival time, the sample consisted of 208,438 patient-level observations. For analyses involving survival, we further excluded cases for which the last contact date was missing or equaled the treatment date, leaving 182,748 patients (Figure 1).
      Figure thumbnail gr1
      Figure 1Study population flow diagram
      Shalowitz et al. Survival implications of time to surgical treatment of endometrial cancers. Am J Obstet Gynecol 2017.

      Variables

      Covariates included patient, facility, and geographic area characteristics. Patient characteristics included age (<45, 45–54, 55–64, 65–74, 75–84, ≥85 years), race (white, black, American Indian, Asian/Pacific Islander, other, and unknown), ethnicity (Hispanic vs not), primary payer (not insured, private, Medicaid, Medicare, other government), stage (1, 2, 3, or 4 based on the higher of pathologic and clinical stages; or unknown), grade (1, 2, 3, or unknown), receipt of systemic (chemotherapy or hormonal therapy) or radiation therapy within 60 days after surgery, treatment and/or diagnosis in the reporting facility, Charlson-Deyo comorbidity score (0, 1, 2+, excluding cancer), performance of lymphadenectomy, and year of diagnosis. Facility characteristics included type (community cancer program, comprehensive community cancer program, academic/research program) and quartile of annual endometrial cancer cases (calculated prior to sample exclusions). Geographic area characteristics included facility’s census region, quartile of straight-line distance from patient’s residential ZIP code to facility, metropolitan location of patient’s ZIP code (yes/no), and quartile of patient’s ZIP code–level median household income (based on 2000 US Census). Lymphadenectomy was collected as a proxy for gynecologic oncologist involvement in surgical treatment and was not expected to be independently associated with survival in these cases, although controversy exists on this point.
      • Todo Y.
      • Kato H.
      • Kaneuchi M.
      • Watari H.
      • Takeda M.
      • Sakuragi N.
      Survival effect of para-aortic lymphadenectomy in endometrial cancer (SEPAL study): a retrospective cohort analysis.
      • Aalders J.G.
      • Thomas G.
      Endometrial cancer–revisiting the importance of pelvic and para aortic lymph nodes.
      The location of lymph nodes removed (eg, pelvic vs paraaortic) was not documented in the NCDB. The interval from diagnosis to surgery was coded as the week postdiagnosis during which surgery occurred; for example, surgery on days 1-7 postdiagnosis was coded as occurring during week 1, and on days 8-14 as week 2. The date of diagnosis was originally recorded as either the date a confirmatory test (eg, biopsy) was performed or the date that a clinical diagnosis was documented, whichever came first.

      Statistical analysis

      The unit of analysis was the individual patient. Covariate and outcome values were compared across the 2 tumor categories (high or low risk) using χ2 tests for categorical variables and nonparametric equality of medians tests for continuous variables. To assess the association between surgical delay and postsurgical time to death or censoring separately for high- and low-risk cancers, the Kaplan-Meier method was used to calculate crude 5-year survival for each week of delay and Cox proportional hazard models were used to estimate crude and adjusted hazard ratios (HR). Linear regression was used to identify independent predictors of time between cancer diagnosis and definitive surgery. To account for the possible role of outliers in the distribution of time from diagnosis to surgery, we also estimated quasimaximum likelihood Poisson models. We report only the linear regression results because the Poisson results are qualitatively identical. In all models, SE were adjusted to account for the clustering of patients within centers. All analyses used software (Stata, Version 14.2; Stata Corp, College Station, TX). Two-tailed P < .05 was considered statistically significant. As the NCDB is a deidentified database, this study was exempted from institutional review board review.

      Results

      In all, 140,078 low-risk and 68,360 high-risk endometrial cancers were included in the descriptive analysis (Table 1). As expected, low-risk cancers occurred comparatively more frequently in younger women and less frequently in black women.
      • Boruta D.M.
      • Gehrig P.A.
      • Fader A.N.
      • Olawaiye A.B.
      Management of women with uterine papillary serous cancer: a Society of Gynecologic Oncology (SGO) review.
      Women with low-risk cancers were more likely to be diagnosed with stage I or II disease than women with high-risk cancers. Women with high-risk cancers were more likely to have Medicare insurance and less likely to be privately insured compared to women with low-risk cancers. Hispanic ethnicity, comorbidity score, and annual hospital case volume were not significantly different between low- and high-risk cancer cases.
      Table 1Descriptive characteristics of included cases
      Overall (N = 208,438)Low risk (N = 140,078)High risk (N = 68,360)P value
      Age at diagnosis, y<.001
       Mean (SD)62.86 (11.63)61.82 (11.59)64.97 (11.44)
      Age category, y, N (%)<.001
       <4511,926 (5.7%)9138 (6.5%)2788 (4.1%)
       45–5434,529 (17%)25,891 (18%)8638 (13%)
       55–6472,751 (35%)50,688 (36%)22,063 (32%)
       65–7453,645 (26%)33,463 (24%)20,182 (30%)
       75–8428,857 (14%)17,018 (12%)11,839 (17%)
       ≥856730 (3.2%)3880 (2.8%)2850 (4.2%)
      Patient race, N (%)<.001
       White179,598 (86%)123,913 (88%)55,685 (81%)
       Black18,880 (9.1%)9332 (6.7%)9548 (14%)
       American Indian584 (0.3%)414 (0.3%)170 (0.2%)
       Asian/Pacific Islander4972 (2.4%)3369 (2.4%)1603 (2.3%)
       Other1511 (0.7%)1024 (0.7%)487 (0.7%)
       Unknown2893 (1.4%)2026 (1.4%)867 (1.3%)
      Hispanic ethnicity, N (%).032
       No181,666 (87%)122,039 (87%)59,627 (87%)
       Yes10,023 (4.8%)6659 (4.8%)3364 (4.9%)
       Unknown16,749 (8.0%)11,380 (8.1%)5369 (7.9%)
      Insurance coverage, N (%)<.001
       Not insured7235 (3.5%)4921 (3.5%)2314 (3.4%)
       Private insurance102,789 (49%)73,658 (53%)29,131 (43%)
       Medicaid9095 (4.4%)6020 (4.3%)3075 (4.5%)
       Medicare83,771 (40%)51,913 (37%)31,858 (47%)
       Other government1783 (0.9%)1258 (0.9%)525 (0.8%)
       Unknown insurance3765 (1.8%)2308 (1.6%)1457 (2.1%)
      Tumor stage, N (%)<.001
       1145,628 (70%)107,468 (77%)38,160 (56%)
       215,196 (7.3%)9608 (6.9%)5588 (8.2%)
       326,777 (13%)13,623 (9.7%)13,154 (19%)
       48953 (4.3%)2685 (1.9%)6268 (9.2%)
       Unknown11,884 (5.7%)6694 (4.8%)5190 (7.6%)
      Charlson-Deyo score,
      Excludes cancer as comorbidity
      N (%)
      .40
       0155,646 (75%)104,509 (75%)51,137 (75%)
       143,068 (21%)29,057 (21%)14,011 (20%)
       2+9724 (4.7%)6512 (4.6%)3212 (4.7%)
      Facility type, N (%)<.001
       Community cancer program12,048 (5.8%)7860 (5.6%)4188 (6.1%)
       Comprehensive community cancer program107,244 (51%)72,399 (52%)34,845 (51%)
       Academic/research program88,747 (43%)59,524 (42%)29,223 (43%)
       Other399 (0.2%)295 (0.2%)104 (0.2%)
      Annual hospital volume quartile, N (%)<.001
       1 Lowest40,735 (20%)26,767 (19%)13,968 (20%)
       244,106 (21%)29,409 (21%)14,697 (21%)
       356,861 (27%)38,434 (27%)18,427 (27%)
       4 Highest66,736 (32%)45,468 (32%)21,268 (31%)
      Distance traveled to recording institution, miles
      Determined by ZIP code of patient’s residence.
       Mean (SD)30.52 (89.29)30.10 (86.67)31.40 (94.43).002
       Median (IQR)11.70 (25.40)11.80 (25.10)11.40 (25.80)<.001
      Metro or adjacent to metro, N (%)
      Determined by ZIP code of patient’s residence.
      <.001
       No22,094 (11%)14,583 (10%)7511 (11%)
       Yes186,344 (89%)125,495 (90%)60,849 (89%)
      Median income quartile, N (%)
      Determined by ZIP code of patient’s residence.
      <.001
       1 Lowest30,131 (15%)18,953 (14%)11,178 (17%)
       242,060 (21%)28,107 (21%)13,953 (21%)
       356,284 (28%)38,207 (28%)18,077 (27%)
       4 Highest74,498 (37%)51,208 (38%)23,290 (35%)
      Household education, quartile, N (%)
      Determined by ZIP code of patient’s residence.
      <.001
       1 Lowest31,766 (16%)20,125 (15%)11,641 (18%)
       249,073 (24%)32,453 (24%)16,620 (25%)
       360,232 (30%)41,311 (30%)18,921 (28%)
       4 Highest61,932 (31%)42,603 (31%)19,329 (29%)
      Lymphadenectomy, N (%)<.001
       No54,719 (26%)38,477 (27%)16,242 (24%)
       Yes153,719 (74%)101,601 (73%)52,118 (76%)
      Adjuvant systemic therapy, N (%)<.001
       No182,641 (88%)130,821 (93%)51,820 (76%)
       Yes25,797 (12%)9257 (6.6%)16,540 (24%)
      Adjuvant radiation therapy, N (%)<.001
       No178,496 (86%)120,644 (86%)57,852 (85%)
       Yes29,942 (14%)19,434 (14%)10,508 (15%)
      IQR, interquartile range.
      Shalowitz et al. Survival implications of time to surgical treatment of endometrial cancers. Am J Obstet Gynecol 2017.
      a Excludes cancer as comorbidity
      b Determined by ZIP code of patient’s residence.

