Literature DB >> 29520338

Gynecologic oncologists involvement on ovarian cancer standard of care receipt and survival.

Sun Hee Rim1, Shawn Hirsch2, Cheryll C Thomas1, Wendy R Brewster3, Darryl Cooney2, Trevor D Thompson1, Sherri L Stewart1.   

Abstract

AIM: To examine the influence of gynecologic oncologists (GO) in the United States on surgical/chemotherapeutic standard of care (SOC), and how this translates into improved survival among women with ovarian cancer (OC).
METHODS: Surveillance, Epidemiology, and End Result (SEER)-Medicare data were used to identify 11688 OC patients (1992-2006). Only Medicare recipients with an initial surgical procedure code (n = 6714) were included. Physician specialty was identified by linking SEER-Medicare to the American Medical Association Masterfile. SOC was defined by a panel of GOs. Multivariate logistic regression was used to determine predictors of receiving surgical/chemotherapeutic SOC and proportional hazards modeling to estimate the effect of SOC treatment and physician specialty on survival.
RESULTS: About 34% received surgery from a GO and 25% received the overall SOC. One-third of women had a GO involved sometime during their care. Women receiving surgery from a GO vs non-GO had 2.35 times the odds of receiving the surgical SOC and 1.25 times the odds of receiving chemotherapeutic SOC (P < 0.01). Risk of mortality was greater among women not receiving surgical SOC compared to those who did [hazard ratio = 1.22 (95%CI: 1.12-1.33), P < 0.01], and also was higher among women seen by non-GOs vs GOs (for surgical treatment) after adjusting for covariates. Median survival time was 14 mo longer for women receiving combined SOC.
CONCLUSION: A survival advantage associated with receiving surgical SOC and overall treatment by a GO is supported. Persistent survival differences, particularly among those not receiving the SOC, require further investigation.

Entities:  

Keywords:  Epidemiology; Guidelines-based care; Gynecologic oncologist; Ovarian neoplasms; Surveillance; and End Result Medicare

Year:  2016        PMID: 29520338      PMCID: PMC5839163          DOI: 10.5317/wjog.v5.i2.187

Source DB:  PubMed          Journal:  World J Obstet Gynecol        ISSN: 2218-6220


INTRODUCTION

Women in the United States with advanced stage epithelial ovarian cancer (OC) have an overall 5-year survival rate of about 30%[. As with many cancers, survival is closely linked with the stage of diagnosis, such that women with localized (stage I) disease have a relative 5-year survival rate of 92%; the prognosis however declines with late stage disease and metastases[. Without an adequate early detection strategy, ensuring that women receive appropriate, standard of care (SOC) treatment is a very important intervention that has demonstrated reduction in OC mortality[. National Comprehensive Cancer Control Network (NCCN) current treatment recommendations for women with epithelial OC include an evaluation prior to initiating chemotherapy along with accurate surgical staging and primary debulking surgery/cytoreduction performed by a gynecologic oncologist (GO)[. In most but not all cases, at least six cycles of platinum and taxane-based chemotherapy administration is recommended for advanced epithelial OCs[. Appropriate care not only constitutes the receipt of SOC treatment, but also quality care from an experienced GO, who is trained to both perform the surgery and administer chemotherapy[. The evidence supporting better guideline-adherent care and outcomes among patients seen by a GO has been previously examined[, and prior studies suggest only 30%–40% of women with OC are treated by a GO[. While NCCN cancer center patients tend to receive guideline-adherent care[, there is potential in exploring whether differences in SOC treatment are affected across patient-level demographic and clinical subgroups. To date, few studies have jointly considered surgical and chemotherapeutic SOC indicators in examining survival in OC patients[. In this study, we examine predictors of both SOC receipt (surgical and chemotherapeutic) and adherence to these treatments among women treated by GOs compared to non-GOs. We further quantified the survival advantage of SOC treatment receipt among OC patients.

MATERIALS AND METHODS

Data source and study population

The study included all women in the Surveillance, Epidemiology, End Results (SEER)-Medicare database[ diagnosed with OC from January 1, 1992 to December 31, 2006 (n = 38972). We excluded women who did not have a primary epithelial OC diagnosis (n = 6175); were Medicare age-ineligible (age < 66) at date of diagnosis (n = 11716); had an invalid month of diagnosis (n = 166); had diagnoses based on autopsy or death certificate only (n = 543); had a nonepithelial ovarian malignancy (n = 3198); and were not continuously enrolled in both Medicare Part A and B or were enrolled in an Health Maintenance Organization plan during the course of treatment (n = 5486). A total of 11688 OC patients met the inclusion criteria for the study.

