Background
Postpartum hysterectomy is an obstetric procedure that carries significant maternal risk that is not well characterized by hospital volume.
Objective
The objective of this study was to determine risk for peripartum hysterectomy for women at low and moderate risk for the procedure.
Study Design
This population-based study used data from the Nationwide Inpatient Sample to characterize risk for peripartum hysterectomy. Women with a diagnosis of placenta accreta or prior cesarean and placenta previa were excluded. Obstetrical risk factors along with demographic and hospital factors were evaluated. Multivariable mixed-effects log-linear regression models were developed to determine adjusted risk. Based on these models receiver operating characteristic curves were plotted, and the area under the curve was determined to assess discrimination.
Results
Peripartum hysterectomy occurred in 1 in 1913 deliveries. Risk factors associated with significant risk for hysterectomy included mode of delivery, stillbirth, placental abruption, fibroids, and antepartum hemorrhage. These factors retained their significance in adjusted models: the risk ratio for stillbirth was 3.44 (95% confidence interval, 2.94–4.02), abruption 2.98 (95% confidence interval, 2.52–3.20), fibroids 3.63 (95% confidence interval, 3.22–4.08), and antepartum hemorrhage 7.15 (95% confidence interval, 6.16–8.32). The area under the curve for the model was 0.833.
Conclusion
Peripartum hysterectomy is a relatively common event that hospitals providing routine obstetric care should be prepared to manage. That specific risk factors are highly associated with risk for hysterectomy supports routine use of hemorrhage risk-assessment tools. However, given that a significant proportion of hysterectomies will be unpredictable, the availability of rapid transfusion protocols may be necessary for hospitals to safely manage these cases.
Introduction
Postpartum hemorrhage is a leading cause of severe maternal morbidity and mortality worldwide. For patients where other medical and surgical treatment options have failed, peripartum hysterectomy may represent a life-saving intervention. The procedure involves frequent morbidity and risk of death ranging from 1-6%. To minimize risk and optimize delivery planning, expert opinion supports referral of women at extremely high risk for peripartum hysterectomy–primarily those with suspected placenta accreta/percreta–to tertiary centers. Research evidence supports that maternal outcomes may be improved at centers that perform peripartum hysterectomy more frequently and use a multidisciplinary approach in planning and delivery.
While referral may benefit these highest risk patients, a significant proportion of peripartum hysterectomies may occur among women without placenta accreta/percreta. For these low-risk women, it is unclear how well hemorrhage leading to peripartum hysterectomy can be anticipated based on risk factors. If peripartum hysterectomy is demonstrated to be: (1) relatively common on a population basis for low-risk women, and (2) only partially accounted for by risk factors, effective hospital-level safety planning would require both routine risk assessment of obstetrical patients as well as general preparedness for catastrophic hemorrhage, an approach supported by major maternal safety initiatives, including hemorrhage bundle recommendations from the National Partnership for Maternal Safety.
Given that unanticipated peripartum hysterectomy may be an important consideration in designing effective hemorrhage safety policies, particularly in low-volume hospitals, the purpose of this study was to characterize hysterectomy risk during delivery hospitalizations among women deemed to be at low and moderate risk for this procedure.
Materials and Methods
Data from the Nationwide Inpatient Sample (NIS) from the Agency for Healthcare Research and Quality were used for the analysis. The NIS is the largest publicly available, all-payer inpatient database in the United States. NIS data sets contain a random sample of approximately 20% of all hospitals within the United States, and through 2011, all discharge claims in a given hospital are included. The sampling frame for the NIS includes nonfederal, general, and specialty-specific hospitals throughout the United States. Sampled hospitals include both academic and community facilities. The NIS included approximately 8 million hospital stays from 45 states in 2010. Sampling weights included in the NIS can be applied to provide national estimates and were used in this analysis. Due to the de-identified nature of the data set, institutional review board exemption was obtained from Columbia University to perform this study.
We analyzed women and girls from 16-49 years of age hospitalized for a delivery from 1998 through 2011. Patients were included if International Classification of Diseases, Ninth Revision billing codes v27.x and 650 were present; these codes identify a large majority of delivery hospitalizations. Since the goal of the study was to evaluate the risk of peripartum hysterectomy in low- and moderate-risk women, those at particularly high risk for peripartum hysterectomy were excluded. Patients with the diagnosis of both placenta previa (641.0x, 641.1x) and prior cesarean delivery (654.2x) were excluded as these patients are at high risk for invasive placentation, as were patients diagnosed with either placenta accreta (667.0x, 667.1x) or vasa previa (663.5x). Women with a diagnosis of ovarian or cervical cancer (180.x and 183.x) were similarly excluded given that they may have been undergoing planned hysterectomy.