      Survival analyses for low-risk cancers

      For patients with low-risk cancers, median survival time was 47.6 months (interquartile range 25.8-73.6), and 14.3% of cases were censored. Five-year crude survival was highest when surgery was performed in the third week after diagnosis, with a linear decline in survival thereafter (Table 2; Supplemental Figure 1). Relative to patients who underwent surgery in the third week after diagnosis, the 11.7% of patients undergoing surgery during the first and second weeks after diagnosis had a higher risk of death (HR, 1.9; 95% confidence interval [CI], 1.7–2.1; and HR, 1.1; 95% CI, 1.1–1.2, respectively). When adjusted for age, stage, race, year of diagnosis, and additional clinical and health system characteristics, surgery in the first and second weeks after diagnosis remained independently associated with death (HR, 1.4; 95% CI, 1.3–1.5; and HR, 1.1; 95% CI, 1.0–1.2, respectively). Mortality risk was significantly higher than baseline when surgery was performed in the eighth week postdiagnosis and worsened as time to surgery increased (Figure 2, A and B).
      Table 2Five-year crude survival by interval between diagnosis and surgery
      Low riskHigh risk
      Time to surgery, wk
      Surgery in wk 1 implies interval between diagnosis and surgery of 1–7 d; in wk 2, 8–14 d, etc.
      Patients5-y Survival (95% CI)Time to surgery, wk
      Surgery in wk 1 implies interval between diagnosis and surgery of 1–7 d; in wk 2, 8–14 d, etc.
      Patients5-y Survival (95% CI)
      1205773.0% (70.6–75.3%)1155146.5% (43.5–49.5%)
      2566585.0% (83.8–86.2%)2339860.7% (58.8–62.6%)
      310,40987.4% (86.5–88.2%)3515366.9% (65.3–68.4%)
      412,59386.2% (85.4–86.9%)4578667.6% (66.0–69.1%)
      511,73686.5% (85.7–87.3%)5499467.1% (65.4–68.7%)
      6935085.4% (84.4–86.4%)6397566.7% (64.8–68.6%)
      7655985.8% (84.7–86.9%)7267565.0% (62.5–67.3%)
      8465584.5% (83.1–85.9%)8190566.3% (63.5–68.9%)
      9304583.0% (81.0–84.8%)9122462.5% (58.8–66.0%)
      10206184.7% (82.4–86.8%)1081764.5% (60.0–68.7%)
      11136782.1% (79.2–84.7%)1163866.0% (61.1–70.5%)
      1297279.7% (75.9–82.9%)1239860.5% (53.8–66.5%)
      1367381.9% (77.5–85.5%)1329655.9% (47.9–63.2%)
      1454081.0% (76.0–85.1%)1424357.8% (47.8–66.5%)
      1540278.6% (72.5–83.5%)1519757.7% (47.4–66.7%)
      1632671.1% (63.4–77.4%)1615349.6% (37.8–60.3%)
      1725077.2% (69.9–83.0%)1711957.5% (44.0–68.8%)
      1820172.9% (63.5–80.2%)1811351.0% (35.7–64.3%)
      1917469.7% (58.2–78.6%)197147.6% (30.4–63.0%)
      2014177.0% (63.5–86.0%)207261.2% (44.7–74.1%)
      >2089874.5% (70.3–78.3%)>2046255.7% (49.3–61.7%)
      Five-year survival and 95% CI generated via Kaplan-Meier method.
      CI, confidence interval.
      Shalowitz et al. Survival implications of time to surgical treatment of endometrial cancers. Am J Obstet Gynecol 2017.
      a Surgery in wk 1 implies interval between diagnosis and surgery of 1–7 d; in wk 2, 8–14 d, etc.
      Figure thumbnail gr2
      Figure 2Unadjusted and adjusted hazard ratios for mortality, by histology
      Dashed lines indicated 95% confidence interval. Hazard ratios adjusted for patient's age, race/ethnicity, insurance status, stage, Charlson-Deyo score, distance traveled to care; income/education quartile and rurality of patient's home ZIP code; reporting hospital type/location/case volume, year of diagnosis, receipt of lymphadenectomy and adjuvant treatment, location of diagnosis and treatment.
      Shalowitz et al. Survival implications of time to surgical treatment of endometrial cancers. Am J Obstet Gynecol 2017.

