Abstract
Objective
To examine changes over time in surgeon and hospital procedural volume for hysterectomy for endometrial cancer, and explore the association between changes in volume and perioperative outcomes.
Methods
We used the Statewide Planning and Research Cooperative System (SPARCS) database to analyze women who underwent abdominal or minimally invasive hysterectomy from 2000 to 2014. Annualized surgeon and hospital volume were estimated. The association between surgeon and hospital volume and perioperative morbidity, mortality, and resource utilization (transfusion, length of stay, hospital charges) were estimated by modeling procedural volume as a continuous and categorical variable.
Results
A total of 44,558 women treated at 218 hospitals were identified. The number of surgeons performing cases each year decreased from 845 surgeons with 2595 patients (mean cases=3) in 2000, to 317 surgeons who operated on 3119 patients (mean cases=10) (P<0.001) in 2014, while the mean hospital volume rose from 14 cases to 32 cases over the same time period (P=0.29). When stratified by surgeon volume quartiles, the morbidity rate was 14.6% among the lowest volume surgeons, 20.8% for medium low, 15.7% for medium-high and 14.1% for high-volume surgeons (P<0.001). In multivariable models where volume was modeled as a continuous variable, there was no association between surgeon volume and the rate of complications, while excessive total charges were lowest and perioperative mortality highest for the high-volume surgeons (P<0.001 for both).
Conclusion
Care of women with endometrial cancer has been concentrated to a smaller number of surgeons and hospitals. The association between surgeon and hospital volume for endometrial cancer is complex with an increased risk of adverse outcomes among medium volume hospitals and surgeons but the lowest complication rates for the highest volume surgeons and centers.
Introduction
Over the past 2 decades the association between procedural volume and surgical outcomes has been studied extensively.1,2 For many surgical interventions, outcomes are superior when the procedure is performed by high-volume surgeons and at high-volume centers.1,2 The improved outcomes achieved by high-volume health care providers are likely due to a multitude of factors including greater technical skill, adherence to guideline-based treatment recommendations, and superior management of complications.3-5
The association between procedural volume and outcomes is most pronounced for high-risk oncologic and cardiovascular surgeries.1,2,5 For endometrial cancer, there appears to be an association between hysterectomy volume and outcomes, although the magnitude of this association may be lower than for other higher risk procedures.6,7 In one report, perioperative surgical and medical complications were approximately 40% lower among women with endometrial cancer who underwent abdominal hysterectomy when the procedure was performed by a high-volume surgeon.7 These findings have contributed, at least in part, to the increased referral of women with uterine cancer to gynecologic oncologists.8
Despite the association between surgical volume and outcomes for endometrial cancer, little is known about how these data have impacted contemporary referral patterns and patient outcomes. We performed a population-based analysis to first examine changes over time in surgeon and hospital procedural volume for endometrial cancer, and second, to explore the association between these changes in volume and perioperative morbidity, mortality, and resource utilization.
Materials and Methods
We used the Statewide Planning and Research Cooperative System (SPARCS) for the analysis. SPARCS is an all-payer reporting system established by the New York State Department of Health.9-12 The database captures patient characteristics, diagnoses, services, and charges for hospital inpatient admissions and outpatient visits. Data quality is ensured by conducting periodic reviews and by comparing SPARCS data with other Department of Health databases.
We identified women diagnosed with uterine cancer (ICD-9 179, 182.x) who underwent hysterectomy from 2000-2014. The sample was limited to those women who either had a minimally invasive (laparoscopic or robotic-assisted, ICD-9 68.31, 68.41, 68.51, 68.61, 68.71, 17.4x; CPT 58541, 58542, 58543, 58544, 58548, 58550, 58552, 58553, 58554, 58570, 58571, 58572, 58573, S2900) or abdominal hysterectomy (ICD-9 68.3, 68.39, 68.4, 68.49, 68.6, 68.69, 68.9; CPT 58150, 58152, 58180, 58200, 58210, 58240, 58950, 58951, 58953, 58956, 58594). Women who had hysterectomy with coding before the hospital admission and those with missing or invalid surgeon identifiers were excluded (Figure 1).
Figure 1.
Flowchart of cohort selection.
Demographic characteristics included year of admission, age (<40, 40-49, 50-59, 60-69, ≥70 years), race/ethnicity (white, black, Hispanic, other), and insurance status (none, private, Medicare, Medicaid, other). Comorbidity was measured by calculating the Elixhauser comorbidity score and categorized as 0, 1, or ≥2 conditions.13 For risk-adjustment of operative morbidity, the performance of the following concomitant procedures were recorded: pelvic exenteration, omentectomy, debulking, lymphadenectomy (LND), small bowel resection, colectomy, rectosigmoid resection, hepatectomy, bladder resection, diaphragm resection, splenectomy, anterior colporrhaphy, posterior colporrhaphy, incontinence repair, oophorectomy, and colpopexy.
We recorded the surgeon and hospital of record for each patient. For each hospital and surgeon, we calculated the mean annual volume as the total number of procedures the surgeon or hospital performed divided by the number of years in which the surgeon or hospital contributed at least one procedure.7,14 The association between volume and outcomes was explored using volume as a continuous variable as well as through classification of volume into quartiles.15 For surgeon and hospital volume, quartiles with approximately equal numbers of patients were defined by visually inspecting the data.