The primary outcome for the analysis was peripartum hysterectomy, defined as hysterectomy (68.3x, 68.4x, 68.9) occurring during the delivery hospitalization. In addition to analyzing the low- to moderate-risk delivery hospitalizations described above, we performed a sensitivity analysis restricted to even lower-risk patients. To create the low-risk cohort, we further excluded women with either placenta previa or prior cesarean delivery. Results are presented for both groups: low-/moderate-risk patients and low-risk patients.
For each hospital, we calculated the total number of delivery hospitalizations and divided this by the number of years in which a hospital had at least 1 delivery. Hospitals were categorized as low (≤1000 deliveries per year), medium (1001-2000 deliveries per year), and high (>2000 deliveries per year) volume. In addition to annualized delivery volume, hospital characteristics included location (urban vs rural), teaching status (teaching vs nonteaching), hospital bed size, and region (Northeast, Midwest, South, or West).
Risk factors for peripartum hysterectomy were determined by a review of the literature and were included in the model. These included mode of delivery (vaginal operative or nonoperative delivery, cesarean delivery), induction of labor, multiple gestation, stillbirth, placental abruption, fibroids, antepartum hemorrhage, chorioamnionitis, polyhydramnios, and preeclampsia/eclampsia. The following patient demographic characteristics were also individually evaluated: maternal age, race (white, black, Hispanic), year of discharge, median income for the ZIP code where the patient resides, and payer status.
Prevalence rates of hysterectomy per 10,000 deliveries were calculated. Risk ratios (RR) for peripartum hysterectomy were derived from fitting weighted log-linear mixed-effects regression models based on the Poisson distribution. These models used the GENMOD procedure in SAS (SAS Institute, Cary, NC) to account for clustering of subjects within hospitals; in these models, hospitals formed the random intercepts. All analyses were weighted by the sampling weights that were included in the data set. To determine how well the mixed-effects log-linear models accounted for patient risk for hysterectomy, receiver operating characteristic (ROC) curves were created and the area under the curve (AUC) was determined. Sensitivity analyses for the ROC curves based on fixed-effects log-linear Poisson models were performed to test the discriminatory ability of the models apart from center clustering effects.
Materials and Methods
Data from the Nationwide Inpatient Sample (NIS) from the Agency for Healthcare Research and Quality were used for the analysis. The NIS is the largest publicly available, all-payer inpatient database in the United States. NIS data sets contain a random sample of approximately 20% of all hospitals within the United States, and through 2011, all discharge claims in a given hospital are included. The sampling frame for the NIS includes nonfederal, general, and specialty-specific hospitals throughout the United States. Sampled hospitals include both academic and community facilities. The NIS included approximately 8 million hospital stays from 45 states in 2010. Sampling weights included in the NIS can be applied to provide national estimates and were used in this analysis. Due to the de-identified nature of the data set, institutional review board exemption was obtained from Columbia University to perform this study.
We analyzed women and girls from 16-49 years of age hospitalized for a delivery from 1998 through 2011. Patients were included if International Classification of Diseases, Ninth Revision billing codes v27.x and 650 were present; these codes identify a large majority of delivery hospitalizations. Since the goal of the study was to evaluate the risk of peripartum hysterectomy in low- and moderate-risk women, those at particularly high risk for peripartum hysterectomy were excluded. Patients with the diagnosis of both placenta previa (641.0x, 641.1x) and prior cesarean delivery (654.2x) were excluded as these patients are at high risk for invasive placentation, as were patients diagnosed with either placenta accreta (667.0x, 667.1x) or vasa previa (663.5x). Women with a diagnosis of ovarian or cervical cancer (180.x and 183.x) were similarly excluded given that they may have been undergoing planned hysterectomy.