      Survival analyses for high-risk cancers

      For patients with high-risk cancers, median survival time was 38.6 months (interquartile range 18.9-67.1), and 14.3% of cases were censored. Five-year crude survival was highest when surgery was performed in the third week after diagnosis, with a linear decline in survival thereafter (Table 2; Supplemental Figure 1). Relative to patients who underwent surgery in the third week after diagnosis, the 15.9% of patients undergoing surgery during the first and second weeks after diagnosis had HR for death of 2.1 (95% CI, 1.9–2.2) and 1.3 (95% CI, 1.2–1.3), respectively. When adjusted for age, stage, race, year of diagnosis, and additional clinical and health system characteristics, surgery in the first and second weeks after diagnosis remained independently associated with death (HR, 1.5; 95% CI, 1.3–1.6; and HR, 1.2; 95% CI, 1.1–1.2, respectively). Apart from an isolated increase seen in the 19th week postdiagnosis, surgery after the third week postdiagnosis was not associated with a statistically significant increase in the adjusted risk of mortality (Figure 2, C and D).

      Characteristics of recipients of early surgery

      Given the finding of increased mortality risk accompanying surgery in the first 2 weeks after diagnosis, we compared clinical and process-based factors for patients undergoing surgery in this time period with patients undergoing surgery 3 and 4 weeks postdiagnosis. Patients with low-risk cancers who underwent surgery in the first week after diagnosis were more likely to be at the extremes of age (<45 or >85 years), black, uninsured or with Medicaid insurance, have advanced stage disease, and undergo both diagnosis and treatment at the reporting CoC hospital. These patients were less likely to be treated at high-volume hospitals or undergo lymphadenectomy (Supplemental Table 1A). Patients with high-risk cancers who underwent surgery in the first week after diagnosis were more likely to be elderly (age >85 years), black, uninsured or with Medicaid insurance, have advanced stage disease, and undergo both diagnosis and treatment at the reporting CoC hospital. These patients were likewise less likely to be treated at high-volume hospitals or undergo lymphadenectomy (Supplemental Table 1B). Additionally, 30-day postoperative mortality was significantly higher among patients treated in the first or second week postdiagnosis, compared to patients treated in the third or fourth week postdiagnosis. For low-risk cancers, this difference was 0.7% vs 0.4% (P < .001); for high-risk cancers the difference was 2.5% vs 1.0% (P < .001).