Definition of variables

Patient-level covariates included age, race, stage at diagnosis, marital status, year of diagnosis, geographic region of SEER registry, and cancer histology. The Charlson-Klabunde comorbidity index score was determined using Medicare claims data for 12 mo prior to and 4 mo after cancer diagnosis date, per prior studies[. We examined all procedure codes in the Medicare claims data falling within a treatment window (defined as two months prior to and one year after the diagnosis date) to determine if a patient received surgical or chemotherapeutic SOC. Since only month and year of diagnosis are reported in the SEER database, the 15th day of the month was assigned as the day of diagnosis for each patient.

SOC definitions

Per recommendation from an experienced group of GOs, consulted specifically for this project (W. Brewster, R.E. Bristow and D.K. Singh), the International Federation of Gynecologists and Obstetricians (FIGO) stage of disease categories were grouped as: I A/I B, I C/II, IIIA/III B and IIIC/IV based on similarities in current surgical and chemotherapeutic treatment regimens. FIGO stage III NOS and stage IV were grouped into stage IIIC/IV group, given that a high proportion of all stage III cases were stage IIIC. Among the women who met the inclusion criteria (n = 11688), we examined receipt of SOC among women receiving any initial surgical care. Thus, we further excluded women who received treatment outside of the treatment window (n = 28), those who had no procedure codes of interest for any surgical care (n = 2464), and women who received neoadjuvant chemotherapy (n = 2482) (given the difficulty of cancer staging for women who are eligible for neoadjuvant chemotherapy) to examine differences in guideline-adherent treatment and survival. We also excluded all OC patients diagnosed with stage I NOS or who were unstaged at diagnosis since minimum SOC parameters are not well defined for these groups. The GO group defined minimum surgical SOC as lymph node dissection, omentectomy and oophorectomy for all patients with FIGO stage IA/IB, IC/II or IIIA/IIIB at diagnosis, but omentectomy and oophorectomy only for women with stage IIIC/IV at diagnosis. Minimum chemotherapy SOC definition depended on: (1) stage of disease at diagnosis; (2) number of chemotherapy cycles received; and (3) type of chemotherapy agent received. For analysis, chemotherapy SOC was defined as an individual receiving the defined number of cycles (three cycles of chemotherapy for stage IC/II and six cycles for stage III/IV), with at least one multi-agent cycle (defined as one platinum based and one non-platinum based agent) using either intravenous or intraperitoneal modes of administration. One cycle of chemotherapy was equal to three weeks of treatment, given that chemotherapy is usually administered every 3–4 wk[. Patients were documented as receiving overall SOC if they received both surgical and adjuvant chemotherapeutic SOC. The GO group recommended surgical and chemotherapy procedure codes for use in determining SOC for each FIGO stage category. Procedure codes included both International Classification of Diseases, Ninth revision, clinical modification codes and American Medical Association (AMA) Current Procedural Terminology codes.

Surgeon specialty definition

Self-reported, physician specialty information from the SEER-Medicare claims file was linked with and verified against the AMA Physician Masterfile using the unique provider identification number (UPIN) for physicians performing (or those in attendance) of an OC procedure of interest. If the operating physician UPIN was not available, but the attending physician UPIN was available, AMA specialty was assigned to the attending physician. If the UPIN for an operating and attending physician was unavailable, the self-reported physician specialty variable found in the Medicare data set was used to define specialty. When a patient received treatment from multiple physicians, care was attributed to the most specialized physician (most to least specialized: GO, gynecologist, general surgeon, and other physician). For analytic purposes, physician specialty was grouped as GO and non-GO.

Statistical analysis

We examined predictors associated with receipt of surgical and chemotherapeutic SOC. A forward selection logistic regression model was used to examine each question. Comparisons of the distribution of OC patients receiving the SOC by physician specialty was examined using the Pearson χ test. Cox proportional hazard methods were used to determine differences in survival time from date of OC diagnosis to date of death. The proportional hazards assumption was examined by testing interactions between time and each covariate in the model. The final models (Model 1 and 2) exclude women (n = 1003) who died within 4.5 mo after diagnosis (i.e., women who did not live long enough to receive chemotherapy SOC). Due to a common category in the chemotherapy variables (chemotherapy SOC and chemotherapy physician specialty), we examined two different models. The first model (Model 1) examined surgery physician specialty and receipt of both SOC measurements, while the second model (Model 2) examined both surgery and chemotherapy physician specialty and receipt of surgery SOC, adjusting for patient-level and clinical factors. All final models were adjusted for covariates that had a statistically significant association from the bivariate analysis or were of importance in the literature. All analyses were performed using SAS version 9.3 (SAS Institute Inc., Cary, NC, United States).