The outcomes of the analysis included perioperative morbidity, in-hospital mortality, and resource utilization. Morbidity was defined as the occurrence of an intraoperative, surgical site, or medical complication as previously described.16 A composite of all-cause morbidity as well as analyses of each individual group of complications were performed. As surrogates for resource utilization, we recorded transfusion, hospital length of stay and hospital charges. Prolonged length of stay (LOS) was defined as a LOS above the 75th percentile for each type of hysterectomy.9 For each patient, total charges were recorded, adjusted for inflation, and reported in 2014 dollars. Excessive total charges were defined as having the inflation-adjusted total charge ranked above the 75th percentile for each procedure type.9
The number of patients, surgeons, and hospitals over time was reported and compared using Spearman's rank correlation. The mean and standard deviation of the surgeon and hospital volume were reported by year and the trend was tested using ANOVA. Patient demographics, concomitant procedures, and outcomes were reported as frequencies and compared across the quartiles of surgeon volume and hospital volume using χ2 tests. The median, interquartile range and range of the volume were also reported for each quartile and compared using Kruskal-Wallis tests.
A mixed-effects log-Poisson model was fit to determine predictors of treatment by the highest volume (by quartile) surgeons. The model included hospital volume as a linear term, age, year, race, insurance status, comorbidity, route of hysterectomy, concomitant lymphadenectomy and omentectomy. Hospital identifiers were included as a random intercept to account for clustering within hospitals. To examine the association between surgeon and hospital volume and each outcome, we fit similar models including surgeon and hospital volume as linear terms, while adjusting for the other covariates described. Due to the presence of clustering within surgeons and within hospitals, random-intercept terms in the regression models were included as nested random intercepts. All analyses were performed with SAS version 9.4 (SAS Institute Inc, Cary, North Carolina). All statistical tests were two-sided. A P-value of <0.05 was considered statistically significant.
Results
We identified a total of 44,558 that underwent hysterectomy for endometrial cancer treated at 218 hospitals in New York. The number of surgeons performing cases each year decreased from 845 surgeons with 2595 patients in 2000, to 317 surgeons who operated on 3119 patients in 2014 (Figure 2A) (P<0.001). The mean surgeon case volume increased from 3 (SD=6) cases in 2000 to 10 (SD=16) cases by 2014 (P<0.001). Similarly, the number of hospitals in which hysterectomies were performed decreased from 182 centers in 2000 to 98 hospitals in 2014 (Figure 2B) (P<0.001). Mean hospital volume rose from 14 cases to 32 cases over the same time period (P=0.29).
Figure 2.
Number of patients, surgeons, and hospital by year. Number of patents and surgeons by year (A); number of patients and hospitals by year (B).
The median procedural volume of physicians in the lowest volume quartile was 2 (IQR, 1-2) per year and increased to 50 (IQR, 46-57) per year in the highest quartile group (Table 1). Similarly, median procedural volume of the lowest volume hospitals was 9 (IQR, 5-18) and rose to 153 (IQR, 127-242) at the highest volume centers (Table 2). Performance of minimally invasive hysterectomy was more common in women treated by high-volume surgeons (P<0.001). High volume surgeons were more likely to operate at high volume hospitals. For example, 5.5% of the operations performed by low volume surgeons were at high volume hospitals, while 60.9% of the procedures performed by high volume surgeons were undertaken at high volume hospitals (P<0.001). In a multivariate model, later year of surgery and the performance of concomitant procedures were associated with treatment by a high-volume surgeon (Table 3).
Table 1.
Demographic characteristics of women with endometrial cancer who underwent hysterectomy stratified by surgeon volume quartiles.
| Annualized surgeon volume | |||||||||
|---|---|---|---|---|---|---|---|---|---|
|
|
|||||||||
| Low | Medium Low | Medium High | High | ||||||
| N | (%) | N | (%) | N | (%) | N | (%) | P-value | |
| Number. of patients | 11,148 | (25.0) | 11,252 | (25.3) | 11,332 | (25.4) | 10,826 | (24.3) | |
| Number of surgeons | 2,610 | (94.9) | 88 | (3.2) | 35 | (1.3) | 16 | (0.6) | |
| Annualized surgeon volume | |||||||||
| Median (IQR) | 2 | (1-2) | 17 | (12-19) | 29 | (26-35) | 50 | (46-57) | <0.001 |