      Factors associated with surgical delay

      Given significant differences in the characteristics and outcomes of patients undergoing surgery <2 weeks after diagnosis, this group was excluded from the linear regression of the diagnosis-to-surgery interval on clinical and process-based characteristics. For patients with low-risk cancers, independent associations with added time to surgery of at least 1 week were seen with black race (1.1 weeks; 95% CI, 0.9–1.4), uninsurance (1.3 weeks; 95% CI, 1.1–1.5), Medicaid insurance (1.7 weeks; 95% CI, 1.5–1.9), and Charlson-Deyo comorbidity score >1 (1.0 weeks; 95% CI, 0.8–1.2) (Table 3). For patients with high-risk cancers, independent associations with added time to surgery of at least 1 week were seen with uninsurance (1.4 weeks; 95% CI, 0.9–1.9) and Medicaid insurance (1.4 weeks; 95% CI, 1.1–1.7). Compared to age <45 years, ages 65-74 and 75-84 years were associated with 1.1 (95% CI, 0.9–1.5) and 1.0 (95% CI, 0.7–1.3) weeks decrease in time to surgery, respectively (Table 3).
      Table 3Association of case factors with incremental change in interval between diagnosis and surgery (weeks)
      Low-risk histologyHigh-risk histology
      Estimate [95% CI]Estimate [95% CI]
      Age, y
      <45ReferenceReference
      45–54–0.65 [–0.80 to –0.49]
      P < .05.
      –0.81 [–1.12 to –0.49]
      P < .05.
      55–64–0.60 [–0.75 to –0.44]
      P < .05.
      –0.90 [–1.19 to –0.61]
      P < .05.
      65–74–0.86 [–1.03 to –0.68]
      P < .05.
      –1.16 [–1.47 to –0.85]
      P < .05.
      75–84–0.71 [–0.90 to –0.53]
      P < .05.
      –1.01 [–1.33 to –0.68]
      P < .05.
      ≥85–0.34 [–0.57 to –0.11]
      P < .05.
      –0.56 [–0.93 to –0.20]
      P < .05.
      Race
      WhiteReferenceReference
      Black1.14 [0.94 to 1.35]
      P < .05.
      0.88 [0.69 to 1.06]
      P < .05.
      American Indian0.53 [–0.35 to 1.42]0.58 [–0.40 to 1.56]
      Asian/Pacific Islander0.02 [–0.31 to 0.35]0.04 [–0.28 to 0.35]
      Other0.34 [–0.10 to 0.78]–0.20 [–0.62 to 0.21]
      Unknown–0.23 [–0.50 to 0.05]0.04 [–0.40 to 0.49]
      Hispanic
      NoReferenceReference
      Yes0.68 [0.46 to 0.89]
      P < .05.
      0.84 [0.53 to 1.16]
      P < .05.
      Unknown0.03 [–0.13 to 0.18]0.11 [–0.06 to 0.28]
      Insurance
      Not insured1.29 [1.05 to 1.54]
      P < .05.
      1.36 [0.87 to 1.86]
      P < .05.
      Private insuranceReferenceReference
      Medicaid1.68 [1.46 to 1.90]
      P < .05.
      1.41 [1.11 to 1.71]
      P < .05.
      Medicare0.58 [0.46 to 0.69]
      P < .05.
      0.46 [0.31 to 0.61]
      P < .05.
      Other government0.49 [0.17 to 0.81]
      P < .05.
      0.31 [–0.09 to 0.71]
      Unknown0.75 [0.34 to 1.15]
      P < .05.
      1.41 [0.88 to 1.94]
      P < .05.
      Stage
      1ReferenceReference
      20.51 [0.39 to 0.63]
      P < .05.
      0.29 [0.14 to 0.45]
      P < .05.
      30.08 [0.00 to 0.17]–0.05 [–0.16 to 0.05]
      4–0.14 [–0.38 to 0.10]–0.39 [–0.56 to –0.22]
      P < .05.
      Unknown0.35 [0.17 to 0.53]
      P < .05.
      0.00 [–0.20 to 0.20]
      Diagnosis/treatment at reporting facility
      Diagnosis onlyReferenceReference
      Diagnosis and treatment–0.70 [–0.91 to –0.49]
      P < .05.
      –0.45 [–0.75 to –0.16]
      P < .05.
      Treatment only–0.33 [–0.54 to –0.11]
      P < .05.
      0.08 [–0.22 to 0.39]
      Charlson-Deyo comorbidity score
      Excludes cancer as comorbidity
      0ReferenceReference
      10.38 [0.30 to 0.46]0.33 [0.22 to 0.44]
      P < .05.
      2+0.99 [0.83 to 1.15]
      P < .05.
      0.85 [0.65 to 1.05]
      P < .05.
      Facility type
      CCPReferenceReference
      Comprehensive CCP–0.01 [–0.22 to 0.20]0.10 [–0.18 to 0.37]
      Academic/research program0.47 [0.21 to 0.73]
      P < .05.
      0.53 [0.21 to 0.84]
      P < .05.
      Other–0.42 [–0.73 to –0.12]
      P < .05.
      –0.59 [–0.94 to –0.24]
      P < .05.
      Facility location
      New EnglandReferenceReference
      Middle Atlantic0.28 [0.00 to 0.57]0.32 [–0.06 to 0.70]
      South Atlantic–0.18 [–0.46 to 0.11]–0.06 [–0.45 to 0.33]
      East North Central–0.10 [–0.37 to 0.18]–0.03 [–0.39 to 0.34]
      East South Central–1.03 [–1.36 to –0.70]
      P < .05.
      –0.80 [–1.30 to –0.29]
      P < .05.
      West North Central–0.58 [–0.91 to –0.25]
      P < .05.
      –0.52 [–0.95 to –0.09]
      P < .05.
      West South Central–0.41 [–0.75 to –0.08]
      P < .05.
      –0.36 [–0.78 to 0.05]
      P < .05.
      Mountain–0.17 [–0.49 to 0.14]–0.27 [–0.69 to 0.14]
      Pacific0.67 [0.24 to 1.10]
      P < .05.
      0.87 [0.29 to 1.45]
      P < .05.
      Hospital case volume quartile
      1 LowestReferenceReference
      20.00 [–0.14 to 0.13]–0.06 [–0.24 to 0.11]
      3–0.03 [–0.20 to 0.15]–0.25 [–0.47 to –0.03]
      P < .05.
      4 Highest–0.14 [–0.33 to 0.05]–0.42 [–0.65 to –0.18]
      P < .05.
      Distance traveled to care, quartile, miles
      Determined by ZIP code of patient residence
      1 LowestReferenceReference
      20.00 [–0.08 to 0.08]0.13 [0.02 to 0.25]
      P < .05.
      30.00 [–0.09 to 0.09]0.07 [–0.05 to 0.19]
      4 Highest0.05 [–0.07 to 0.18]–0.03 [–0.19 to 0.12]
      Household education, quartile
      Determined by ZIP code of patient residence
      1 LowestReferenceReference
      2–0.21 [–0.33 to –0.08]
      P < .05.
      –0.38 [–0.56 to –0.21]
      P < .05.
      3–0.33 [–0.47 to –0.19]
      P < .05.
      –0.60 [–0.80 to –0.40]
      P < .05.
      4 Highest–0.57 [–0.74 to –0.40]
      P < .05.
      –0.80 [–1.03 to –0.57]
      P < .05.
      Lymphadenectomy
      NoReferenceReference
      Yes–0.53 [–0.62 to –0.44]
      P < .05.
      –0.86 [–1.01 to –0.71]
      P < .05.
      Unknown–0.07 [–0.63 to 0.49]0.22 [–0.56 to 1.01]
      Linear regression model also includes year of diagnosis, metropolitan/nonmetropolitan location, median household income of patient ZIP code of residence.
      CCP, community cancer program; CI, confidence interval.
      Shalowitz et al. Survival implications of time to surgical treatment of endometrial cancers. Am J Obstet Gynecol 2017.
      a Excludes cancer as comorbidity
      b Determined by ZIP code of patient residence
      c P < .05.
      For patients with either low- or high-risk cancers, disease stage was not consistently associated with the interval between diagnosis and surgery. Compared to stage I disease, stage II disease was associated with a 2- to 4-day longer time to surgery; stage IV disease was associated with a 1- to 3-day shorter time to surgery.

