In 2010, an estimated 21,880 women had ovarian cancer diagnosed and 13,850 women died of ovarian cancer in the United States, making it the most lethal gynecologic malignancy; standard treatment consists of cytoreductive surgery and platinum-based chemotherapy.1,2 The amount of residual tumor after primary cytoreduction is inversely related to outcomes and is one of the strongest prognostic factors.3,4 Recently, improved success of complete cytoreduction by the incorporation of more extensive upper abdominal procedures has been reported at specialized cancer centers, and these improvements have been associated with improved median survival.5,6
Extensive surgical procedures are associated with substantial postoperative morbidity and mortality rates. Published reports of 30-day mortality after primary debulking surgery for advanced ovarian cancer have recently been reviewed by Gerestein et al.7 The majority of published data consists of single institution reports, with an average 30-day mortality rate of 2.5%. Population-based reports from Denmark and the Netherlands have reported slightly higher 30-day mortality rates ranging from 2.5% to 4.4%.8,9
Almost half of American women are age 65 years or older when ovarian cancer is diagnosed.10 Increasing age has been strongly associated with increased postoperative mortality after abdominal surgery.11 The 30-day mortality among women older than 80 years with all stages of ovarian cancer has been reported to range from 5.4% to 9.8%.7,12 In addition to age, postoperative mortality may be related to patient characteristics such as medical comorbidities and stage of disease, but most reports lack sufficient power to evaluate these associations. Others have suggested that hospital and surgeon characteristics or procedure volume may be related to short-term outcomes after surgery for advanced ovarian cancer.13 The ability to identify subpopulations of patients at very high risk for poor postoperative outcomes may allow providers to select patients who may benefit from an alternative treatment approach such as the use of neoadjuvant chemotherapy. Recent data have suggested that the use of neoadjuvant chemotherapy before cytoreductive surgery results in similar long-term survival rates with lower postoperative morbidity and mortality in advanced ovarian cancer.14
The objective of this study was to estimate 30-day mortality in a large population-based cohort of elderly women with advanced ovarian cancer and to identify patient and treatment characteristics associated with 30-day mortality. From this analysis, we aimed to identify criteria that could be identified preoperatively and used to predict postoperative mortality in elderly women admitted routinely for ovarian cancer surgery.
MATERIALS AND METHODS
Internal Review Board approval was obtained from the Human Subjects Division of the University of Washington (Institutional Review Board 37473).15 Data for this analysis came from a linkage between the Surveillance, Epidemiology, and End Results (SEER) database provided by the National Cancer Institute and Medicare health care claims records provided by the Center for Medical Services.16 SEER registries identify 97% of all incident cancer cases among persons residing in SEER regions (14% of the U.S. population in 1995 and 26% in 2005),17 and 93% of persons in these registries older than 65 years had Medicare data successfully matched to SEER records in the linkage process.16
This study identified all women older than age 65 years in the SEER Medicare database with ovarian cancer diagnosed from January 1, 1995 to December 31, 2005. Women were included if they had American Joint Cancer Committee stage III or IV ovarian cancer (n=13,998). Women were excluded if they had a diagnosis based on autopsy or death certificate only, noninvasive pathology, disease that was not pathologically confirmed, nonepithelial malignancies, or if they had a second primary malignancy diagnosed any time in the 6 months before or after the date of the ovarian cancer diagnosis (1,488 excluded). Women had to be continuously covered by Medicare parts A and B and not be enrolled in an health maintenance organization from the 12 months before diagnosis and at least 9 months after diagnosis (4,264 excluded). This study was further limited to the 5,475 women from this cohort who had evidence in Medicare records of a debulking surgery for their ovarian cancer. The first episode of cytoreductive surgery for ovarian cancer in the year after the diagnosis date was identified as defined from the Medicare records that were available for claims through December 31, 2007.