| Range | 1-7 | 7-23 | 24-42 | 42-64 | |||||
| Hysterectomy | <0.001 | ||||||||
| Abdominal | 9,476 | (85.0) | 8,160 | (72.5) | 6,276 | (55.4) | 5,329 | (49.2) | |
| Robotic/Laparoscopic | 1,672 | (15.0) | 3,092 | (27.5) | 5,056 | (44.6) | 5,497 | (50.8) | |
| Age (years) | <0.001 | ||||||||
| <40 | 293 | (2.6) | 309 | (2.7) | 283 | (2.5) | 236 | (2.2) | |
| 40-49 | 1,333 | (12.0) | 944 | (8.4) | 954 | (8.4) | 863 | (8.0) | |
| 50-59 | 3,314 | (29.7) | 3,039 | (27.0) | 3,060 | (27.0) | 2,980 | (27.5) | |
| 60-69 | 3,211 | (28.8) | 3,795 | (33.7) | 3,813 | (33.6) | 3,459 | (32.0) | |
| ≥70 | 2,997 | (26.9) | 3,165 | (28.1) | 3,222 | (28.4) | 3,288 | (30.4) | |
| Year | <0.001 | ||||||||
| 2000 | 1,244 | (11.2) | 529 | (4.7) | 464 | (4.1) | 358 | (3.3) | |
| 2001 | 1,230 | (11.0) | 639 | (5.7) | 523 | (4.6) | 410 | (3.8) | |
| 2002 | 1,107 | (9.9) | 588 | (5.2) | 551 | (4.9) | 432 | (4.0) | |
| 2003 | 1,051 | (9.4) | 671 | (6.0) | 539 | (4.8) | 517 | (4.8) | |
| 2004 | 1,003 | (9.0) | 683 | (6.1) | 554 | (4.9) | 599 | (5.5) | |
| 2005 | 898 | (8.1) | 725 | (6.4) | 611 | (5.4) | 732 | (6.8) | |
| 2006 | 804 | (7.2) | 721 | (6.4) | 616 | (5.4) | 724 | (6.7) | |
| 2007 | 675 | (6.1) | 740 | (6.6) | 670 | (5.9) | 803 | (7.4) | |
| 2008 | 648 | (5.8) | 846 | (7.5) | 799 | (7.1) | 848 | (7.8) | |
| 2009 | 519 | (4.7) | 732 | (6.5) | 815 | (7.2) | 892 | (8.2) | |
| 2010 | 505 | (4.5) | 828 | (7.4) | 871 | (7.7) | 961 | (8.9) | |
| 2011 | 393 | (3.5) | 829 | (7.4) | 1,022 | (9.0) | 930 | (8.6) | |
| 2012 | 406 | (3.6) | 852 | (7.6) | 1,028 | (9.1) | 930 | (8.6) | |
| 2013 | 344 | (3.1) | 970 | (8.6) | 1,163 | (10.3) | 897 | (8.3) | |
| 2014 | 321 | (2.9) | 899 | (8.0) | 1,106 | (9.8) | 793 | (7.3) | |
| Race | <0.001 | ||||||||
| White | 8,287 | (74.3) | 6,276 | (55.8) | 7,444 | (65.7) | 8,641 | (79.8) | |
| Black | 990 | (8.9) | 1,957 | (17.4) | 1,547 | (13.7) | 499 | (4.6) | |
| Hispanic | 662 | (5.9) | 1,048 | (9.3) | 718 | (6.3) | 395 | (3.6) | |
| Other | 752 | (6.7) | 1,627 | (14.5) | 1,287 | (11.4) | 656 | (6.1) | |
| Unknown | 457 | (4.1) | 344 | (3.1) | 336 | (3.0) | 635 | (5.9) | |
| Insurance status | <0.001 | ||||||||
| None | 237 | (2.1) | 291 | (2.6) | * | * | 172 | (1.6) | |
| Private | 6,586 | (59.1) | 6,036 | (53.6) | 6,870 | (60.6) | 6,035 | (55.7) | |
| Medicare | 3,695 | (33.1) | 3,969 | (35.3) | 3,742 | (33.0) | 4,094 | (37.8) | |
| Medicaid | 548 | (4.9) | 925 | (8.2) | 427 | (3.8) | 349 | (3.2) | |
| Other | 12 | (0.1) | * | * | * | * | 12 | (0.1) | |
| Unknown | 70 | (0.6) | * | * | 182 | (1.6) | 164 | (1.5) | |
| Comorbidity | <0.001 | ||||||||
| 0 | 2,909 | (26.1) | 2,453 | (21.8) | 2,251 | (19.9) | 2,033 | (18.8) | |
| 1 | 3,122 | (28.0) | 2,956 | (26.3) | 3,070 | (27.1) | 2,863 | (26.4) | |
| ≥2 | 5,117 | (45.9) | 5,843 | (51.9) | 6,011 | (53.0) | 5,930 | (54.8) | |
| Annualized hospital volume | <0.001 | ||||||||
| Low | 6,003 | (53.8) | 3,184 | (28.3) | 815 | (7.2) | 1,083 | (10.0) | |
| Medium Low | 2,628 | (23.6) | 3,371 | (30.0) | 2,832 | (25.0) | 2,256 | (20.8) | |
| Medium High | 1,907 | (17.1) | 4,114 | (36.6) | 3,808 | (33.6) | 890 | (8.2) | |
| High | 610 | (5.5) | 583 | (5.2) | 3,877 | (34.2) | 6,597 | (60.9) | |
| Other procedures | |||||||||
| Exenteration | * | * | * | * | * | * | * | * | 0.003 |
| Omentectomy | 1,247 | (11.2) | 2,592 | (23.0) | 2,521 | (22.2) | 1,767 | (16.3) | <0.001 |
| Debulk | 16 | (0.1) | 39 | (0.3) | 30 | (0.3) | 36 | (0.3) | 0.01 |
| LND | 4,638 | (41.6) | 7,407 | (65.8) | 8,114 | (71.6) | 7,854 | (72.5) | <0.001 |
| Small bowel resection | 75 | (0.7) | 109 | (1.0) | 65 | (0.6) | 85 | (0.8) | 0.005 |
| Colon resection | 96 | (0.9) | 82 | (0.7) | 95 | (0.8) | 77 | (0.7) | 0.48 |
| Rectosigmoid resection | 97 | (0.9) | 84 | (0.7) | 87 | (0.8) | 70 | (0.6) | 0.30 |
| Liver resection | * | * | 14 | (0.1) | 15 | (0.1) | * | * | 0.09 |
| Bladder resection | * | * | * | * | * | * | * | * | 0.52 |
| Diaphragm resection | * | * | 26 | (0.2) | 54 | (0.5) | 19 | (0.2) | <0.001 |
| Splenectomy | * | * | 20 | (0.2) | 17 | (0.2) | 16 | (0.1) | 0.06 |
| Anterior repair | 71 | (0.6) | 34 | (0.3) | 65 | (0.6) | 89 | (0.8) | <0.001 |
| Posterior repair | 62 | (0.6) | 25 | (0.2) | 42 | (0.4) | 15 | (0.1) | <0.001 |
| Incontinence repair | 130 | (1.2) | 61 | (0.5) | 82 | (0.7) | 86 | (0.8) | <0.001 |
| Oophorectomy | 10,607 | (95.1) | 10,909 | (97.0) | 10,992 | (97.0) | 10,585 | (97.8) | <0.001 |
| Colpopexy | 197 | (1.8) | 71 | (0.6) | 420 | (3.7) | 65 | (0.6) | <0.001 |
Quartiles of annualized surgeon and hospital volume were calculated at patient-level.
ome cell size of 10 or less.