      Comment

      We identified 2 populations of endometrial cancer patients at risk for decreased survival related to the interval between diagnosis and surgery. First, patients who underwent surgery in the first or second week after diagnosis had consistently worse survival outcomes than patients treated in the third or fourth week after diagnosis, even after adjustment for observed clinical, demographic, and process-based factors. Crude survival at 5 years was decreased by 14% for low-risk patients and by 20% for high-risk patients (Table 2). Second, delay of ≥8 weeks in surgical treatment of low-risk endometrial cancers was independently associated with worsened 5-year survival. For example, 5-year survival for patients undergoing surgery 16 weeks postdiagnosis was 16% worse than for patients undergoing surgery 3 weeks postdiagnosis (Table 2). In contrast, delay of up to 21 weeks in surgical treatment of high-risk endometrial cancers did not appear to independently affect survival outcomes.
      Elit et al
      • Elit L.M.
      • O’Leary E.M.
      • Pond G.R.
      • Seow H.-Y.
      Impact of wait times on survival for women with uterine cancer.
      previously noted an increased mortality associated with surgery <2 weeks after diagnosis, but were unable to identify contributing factors to this phenomenon given relatively small case numbers. As there is not a cancer-specific reason that patients receiving rapid, appropriate surgical care would have worsened survival, this finding is likely related to the delivery of care. We found that patients treated earliest were more likely than patients treated 3 or 4 weeks postdiagnosis to have no insurance or Medicaid, have advanced disease, be black, be diagnosed and treated at the same hospital, be treated in hospitals with the lowest case-volume quartile, and not undergo lymphadenectomy. These findings suggest that access to care, delays in presentation (resulting in advanced disease), and lack of referral to a specialty center may factor into this group’s relatively poor outcomes. Additionally, the increased risk seen after adjustment for observable characteristics may indicate inadequate preoperative workup or clinical acuity not captured by comorbidity score, potentially reflected in this population’s elevated rate of mortality in the first 30 postoperative days.
      Likewise, for patients experiencing a time to surgery >2 weeks, the association of race and insurance status with prolonged time to surgery suggests that access to specialty care may contribute to delay. These findings are consistent with prior studies.
      • Dolly D.
      • Mihai A.
      • Rimel B.J.
      • et al.
      A delay from diagnosis to treatment is associated with a decreased overall survival for patients with endometrial cancer.
      • Matsuo K.
      • Opper N.R.
      • Ciccone M.A.
      • et al.
      Time interval between endometrial biopsy and surgical staging for type I endometrial cancer: association between tumor characteristics and survival outcome.
      • Vandborg M.P.
      • Christensen R.D.
      • Kragstrup J.
      • et al.
      Reasons for diagnostic delay in gynecological malignancies.
      For patients with low-risk cancers, the association between high comorbidity scores and treatment delay suggests that differences in patients’ need for preoperative medical optimization may also delay definitive surgical treatment. Interestingly, the finding that an increased interval between diagnosis and surgery was not independently associated with advancing stage suggests that extent of disease at diagnosis contributes more to survival outcomes than progression of disease during the wait for surgery.
      Three major processes contribute to the interval between diagnosis and surgery. First, many patients require referral for definitive surgical treatment. Transfer of care carries the burden of insurance/financial access to a referral center and the logistics of coordinating and obtaining an appointment with a specialist. In some areas of the United States, distance to the closest referral center may significantly delay treatment or increase the probability that patients are treated by nonspecialists.
      • Shalowitz D.I.
      • Vinograd A.M.
      • Giuntoli R.L.
      Geographic access to gynecologic cancer care in the United States.
      Second, preoperative medical optimization and logistics may affect the timing of surgical treatment. Patients with comorbid disease may require specialty medical clearance prior to surgery, some of which may involve imaging or other procedures (eg, cardiac stress testing). Third, after preoperative evaluation and optimization, surgical schedule availability is likely to be highly surgeon- and institution-dependent. Indeed, surgical wait times at referral institutions appear to be longer than at community hospitals.
      • Bilimoria K.Y.
      • Ko C.Y.
      • Tomlinson J.S.
      • et al.
      Wait times for cancer surgery in the United States: trends and predictors of delays.
      Perhaps as a consequence of the benefits and burdens associated with referral, previous reports regarding the association between subspecialty care and outcomes for endometrial cancer patients have been mixed.
      • Becker J.H.
      • Ezendam N.P.M.
      • Boll D.
      • van der Aa M.
      • Pijnenborg J.M.A.
      Effects of surgical volumes on the survival of endometrial carcinoma.
      • Fader A.N.
      • Weise R.M.
      • Sinno A.K.
      • et al.
      Utilization of minimally invasive surgery in endometrial cancer care: a quality and cost disparity.
      • Chan J.K.
      • Sherman A.E.
      • Kapp D.S.
      • et al.
      Influence of gynecologic oncologists on the survival of patients with endometrial cancer.
      What should be the target interval between diagnosis and treatment of endometrial cancers? For patients who experience treatment delay as a result of poor access to care, decreasing time to surgery through improved access may also improve outcomes. However, for some patients, complex presurgical optimization may require prolongation of the diagnosis-to-surgery interval. For the latter group, more rapid surgical intervention may increase risk of mortality, as seen in the patient population undergoing surgery in the first 2 weeks postdiagnosis.
      We suggest that the recommended interval between diagnosis and treatment of endometrial cancers should be ≤8 weeks, especially for patients with low-risk histologies on biopsy. In the majority of cases, this interval should allow adequate time for: (1) pathologic analysis of the diagnostic biopsy, (2) subspecialty referral if needed, (3) preoperative evaluation and medical optimization, and (4) surgical scheduling. However, we emphasize that medical optimization should not be abbreviated to attempt to shorten time to surgery. Additionally, as the outcomes of patients with high-risk histologies appear less sensitive to delays in surgical treatment, referral to a gynecologic oncologist should not be neglected out of concern over surgical wait time. Patients with high-risk histologies on initial biopsy, or other known adverse clinical predictors (eg, significant medical comorbidity or evidence of extrauterine disease) should be triaged to centers with expertise in the management of gynecologic cancers. These patients are likely to require specialized surgical care (eg, lymphadenectomy or cytoreduction) in addition to adjuvant therapy, consistent with the standard of care for their disease.

      National Comprehensive Cancer Network. NCCN Clinical Practice Guidelines in Oncology: Uterine Neoplasms Version 1.2017. Available at: https://www.nccn.org/professionals/physician_gls/pdf/uterine.pdf. Accessed December 30, 2016.

      Approximately 37% of endometrial cancer cases were excluded from this analysis because of missing values for the interval between diagnosis and surgery. Survival data were also not available for these cases, suggesting that the original date of diagnosis may not have been documented. Although there was no a priori reason to suspect that the primary outcome of interest (ie, the effect of time to surgery on survival) was affected by these exclusions, we were limited in our ability to determine whether significant bias occurred. For low-risk histologies, the stage distribution between included and excluded cases was statistically different given the large patient sample, but clinically identical. For high-risk histologies, slightly more cases were stage I/II in the excluded group than in the included group (71% vs 64%, respectively) (Supplemental Table 2). As the survival data contained in the NCDB are not cancer-specific, we selected 5-year survival as the outcome of interest, since the greatest risk for death from endometrial cancer occurs in this interval.
      • Ward K.K.
      • Shah N.R.
      • Saenz C.C.
      • McHale M.T.
      • Alvarez E.A.
      • Plaxe S.C.
      Cardiovascular disease is the leading cause of death among endometrial cancer patients.
      Our survival analyses were limited by the extent to which patient follow-up was recorded in the NCDB. Additionally, our results reflect average relationships across patients and may mask important clinical and nonclinical heterogeneity.
      While retrospective analyses can help identify populations that are vulnerable to worsened survival as a result of surgical timing, prospective investigation is required to identify points in the process of care that are amenable to intervention. In the meantime, gynecologic oncologists and policy makers should use available data to develop national practice standards for endometrial cancer care delivery in the United States, following similar efforts internationally.
      • Elit L.M.
      • O’Leary E.M.
      • Pond G.R.
      • Seow H.-Y.
      Impact of wait times on survival for women with uterine cancer.
      • Kang M.Y.
      • Sykes P.
      • Herbison P.Y.
      • Petrich S.
      Retrospective analysis on timeframes of referral, diagnosis and treatment of patients with endometrial carcinomas in Dunedin Hospital, 2008-2011.
      Priority should be given to policies that minimize morbidity from disparities in access to the standard of gynecologic cancer care.