RESULTS

Among the 11688 OC patients, 57.4% (n = 6714) received an initial surgical procedure code of interest. Table 1 shows the patient and tumor characteristics by the type of physician performing the initial surgery. The mean age of patients was mid to late-70s; most women were white, married or widowed, had no comorbidities, had FIGO stage IIIC/IV disease, and serous histology. More women received an initial surgical procedure from OB/GYNs (n = 3088) than GOs (n = 2254), general surgeons (n = 914), or other non-GO/unknown specialties (n = 419).
Table 1

Characteristics of ovarian cancer patients who received any initial surgical procedure by physician specialty (n = 6714)

CharacteristicSurgeon specialty1
GONon-GO
OBGYNGeneral surgeonOther2
No. of patients22543088914419
Mean age at diagnosis (stddev)74.6 (5.9)74.8 (6.1)77.0 (6.8)    75.5 (6.2)
Race n (%)
 White1995 (88.5)2844 (92.1)827 (90.5)379 (90.5)
 African American121 (5.4)104 (3.4)49 (5.4)26 (6.2)
 Hispanic  35 (1.6)  31 (1.0)33
 Asian  53 (2.4)  66 (2.1)33
 Other4  47 (2.1)  37 (1.2)33
Marital status
 Married1052 (46.7)1424 (46.1)327 (35.8)170 (40.6)
 Single159 (7.1)221 (7.2)53 (5.8)31 (7.4)
 Divorced148 (6.6)166 (5.4)58 (6.3)29 (6.9)
 Widowed  799 (35.4)1168 (37.8)458 (50.1)176 (42.0)
 Separated/unknown  96 (4.2)109 (3.5)33
Charlson-Klabunde comorbidity score
 01521 (67.5)2133 (69.1)605 (66.2)266 (63.5)
 1  498 (22.1)  644 (20.9)188 (20.6)  93 (22.2)
 2175 (7.8)189 (6.1)78 (8.5)38 (9.1)
 3  45 (2.0)  80 (2.6)29 (3.2)3
 4 or more3  42 (1.4)33
FIGO treatment stage
 IA/IB200 (8.9)  383 (12.4)66 (7.2)  43 (10.3)
 IC/II  276 (12.2)  516 (16.7)90 (9.8)40 (9.5)
 IIIA/IIIB119 (5.3)179 (5.8)59 (6.5)3
 IIIC/IV1580 (70.1)1898 (61.5)660 (72.2)308 (73.5)
 Unstaged/NOS  79 (3.5)112 (3.7)39 (4.2)3
Histology
 Serous1460 (64.8)1897 (61.4)554 (60.6)254 (60.6)
 Endometrioid  238 (10.6)  381 (12.3)73 (8.0)  46 (11.0)
 Mucinous129 (5.7)235 (7.6)79 (8.6)25 (6.0)
 Clear cell  84 (3.7)127 (4.1)33
 Adenocarcinoma  275 (12.2)  344 (11.1)175 (19.1)  66 (15.8)
 Other5  68 (3.1)104 (3.3)20 (2.2)3

Surgeon specialty was categorized according to the most specialized care received during the course of the treatment window

39 women received a surgery procedure code during the treatment window (defined as a period of two months prior and one year after a patient’s diagnosis date in which procedures were performed) but surgeon specialty could not be identified

Denotes cell size suppression of less than 20

Other race includes designation of “Other” or Native American

Other histology includes Transitional. GO: Gynecologic oncologists; FIGO: International Federation of Gynecologists and Obstetricians; NOS: Not otherwise specified.

Among women treated by a GO, 79.2% received the surgical SOC and 52.8% received the chemotherapy SOC (Figure 1). Regardless of stage at diagnosis, women more frequently received surgical and chemotherapeutic SOC from a GO than from a non-GO.
Figure 1

Surgical standard of care (n = 4434) and adjuvant chemotherapy standard of care (n = 2595) receipt by physician specialty and International Federation of Gynecologists and Obstetricians stage

(1) Surgery SOC treatment was based on ovarian cancer patients receiving surgery prior to chemotherapy (n = 6714); (2) Stages 1, not otherwise specified and Unknown/unstaged were removed from analysis; (3) Surgeon specialty and chemotherapy specialty was categorized according to the most specialized care received during the course of the treatment window; (4) Women who received surgery SOC by a surgeon specialty who could not be identified are not shown (n = 17); (5) There were 177 women who received a chemotherapy procedure code of interest but for whom physician specialty could not be identified and 1238 women who did not receive a chemotherapy procedure code of interest.[1] Denote that the estimate is statistically significantly higher for GO compared to Non-GO. SOC: Standard of care; GO: Gynecologic oncologist.

