Objective
There is growing recognition that, in addition to occurrence of perioperative complications, the treatment of patients with complications influences outcome. We examined complications, failure to rescue (death in patients with a complication), and mortality rates for women who underwent abdominal hysterectomy.
Study Design
Women who underwent abdominal hysterectomy from 1998-2010 and whose data were recorded in the Nationwide Inpatient Sample were identified. Hospitals were stratified based on risk-adjusted mortality rates into 5 quintiles, and rates of complications and failure to rescue were examined.
Results
A total of 664,229 women who had been treated at 741 hospitals were identified. The overall mortality rate for the cohort was 0.17%. The risk-adjusted, hospital-level mortality rate ranged from 0–1.12%. The complication rate was 6.5% at the hospitals with the lowest mortality rates, 9.9% at the second quintile hospitals, 9.5% at both the third and fourth quintile hospitals, and 7.9% at the hospitals with the highest mortality rates. In contrast to complications, the failure-to-rescue rate increased with each successive risk-adjusted mortality quintile. The failure-to-rescue rate was 0% at the hospitals with the lowest mortality rates and increased with each successive quintile to 1.1%, 2.1%, 2.7%, and 4.4% in the hospitals with the highest mortality rates ( P < .0001).
Conclusion
For women who underwent abdominal hysterectomy, hospital complication rates correlated poorly with mortality rates; failure-to-rescue is strongly associated with in-hospital mortality rates. The treatment of complications, not the actual development of a complication, is the most important factor to use to predict death after major gynecologic surgery.
For Editors’ Commentary, see Contents
The measurement and reporting of quality has become a major focus of medicine in the United States. For women, hysterectomy is 1 of the most commonly performed surgical procedures and an ideal target for quality improvement initiatives. In spite of the frequency of hysterectomy, systematic efforts to improve quality have been largely lacking.
For many surgical procedures, complication rates have been used as a surrogate for quality. Perioperative complications are often associated with substantial pain and suffering, are costly to treat, and can be associated with devastating long-term consequences. Further, for some diseases like cancer, complications can delay downstream treatments and ultimately compromise long-term outcomes. Although the major complication rate after hysterectomy is modest compared with other high-risk procedures, an appreciable number of women still experience significant complications at the time of the procedure.
Despite the importance of complications, there is growing recognition that perhaps the most important determinant of outcomes is not the occurrence of a complication, but rather the treatment of atients with complications. In a large analysis of patients who underwent general and vascular surgery, complication rates were similar at hospitals with high and low mortality rates. However, death in patients with complications was nearly twice as high in hospitals with a high mortality rate, compared with hospitals with low mortality rates. The concept of failure to rescue or death after a complication has now gained prominence as an important determinant of outcome for patients who undergo surgery.
To date, failure to rescue has received little attention in gynecologic surgery. We performed a population-based analysis to examine factors that are associated with poor outcome in women who undergo major gynecologic surgery. The primary objective of our study was to determine whether complication rates correlate with hospital-level mortality rates in women who undergo hysterectomy. Specifically, we examined hospital-level rates of complications, failure to rescue, and death in women who undergo abdominal hysterectomy.
Methods
Data
Data from the Nationwide Inpatient Sample (NIS), which was developed and is maintained by the Agency for Healthcare Research and Quality, were analyzed. NIS includes a random sample of 20% of all hospital discharges from the United States and is the largest all-payer inpatient care database in the United States. The sampling scheme for NIS includes all nonfederal, general, and specialty-specific hospitals from across the United States. Hospitals that are sampled include not only academic but also community facilities. NIS captured approximately 8 million hospital stays from 45 states in 2010. The study was deemed exempt by the Columbia University Institutional Review Board.
Demographic and clinical characteristics
The data for women who were ≥18 years old and who underwent abdominal hysterectomy between 1998 and 2010 were analyzed. Abdominal hysterectomy was defined by International Classification of Diseases, 9th revision , codes 68.3, 68.39, 68.4, 68.49, and 68.9 and was classified further as either a total or subtotal (supracervical) hysterectomy. To enhance the reliability of our estimates, only hospitals that had performed at least 400 abdominal hysterectomies were included. Concomitant procedures that were performed at the time of abdominal hysterectomy and that included oophorectomy (either unilateral or bilateral), anterior colporrhaphy, posterior colporrhaphy, antiincontinence procedures, small-bowel resection, large-bowel resection, and lymphadenectomy were recorded with the use of International Classification of Diseases, 9th revision , coding. Surgical indications included leiomyoma, endometriosis, abnormal bleeding, benign ovarian neoplasms, pelvic organ prolapse, uterine cancer, and ovarian cancer. Patients may have had multiple indications for surgery.