SEER data were used to identify and categorize age (5-year groups), race (white, black, or other), and marital status (married or unmarried). SEER registries were grouped according to geographic region (Northeast, Midwest, South, or West). Population density of area of residence was categorized as defined in the SEER files. Median household income from postal code of residence was used as a proxy for socioeconomic status and was derived from 2000 census data included in the SEER files (categorized into quartiles: low=1 to high=4). Tumor stage, grade, and histology were determined from SEER. Tumor grade was missing for more than 20% of the individuals and so was not utilized as a variable in the analysis. Comorbidity score was determined using claims for the 12 months before ovarian cancer diagnosis to calculate the Deyo adaptation18 of the Charleson comorbidity index.19,20 One point is assigned for evidence of each of the following: dementia, congestive heart failure, coronary artery disease (heart attack, angina or revascularization), diabetes, hypertension, peripheral vascular disease, pulmonary disease, renal disease, or stroke. Two points are assigned for previous malignancy, and three points are assigned for hepatic disease.
Hospital volume was determined as the number of cases from January 1, 1995 to December 31, 2006, for hospitals that were located within SEER registry areas as indicated in the hospital files in the SEER Medicare database. Hospital volume was categorized by the number of ovarian cancer cases over the study period as described by Schrag et al21 as low (1–12), intermediate (13–28), and high (more than 28).21 Only women whose surgeries occurred at hospitals located in SEER areas were included in the multivariable analysis that accounted for treatment variables to ensure that all Medicare cases for that hospital were identified. Although this volume does not include non-Medicare cases, it has been shown to correlate with overall hospital volume and to allow ranking of hospitals into volume categories.22 Ovarian cancer surgeon was determined by the use of a unique provider identification number when available on provider claims (when available) associated with ovarian cancer surgeries, and surgeon volume was categorized into low (1–4), intermediate (5–25), and high (more than 25). Provider specialty was determined from both Medicare files and American Medical Association files.23 Surgeon specialty was categorized as gynecologic oncologist, gynecologist, surgeon, or other or unknown.
Admission type was identified in 97.5% of the patients from the inpatient hospital billing records for the surgical episode. Patients were categorized as having an emergent admission if they were admitted through the emergency department or if the admitting physician indicated on the billing claim that the admission was an emergency.
Surgical treatment for ovarian cancer was identified in the MEDPAR files using International Classification of Diseases, 9th Revision (ICD-9) procedure codes and, in the physician claims, using Physicians' Current Procedural Terminology Coding System codes indicating surgical resection of the primary tumor as previously described.24 Complexity of the primary surgery may influence 30-day mortality. Identification of upper abdominal procedures at the time of the primary surgery was performed by searching for ICD-9 codes in the inpatient billing records. Patients were classified as having an upper abdominal procedure if they were noted to have a liver (50.22, 50.3), diaphragm (34.81), spleen (41.2, 41.3, 41.5), or pancreatic (52.5, 52.6) resection. Large bowel resections were identified from ICD-9 codes (45.52, 45.7 45.8, 45.92, 45.95, 45.93, 45.94, 48.4, 48.5, 48.6) of the inpatient records. Chemotherapy was identified as previously defined if the inpatient record, outpatient file, or physician claims indicated that chemotherapy was administered.15 Chemotherapy was classified as neoadjuvant if administered before the date of the primary surgical episode. Previous studies have determined a high level of agreement between Medicare data and chart review in the identification of surgery and chemotherapy among cancer patients.25,26
The primary outcome in this study was 30-day mortality after surgery, defined as death from any cause in the 30 days after the primary surgical episode. Death was identified from the Medicare records and was verified with the social security administration and captured all patient deaths regardless of location (in hospital, home, hospice).