Table 2 reports the factors associated with receipt of surgical SOC after adjusting for other covariates. Surgery performed by a GO was strongly associated with receiving surgical SOC [odds ratio (OR) for GO = 2.35; 95%CI: 2.03–2.71]. Other factors associated with greater odds of surgical SOC receipt included: More advanced stage of disease, white vs African-American race, younger age at diagnosis, serous vs adenocarcinoma not otherwise specified histologic type, being married vs not married, and diagnosis during the later years of the study period.
Table 2

Predictors of receipt of minimum surgical and chemotherapeutic standard of care1

Surgical standard of care2
Chemotherapeutic standard of care2
Odds ratio (95%CI)P valueOdds ratio (95%CI)P value
Physician specialty3
 Gynecologic oncologist2.35 (2.03–2.71)< 0.011.25 (1.07–1.47)   0.006
 Non-gynecologic oncologist1.001.00
Age at diagnosis
 66–691.001.00
 70–740.80 (0.67–0.96)   0.0170.93 (0.78–1.09)   0.393
 75–790.83 (0.69–1.0)     0.0530.79 (0.66–0.94)   0.008
 80–840.58 (0.47–0.71)< 0.010.61 (0.48–75)  < 0.001
 ⩾ 850.40 (0.31–0.51)< 0.010.31 (0.21–0.48)< 0.001
Race4
 White1.00
 African American0.67 (0.50–0.91)   0.01
 Other0.83 (0.62–1.10)   0.208
Treatment stage5
 IA/IB0.08 (0.07–0.10)< 0.01NA   NA
 IC/II0.08 (0.07–0.10)< 0.013.46 (2.86–4.18)< 0.001
 IIIA/IIIB0.05 (0.04–0.07)< 0.010.83 (0.64–1.09)   0.182
 IIIC/IV1.001.00
Charlson-Klabunde comorbidity score
 01.001.00
 10.84 (0.72–0.98)   0.0290.84 (0.71–0.99)   0.029
 20.81 (0.63–1.02)   0.0840.78 (0.60–1.03)   0.078
 30.65 (0.44–0.97)   0.0390.49 (0.31–0.80)   0.005
 4 or more1.09 (0.60–1.97)   0.7710.63 (0.29–1.37)   0.247
Histology
 Serous1.001.00
 Endometrioid1.10 (0.90–1.35)   0.3560.70 (0.56–0.89)   0.003
 Mucinous0.95 (0.74–1.35)   0.670.49 (0.34–0.70)< 0.001
 Clear cell1.29 (0.93–1.78)   0.130.62 (0.41–0.93)   0.026
 Transitional0.70 (0.27–1.79)   0.4540.76 (0.30–1.97)   0.572
 Adenocarcinoma (NOS)0.44 (0.37–0.54)< 0.0011.04 (0.86–1.27)   0.695
 Other1.05 (0.70–1.56)   0.8130.74 (0.47–1.13)   0.168
Marital status
 Married1.001.00
 Not married0.83 (0.72–0.95)   0.0070.75 (0.66–0.86)< 0.001
 Unknown1.03 (0.69–1.52)   0.870.73 (0.48–1.09)   0.127
Year of diagnosis
 1993–19970.62 (0.52–0.73)< 0.010.28 (0.23–0.33)< 0.001
 1998–20020.79 (0.68–0.92)   0.0031.09 (0.94–1.26)   0.261
 2003–20061.001.00
SEER region4
 Northeast1.00
 Midwest0.76 (0.62–0.93)   0.009
 South1.09 (0.88–1.37)   0.424
 West0.93 (0.78–1.10)   0.391

Minimum SOC treatment was based on patients receiving surgery prior to chemotherapy (n = 6714)

Surgery SOC (n = 4434) and chemotherapy SOC (n = 2595)

Physician specialty was categorized according to the most specialized care received during the course of the treatment window; there were 39 and 177 cases where physician specialty could not be identified for surgery or chemotherapy procedures, respectively (results for this group not shown)

Race was not entered into the chemotherapy SOC model based on forward selection entry criteria (P ≤ 0.10); Region was not entered into the surgery SOC model based on forward selection entry criteria (P ≤ 0.10)

Stage I NOS, Stage IA/IB (for chemotherapy SOC) and unknown/unstaged were removed from the analysis since current guidelines recommend early stage patients not receive chemotherapy treatment. NOS: Not otherwise specified; SOC: Standard of care; SEER: Surveillance, Epidemiology, and End Result; NA: Not applicable.