Table 2.
Demographic characteristics of the cohort stratified by hospital volume.
| Annualized hospital volume | ||||||||||
|---|---|---|---|---|---|---|---|---|---|---|
|
|
||||||||||
| Low | Medium Low | Medium High | High | |||||||
| N | (%) | N | (%) | N | (%) | N | (%) | P-value | ||
| Number of patients | 11,085 | (24.9) | 11,087 | (24.9) | 10,719 | (24.1) | 11,667 | (26.2) | ||
| Number of hospitals | 184 | (84.4) | 20 | (9.2) | * | * | * | * | ||
| Annualized hospital volume | ||||||||||
| Median (IQR) | 9 | (5-18) | 41 | (32-48) | 82 | (67-98) | 153 | (127-242) | <0.001 | |
| Range | 1-23 | 23-55 | 57-109 | 127-242 | ||||||
| Hysterectomy | <0.001 | |||||||||
| Abdominal | 8,953 | (80.8) | 7,627 | (68.8) | 7,184 | (67.0) | 5,477 | (46.9) | ||
| Robotic/Laparoscopic | 2,132 | (19.2) | 3,460 | (31.2) | 3,535 | (33.0) | 6,190 | (53.1) | ||
| Age (years) | <0.001 | |||||||||
| <40 | 321 | (2.9) | 217 | (2.0) | 279 | (2.6) | 304 | (2.6) | ||
| 40-49 | 1,198 | (10.8) | 923 | (8.3) | 979 | (9.1) | 994 | (8.5) | ||
| 50-59 | 3,072 | (27.7) | 2,999 | (27.0) | 3,042 | (28.4) | 3,280 | (28.1) | ||
| 60-69 | 3,421 | (30.9) | 3,595 | (32.4) | 3,419 | (31.9) | 3,843 | (32.9) | ||
| ≥70 | 3,073 | (27.7) | 3,353 | (30.2) | 3,000 | (28.0) | 3,246 | (27.8) | ||
| Year | <0.001 | |||||||||
| 2000 | 1,026 | (9.3) | 599 | (5.4) | 595 | (5.6) | 375 | (3.2) | ||
| 2001 | 936 | (8.4) | 753 | (6.8) | 658 | (6.1) | 455 | (3.9) | ||
| 2002 | 859 | (7.7) | 706 | (6.4) | 644 | (6.0) | 469 | (4.0) | ||
| 2003 | 884 | (8.0) | 693 | (6.3) | 650 | (6.1) | 551 | (4.7) | ||
| 2004 | 856 | (7.7) | 716 | (6.5) | 679 | (6.3) | 588 | (5.0) | ||
| 2005 | 787 | (7.1) | 759 | (6.8) | 792 | (7.4) | 628 | (5.4) | ||
| 2006 | 780 | (7.0) | 749 | (6.8) | 654 | (6.1) | 682 | (5.8) | ||
| 2007 | 733 | (6.6) | 743 | (6.7) | 684 | (6.4) | 728 | (6.2) | ||
| 2008 | 763 | (6.9) | 784 | (7.1) | 747 | (7.0) | 847 | (7.3) | ||
| 2009 | 620 | (5.6) | 670 | (6.0) | 686 | (6.4) | 982 | (8.4) | ||
| 2010 | 677 | (6.1) | 775 | (7.0) | 743 | (6.9) | 970 | (8.3) | ||
| 2011 | 573 | (5.2) | 764 | (6.9) | 793 | (7.4) | 1,044 | (8.9) | ||
| 2012 | 560 | (5.1) | 771 | (7.0) | 767 | (7.2) | 1,118 | (9.6) | ||
| 2013 | 578 | (5.2) | 817 | (7.4) | 818 | (7.6) | 1,161 | (10.0) | ||
| 2014 | 453 | (4.1) | 788 | (7.1) | 809 | (7.5) | 1,069 | (9.2) | ||
| Race | <0.001 | |||||||||
| White | 6,744 | (60.8) | 7,480 | (67.5) | 7,548 | (70.4) | 8,876 | (76.1) | ||
| Black | 1,788 | (16.1) | 1,421 | (12.8) | 1,151 | (10.7) | 633 | (5.4) | ||
| Hispanic | 1,012 | (9.1) | 632 | (5.7) | 647 | (6.0) | 532 | (4.6) | ||
| Other | 1,271 | (11.5) | 1,161 | (10.5) | 974 | (9.1) | 916 | (7.9) | ||
| Unknown | 270 | (2.4) | 393 | (3.5) | 399 | (3.7) | 710 | (6.1) | ||
| Insurance status | <0.001 | |||||||||
| None | 437 | (3.9) | 162 | (1.5) | 113 | (1.1) | 98 | (0.8) | ||
| Private | 5,719 | (51.6) | 6,074 | (54.8) | 6,974 | (65.1) | 6,760 | (57.9) | ||
| Medicare | 3,774 | (34.0) | 4,238 | (38.2) | 3,336 | (31.1) | 4,152 | (35.6) | ||
| Medicaid | 1,080 | (9.7) | 566 | (5.1) | 278 | (2.6) | 325 | (2.8) | ||
| Other | 18 | (0.2) | * | * | * | * | * | * | ||
| Unknown | 57 | (0.5) | 42 | (0.4) | 14 | (0.1) | 328 | (2.8) | ||
| Comorbidity | <0.001 | |||||||||
| 0 | 2,755 | (24.9) | 2,368 | (21.4) | 2,283 | (21.3) | 2,240 | (19.2) | ||