The χ2 test was used to compare the frequency distributions of categorical variables. All analyses were stratified based on the admission type and all models empirically included year of diagnosis as a confounding variable to account for possible temporal changes. Because the outcome of interest was not rare, a Poisson regression was used to model incident rate ratios, which were interpreted as a relative risk (RR) for the outcome of interest. Outcomes at a particular institution may be related to unmeasured factors from the individual institution; therefore, as described previously, a generalized estimating equation was used to account for clustering by hospital.27 Models were fit using generalized estimating equations with a Poisson family, a log link, and the hospital identifier as a clustering variable. Variables of interest were classified as either patient-related (age, race, median household income, marital status, geographic region, size of area of residence, stage, histology, and comorbidity score) or treatment-related (hospital and surgeon volume, surgeon specialty, upper abdominal procedures, large bowel resection, neoadjuvant chemotherapy). The first model fit included all patient-related variables significantly associated with 30-day mortality on univariable analysis. The second model included all variables in the first model and the treatment related variables found to be significant on univariable analysis. Model fit was assessed by the use of generalized Pearson residuals. All P values are two-sided and P<.05 was considered significant. No statistical corrections were made for multiple comparisons. Stata SE 11.0 (College Station, TX) was used for all calculations.
Last, a decision tree was constructed for women with routine admissions. Because we aimed to identify women who may benefit from alternative treatment strategies such as neoadjuvant chemotherapy, women who already received neoadjuvant chemotherapy were excluded from this tree (n=605). Similarly, only women admitted routinely were included in this tree, because it was hypothesized that women admitted emergently were much more likely to have bowel obstructions and other acute symptoms that would dictate the need for immediate surgery, regardless of the surgical mortality. Only variables that were significant in the multivariable model were included, and we limited variables in the analysis to the patient-related variables because these were easily ascertained preoperatively and objective. Age was dichotomized at age 75 (65–75 compared with 75 or older), stage was classified as III or IV, and comorbidity score was categorized as 0–1 compared with 2 or more. We then were able to categorize women into risk groups based on the observed 30-day mortality in these subgroups.
Of the 5,475 women having surgery for advanced ovarian cancer, 4,517 (84.4%) had an elective admission, 835 (15.6%) were admitted emergently, and 123 (2.2%) had an unknown admission status. Demographic, clinical, and pathologic characteristics are listed for the entire cohort and stratified by admission type in Table 1. Women admitted emergently tended to be slightly older (median age 76.9 compared with 75.1 years), had higher comorbidity scores, were more likely to have stage IV disease (41.9% compared with 32.9%), to be of nonwhite race, and to be unmarried when compared with women with elective admissions (all P<.001). Geographic variability was noted, with higher proportions of emergent admissions observed in women living in the Northeast and the Midwest than in the South and the West (P<.001). Neoadjuvant chemotherapy was used in 649 (11.85%) patients, the majority of whom were admitted electively.
Hospital, surgeon, and treatment characteristics are summarized in Table 2. Women admitted emergently were more likely to undergo operation in low-volume hospitals, by low-volume physicians, and by nongynecologic oncologists (all P<.001). There was no difference in the performance of upper abdominal procedures, but women admitted emergently were more likely to have large bowel resections during surgery (23.95% compared with 18.88%, P=.001). Women admitted on an emergent basis were much less likely than those admitted routinely to have been previously treated with neoadjuvant chemotherapy (2.99% compared with 13.39%, P<.001).
The 30-day mortality among the entire cohort was 8.22%. Women admitted electively had a much lower 30-day mortality of 5.56% compared with 20.12% for those admitted emergently (P<.001). In a univariable analysis (Table 3), age was strongly associated with 30-day mortality in both elective and emergent admissions (P<.001 elective, P=.03 emergent). Among those with elective admissions, 30-day mortality was more than five-times higher for women older than age 85 years compared with those aged 65–69 years (17.52% compared with 3.19%, P<.001). When age was entered into the model as a continuous variable in the group, each additional year older than 65 was associated with a 7.5% increase in the risk of 30-day morality (95% confidence interval [CI] 1.06–1.10). The relationship between advancing age and 30-day mortality among women admitted emergently was not as strong, with less than a twofold difference observed between the same groups (26.32% compared with 14.29%, P=.03). As a continuous variable in this group, each increase in age of 1 year older than 65 was associated with a 2.8% increase in the risk of 30-day mortality (95% CI 1.01–1.05). Marital status was associated with 30-day mortality, with unmarried women in all groups having more than a twofold increase in observed 30-day mortality (elective: 7.06% compared with 3.39%, P<.001; emergent 23.75% compared with 13.88%, P=.001). Stage IV disease and mucinous histology were associated with higher 30-day mortality for both emergent and elective admissions (all P<.05). Increasing comorbidity score was strongly associated with higher 30-day mortality in all groups (all P≤.001). No differences in 30-day mortality were observed in either group by race, median household income, geographical region, or size of area of residence.