Table 2 also reports factors associated with receipt of the minimum chemotherapy SOC after adjusting for other covariates. Women who obtained chemotherapy from a GO had a higher odds of receiving chemotherapeutic SOC (OR = 1.25, 95%CI: 1.07–1.47). Other statistically significant factors for higher odds of chemotherapeutic SOC included: Less advanced stage of disease, younger age at diagnosis, histologic type (serous compared with endometrioid/mucinous/clear cell), being married compared with unmarried, living in the SEER Midwest region (compared to the SEER Northeast), and diagnosis during more recent years. Table 3 shows the Cox regression model of time to death among the sample of OC patients who received a primary surgery procedure who did not die within 4.5 mo after diagnosis. In Table 3 (Model 1), women who did not receive surgery SOC had increased mortality compared to women who did [hazard ratio 1.22 (95%CI: 1.12–1.33)]. Similarly, women who did not receive any chemotherapy SOC had a higher risk of earlier death compared to women who received the full contingent of chemotherapy [hazard ratio 1.29 (95%CI: 1.14–1.46)]. Increasing age, late stage disease, higher number of comorbidities, and mucinous histology compared to serous histology were all associated with increased death (Table 3, Model 1). Similar patterns were observed in Table 3, Model 2 after controlling for chemotherapy physician specialty (as opposed to chemotherapy SOC). For Model 2, women who received surgery from a GO had better survival. Although there was no significant difference in survival between chemotherapy treatment from a GO compared to non-GO, those not receiving any chemotherapy had a significantly shorter survival time (Table 3, Model 2). The median survival time for women who received the overall SOC was 52 mo compared to 38 mo for women that did not receive the overall surgical and chemotherapeutic SOC (Figure 2).
Table 3

Cox proportional hazard model of time-to-death among ovarian cancer patients

PredictorModel 11
Model 21
Hazard ratio (95%CI)P valueHazard ratio (95%CI)P value
Received surgery SOC2
 Yes1.001.00
 No1.22 (1.12–1.33)< 0.011.21 (1.11–1.31)< 0.01
Received chemotherapy SOC24
 Yes1.004
 No, but received some chemotherapy0.95 (0.89–1.02)0.184
 Received no chemotherapy1.29 (1.14–1.46)< 0.014
Age at diagnosis
 66–691.001.00
 70–741.07 (0.98–1.17)0.131.05 (0.97–1.15)0.24
 75–791.23 (1.12–1.34)< 0.011.21 (1.10–1.32)< 0.01
 80–841.52 (1.37–1.69)< 0.011.48 (1.33–1.65)< 0.01
 ⩾ 851.96 (1.70–2.26)< 0.011.92 (1.67–2.21)< 0.01
Race
 White1.001.00
 African American1.11 (0.95–1.29)0.181.13 (0.97–1.32)0.12
 Other0.90 (0.78–1.05)0.170.88 (0.75–1.02)0.09
Year of diagnosis
 1993–19971.27 (1.17–1.38)< 0.011.24 (1.14–1.35)< 0.01
 1998–20021.18 (1.09–1.27)< 0.011.17 (1.08–1.27)< 0.01
 2003–20061.001.00
Treatment stage
 IA/IB0.20 (0.18–0.23)< 0.010.17 (0.15–0.20)< 0.01
 IC/II0.35 (0.32–0.40)< 0.010.36 (0.32–0.40)< 0.01
 IIIA/IIIB0.61 (0.53–0.71)< 0.010.62 (0.54–0.71)< 0.01
 IIIC/IV1.001.00
Charlson-Klabunde comorbidity score
 01.001.00
 11.28 (1.18–1.38)< 0.011.26 (1.17–1.36)< 0.01
 21.38 (1.22–1.56)< 0.011.37 (1.21–1.55)< 0.01
 31.64 (1.34–2.00)< 0.011.64 (1.34–2.01)< 0.01
 ⩾ 42.33 (1.73–3.15)< 0.012.27 (1.67–3.09)< 0.01
Histology
 Serous1.001.00
 Endometrioid0.76 (0.68–0.85)< 0.010.75 (0.68–0.84)< 0.01
 Mucinous1.22 (1.06–1.41)< 0.011.22 (1.06–1.41)< 0.01
 Clear cell0.83 (0.69–1.00)0.050.83 (0.69–1.00)0.05
 Transitional0.79 (0.47–1.31)0.360.79 (0.48–1.32)0.37
 Adenocarcinoma (NOS)1.07 (0.98–1.18)0.141.07 (0.97–1.17)0.2
 Other1.02 (0.82–1.28)0.851.02 (0.82–1.28)0.85
Marital status
 Married1.001.00
 Not Married1.07 (1.00–1.14)0.051.07 (1.00–1.14)0.05
 Unknown1.00 (0.82–1.23)0.970.99 (0.80–1.21)0.89
Surgeon specialty3
 Non-GO1.001.00
 GO0.90 (0.84–0.96)< 0.010.90 (0.84–0.97)< 0.01
Chemotherapy specialty3
 Non-GO41.00
 GO40.98 (0.89–1.08)0.68
 Did not receive chemotherapy41.33 (1.19–1.47)< 0.01