| 1 | 2,990 | (27.0) | 2,964 | (26.7) | 2,860 | (26.7) | 3,197 | (27.4) | ||
| ≥2 | 5,340 | (48.2) | 5,755 | (51.9) | 5,576 | (52.0) | 6,230 | (53.4) | ||
| Annualized surgeon volume | ||||||||||
| Low | 6,003 | (54.2) | 2,628 | (23.7) | 1,907 | (17.8) | 610 | (5.2) | ||
| Medium Low | 3,184 | (28.7) | 3,371 | (30.4) | 4,114 | (38.4) | 583 | (5.0) | ||
| Medium High | 815 | (7.4) | 2,832 | (25.5) | 3,808 | (35.5) | 3,877 | (33.2) | ||
| High | 1,083 | (9.8) | 2,256 | (20.3) | 890 | (8.3) | 6,597 | (56.5) | ||
| Other procedures | ||||||||||
| Exenteration | * | * | * | * | * | * | * | * | 0.21 | |
| Omentectomy | 1,637 | (14.8) | 2,419 | (21.8) | 2,047 | (19.1) | 2,024 | (17.3) | <0.001 | |
| Debulk | 24 | (0.2) | 37 | (0.3) | 28 | (0.3) | 32 | (0.3) | 0.41 | |
| LND | 5,563 | (50.2) | 6,893 | (62.2) | 6,955 | (64.9) | 8,602 | (73.7) | <0.001 | |
| Small bowel resection | 77 | (0.7) | 96 | (0.9) | 82 | (0.8) | 79 | (0.7) | 0.34 | |
| Colon resection | 62 | (0.6) | 85 | (0.8) | 96 | (0.9) | 107 | (0.9) | 0.01 | |
| Rectosigmoid resection | 69 | (0.6) | 85 | (0.8) | 67 | (0.6) | 117 | (1.0) | 0.002 | |
| Liver resection | * | * | * | * | 14 | (0.1) | 23 | (0.2) | <0.001 | |
| Bladder resection | 11 | (0.1) | * | * | * | * | * | * | 0.41 | |
| Diaphragm resection | * | * | 12 | (0.1) | 22 | (0.2) | 63 | (0.5) | <0.001 | |
| Splenectomy | * | * | * | * | 15 | (0.1) | 29 | (0.2) | <0.001 | |
| Anterior repair | 50 | (0.5) | 56 | (0.5) | 44 | (0.4) | 109 | (0.9) | <0.001 | |
| Posterior repair | 44 | (0.4) | 50 | (0.5) | 25 | (0.2) | 25 | (0.2) | 0.002 | |
| Incontinence repair | 105 | (0.9) | 80 | (0.7) | 57 | (0.5) | 117 | (1.0) | <0.001 | |
| Oophorectomy | 10,634 | (95.9) | 10,702 | (96.5) | 10,373 | (96.8) | 11,384 | (97.6) | <0.001 | |
| Colpopexy | 226 | (2.0) | 336 | (3.0) | 106 | (1.0) | 85 | (0.7) | <0.001 | |
Quartiles of annualized surgeon and hospital volume were calculated at patient-level.
Some cell size of 10 or less.
Table 3.
Predictors of the highest quartile of surgeon volume.
| aRR | |
|---|---|
| Hospital volume | |
| 1 case increase | 1.01 (0.997-1.02) |
| Age (years) | |
| <40 | Referent |
| 40-49 | 0.96 (0.83-1.11) |
| 50-59 | 1.01 (0.88-1.15) |
| 60-69 | 1.01 (0.89-1.16) |
| ≥70 | 1.04 (0.90-1.19) |
| Year of admission | |
| 2000 | Referent |
| 2001 | 1.02 (0.88-1.18) |
| 2002 | 1.12 (0.97-1.29) |
| 2003 | 1.23 (1.07-1.41)* |
| 2004 | 1.41 (1.23-1.62)* |
| 2005 | 1.52 (1.33-1.73)* |
| 2006 | 1.52 (1.33-1.73)* |
| 2007 | 1.51 (1.32-1.72)* |
| 2008 | 1.38 (1.21-1.57)* |
| 2009 | 1.40 (1.23-1.60)* |
| 2010 | 1.36 (1.20-1.56)* |
| 2011 | 1.27 (1.11-1.46)* |
| 2012 | 1.24 (1.08-1.42)* |
| 2013 | 1.14 (1.00-1.31)* |
| 2014 | 1.16 (1.01-1.33)* |
| Race | |
| White | Referent |
| Black | 0.95 (0.86-1.04) |
| Hispanic | 0.97 (0.87-1.08) |
| Other | 0.98 (0.91-1.07) |
| Unknown | 1.16 (1.05-1.27)* |
| Insurance status | |
| Private | Referent |
| Medicare | 1.04 (0.99-1.09) |
| Medicaid | 0.99 (0.88-1.10) |
| Other | 0.97 (0.55-1.72) |
| None | 1.06 (0.90-1.24) |
| Unknown | 0.49 (0.41-0.58)* |
| Comorbidity | |
| 0 | Referent |
| 1 | 1.03 (0.97-1.09) |
| ≥2 | 1.04 (0.99-1.10) |
| Lymphadenectomy | |
| No | Referent |
| Yes | 1.11 (1.06-1.16)* |
| Omentectomy | |
| No | Referent |
| Yes | 1.06 (1.01-1.13)* |
| Hysterectomy | |
| Abdominal | Referent |
| MIS | 1.19 (1.13-1.25)* |
aRR: adjusted risk ratio.