When stratified by admission type, hospital volume was not associated with 30-day mortality (Table 3). Surgeon specialty and provider volume were associated with 30-day mortality; however, this association appears to be mostly driven by the higher mortality observed when provider status was other or unknown (30-day mortality 17.94%) and procedure volume was missing (30-day mortality 19.37%). When cases with missing provider status and provider procedure volumes are excluded from the analysis, all P values are not significant for an association with 30-day mortality. Women whose surgeries included upper abdominal procedures or large bowel resections had no significant difference in 30-day mortality. Among women admitted electively, those having neoadjuvant chemotherapy had more than 70% lower 30-day mortality than those without neoadjuvant chemotherapy (1.82% compared with 6.13%, P<.001).
Multivariable analysis demonstrated a significant relationship between advancing age, marital status, increasing stage, increasing comorbidity score, and 30-day mortality (all P<.05) among women admitted electively (Table 4). Adjusting for marital status, stage, histology, comorbidity score, and year of diagnosis, women aged 80–84 years had at least a twofold increase in 30-day mortality (RR 2.10, 95% CI 1.36–3.24), and women 85 years and older had almost a fivefold increase in 30-day mortality (RR 4.77, 95% CI 3.07–7.42) compared with women aged 65–69 years. Higher stage, nonmarried status, and advancing comorbidity score were significantly associated with increased short-term mortality among women admitted emergently (all P<.05). Hospital volume, provider volume, and surgeon specialty were not significantly associated with 30-day mortality in either elective or emergent admissions after controlling for patient-related variables. Neoadjuvant chemotherapy remained significantly associated with a lower 30-day mortality in the adjusted model among women admitted electively (RR 0.37, 95% CI 0.17–0.83). When all women were considered in a combined multivariable model adjusting for patient characteristics and hospital admission type (emergent compared with elective), women with emergent admissions had almost a threefold increase in 30-day mortality compared with women admitted electively (RR 2.77, 95% CI 2.25–3.41).
Figure 1 illustrates 30-day mortality for women admitted electively who had not previously received neoadjuvant chemotherapy. Age, stage, and comorbidity score were used to stratify the risk of 30-day mortality into low-risk (less than 5%), intermediate-risk (5–10%), and high-risk (more than 10%) groups. The high-risk group includes all women aged 75 years or older with stage IV disease and women aged 75 years or older with stage III disease and a comorbidity score of 1 or more.
This group represented 25.7% of the patients and had a 30-day average mortality rate of 12.71% (95% CI 10.72%–14.92%), representing almost 50% of the deaths in the cohort. The low-risk group includes women aged 65–74 years, with either stage III or IV disease and a comorbidity score of 1 or less. This group comprises 48.7% of the population and had an average 30-day mortality of 3.64% (95% CI 2.85%–4.58%). The remaining patients constitute the intermediate-risk group, with an average 30-day mortality of 6.05%. (95% CI 4.66%–7.70%).
This population-based study of surgical outcomes among elderly women in the United States after surgery for advanced ovarian cancer reports an overall 30-day mortality rate of 8.22% among all women. When stratified by admission severity, we found a 30-day mortality rate of 5.56% for women admitted electively and 20.12% for women admitted emergently. After correcting for patient characteristics, emergent admission was associated with almost a threefold increase in the risk of 30-day mortality. Among women admitted electively (approximately 85% of cohort) age, stage, and comorbidity score were associated with 30-day mortality. These parameters were combined to identify a high-risk group of women aged 75 or older with stage IV disease and women aged 75 or older with stage III disease and a comorbidity score of 1 or more with an observed 30-day mortality of 12.7%.