The primary outcome for the analysis was peripartum hysterectomy, defined as hysterectomy (68.3x, 68.4x, 68.9) occurring during the delivery hospitalization. In addition to analyzing the low- to moderate-risk delivery hospitalizations described above, we performed a sensitivity analysis restricted to even lower-risk patients. To create the low-risk cohort, we further excluded women with either placenta previa or prior cesarean delivery. Results are presented for both groups: low-/moderate-risk patients and low-risk patients.
For each hospital, we calculated the total number of delivery hospitalizations and divided this by the number of years in which a hospital had at least 1 delivery. Hospitals were categorized as low (≤1000 deliveries per year), medium (1001-2000 deliveries per year), and high (>2000 deliveries per year) volume. In addition to annualized delivery volume, hospital characteristics included location (urban vs rural), teaching status (teaching vs nonteaching), hospital bed size, and region (Northeast, Midwest, South, or West).
Risk factors for peripartum hysterectomy were determined by a review of the literature and were included in the model. These included mode of delivery (vaginal operative or nonoperative delivery, cesarean delivery), induction of labor, multiple gestation, stillbirth, placental abruption, fibroids, antepartum hemorrhage, chorioamnionitis, polyhydramnios, and preeclampsia/eclampsia. The following patient demographic characteristics were also individually evaluated: maternal age, race (white, black, Hispanic), year of discharge, median income for the ZIP code where the patient resides, and payer status.
Prevalence rates of hysterectomy per 10,000 deliveries were calculated. Risk ratios (RR) for peripartum hysterectomy were derived from fitting weighted log-linear mixed-effects regression models based on the Poisson distribution. These models used the GENMOD procedure in SAS (SAS Institute, Cary, NC) to account for clustering of subjects within hospitals; in these models, hospitals formed the random intercepts. All analyses were weighted by the sampling weights that were included in the data set. To determine how well the mixed-effects log-linear models accounted for patient risk for hysterectomy, receiver operating characteristic (ROC) curves were created and the area under the curve (AUC) was determined. Sensitivity analyses for the ROC curves based on fixed-effects log-linear Poisson models were performed to test the discriminatory ability of the models apart from center clustering effects.
Results
Of 55,214,208 low- and moderate-risk deliveries included in the cohort, there were 28,862 (5.2 per 10,000, or 1 in 1913, deliveries) cases of peripartum hysterectomy. To arrive at this cohort, 384,899 deliveries were excluded. When risk factors were present, the hysterectomy frequency was much higher for women with abruption, fibroids, placenta previa (in the absence of a prior cesarean delivery), antepartum hemorrhage, or stillbirth. The rates of hysterectomy for these conditions were 31.9, 57.3, 146.7, 72.0, and 24.2 per 10,000 deliveries, respectively ( Table 1 ). Risk was higher during cesarean compared to vaginal delivery and among older women with hysterectomy rates of 12.0, 20.6, and 59.7 per 10,000 deliveries at maternal ages of 35-39, 40-44, and 45-49 years, respectively. When the cohort was restricted to low-risk women (cases of repeat cesarean delivery and placenta previa were excluded), risk of hysterectomy was lower; of 46,834,460 deliveries in the low-risk cohort there were 15,444 cases of hysterectomy, occurring at a frequency of 3.3 per 10,000. Obstetric risk factors and increasing maternal age were similarly associated with increased risk among the low-risk cohort. Comparing risk at the beginning and end of the study period, hysterectomy rates increased 25.7% in the low-risk and 25.8% in the moderate-risk group between 1998 through 1999 and 2010 through 2011 (both P < .001).