Mixed-effects log-Poisson models included hospital volume as a linear term, age, year, race, insurance status, comorbidity, route of hysterectomy, concomitant lymphadenectomy and omentectomy. Hospital identifiers were included as random interceptto account for hospital level of clustering.
P-value<0.05
When stratified by surgeon volume quartiles, the morbidity rate was 14.6% among the lowest-volume surgeons, 20.8% for medium low, 15.7% for medium high and 14.1% for high volume surgeons (P<0.001) (Tables 4 and 5). Similar trends were noted for surgical site and medical complications in which the complication rates rose slightly for the medium low and medium high-volume surgeons and then decreased for the highest-volume surgeons. The rates of prolonged length of stay and excessive total charges were lowest for the high-volume surgeons (P<0.001 for both). There was no statistically significant difference in the mortality rate across the quartiles.
Table 4.
Outcomes of the endometrial cancer patients who had hysterectomy stratified by surgeon and hospital volume.
| Low | Medium Low | Medium High | High | ||||||
|---|---|---|---|---|---|---|---|---|---|
|
|
|||||||||
| N | (%) | N | (%) | N | (%) | N | (%) | P-value | |
| Outcomes by surgeon volume | |||||||||
| Any morbidity | 1,628 | (14.6) | 2,344 | (20.8) | 1,781 | (15.7) | 1,530 | (14.1) | <0.001 |
| Intraoperative complications | 395 | (3.5) | 504 | (4.5) | 475 | (4.2) | 483 | (4.5) | 0.001 |
| Surgical site complications | 744 | (6.7) | 1,140 | (10.1) | 764 | (6.7) | 603 | (5.6) | <0.001 |
| Medical complications | 963 | (8.6) | 1,445 | (12.8) | 1,032 | (9.1) | 855 | (7.9) | <0.001 |
| Mortality | 67 | (0.6) | 81 | (0.7) | 51 | (0.5) | 64 | (0.6) | 0.07 |
| Transfusion | 1,746 | (15.7) | 2,656 | (23.6) | 1,680 | (14.8) | 1,940 | (17.9) | <0.001 |
| Prolonged LOS | 2,685 | (24.1) | 3,818 | (33.9) | 2,355 | (20.8) | 2,135 | (19.7) | <0.001 |
| Excessive total charges | 1,675 | (15.0) | 4,239 | (37.7) | 3,808 | (33.6) | 1,417 | (13.1) | <0.001 |
| Outcomes by hospital volume | |||||||||
| Any morbidity | 1,651 | (14.9) | 2,052 | (18.5) | 1,957 | (18.3) | 1,623 | (13.9) | <0.001 |
| Intraoperative complications | 398 | (3.6) | 495 | (4.5) | 379 | (3.5) | 585 | (5.0) | <0.001 |
| Surgical site complications | 803 | (7.2) | 910 | (8.2) | 921 | (8.6) | 617 | (5.3) | <0.001 |
| Medical complications | 920 | (8.3) | 1,304 | (11.8) | 1,228 | (11.5) | 843 | (7.2) | <0.001 |
| Mortality | 84 | (0.8) | 84 | (0.8) | 56 | (0.5) | 39 | (0.3) | <0.001 |
| Transfusion | 1,795 | (16.2) | 2,202 | (19.9) | 2,174 | (20.3) | 1,851 | (15.9) | <0.001 |
| Prolonged LOS | 2,991 | (27.0) | 3,154 | (28.4) | 2,601 | (24.3) | 2,247 | (19.3) | <0.001 |
| Excessive total charges | 1,721 | (15.5) | 3,072 | (27.7) | 3,562 | (33.2) | 2,784 | (23.9) | <0.001 |
Table 5. Pairwise comparison of outcomes by quartiles of surgeon and hospital volume with Bonferroni adjustment of P-values for multiple testing.