Our findings are consistent with previous publications, reporting higher 30-day mortality rates for population-based studies compared with single institution reports.7 Our overall 30-day mortality rate of 8.22% is considerably higher than those of previous population-based reports.9,12 However, this may be accounted for by the older age and advanced stage of the patients in our cohort. Increasing age has been strongly associated with an increase in operative mortality. A population-based report from the Netherlands for women with all stages of ovarian cancer reported postoperative mortality of 6.6% for women aged 70–79 years and 9.8% for women aged 80 and older.7 A previous single institution report of outcomes among women older than age 80 years with advanced ovarian cancer having surgery revealed 13% had in-hospital mortality and 20% died within 60 days of surgery.28 The observed high mortality among older women in this study is consistent with the findings of our analysis.
Our primary study objective was to report postoperative mortality and to identify risk factors independently associated with 30-day mortality in both elective and emergently admitted elderly women having surgery for advanced ovarian cancer A secondary objective was to characterize a subgroup of women at very high risk for 30-day mortality who may benefit from an alternative primary treatment approach. In trying to characterize this subpopulation, we performed another analysis limited to patients who had not already received neoadjuvant chemotherapy. Women admitted emergently were also excluded, because we hypothesized that these patients had a high probability of having severe symptoms and bowel obstructions that would favor a primary surgical approach for symptom management. In the remaining women, age, stage, and comorbidity were three factors that allowed the group to be stratified into risk groups, with 25.7% of patients in the highest risk group. These findings were similar to those reported in a series of 567 patients undergoing operation at four U.S. centers.29 In that analysis, a subgroup of women aged 75 years and older with poor performance or nutritional status and disseminated disease represented 6.6% of the cohort and had a 90-day mortality rate of 18.4%.
In addition to exploring patient-related factors associated with postoperative mortality, we also examined the relationship between the treatment environment and short-term mortality. The relationship between surgeon and hospital volume and short-term outcomes in advanced ovarian cancer has been uncertain. A Canadian population-based trial of more than 3,800 women with all stages of ovarian cancer did not find a significant relationship between hospital or surgeon volume and 30-day postoperative mortality.30 Conversely, Bristow et al13 in a U.S. population-based report showed a significant relationship between surgeon volume but not hospital volume, with high-volume surgeons having 69% lower in-hospital mortality rates than low-volume surgeons.13 In our analysis, we did not observe a significant relationship between hospital or surgeon volume and 30-day mortality after adjusting for patient characteristics. In our analysis, we were able to adjust for urgency of admission, a comorbidity score that was related to treatment in the 1 year before (as opposed to those billed for time during the surgical admission only), and tumor-specific characteristics such as stage. These differences in the methods of adjustment for covariates may account for the observed differences in findings between these analyses. A previous report utilizing a surgical complexity score failed to demonstrate an association between 3-month mortality and increasing surgical complexity.31 Our results were consistent with this study, with no association seen between performance of either upper abdominal procedures or large bowel resections and the 30-day surgical mortality. The small number of upper abdominal procedures performed in this cohort also may have limited our power to detect an associated between upper abdominal procedures and 30-day mortality.