| Low-risk patients | Low- and moderate-risk patients | |||
|---|---|---|---|---|
| Total, n (%) | Hysterectomy rate per 10,000 deliveries, n (%) | Total, n (%) | Hysterectomy rate per 10,000 deliveries, n (%) | |
| All deliveries | 46,834,460 | 3.3 (15,444) | 55,214,208 | 5.2 (28,862) |
| Obstetric factors | ||||
| Mode of delivery | ||||
| Primary cesarean | 9,010,656 (19.2) | 9.8 (8855) | 9,240,373 (16.7) | 12.9 (11,936) |
| Repeat cesarean | NA | NA | 6,700,366 (12.1) | 14.0 (9389) |
| Operative vaginal | 3,606,723 (7.7) | 3.0 (1093) | 3,778,882 (6.8) | 3.4 (1288) |
| Nonoperative vaginal | 34,217,081 (73.1) | 1.6 (5497) | 35,494,587 (64.3) | 1.8 (6249) |
| Labor induction | 9,508,646 (20.3) | 3.3 (3170) | 9,850,070 (17.8) | 3.6 (3517) |
| Multiple gestation | 812,216 (1.7) | 15.3 (1239) | 948,799 (1.7) | 18.8 (1782) |
| Stillbirth | 323,929 (0.7) | 16.9 (549) | 368,170 (0.7) | 24.2 (890) |
| Placental abruption | 484,026 (1.0) | 23.9 (1156) | 573,723 (1.0) | 31.9 (1831) |
| Fibroids | 296,360 (0.6) | 44.6 (1322) | 398,707 (0.7) | 57.3 (2285) |
| Antepartum hemorrhage | 116,424 (0.5) | 48.9 (569) | 140,432 (0.3) | 72.0 (1011) |
| Polyhydramnios | 280,679 (0.6) | 9.2 (258) | 350,458 (0.6) | 11.1 (388) |
| Preeclampsia/Eclampsia | 1,790,595 (3.8) | 7.4 (1320) | 2,054,445 (3.7) | 9.2 (1893) |
| Chorioamnionitis | 873,730 (1.9) | 9.1 (797) | 952,974 (1.7) | 11.5 (1101) |
| Placenta previa | NA | NA | 217,333 (0.4) | 146.7 (3189) |
| Demographic factors | ||||
| Age, y | ||||
| 16–17 | 1,374,088 (2.9) | 0.6 (78) | 1,693,219 (3.1) | 0.6 (97) |
| 18–20 | 3,723,756 (8.0) | 0.9 (320) | 3,889,511 (7.0) | 1.1 (411) |
| 20–24 | 12,168,549 (26.0) | 1.5 (1795) | 13,592,877 (24.6) | 2.1 (2908) |
| 25–29 | 12,996,344 (27.7) | 2.5 (3244) | 15,189,048 (27.5) | 3.9 (5963) |
| 30–34 | 10,583,766 (22.6) | 4.2 (4449) | 13,036,606 (23.6) | 6.6 (8634) |
| ≥35 | 5,978,469 (12.8) | 9.3 (5558) | 7,812,810 (14.2) | 13.9 (10,849) |
| Discharge year | ||||
| 1998 | 3,079,390 (6.5) | 2.6 (807) | 3,505,578 (6.3) | 4.5 (1580) |
| 1999 | 3,207,632 (6.8) | 3.1 (1010) | 3,656,664 (6.6) | 4.9 (1805) |
| 2000 | 3,400,919 (7.3) | 2.9 (971) | 3,884,785 (7.0) | 4.4 (1720) |
| 2001 | 3,318,450 (7.1) | 3.2 (1052) | 3,815,910 (6.9) | 4.9 (1873) |
| 2002 | 3,454,729 (7.4) | 2.7 (939) | 3,988,487 (7.2) | 4.9 (1945) |
| 2003 | 3,380,353 (7.2) | 3.3 (1111) | 3,927,782 (7.1) | 5.0 (1966) |
| 2004 | 3,484,192 (7.4) | 3.2 (1102) | 4,070,598 (7.4) | 5.1 (2087) |
| 2005 | 3,486,450 (7.4) | 3.3 (1153) | 4,096,636 (7.4) | 5.2 (2115) |
| 2006 | 3,508,993 (7.5) | 3.3 (1163) | 4,140,424 (7.5) | 5.1 (2096) |
| 2007 | 3,720,572 (7.9) | 3.3 (1246) | 4,418,172 (8.0) | 5.1 (2253) |
| 2008 | 3,463,935 (7.4) | 3.6 (1260) | 4,126,560 (7.5) | 5.9 (2441) |
| 2009 | 3,257,933 (7.0) | 4.4 (1423) | 4,044,407 (7.3) | 6.2 (2499) |
| 2010 | 3,049,541 (6.5) | 3.1 (960) | 3,786,824 (6.9) | 5.2 (1986) |