      Appendix

      Figure thumbnail fx1
      Supplemental Figure 1Kaplan-Meier survival curves for selected delay times by patient risk level
      Type 1 = low risk; type 2 = high risk.
      Shalowitz et al. Survival implications of time to surgical treatment of endometrial cancers. Am J Obstet Gynecol 2017.
      Supplemental Table 1ACharacteristics of cases with time to surgery <2 weeks–low-risk histologies
      0–1 wk

      N = 4264
      1–2 wk

      N = 11,702
      2–4 wk

      N = 44,375
      P value
      Age category, N (%)<.001
       <45 y322 (7.6%)689 (5.9%)2565 (5.8%)
       45–54 y889 (21%)2383 (20%)8443 (19%)
       55–64 y1412 (33%)4128 (35%)16,375 (37%)
       65–74 y898 (21%)2777 (24%)10,697 (24%)
       75–84 y576 (14%)1422 (12%)5212 (12%)
       ≥85 y167 (3.9%)303 (2.6%)1083 (2.4%)
      Patient race, N (%)<.001
       White3751 (88%)10,626 (91%)40,089 (90%)
       Black300 (7.0%)540 (4.6%)2218 (5.0%)
       American Indian6 (0.1%)24 (0.2%)110 (0.2%)
       Asian/Pacific Islander108 (2.5%)259 (2.2%)1007 (2.3%)
       Other43 (1.0%)84 (0.7%)284 (0.6%)
       Unknown56 (1.3%)169 (1.4%)667 (1.5%)
      Hispanic ethnicity, N (%)<.001
       No3732 (88%)10,382 (89%)39,038 (88%)
       Yes242 (5.7%)388 (3.3%)1637 (3.7%)
       Unknown290 (6.8%)932 (8.0%)3700 (8.3%)
      Primary payor, N (%)<.001
       Not insured204 (4.8%)261 (2.2%)1147 (2.6%)
       Private insurance2124 (50%)6591 (56%)25,024 (56%)
       Medicaid226 (5.3%)322 (2.8%)1253 (2.8%)
       Medicare1550 (36%)4196 (36%)15,887 (36%)
       Other government38 (0.9%)103 (0.9%)387 (0.9%)
       Insurance status unknown122 (2.9%)229 (2.0%)677 (1.5%)
      Tumor stage, N (%)<.001
       12703 (63%)8807 (75%)34,338 (77%)
       2360 (8.4%)741 (6.3%)2769 (6.2%)
       3665 (16%)1304 (11%)4386 (9.9%)
       4341 (8.0%)306 (2.6%)806 (1.8%)
       Unknown195 (4.6%)544 (4.6%)2076 (4.7%)
      Diagnosis/treatment at reporting facility, N (%)<.001
       Diagnosis only48 (1.1%)242 (2.1%)1086 (2.4%)
       Both2677 (63%)6231 (53%)21,274 (48%)
       Treatment only1539 (36%)5229 (45%)22,015 (50%)
      Charlson-Deyo score,
      Excludes cancer as comorbidity
      N (%)
      .004
       03273 (77%)9208 (79%)34,405 (78%)
       1807 (19%)2112 (18%)8383 (19%)
       2+184 (4.3%)382 (3.3%)1587 (3.6%)
      Facility type, N (%)<.001
       Community cancer program329 (7.7%)879 (7.5%)2621 (5.9%)
       Comprehensive community cancer program2391 (56%)6826 (58%)24,438 (55%)
       Academic/research program1536 (36%)3965 (34%)17,186 (39%)
       Other8 (0.2%)32 (0.3%)130 (0.3%)
      Facility location, N (%)<.001
       New England211 (4.9%)628 (5.4%)3006 (6.8%)
       Middle Atlantic677 (16%)1510 (13%)6172 (14%)
       South Atlantic839 (20%)2173 (19%)8767 (20%)
       East North Central762 (18%)2386 (20%)9560 (22%)
       East South Central318 (7.5%)973 (8.3%)3032 (6.8%)
       West North Central409 (9.6%)1282 (11%)4223 (9.5%)
       West South Central450 (11%)1105 (9.4%)3080 (6.9%)
       Mountain246 (5.8%)673 (5.8%)2414 (5.4%)
       Pacific352 (8.3%)972 (8.3%)4121 (9.3%)
      Annual hospital volume quartile, N (%)<.001
       1 Lowest1112 (26%)2909 (25%)8931 (20%)
       21017 (24%)2728 (23%)9453 (21%)
       31161 (27%)3090 (26%)12,109 (27%)
       4 Highest974 (23%)2975 (25%)13,882 (31%)
      Distance traveled to care quartile,
      Determined by ZIP code of patient residence.
      N (%)
      <.001
       1 Lowest1171 (28%)2981 (26%)10,820 (25%)
       21064 (25%)2960 (26%)10,984 (25%)
       31001 (24%)2880 (25%)11,144 (26%)
       4 Highest941 (23%)2682 (23%)10,673 (24%)
      No high-school degree quartile,
      Determined by ZIP code of patient residence.
      N (%)
      <.001
       1 Lowest659 (16%)1435 (13%)5495 (13%)
       2953 (23%)2477 (22%)9765 (23%)
       31216 (29%)3397 (30%)13,073 (30%)
       4 Highest1306 (32%)4073 (36%)14,879 (34%)
      Year of diagnosis, N (%)<.001
       2003444 (10%)1194 (10%)3654 (8.2%)
       2004467 (11%)1306 (11%)4070 (9.2%)
       2005485 (11%)1323 (11%)4323 (9.7%)
       2006489 (11%)1253 (11%)4488 (10%)
       2007429 (10%)1180 (10%)4494 (10%)
       2008433 (10%)1146 (9.8%)4569 (10%)
       2009403 (9.5%)1073 (9.2%)4594 (10%)
       2010407 (9.5%)1105 (9.4%)4748 (11%)
       2011361 (8.5%)1097 (9.4%)4776 (11%)
       2012346 (8.1%)1025 (8.8%)4659 (10%)
      Lymphadenectomy, N (%)<.001
       No1369 (32%)3196 (27%)11,331 (26%)
       Yes2895 (68%)8506 (73%)33,044 (74%)
      Shalowitz et al. Survival implications of time to surgical treatment of endometrial cancers. Am J Obstet Gynecol 2017.
      a Excludes cancer as comorbidity
      b Determined by ZIP code of patient residence.
      Supplemental Table 1BCharacteristics of cases with time to surgery <2 weeks–high-risk histologies
      0–1 wk