There are several important limitations of this analysis. SEER Medicare data lack important information on laboratory values (such as albumin), performance status, and American Society of Anesthesiologist score, all of which have been previously correlated with perioperative morbidity and mortality.31 The use of median household income as an estimation of socioeconomic status may inadequately classify patients and result in an inability to determine an association between socioeconomic status and 30-day mortality. No information was available on the completeness of the surgery. The use of claims data to identify treatment and comorbidities is likely to result in some underascertainment of both of these variables because of inaccurate coding and alternative payment sources. Medicare data and chart review have been shown to have a high level of agreement in the identification of surgery and chemotherapy, but the accuracy of diagnostic codes is lower for comorbid conditions and treatment complications.20,25,26 The ability to accurately assess provider or hospital volume from Medicare data only may be inaccurate. However, this method has been previously validated and shown to correlate well with total volume.21,22
We report postoperative 30-day mortality rates for women aged 65 years and older that are substantial and increase sharply with increasing age for both elective and emergent ovarian cancer admissions. We identified a subpopulation of previously untreated women admitted electively at age 75 years and older with stage IV disease or with stage III disease and a comorbidity score of 1 or higher who, in our cohort, are at high risk for death after surgery for advanced ovarian cancer. The completeness of the surgical resection and thus the proportion of women optimally debulked are not known from these data, and the resulting effect on short-term and long-term mortality would be critical in informing clinicians faced with decision-making. These findings need to be replicated prospectively and correlated with completeness of surgical debulking. If validated, then this subpopulation may benefit from better risk counseling and may be considered for less risky alterative treatment strategies.
3. Bristow RE, Chi DS. Platinum-based neoadjuvant chemotherapy and interval surgical cytoreduction for advanced ovarian cancer: a meta-analysis. Gynecol Oncol 2006;103:1070–6.
4. du Bois A, Reuss A, Pujade-Lauraine E, Harter P, Ray-Coquard I, Pfisterer J. Role of surgical outcome as prognostic factor in advanced epithelial ovarian cancer: a combined exploratory analysis of 3 prospectively randomized phase 3 multicenter trials: by the Arbeitsgemeinschaft Gynaekologische Onkologie Studiengruppe Ovarialkarzinom (AGO-OVAR) and the Groupe d'Investigateurs Nationaux Pour les Etudes des Cancers de l'Ovaire (GINECO). Cancer 2009;115:1234–44.
5. Chi DS, Eisenhauer EL, Zivanovic O, Sonoda Y, Abu-Rustum NR, Levine DA, et al.. Improved progression-free and overall survival in advanced ovarian cancer as a result of a change in surgical paradigm. Gynecol Oncol 2009;114:26–31.
6. Aletti GD, Dowdy SC, Gostout BS, Jones MB, Stanhope CR, Wilson TO, et al.. Aggressive surgical effort and improved survival in advanced-stage ovarian cancer. Obstet Gynecol 2006;107:77–85.
7. Gerestein CG, Damhuis RA, Burger CW, Kooi GS. Postoperative mortality after primary cytoreductive surgery for advanced stage epithelial ovarian cancer: a systematic review. Gynecol Oncol 2009;114:523–7.
8. Marx C, Bendixen A, Høgdall C, Ottosen C, Kehlet H, Ottesen B. Organisation and quality of primary surgical intervention for ovarian cancer in Denmark. Acta Obstet Gynecol Scand 2007;86:1496–502.
9. Engelen MJ, Kos HE, Willemse PH, Aalders JG, de Vries EG, Schaapveld M, et al.. Surgery by consultant gynecologic oncologists improves survival in patients with ovarian carcinoma. Cancer 2006;106:589–98.
10. Altekruse SF, Krapcho M, Neyman N, Aminou R, Waldron W, Ruhl J, et al.. SEER cancer statistics review, 1975–2007. In: Institute NC, editor. Bethesda (MD); 2010.
11. Massarweh NN, Legner VJ, Symons RG, McCormick WC, Flum DR. Impact of advancing age on abdominal surgical outcomes. Arch Surg 2009;144:1108–14.
12. Diaz-Montes TP, Zahurak ML, Giuntoli RL 2nd, Gardner GJ, Gordon TA, Armstrong DK, et al.. Surgical care of elderly women with ovarian cancer: a population-based perspective. Gynecol Oncol 2005;99:352–7.
13. Bristow RE, Zahurak ML, Diaz-Montes TP, Giuntoli RL, Armstrong DK. Impact of surgeon and hospital ovarian cancer surgical case volume on in-hospital mortality and related short-term outcomes. Gynecol Oncol 2009;115:334–8.