Race was classified as white, black, Hispanic, and other; age was grouped into 10-year increments. Household income was classified by the NIS as low, medium, high, or highest. Insurance status at the time of hysterectomy was categorized as private, Medicare, Medicaid, self-pay, other, and unknown. The presence of comorbid medical conditions was measured with the Elixhauser Comorbidity Index. Patients were categorized based on the number of medical comorbidities into 0, 1, and ≥2, as previously described. Area of residence was categorized as either urban or rural, and location was classified as northeast, midwest, west, or south. The hospital in which each patient underwent surgery was classified as either a teaching or nonteaching facility and, based on size, as a small, medium, or large facility.
Complications, failure to rescue, and risk-adjusted mortality rates
Risk-adjusted mortality rates for each hospital were estimated as previously reported. Logistic regression models were used to estimate the probability of death for each patient. These models included all of the clinical and demographic characteristics, indications for surgery, and concomitant procedures. The predicted probabilities of all patients at a given hospital were then summed to determine the expected mortality rate for each hospital. The risk-adjusted mortality rate for a given hospital was then calculated by multiplying the ratio of the observed to expected mortality rate by the overall mortality rate of the entire study cohort. Hospitals were then classified into 5 quintiles based on their risk-adjusted mortality rates (lowest risk-adjusted to highest risk-adjusted mortality rate). In this stratification, there were 284 hospitals without any observed deaths. These 284 hospitals made up the lowest risk-adjusted mortality quintile. Stratification of the remaining hospitals into the risk-adjusted mortality quintiles was performed so that approximately equal numbers of hospitals were included in each quintile.
Major perioperative complications were analyzed. For the analysis, complications were divided into 2 groups: medical complications (myocardial infarction, cardiopulmonary arrest, renal failure, respiratory failure, venous thromboembolism, hemorrhage, cerebrovascular accident, shock, gastrointestinal bleed) and infectious complications (wound complications, abscess, pneumonia, bacteremia/sepsis). An analysis of any complication, which is a composite measure that included both medical and infectious complications, was also performed. Failure to rescue was defined as death in a patient with any of the recorded complications. For each hospital, the overall complication rate and the rate of failure to rescue were determined by dividing the number of patients with a complication or death after a complication, respectively, by the total number of women who underwent an abdominal hysterectomy at that institution.
Statistical analysis
Frequency distributions for categoric variables were compared across the risk-adjusted mortality quintiles with χ 2 tests. The complication rates and rate of failure to rescue are reported descriptively over time and across the risk-adjusted mortality quintiles. Separate analyses were performed for the overall complication rate and failure to rescue and for medical and infectious complications and death after these complications, respectively. To verify the reliability of our estimates, we performed a sensitivity analysis in which the hospitals were grouped into 4 quartiles. The analysis of risk-adjusted mortality rates, complications, and failure to rescue was repeated in this analysis. A probability value of < .05 was considered statistically significant. All analyses were conducted with SAS software (version 9.3; SAS Institute, Cary, NC). All statistical tests were 2-sided.
Results
A total of 664,229 women who underwent abdominal hysterectomy were identified. The overall mortality rate for the cohort was 0.17%. The risk-adjusted mortality rate across the 741 hospitals ranged from 0–1.12%. When stratified into 5 quintiles, there were 284 hospitals without any deaths. The risk-adjusted mortality rates ranged from 0.35–1.12% for the highest mortality quintile of hospitals ( Table 1 ).