      N = 3399
      1–2 wk

      N = 7243
      2–4 wk

      N = 22,185
      P value
      Age category, N (%)<.001
       <45 y132 (3.9%)279 (3.9%)770 (3.5%)
       45–54 y493 (15%)952 (13%)2912 (13%)
       55–64 y1034 (30%)2332 (32%)7197 (32%)
       65–74 y942 (28%)2202 (30%)6804 (31%)
       75–84 y619 (18%)1230 (17%)3703 (17%)
       ≥85 y179 (5.3%)248 (3.4%)799 (3.6%)
      Patient race, N (%)<.001
       White2645 (78%)6078 (84%)18,759 (85%)
       Black588 (17%)820 (11%)2433 (11%)
       American Indian10 (0.3%)18 (0.2%)53 (0.2%)
       Asian/Pacific Islander84 (2.5%)174 (2.4%)513 (2.3%)
       Other29 (0.9%)54 (0.7%)149 (0.7%)
       Unknown43 (1.3%)99 (1.4%)278 (1.3%)
      Hispanic ethnicity, N (%)<.001
       No3016 (89%)6398 (88%)19,644 (89%)
       Yes166 (4.9%)262 (3.6%)789 (3.6%)
       Unknown217 (6.4%)583 (8.0%)1752 (7.9%)
      Primary payor, N (%)<.001
       Not insured179 (5.3%)210 (2.9%)567 (2.6%)
       Private insurance1323 (39%)3276 (45%)10,170 (46%)
       Medicaid203 (6.0%)235 (3.2%)692 (3.1%)
       Medicare1577 (46%)3311 (46%)10,226 (46%)
       Other government24 (0.7%)54 (0.7%)160 (0.7%)
       Unknown93 (2.7%)157 (2.2%)370 (1.7%)
      Tumor stage, N (%)<.001
       11187 (35%)3714 (51%)12,753 (57%)
       2255 (7.5%)545 (7.5%)1648 (7.4%)
       3768 (23%)1452 (20%)4207 (19%)
       4849 (25%)981 (14%)1916 (8.6%)
       Unknown340 (10%)551 (7.6%)1661 (7.5%)
      Class of case, N (%)<.001
       Diagnosis only61 (1.8%)155 (2.1%)582 (2.6%)
       Both2243 (66%)3791 (52%)10,389 (47%)
       Treatment only1095 (32%)3297 (46%)11,214 (51%)
      Charlson-Deyo score,
      Charlson-Deyo score excludes cancer as comorbidity
      N (%)
      .064
       02603 (77%)5688 (79%)17,124 (77%)
       1651 (19%)1289 (18%)4228 (19%)
       2+145 (4.3%)266 (3.7%)833 (3.8%)
      Facility type, N (%)<.001
       Community cancer program252 (7.4%)488 (6.7%)1436 (6.5%)
       Comprehensive community cancer program1829 (54%)4055 (56%)11,881 (54%)
       Academic/research program1315 (39%)2688 (37%)8817 (40%)
       Other specified types of cancer programs3 (0.1%)12 (0.2%)51 (0.2%)
      Facility location, N (%)<.001
       New England174 (5.1%)366 (5.1%)1422 (6.4%)
       Middle Atlantic497 (15%)946 (13%)3307 (15%)
       South Atlantic775 (23%)1458 (20%)4606 (21%)
       East North Central556 (16%)1290 (18%)4045 (18%)
       East South Central328 (9.6%)746 (10%)1851 (8.3%)
       West North Central300 (8.8%)781 (11%)2139 (9.6%)
       West South Central313 (9.2%)646 (8.9%)1539 (6.9%)
       Mountain153 (4.5%)361 (5.0%)986 (4.4%)
       Pacific303 (8.9%)649 (9.0%)2290 (10%)
      Annual hospital volume quartile, N (%)<.001
       1 Lowest815 (24%)1721 (24%)4738 (21%)
       2811 (24%)1659 (23%)4613 (21%)
       3890 (26%)1914 (26%)5927 (27%)
       4 Highest883 (26%)1949 (27%)6907 (31%)
      Distance quartile,
      Determined by ZIP code of patient residence.
      N (%)
      <.001
       1 Lowest992 (30%)1891 (27%)5666 (26%)
       2821 (25%)1652 (23%)5242 (24%)
       3706 (21%)1664 (23%)5280 (24%)
       4 Highest809 (24%)1877 (26%)5571 (26%)
      Median income quartile (unified), N (%)<.001
       1 Lowest612 (18%)1088 (15%)3292 (15%)
       2692 (21%)1452 (21%)4465 (21%)
       3919 (28%)1898 (27%)5884 (27%)
       4 Highest1088 (33%)2583 (37%)7956 (37%)
      No high-school degree quartile (unified),
      Determined by ZIP code of patient residence.
      N (%)
      <.001
       1 Lowest641 (19%)1140 (16%)3270 (15%)
       2849 (26%)1654 (24%)5162 (24%)
       3908 (27%)1931 (28%)6219 (29%)
       4 Highest915 (28%)2294 (33%)6951 (32%)
      Year of diagnosis, N (%)<.001
       2003415 (12%)860 (12%)2231 (10%)
       2004375 (11%)804 (11%)2143 (9.7%)
       2005335 (9.9%)766 (11%)2097 (9.5%)
       2006324 (9.5%)692 (9.6%)2050 (9.2%)
       2007339 (10.0%)705 (9.7%)2193 (9.9%)
       2008317 (9.3%)689 (9.5%)2193 (9.9%)
       2009355 (10%)705 (9.7%)2246 (10%)
       2010316 (9.3%)704 (9.7%)2295 (10%)
       2011331 (9.7%)669 (9.2%)2410 (11%)
       2012292 (8.6%)649 (9.0%)2327 (10%)
      Lymphadenectomy, N (%)<.001
       No1206 (35%)1778 (25%)4679 (21%)
       Yes2193 (65%)5465 (75%)17,506 (79%)
      Shalowitz et al. Survival implications of time to surgical treatment of endometrial cancers. Am J Obstet Gynecol 2017.
      a Charlson-Deyo score excludes cancer as comorbidity
      b Determined by ZIP code of patient residence.
      Supplemental Table 2Stage comparison of included and excluded cases
      A: Low-risk histologies
      StageIncludedExcludedTotal
      1107,46843,147150,615
      %76.7275.4976.36
      29608456414,172
      %6.867.997.19
      313,623528018,903
      %9.739.249.58
      4268513164001
      %1.922.302.03
      Unknown669428489542
      %4.784.984.84
      Total140,07857,155197,233
      %100.00100.00100.00
      Pearson χ2 (4) = 123.0128P < .001
      B: High-risk histologies
      StageIncludedExcludedTotal
      138,16040,10678,266
      %55.8263.7659.63
      25588473610,324
      %8.177.537.87
      313,154793221,086
      %19.2412.6116.06
      46268384310,111
      %9.176.117.70
      Unknown5190628411,474
      %7.599.998.74
      Total68,36062,901131,261
      %100.00100.00100.00
      Pearson χ2 (4) = 1900P < .001
      Shalowitz et al. Survival implications of time to surgical treatment of endometrial cancers. Am J Obstet Gynecol 2017.