14. Vergote I, Trope CG, Amant F, Kristensen GB, Ehlen T, Johnson N, et al.. Neoadjuvant chemotherapy or primary surgery in stage IIIC or IV ovarian cancer. N Engl J Med 2010;363:943–53.
15. Thrall MM, Gray HJ, Symons RG, Weiss NS, Flum DR, Goff BA. Trends in treatment of advanced epithelial ovarian cancer in the Medicare population. Gynecol Oncol 2011;122:100–6.
16. Warren JL, Klabunde CN, Schrag D, Bach PB, Riley GF. Overview of the SEER-Medicare data: content, research applications, and generalizability to the United States elderly population. Med Care 2002;40(Suppl 8):IV-3–18.
17. Zippin C, Lum D, Hankey BF. Completeness of hospital cancer case reporting from the SEER Program of the National Cancer Institute. Cancer 1995;76:2343–50.
18. Deyo RA, Cherkin DC, Ciol MA. Adapting a clinical comorbidity index for use with ICD-9-CM administrative databases. J Clin Epidemiol 1992;45:613–9.
19. Charlson ME, Pompei P, Ales KL, MacKenzie CR. A new method of classifying prognostic comorbidity in longitudinal studies: development and validation. J Chronic Dis 1987;40:373–83.
20. Klabunde CN, Warren JL, Legler JM. Assessing comorbidity using claims data: an overview. Med Care 2002;40(Suppl 8):IV-26–35.
21. Schrag D, Earle C, Xu F, Panageas KS, Yabroff KR, Bristow RE, et al.. Associations between hospital and surgeon procedure volumes and patient outcomes after ovarian cancer resection. J Natl Cancer Inst 2006;98:163–71.
22. Bach PB, Cramer LD, Schrag D, Downey RJ, Gelfand SE, Begg CB. The influence of hospital volume on survival after resection for lung cancer. N Engl J Med 2001;345:181–8.
23. Pollack LA, Adamache W, Eheman CR, Ryerson AB, Richardson LC. Enhancement of identifying cancer specialists through the linkage of Medicare claims to additional sources of physician specialty. Health Serv Res 2009;44(2 Pt 1):562–76.
24. Earle CC, Schrag D, Neville BA, Yabroff KR, Topor M, Fahey A, et al.. Effect of surgeon specialty on processes of care and outcomes for ovarian cancer patients. J Natl Cancer Inst 2006;98:172–80.
25. Cooper GS, Virnig B, Klabunde CN, Schussler N, Freeman J, Warren JL. Use of SEER-Medicare data for measuring cancer surgery. Med Care 2002;40(Suppl 8):IV-43–8.
26. Warren JL, Harlan LC, Fahey A, Virnig BA, Freeman JL, Klabunde CN, et al.. Utility of the SEER-Medicare data to identify chemotherapy use. Med Care 2002;40(Suppl 8):IV-55–61.
27. Panageas KS, Schrag D, Riedel E, Bach PB, Begg CB. The effect of clustering of outcomes on the association of procedure volume and surgical outcomes. Ann Intern Med 2003;139:658–65.
28. Moore KN, Reid MS, Fong DN, Myers TK, Landrum LM, Moxley KM, et al.. Ovarian cancer in the octogenarian: does the paradigm of aggressive cytoreductive surgery and chemotherapy still apply? Gynecol Oncol 2008;110:133–9.
29. Aletti GD, Eisenhauer EL, Santillan A, Axtell A, Aletti G, Holschneider C, et al.. Identification of patient groups at highest risk from traditional approach to ovarian cancer treatment. Gynecol Oncol 2011;120:23–8.
30. Elit L, Bondy SJ, Paszat L, Przybysz R, Levine M. Outcomes in surgery for ovarian cancer. Gynecol Oncol 2002;87:260–7.
31. Aletti GD, Santillan A, Eisenhauer EL, Hu J, Aletti G, Podratz KC, et al.. A new frontier for quality of care in gynecologic oncology surgery: multi-institutional assessment of short-term outcomes for ovarian cancer using a risk-adjusted model. Gynecol Oncol 2007;107:99–106.