| Variable | Quintile | P value | ||||
|---|---|---|---|---|---|---|
| Lowest, n | Second, n | Third, n | Fourth, n | Highest, n | ||
| Patients | 193,904 | 149,212 | 132,561 | 108,605 | 79,947 | |
| Hospitals | 284 | 114 | 114 | 115 | 114 | |
| Year of procedure | < .0001 | |||||
| 1998 | 14,795 (7.6%) | 10,219 (6.9%) | 7783 (5.9%) | 7590 (7.0%) | 6656 (8.3%) | |
| 1999 | 16,194 (8.4%) | 13,194 (8.8%) | 11,483 (8.7%) | 8666 (8.0%) | 7067 (8.8%) | |
| 2000 | 15,395 (7.9%) | 15,100 (10.1%) | 13,182 (9.9%) | 10,289 (9.5%) | 5272 (6.6%) | |
| 2001 | 19,301 (10.0%) | 10,280 (6.9%) | 12,752 (9.6%) | 9860 (9.1%) | 6209 (7.8%) | |
| 2002 | 20,148 (10.4%) | 17,886 (12.0%) | 14,406 (10.9%) | 9582 (8.8%) | 6674 (8.4%) | |
| 2003 | 19,193 (9.9%) | 12,759 (8.6%) | 11,395 (8.6%) | 9984 (9.2%) | 7884 (9.9%) | |
| 2004 | 16,623 (8.6%) | 13,921 (9.3%) | 12,761 (9.6%) | 8649 (8.0%) | 6043 (7.6%) | |
| 2005 | 14,922 (7.7%) | 12,330 (8.3%) | 9869 (7.4%) | 10,047 (9.3%) | 5763 (7.2%) | |
| 2006 | 14,697 (7.6%) | 9344 (6.3%) | 9229 (7.0%) | 7543 (7.0%) | 9271 (11.6%) | |
| 2007 | 12,057 (6.2%) | 12,401 (8.3%) | 9324 (7.0%) | 7252 (6.7%) | 6481 (8.1%) | |
| 2008 | 11,521 (5.9%) | 9114 (6.1%) | 7884 (6.0%) | 8080 (7.4%) | 4905 (6.1%) | |
| 2009 | 11,504 (5.9%) | 6817 (4.6%) | 8088 (6.1%) | 5319 (4.9%) | 4035 (5.1%) | |
| 2010 | 7554 (3.9%) | 5847 (3.9%) | 4405 (3.3%) | 5744 (5.3%) | 3687 (4.6%) | |
| Age, y | < .0001 | |||||
| <40 | 51,131 (26.4%) | 32,437 (21.7%) | 26,623 (20.1%) | 23,678 (21.8%) | 19,549 (24.5%) | |
| 40-49 | 90,698 (46.8%) | 66,293 (44.4%) | 57,954 (43.7%) | 49,373 (45.5%) | 37,780 (47.3%) | |
| 50-59 | 32,353 (16.7%) | 28,026 (18.8%) | 25,721 (19.4%) | 20,212 (18.6%) | 13,465 (16.8%) | |
| 60-69 | 11,134 (5.7%) | 12,109 (8.1%) | 11,913 (9.0%) | 8336 (7.7%) | 4979 (6.2%) | |
| 70-79 | 6434 (3.3%) | 7522 (5.0%) | 7518 (5.7%) | 5182 (4.8%) | 3139 (3.9%) | |
| ≥80 | 2154 (1.1%) | 2825 (1.9%) | 2832 (2.1%) | 1824 (1.7%) | 1035 (1.3%) | |
| Race | < .0001 | |||||
| White | 93,605 (48.3%) | 72,146 (48.4%) | 60,664 (45.8%) | 54,496 (50.2%) | 37,820 (47.3%) | |
| Black | 22,918 (11.8%) | 24,363 (16.3%) | 15,715 (11.9%) | 17,355 (16.0%) | 11,803 (14.8%) | |
| Hispanic | 11,831 (6.1%) | 9776 (6.6%) | 10,581 (8.0%) | 7663 (7.1%) | 5903 (7.4%) | |
| Other | 7862 (4.1%) | 6609 (4.4%) | 5108 (3.9%) | 5139 (4.7%) | 3116 (3.9%) | |
| Unknown | 57,688 (29.8%) | 36,318 (24.3%) | 40,493 (30.6%) | 23,952 (22.1%) | 21,305 (26.7%) | |
| Income | < .0001 | |||||
| Low | 28,960 (14.9%) | 24,356 (16.3%) | 15,239 (11.5%) | 16,489 (15.2%) | 13,094 (16.4%) | |
| Medium | 49,564 (25.6%) | 32,302 (21.7%) | 26,481 (20.0%) | 24,116 (22.2%) | 19,834 (24.8%) | |
| High | 50,183 (25.9%) | 36,913 (24.8%) | 35,726 (27.0%) | 29,578 (27.2%) | 22,294 (27.9%) | |