| Any morbidity | Intraoperative complications | Surgical site complications | Medical complications | Mortality | Transfusion | Prolonged LOS | Excessive total charges | |
|---|---|---|---|---|---|---|---|---|
| Surgeon volume | ||||||||
| Low vs. Medium Low | <0.001 | 0.002 | <0.001 | <0.001 | 1.00 | <0.001 | <0.001 | <0.001 |
| Low vs. Medium High | 0.12 | 0.07 | 1.00 | 1.00 | 0.70 | 0.49 | <0.001 | <0.001 |
| Low vs. High | 1.00 | 0.003 | 0.004 | 0.28 | 1.00 | <0.001 | <0.001 | <0.001 |
| Medium Low vs. Medium High | <0.001 | 1.00 | <0.001 | <0.001 | 0.05 | <0.001 | <0.001 | <0.001 |
| Medium Low vs. High | <0.001 | 1.00 | <0.001 | <0.001 | 1.00 | <0.001 | <0.001 | <0.001 |
| Medium High vs. High | 0.01 | 1.00 | 0.002 | 0.01 | 0.86 | <0.001 | 0.30 | <0.001 |
| Hospital volume | ||||||||
| Low vs. Medium Low | <0.001 | 0.01 | 0.04 | <0.001 | 1.00 | <0.001 | 0.09 | <0.001 |
| Low vs. Medium High | <0.001 | 1.00 | 0.001 | <0.001 | 0.18 | <0.001 | <0.001 | <0.001 |
| Low vs. High | 0.21 | <0.001 | <0.001 | 0.01 | <0.001 | 1.00 | <0.001 | <0.001 |
| Medium Low vs. Medium High | 1.00 | 0.003 | 1.00 | 1.00 | 0.18 | 1.00 | <0.001 | <0.001 |
| Medium Low vs. High | <0.001 | 0.31 | <0.001 | <0.001 | <0.001 | <0.001 | <0.001 | <0.001 |
| Medium High vs. High | <0.001 | <0.001 | <0.001 | <0.001 | 0.18 | <0.001 | <0.001 | <0.001 |
When surgeon volume was analyzed as a continuous variable, there was no association between volume and overall morbidity, any of the specific morbidity subgroups, transfusion, or prolonged length of stay (P>0.05 for all) (Table 6). The rate of excessive charges decreased with increasing surgical volume while the mortality rate increased.
Table 6.
Predictors of morbidity, mortality and resource utilization stratified by surgeon and hospital volume.
| Increase of 1 case | Increase of 5 cases | Increase of 10 cases | Increase of 15 cases | Increase of 20 cases | |
|---|---|---|---|---|---|
| Surgeon volume | |||||
| Any morbidity | 1.001 (0.999-1.003) | 1.00 (0.99-1.02) | 1.01 (0.99-1.03) | 1.01 (0.98-1.05) | 1.02 (0.97-1.06) |
| Intraoperative complications | 1.001 (0.997-1.005) | 1.01 (0.98-1.03) | 1.01 (0.97-1.05) | 1.02 (0.96-1.08) | 1.02 (0.94-1.11) |
| Surgical site complications | 1.000 (0.997-1.003) | 1.00 (0.99-1.02) | 1.00 (0.97-1.03) | 1.00 (0.96-1.05) | 1.00 (0.94-1.07) |
| Medical complications | 1.000 (0.997-1.002) | 1.00 (0.98-1.01) | 1.00 (0.97-1.02) | 0.99 (0.95-1.04) | 0.99 (0.94-1.05) |
| Mortality+ | 1.01 (1.003-1.02)* | 1.05 (1.02-1.09)* | 1.11 (1.03-1.19)* | 1.17 (1.05-1.31)* | 1.24 (1.07-1.43)* |
| Transfusion | 1.001 (0.998-1.003) | 1.00 (0.99-1.02) | 1.01 (0.98-1.03) | 1.01 (0.98-1.05) | 1.02 (0.97-1.07) |
| Prolonged LOS | 1.001 (0.999-1.003) | 1.01 (0.997-1.02) | 1.01 (0.99-1.03) | 1.02 (0.99-1.05) | 1.03 (0.99-1.07) |
| Excessive total charges | 0.997 (0.994-0.999)* | 0.98 (0.97-0.997)* | 0.97 (0.94-0.99)* | 0.95 (0.91-0.99)* | 0.93 (0.88-0.99)* |
| Hospital volume | |||||
| Any morbidity | 1.002 (1.001-1.004)* | 1.01 (1.004-1.02)* | 1.02 (1.01-1.04)* | 1.04 (1.01-1.06)* | 1.05 (1.02-1.08)* |
| Intraoperative complications | 1.002 (1.000-1.004)* | 1.01 (1.001-1.02)* | 1.02 (1.002-1.04)* | 1.03 (1.004-1.06)* | 1.04 (1.005-1.08)* |
| Surgical site complications | 1.001 (1.000-1.003) | 1.01 (0.998-1.02) | 1.01 (0.997-1.03) | 1.02 (0.99-1.05) | 1.03 (0.99-1.07) |
| Medical complications | 1.003 (1.001-1.005)* | 1.01 (1.004-1.03)* | 1.03 (1.01-1.05)* | 1.04 (1.01-1.08)* | 1.06 (1.02-1.10)* |
| Mortality+ | 0.996 (0.993-0.999)* | 0.98 (0.97-0.99)* | 0.96 (0.94-0.99)* | 0.94 (0.91-0.98)* | 0.92 (0.88-0.97)* |
| Transfusion | 1.003 (1.001-1.005)* | 1.01 (1.003-1.03)* | 1.03 (1.01-1.05)* | 1.05 (1.01-1.08)* | 1.06 (1.01-1.11)* |
| Prolonged LOS | 1.002 (1.000-1.004) | 1.01 (0.998-1.02) | 1.02 (0.997-1.04) | 1.03 (0.995-1.06) | 1.03 (0.99-1.08) |
| Excessive total charges | 1.006 (1.002-1.010)* | 1.03 (1.01-1.05)* | 1.06 (1.02-1.11)* | 1.09 (1.02-1.16)* | 1.12 (1.03-1.22)* |
aRR: adjusted risk ratio.
Mixed-effects log-Poisson models included surgeon and hospital volume as linear terms, age, year, race, insurance status, comorbidity, route of hysterectomy, concomitant lymphadenectomy and omentectomy. Surgeon and hospital identifiers were included as nested random intercepts to account for surgeon and hospital level of clustering for the outcomes of intraoperative, surgical site, and medical complications, any morbidity, transfusion, excessive LOS and total charges.