| 2011 | 3,021,372 (6.5) | 4.1 (1247) | 3,751,381 (6.8) | 6.7 (2496) |
| Household income | ||||
| Lowest quartile | 9,021,900 (19.3) | 3.3 (2970) | 10,849,296 (19.6) | 5.5 (5914) |
| Second quartile | 11,429,017 (24.4) | 3.4 (3896) | 13,432,555 (24.3) | 5.3 (7178) |
| Third quartile | 11,854,223 (25.3) | 3.1 (3712) | 13,873,606 (25.1) | 5.0 (6869) |
| Highest quartile | 13,690,449 (29.2) | 3.3 (4554) | 16,066,390 (29.1) | 5.2 (8340) |
| Unknown | 838,872 (1.8) | 3.7 (313) | 992,362 (1.8) | 5.6 (561) |
| Insurance status | ||||
| Medicare | 218,116 (0.5) | 6.1 (133) | 268,846 (0.5) | 7.9 (212) |
| Medicaid | 18,402,957 (39.3) | 2.9 (5325) | 21,779,950 (39.4) | 4.9 (10,697) |
| Private | 25,220,421 (53.9) | 3.6 (9002) | 29,663,361 (53.7) | 5.5 (16,176) |
| Self-pay | 1,601,392 (3.4) | 3.3 (529) | 1,875,371 (3.4) | 5.1 (962) |
| Other | 1,287,255 (2.7) | 3.3 (419) | 1,504,706 (2.7) | 5.0 (753) |
| Unknown | 104,319 (0.2) | 3.6 (37) | 121,973 (0.2) | 5.1 (62) |
| Race | ||||
| White | 19,336,275 (41.3) | 3.0 (5782) | 22,689,016 (41.1) | 4.8 (10,782) |
| Black | 4,731,018 (10.1) | 4.4 (2102) | 5,673,336 (10.3) | 6.9 (3908) |
| Hispanic | 8,034,011 (17.2) | 3.4 (2696) | 9,741,891 (17.6) | 6.0 (5820) |
| Other | 3,699,890 (7.9) | 4.6 (1684) | 4,326,777 (7.8) | 6.7 (2892) |
| Unknown | 11,033,266 (23.6) | 2.9 (3181) | 12,784,189 (23.2) | 4.3 (5459) |
| Hospital factors | ||||
| Hospital bed size | ||||
| Small | 5,363,930 (11.5) | 2.5 (1347) | 6,279,308 (11.4) | 4.0 (2481) |
| Medium | 12,684,770 (27.1) | 3.0 (3821) | 14,926,948 (27.0) | 4.8 (7100) |
| Large | 28,648,663 (61.2) | 3.6 (10,227) | 33,844,564 (61.3) | 5.7 (19,206) |
| Unknown | 137,097 (0.3) | 3.6 (50) | 163,388 (0.3) | 4.7 (76) |
| Hospital location | ||||
| Rural | 6,165,046 (13.2) | 2.7 (1685) | 7,237,070 (13.1) | 4.0 (2890) |
| Urban | 40,532,317 (86.5) | 3.4 (13,710) | 47,813,750 (86.6) | 5.4 (25,896) |
| Unknown | 137,097 (0.3) | 3.6 (50) | 163,388 (0.3) | 4.7 (76) |
| Hospital region | ||||
| Northeast | 7,847,267 (16.8) | 2.9 (2289) | 9,216,075 (16.7) | 5.1 (4677) |
| Midwest | 10,219,284 (21.8) | 2.7 (2740) | 11,912,669 (21.6) | 4.2 (5045) |
| South | 17,182,078 (36.7) | 3.6 (6269) | 20,465,868 (37.1) | 5.7 (11,588) |
| West | 11,585,832 (24.7) | 3.6 (4146) | 13,619,596 (24.7) | 5.5 (7552) |
| Annualized delivery volume | ||||
| ≤1000 | 9,603,149 (20.5) | 2.6 (2516) | 11,230,340 (20.3) | 3.9 (4375) |
| 1001–2000 | 11,565,167 (24.7) | 2.9 (3376) | 13,616,401 (24.6) | 4.7 (6386) |
| >2000 | 25,555,144 (54.8) | 3.7 (9552) | 30,367,467 (55.0) | 6.0 (18,102) |
| Hospital teaching | ||||
| Nonteaching | 25,320,180 (54.1) | 2.9 (7400) | 29,899,914 (54.2) | 4.3 (12.962) |
| Teaching | 21,377,183 (45.6) | 3.7 (7995) | 25,150,907 (45.6) | 6.3 (15,824) |
| Unknown | 137,097 (0.3) | 3.6 (50) | 163,388 (0.3) | 4.7 (76) |
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