| Highest | 61,254 (31.6%) | 52,666 (35.3%) | 52,807 (39.8%) | 36,419 (33.5%) | 23,317 (29.2%) | |
| Unknown | 3943 (2.0%) | 2975 (2.0%) | 2308 (1.7%) | 2003 (1.8%) | 1408 (1.8%) | |
| Insurance | < .0001 | |||||
| Private | 151,925 (78.4%) | 108,294 (72.6%) | 97,232 (73.4%) | 79,349 (73.1%) | 59,122 (74.0%) | |
| Medicare | 16,476 (8.5%) | 17,817 (11.9%) | 16,870 (12.7%) | 12,126 (11.2%) | 7793 (9.8%) | |
| Medicaid | 13,723 (7.1%) | 11,327 (7.6%) | 8668 (6.5%) | 9136 (8.4%) | 7442 (9.3%) | |
| Self-pay | 5328 (2.8%) | 5808 (3.9%) | 3330 (2.5%) | 3227 (3.0%) | 2386 (3.0%) | |
| Other | 5941 (3.1%) | 5635 (3.8%) | 6291 (4.8%) | 4571 (4.2%) | 3025 (3.8%) | |
| Unknown | 511 (0.3%) | 331 (0.2%) | 161 (0.1%) | 196 (0.2%) | 179 (0.2%) | |
| Comorbidity | < .0001 | |||||
| 0 | 162,397 (83.8%) | 111,611 (74.8%) | 97,157 (73.3%) | 82,732 (76.2%) | 64,075 (80.2%) | |
| 1 | 23,312 (12.0%) | 24,066 (16.1%) | 22,714 (17.1%) | 17,011 (15.7%) | 11,098 (13.9%) | |
| ≥2 | 8195 (4.2%) | 13,535 (9.1%) | 12,690 (9.6%) | 8862 (8.2%) | 4774 (6.0%) | |
| Region | < .0001 | |||||
| Northeast | 17,974 (9.3%) | 20,486 (13.7%) | 19,361 (14.6%) | 21,739 (20.0%) | 16,032 (20.1%) | |
| Midwest | 47,180 (24.3%) | 25,030 (16.8%) | 29,836 (22.5%) | 24,451 (22.5%) | 15,332 (19.2%) | |
| South | 90,573 (46.7%) | 77,798 (52.1%) | 57,889 (43.7%) | 39,871 (36.7%) | 38,771 (48.5%) | |
| West | 38,177 (19.7%) | 25,898 (17.4%) | 25,475 (19.2%) | 22,544 (20.8%) | 9812 (12.3%) | |
| Area of residence | ||||||
| Metropolitan | 173,066 (89.3%) | 147,537 (98.9%) | 128,725 (97.1%) | 104,175 (95.9%) | 77,518 (97.0%) | |
| Non-metropolitan | 20,838 (10.8%) | 1675 (1.1%) | 3836 (2.9%) | 4430 (4.1%) | 2429 (3.0%) | |
| Hospital size | < .0001 | |||||
| Small | 20,546 (10.6%) | 8177 (5.5%) | 5549 (4.2%) | 3089 (2.8%) | 7424 (9.3%) | |
| Medium | 47,967 (24.7%) | 29,765 (20.0%) | 25,065 (18.9%) | 27,051 (24.9%) | 21,747 (27.2%) | |
| Large | 125,391 (64.7%) | 111,270 (74.6%) | 101,947 (76.9%) | 78,465 (72.3%) | 50,776 (63.5%) | |
| Hospital teaching status | < .0001 | |||||
| Nonteaching | 125,576 (64.8%) | 54,888 (36.8%) | 36,283 (27.4%) | 36,345 (33.5%) | 40,022 (50.1%) | |
| Teaching | 68,328 (35.2%) | 94,324 (63.2%) | 96,278 (72.6%) | 72,260 (66.5%) | 39,925 (49.9%) | |
| Indication for surgery | ||||||
| Leiomyoma | 114,155 (58.9%) | 86,754 (58.1%) | 74,831 (56.5%) | 63,938 (58.9%) | 48,543 (60.7%) | < .0001 |
| Endometriosis | 68,932 (35.6%) | 44,494 (29.8%) | 38,829 (29.3%) | 32,896 (30.3%) | 26,845 (33.6%) | < .0001 |
| Abnormal bleeding | 86,890 (44.8%) | 54,530 (36.6%) | 48,131 (36.3%) | 41,596 (38.3%) | 32,569 (40.7%) | < .0001 |
| Benign neoplasm | 55,215 (28.5%) | 38,253 (25.6%) | 33,505 (25.3%) | 28,755 (26.5%) | 23,218 (29.0%) | < .0001 |