      References

        • Dolly D.
        • Mihai A.
        • Rimel B.J.
        • et al.
        A delay from diagnosis to treatment is associated with a decreased overall survival for patients with endometrial cancer.
        Front Oncol. 2016; 6: 31
        • Richards M.A.
        • Westcombe A.M.
        • Love S.B.
        • Littlejohns P.
        • Ramirez A.J.
        Influence of delay on survival in patients with breast cancer: a systematic review.
        Lancet. 1999; 353: 1119-1126
        • Bleicher R.J.
        • Ruth K.
        • Sigurdson E.R.
        • et al.
        Time to surgery and breast cancer survival in the United States.
        JAMA Oncol. 2016; 2: 330-339
        • Yun Y.H.
        • Kim Y.A.
        • Min Y.H.
        • et al.
        The influence of hospital volume and surgical treatment delay on long-term survival after cancer surgery.
        Ann Oncol. 2012; 23: 2731-2737
        • Lee C.T.
        • Madii R.
        • Daignault S.
        • et al.
        Cystectomy delay more than 3 months from initial bladder cancer diagnosis results in decreased disease specific and overall survival.
        J Urol. 2006; 175: 1262-1267
        • Kötz B.S.
        • Croft S.
        • Ferry D.R.
        Do delays between diagnosis and surgery in resectable esophageal cancer affect survival? A study based on West Midlands cancer registration data.
        Br J Cancer. 2006; 95: 835-840
        • Stec A.A.
        • Coons B.J.
        • Chang S.S.
        • et al.
        Waiting time from initial urological consultation to nephrectomy for renal cell carcinoma–does it affect survival?.
        J Urol. 2008; 179: 2152-2157
        • Umezu T.
        • Shibata K.
        • Kajiyama H.
        • Yamamoto E.
        • Mizuno M.
        • Kikkawa F.
        Prognostic factors in stage IA-IIA cervical cancer patients treated surgically: does the waiting time to the operation affect survival?.
        Arch Gynecol Obstet. 2012; 285: 493-497
        • Pirog E.C.
        • Heller D.S.
        • Westhoff C.
        Endometrial adenocarcinoma–lack of correlation between treatment delay and tumor stage.
        Gynecol Oncol. 1997; 67: 303-308
        • Menczer J.
        • Krissi H.
        • Chetrit A.
        • et al.
        The effect of diagnosis and treatment delay on prognostic factors and survival in endometrial carcinoma.
        Am J Obstet Gynecol. 1995; 173: 774-778
        • Levy T.
        • Golan A.
        • Menczer J.
        Endometrial endometrioid carcinoma: a glimpse at the natural course.
        Am J Obstet Gynecol. 2006; 195: 454-457
        • Elit L.M.
        • O’Leary E.M.
        • Pond G.R.
        • Seow H.-Y.
        Impact of wait times on survival for women with uterine cancer.
        J Clin Oncol. 2014; 32: 27-33
        • Elit L.M.
        • Pond G.
        • Seow H.-Y.
        Treatment delay in endometrial cancer. Reply to J. Menczer.
        J Clin Oncol. 2014; 32: 2114
        • Matsuo K.
        • Opper N.R.
        • Ciccone M.A.
        • et al.
        Time interval between endometrial biopsy and surgical staging for type I endometrial cancer: association between tumor characteristics and survival outcome.
        Obstet Gynecol. 2015; 125: 424-433
        • Elit L.M.
        • Pond G.
        • Seow H.
        Time interval between endometrial biopsy and surgical staging for type I endometrial cancer: association between tumor characteristics and survival outcome.
        Obstet Gynecol. 2015; 125: 1497-1498
        • Strohl A.E.
        • Feinglass J.M.
        • Shahabi S.
        • Simon M.A.
        Surgical wait time: a new health indicator in women with endometrial cancer.
        Gynecol Oncol. 2016; 141: 511-515
      1. American College of Surgeons. National Cancer Database. Available at: https://www.facs.org/quality programs/cancer/ncdb. Accessed Feb. 4, 2016.

        • Cantrell L.A.
        • Blank S.V.
        • Duska L.R.
        Uterine carcinosarcoma: a review of the literature.
        Gynecol Oncol. 2015; 137: 581-588
        • Todo Y.
        • Kato H.
        • Kaneuchi M.
        • Watari H.
        • Takeda M.
        • Sakuragi N.
        Survival effect of para-aortic lymphadenectomy in endometrial cancer (SEPAL study): a retrospective cohort analysis.
        Lancet. 2010; 375: 1165-1172
        • Aalders J.G.
        • Thomas G.
        Endometrial cancer–revisiting the importance of pelvic and para aortic lymph nodes.
        Gynecol Oncol. 2007; 104: 222-231
        • Boruta D.M.
        • Gehrig P.A.
        • Fader A.N.
        • Olawaiye A.B.
        Management of women with uterine papillary serous cancer: a Society of Gynecologic Oncology (SGO) review.
        Gynecol Oncol. 2009; 115: 142-153
        • Vandborg M.P.
        • Christensen R.D.
        • Kragstrup J.
        • et al.
        Reasons for diagnostic delay in gynecological malignancies.
        Int J Gynecol Cancer. 2011; 21: 967-974
        • Shalowitz D.I.
        • Vinograd A.M.
        • Giuntoli R.L.
        Geographic access to gynecologic cancer care in the United States.
        Gynecol Oncol. 2015; 138: 115-120
        • Bilimoria K.Y.
        • Ko C.Y.
        • Tomlinson J.S.
        • et al.
        Wait times for cancer surgery in the United States: trends and predictors of delays.
        Ann Surg. 2011; 253: 779-785
        • Becker J.H.
        • Ezendam N.P.M.
        • Boll D.
        • van der Aa M.
        • Pijnenborg J.M.A.
        Effects of surgical volumes on the survival of endometrial carcinoma.
        Gynecol Oncol. 2015; 139: 306-311
        • Fader A.N.
        • Weise R.M.
        • Sinno A.K.
        • et al.
        Utilization of minimally invasive surgery in endometrial cancer care: a quality and cost disparity.
        Obstet Gynecol. 2016; 127: 91-100
        • Chan J.K.
        • Sherman A.E.
        • Kapp D.S.
        • et al.
        Influence of gynecologic oncologists on the survival of patients with endometrial cancer.
        J Clin Oncol. 2011; 29: 832-838
      2. National Comprehensive Cancer Network. NCCN Clinical Practice Guidelines in Oncology: Uterine Neoplasms Version 1.2017. Available at: https://www.nccn.org/professionals/physician_gls/pdf/uterine.pdf. Accessed December 30, 2016.

        • Ward K.K.
        • Shah N.R.
        • Saenz C.C.
        • McHale M.T.
        • Alvarez E.A.
        • Plaxe S.C.
        Cardiovascular disease is the leading cause of death among endometrial cancer patients.
        Gynecol Oncol. 2012; 126: 176-179
        • Kang M.Y.
        • Sykes P.
        • Herbison P.Y.
        • Petrich S.
        Retrospective analysis on timeframes of referral, diagnosis and treatment of patients with endometrial carcinomas in Dunedin Hospital, 2008-2011.
        N Z Med J. 2013; 126: 84-95