Model 1 and Model 2: Includes OC patients who did not have an unknown FIGO stage at diagnosis, and survived at least 4.5 mo after diagnosis;

Minimum SOC procedure codes for surgery

Missing surgeon and physician specialty excluded from analysis

Excluded from the model based inclusion criteria. Chemotherapy SOC and chemotherapy physician specialty cannot be included in the same model because the common level of “did not receive chemotherapy” would introduce a singularity and prevent model convergence. OC: Ovarian cancer; SOC: Standard of care; GO: Gynecologic oncologist; FIGO: International Federation of Gynecologists and Obstetricians; NOS: Not otherwise specified.

Figure 2

Ovarian cancer survivor curves1 by receipt of overall standard of care2 (n = 1678)

1All covariates held at the reference level noted in Table 3; 20 = Did not receive overall standard of care; 1 = Did receive overall SOC. SOC: Standard of care.

DISCUSSION

Our findings show that among OC patients receiving initial surgical treatment, only 25% of women received the overall SOC as defined by our panel of GOs. Few women (approximately one-third of women receiving a surgical procedure) had a GO involved at any point during their care. Women who obtained surgery from a GO however, were more likely to receive the surgical SOC and chemotherapeutic SOC than women who obtained treatment from a non-GO. The median survival time was 14 mo longer for women who received the overall SOC compared to women who did not receive overall SOC. Our results are consistent with prior studies that suggest that appropriate surgical treatment in the United States is more frequently performed when a GO is the treating physician[. Data from a single state cancer registry study by Chan et al[ showed that women with OC under the care of GOs were more likely to receive appropriate staging and chemotherapy treatments, controlling for age, stage, and grade of disease. Also similar to previous studies, our results suggest that greater utilization of GOs in the care of OC patients would be beneficial[. Although the level of detail in our analysis is unable to discriminate the factors underlying the low utilization, it is likely that our results reflect a complex interaction of both preference and access-relevant effects, such as the influence of a patient’s choice in receipt of GO care vs a shortage of available GOs in some areas. While patient treatment preferences can independently and significantly affect chemotherapy receipt[, geographic access may also play an important role in (both chemotherapeutic or surgical) treatment receipt from a GO. For example, a previous analysis reported on the unequal distribution of GOs in the United States[. A recently published study suggested that OC mortality may be a function of distance to a practicing GO as counties located more than 50 miles from a gynecologic oncology practice had almost 60% increased likelihood of OC mortality than those physically closer to a practice location[. While earlier research efforts have indicated that treatment of OC can be improved by early referral to a GO[, referral and consultation from GOs have generally been low, with only about 39% of family physicians and 51% of general internists self-reporting referrals to a GO[. Given that surgery is an important determinant of outcomes for OC patients, receiving surgery/treatment from surgeons with specialized training in pelvic surgery (i.e., GOs)[, who see a high volume of cases[ at high volume facilities treating more than 20 OC cases per year[, might help improve outcomes. It is important to note that there are still subgroups that require further research. Although African-American women were more likely than their white counterparts to receive their initial surgical procedure from a GO (data not shown), they had lower odds of receiving the surgical SOC and there was no difference in survival after adjusting for physician specialty, surgical SOC, and other tumor and sociodemographic characteristics. The increased risk of death among African American women noted in other studies, when controlling for receipt of chemotherapeutic SOC, suggests that there may be some important nuanced differences in the definitions of chemotherapeutic SOC[, chemotherapeutic agents, and/or interaction effects between age, comorbidity, stage, and race that have not been adequately explored. Bristow et al[ have previously suggested similar differences in survival between African-American and white OC patients and the complexity of examining race-based survival associations[. The findings in this study should be considered in light of several limitations: (1) our analysis was focused on fee for service Medicare; women who received treatment under managed care were not included because the managed care cases did not include codes to identify specific treatment procedures; (2) neoadjuvant chemotherapy cases, which could have later received surgical SOC, were excluded; and (3) it is a challenge to operationalize NCCN recommendations into an analytic/computer program because the recommendations are relatively complex, and some information required for the NCCN decision algorithms is not available in claims data. However, our panel of experienced GOs developed a simpler, but accurate definition of the SOC so that recommendations could be converted into analytic code. Similarly, since SOC definitions were varied for each stage at diagnosis, if claims data were not available for the full contingency of treatment procedures, it is possible that there was an underestimation of patients identified as receiving overall SOC in that subgroup. Fourth, given the limitations of Medicare data, inaccuracies or incomplete data in billing, drug, or procedure codes could have resulted in an underestimate or overestimate of the total number of surgeries and/or chemotherapy procedures performed, thus biasing the estimate. Previous studies have noted some concerns in the validation of chemotherapeutic agents within Medicare claims data[. Fifth, there is potential for misclassification of physician specialty, given the use of multiple data sources including operating physician, attending physician, and self-reported physician specialty[. Furthermore, in our analysis, receipt of treatment from a GO was designated as such if a GO had been seen at any point during the care. Lastly, since we assumed each cycle of treatment lasted three weeks, we calculated that it would take at least 4.5 mo for women diagnosed with stage IIIC or IV to complete the chemotherapy SOC as defined in our study. Thus, women who died within five months of the diagnosis date would not have had the opportunity to receive chemotherapy SOC. Our definition of chemotherapy SOC may have been too rigorous and potentially introduce selection or survival bias. Our study showed that GOs more often provided the surgical and chemotherapeutic SOC. The receipt of surgical standards was associated with better survival outcomes, even after adjusting for provider specialty. As such, these two NCCN-recommendations (i.e., treatment from a GO and receipt of SOC) continue to be critical points of intervention for improving survival time and reducing deaths from OC. Although it is difficult to determine when adjuvant chemotherapy is warranted based on sound clinical judgement (i.e., taking into consideration the patient’s comorbidities, toxicities, age, etc.) or patient refusal, one area that has not been carefully examined is the potential that race/ethnicity-based differences in patient and caregiver preferences may have for OC care. Future research may further explore this and the interaction effects of race, age, comorbidities on survival.
  30 in total