The model for mortality did not account for hospital- and surgeon-level clustering because of convergence issue.
P-value<0.05
When hospital volume quartiles were analyzed, morbidity increased at medium low and medium high-volume hospitals and then declined to the lowest rates in the highest volume hospitals (Table 4). Increased hospital volume was associated with lower rates of surgical site and medical complications, mortality, transfusion, and prolonged length of stay (Table 4). The rate of excessive charges was 15.5% in low volume hospitals, rose in the medium volume quartiles, and then decreased to 23.9% in the high-volume quartile (P<0.001). When hospital volume was analyzed as a continuous variable, increased volume was associated with a higher morbidity rate, increased transfusion and excessive charges but lower mortality (Table 5).
Discussion
These data suggest that practice patterns for endometrial cancer have gradually shifted over the last two decades with a concentration of patients to a smaller number of surgeons and hospitals. The association between surgeon and hospital volumes for endometrial cancer is complex, with an increased risk of adverse outcomes among medium volume hospitals and surgeons but superior outcomes for the highest volume surgeons and centers. However, the differences in outcomes based on volume are modest compared to other higher risk surgical procedures.
The increased national focus on surgical volume has led to changes in practice patterns for some procedures in which outcomes are strongly associated with procedural volume.17-19 In one report of patients undergoing cardiovascular and cancer surgery from 1999 to 2008, Birkmeyer and colleagues noted that the median hospital volume for all four cancer operations studied increased substantially over time. The increased volume was due to a combination of market concentration of procedures to a smaller number of hospitals as well as an increase in the overall number of cases for some procedures. Importantly, increased hospital volume was associated with decreased mortality. For pancreatectomy, operative mortality declined by 19% and increased hospital volume accounted for two thirds of this reduction in mortality.17
For women with endometrial cancer undergoing hysterectomy, we noted similar trends with an increase in mean surgeon and hospital volume over time. While there was a small increase in the overall number of patients treated per year, market concentration to a smaller number of gynecologic surgeons and hospitals had a greater effect on increasing the mean procedural volumes. Concentration of endometrial cancer to a smaller number of surgeons and centers is likely driven by multiple factors. The increased complexity of treatment for endometrial cancer has resulted in fewer general gynecologists treating these women and, professional societies now recommend referral of women with endometrial cancer to gynecologic oncology subspecialists.20 In one sample of women who underwent hysterectomy in 2014-2015, gynecologic oncologists rendered care to over 90% of the patients.8
These data demonstrate that the association between surgeon and hospital volume and outcomes for endometrial cancer is complex. The overall morbidity rate was higher for intermediate volume surgeons and hospitals compared to low volume providers but was the lowest for the highest volume surgeons and hospitals. However, in a multivariable analysis modeling surgeon and hospital volume as continuous variables, there was no association between surgeon volume and complications while mortality was higher among high volume surgeons. In contrast, mortality declined with increasing hospital volume but complication rates were higher at high volume hospitals.
These findings are likely driven by several factors. First, among low volume surgeons, many hysterectomies for endometrial cancer were likely very early tumors or cases in which endometrial cancer was incidentally identified. These cases were likely technically less complex and thus associated with good outcomes. Morbidity was higher in the intermediate volume strata but then decreased for the highest volume providers perhaps reflecting an effect of volume on outcomes for these patients. Second, prior work has consistently shown that the volume-outcome paradigm is most pronounced for high-risk procedures that are associated with significant morbidity. For moderate risk procedures, the effect of volume is often much smaller.21-23 Our findings are similar, the magnitude of association between volume and outcomes we found was small. Importantly, as in other studies of hysterectomy, we noted that increased surgeon volume was associated with decreased resource utilization including hospital charges.14,23 These findings highlight the methodologic complexity of analyzing surgical volume for endometrial cancer.
While our study benefits from the inclusion of a large sample of patients, we recognize a number of important limitations. First, we lacked data on tumor characteristics such as stage, histology, and uterine size that undoubtedly influenced treatment planning and outcome. To mitigate this bias, we adjusted for the performance of concomitant surgical procedures including lymphadenectomy and cytoreduction that are surrogates for extent of disease. Second, SPARCS data is limited to patients in New York State and the findings we documented may not be representative of other areas in the U.S. Third, while SPARCS has been widely used in prior work, there may be misclassification of covariates, outcomes or surgeons in a small number of cases. Finally, while we examined short term outcomes, we are unable to assess downstream care and long-term outcomes and quality of life.
From a policy perspective, these data suggest that surgeon and hospital volume are of limited value as quality metrics for endometrial cancer. Given prior work demonstrating a more robust association between surgical specialty and outcomes, efforts to direct the surgical care of women with endometrial cancer to gynecologic oncologists may be of more utility.24 Future efforts to determine the association between surgeon and hospital characteristics and downstream survival and quality of life will be of great interest.
Acknowledgments
Dr. Wright (NCI R01CA169121-01A1) and Dr. Hershman (NCI R01 CA166084) are recipients of grants from the National Cancer Institute. Dr. Hershman is the recipient of a grant from the Breast Cancer Research Foundation/Conquer Cancer Foundation.
Footnotes
Financial Disclosure: Dr. Wright has served as a consultant for Tesaro and Clovis Oncology. Dr. Neugut has served as a consultant to Pfizer, Teva, Otsuka, and United Biosource Corporation. He is on the scientific advisory board of EHE, Intl. The other authors did not report any potential conflicts of interest.
References
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