| Pelvic organ prolapse | 13,099 (6.8%) | 7992 (5.4%) | 7259 (5.5%) | 5524 (5.1%) | 4269 (5.3%) | < .0001 |
| Uterine cancer | 10,035 (5.2%) | 14,140 (9.5%) | 14,002 (10.6%) | 9405 (8.7%) | 5486 (6.9%) | < .0001 |
| Ovarian cancer | 4061 (2.1%) | 7455 (5.0%) | 6857 (5.2%) | 4501 (4.1%) | 2280 (2.9%) | < .0001 |
| Concomitant procedures | ||||||
| Salpingo-oophorectomy | 139,411 (71.9%) | 109,345 (73.3%) | 98,660 (74.4%) | 78,059 (71.9%) | 55,885 (71.9%) | < .0001 |
| Anterior colporrhaphy | 2609 (1.4%) | 1517 (1.0%) | 1772 (1.3%) | 1071 (1.0%) | 825 (1.0%) | < .0001 |
| Posterior colporrhaphy | 3536 (1.8%) | 2359 (1.6%) | 2433 (1.8%) | 1613 (1.5%) | 1188 (1.5%) | < .0001 |
| Antiincontinence procedure | 13,338 (6.9%) | 7595 (5.1%) | 6902 (5.2%) | 5670 (5.2%) | 4307 (5.4%) | < .0001 |
| Small-bowel resection | 545 (0.3%) | 842 (0.6%) | 788 (0.6%) | 551 (0.5%) | 315 (0.4%) | < .0001 |
| Colon resection | 884 (0.5%) | 1325 (0.9%) | 1294 (1.0%) | 839 (0.8%) | 468 (0.6%) | < .0001 |
| Lymphadenectomy | 8002 (4.1%) | 14,798 (9.9%) | 14,298 (10.8%) | 9747 (9.0%) | 5032 (6.3%) | < .0001 |
| Hysterectomy type | < .0001 | |||||
| Total | 179,711 (92.7%) | 137,767 (92.3%) | 119,400 (90.1%) | 97,700 (90.0%) | 73,329 (91.7%) | |
| Subtotal | 14,193 (7.3%) | 11,445 (7.7%) | 13,161 (9.9%) | 10,905 (10.0%) | 6618 (8.3%) | |
Table 1 displays the characteristics of the cohort that are stratified by hospital-level risk-adjusted mortality rates. Although the absolute differences between mortality groups were low for most covariates, all of the characteristics were statistically significantly different across the groups. Compared with the hospitals with the highest mortality rates, the hospitals with the lowest mortality rates had a lower percentage of black patients (11.8% vs 14.8%), fewer Medicaid recipients (7.1% vs 9.3%), and fewer women with ≥2 comorbidities (4.2% vs 6.0%; P < .0001 for all). Compared with hospitals in the second, third, and fourth mortality groups, centers in the highest mortality quintile were more often small hospitals (9.3%; P < .0001) and nonteaching facilities (50.1%; P < .0001).
The overall complication rate was 8.5% and ranged from 6.2% in 1998 to 5.3% in 2010 ( P < .0001; Figure 1 ). The complication rate was 6.5% at the hospitals with the lowest mortality rates, 9.9% at the second quintile hospitals, 9.5% at both the third and fourth quintile hospitals, and 7.9% at the highest mortality quintile hospitals ( P < .0001) ( Table 2 ). Similar trends in complications rates were noted for both medical complications and infectious complications. The rates of both medical and infectious complications were highest in the second to lowest risk-adjusted mortality quintile.