1.  Involvement of gynecologic oncologists in the treatment of patients with a suspicious ovarian mass.

Authors:  Barbara A Goff; Jacqueline W Miller; Barbara Matthews; Katrina F Trivers; C Holly A Andrilla; Denise M Lishner; Laura-Mae Baldwin
Journal:  Obstet Gynecol       Date:  2011-10       Impact factor: 7.661

2.  Ovarian cancer staging: does it require a gynecologic oncologist?

Authors:  A R Mayer; S K Chambers; E Graves; C Holm; P C Tseng; B E Nelson; P E Schwartz
Journal:  Gynecol Oncol       Date:  1992-11       Impact factor: 5.482

Review 3.  The outcomes of ovarian cancer treatment are better when provided by gynecologic oncologists and in specialized hospitals: a systematic review.

Authors:  Flora Vernooij; Peter Heintz; Els Witteveen; Yolanda van der Graaf
Journal:  Gynecol Oncol       Date:  2007-04-12       Impact factor: 5.482

4.  Surgery by consultant gynecologic oncologists improves survival in patients with ovarian carcinoma.

Authors:  Mirjam J A Engelen; Henrike E Kos; Pax H B Willemse; Jan G Aalders; Elisabeth G E de Vries; Michael Schaapveld; Renee Otter; Ate G J van der Zee
Journal:  Cancer       Date:  2006-02-01       Impact factor: 6.860

5.  The implications of age and comorbidity on survival following epithelial ovarian cancer: summary and results from a Centers for Disease Control and Prevention study.

Authors:  Cynthia D O'Malley; Sarah J Shema; Rosemary D Cress; Katrina Bauer; Amy R Kahn; Maria J Schymura; Jennifer M Wike; Sherri L Stewart
Journal:  J Womens Health (Larchmt)       Date:  2012-07-20       Impact factor: 2.681

6.  Identifying specific chemotherapeutic agents in Medicare data: a validation study.

Authors:  Jennifer L Lund; Til Stürmer; Linda C Harlan; Hanna K Sanoff; Robert S Sandler; Maurice Alan Brookhart; Joan L Warren
Journal:  Med Care       Date:  2013-05       Impact factor: 2.983

7.  Impact of surgeon and hospital ovarian cancer surgical case volume on in-hospital mortality and related short-term outcomes.

Authors:  Robert E Bristow; Marianna L Zahurak; Teresa P Diaz-Montes; Robert L Giuntoli; Deborah K Armstrong
Journal:  Gynecol Oncol       Date:  2009-09-18       Impact factor: 5.482

8.  Predictors of comprehensive surgical treatment in patients with ovarian cancer.

Authors:  Barbara A Goff; Barbara J Matthews; Eric H Larson; C Holly A Andrilla; Michelle Wynn; Denise M Lishner; Laura-Mae Baldwin
Journal:  Cancer       Date:  2007-05-15       Impact factor: 6.860

9.  The benefits of a gynecologic oncologist: a pattern of care study for endometrial cancer treatment.

Authors:  P Y Roland; F J Kelly; C Y Kulwicki; P Blitzer; M Curcio; J W Orr
Journal:  Gynecol Oncol       Date:  2004-04       Impact factor: 5.482

10.  Quality of care in advanced ovarian cancer: the importance of provider specialty.

Authors:  Cheryl Mercado; David Zingmond; Beth Y Karlan; Evan Sekaris; Jenny Gross; Melinda Maggard-Gibbons; James S Tomlinson; Clifford Y Ko
Journal:  Gynecol Oncol       Date:  2010-01-27       Impact factor: 5.482

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  8 in total

1.  Sensitivity of Medicare Data to Identify Oncologists.

Authors:  Joan L Warren; Michael J Barrett; Dolly P White; Robert Banks; Susannah Cafardi; Lindsey Enewold
Journal:  J Natl Cancer Inst Monogr       Date:  2020-05-01

2.  Analytical Validation of a Deep Neural Network Algorithm for the Detection of Ovarian Cancer.

Authors:  Gerard Reilly; Rowan G Bullock; Jessica Greenwood; Daniel R Ure; Erin Stewart; Pierre Davidoff; Justin DeGrazia; Herbert Fritsche; Charles J Dunton; Nitin Bhardwaj; Lesley E Northrop
Journal:  JCO Clin Cancer Inform       Date:  2022-06

3.  Pelvic Inflammatory Disease: Possible Catches and Correct Management in Young Women.

Authors:  Chiara Di Tucci; Daniele Di Mascio; Michele Carlo Schiavi; Giorgia Perniola; Ludovico Muzii; Pierluigi Benedetti Panici
Journal:  Case Rep Obstet Gynecol       Date:  2018-07-11

4.  Malignancy Assessment Using Gene Identification in Captured Cells Algorithm for the Prediction of Malignancy in Women With a Pelvic Mass.

Authors:  Richard George Moore; Negar Khazan; Madeline Ann Coulter; Rakesh Singh; Michael Craig Miller; Umayal Sivagnanalingam; Brent DuBeshter; Cynthia Angel; Cici Liu; Kelly Seto; David Englert; Philip Meachem; Kyu Kwang Kim
Journal:  Obstet Gynecol       Date:  2022-09-08       Impact factor: 7.623

5.  Clinical Performance of a Multivariate Index Assay in Detecting Early-Stage Ovarian Cancer in Filipino Women.

Authors:  Clarissa L Velayo; Kareen N Reforma; Renee Vina G Sicam; Michele H Diwa; Alvin Duke R Sy; Ourlad Alzeus G Tantengco
Journal:  Int J Environ Res Public Health       Date:  2022-08-11       Impact factor: 4.614

Review 6.  Improving the quality of care for patients with advanced epithelial ovarian cancer: Program components, implementation barriers, and recommendations.

Authors:  Sarah M Temkin; Matthew P Smeltzer; Monique D Dawkins; Leigh M Boehmer; Leigha Senter; Destin R Black; Stephanie V Blank; Anna Yemelyanova; Anthony M Magliocco; Mollie A Finkel; Tracy E Moore; Premal H Thaker
Journal:  Cancer       Date:  2021-11-17       Impact factor: 6.921

7.  Increased expression of MMP17 predicts poor clinical outcomes in epithelial ovarian cancer patients.

Authors:  Chao Xiao; Yao Wang; Qijun Cheng; Yuchao Fan
Journal:  Medicine (Baltimore)       Date:  2022-08-26       Impact factor: 1.817

8.  Impact of Surgeon Type and Rurality on Treatment and Survival of Ovarian Cancer Patients.

Authors:  Kristin S Weeks; Charles F Lynch; Michele M West; Ryan M Carnahan; Michael A O'Rorke; Jacob J Oleson; Megan E McDonald; Mary E Charlton
Journal:  Am J Clin Oncol       Date:  2021-10-01       Impact factor: 2.787

  8 in total

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