Abstract
Study Objective:
To develop a preoperative risk assessment tool that quantifies the risk of postoperative complications within 30 days of hysterectomy.
Design:
Retrospective analysis.
Setting:
Michigan Surgical Quality Collaborative hospitals.
Patients:
Women who underwent hysterectomy for gynecologic indications.
Interventions:
Development of a nomogram to create a clinical risk assessment tool.
Measurements and Main Results:
Postoperative complications within 30 days were the primary outcome. Bivariate analysis was performed comparing women who did and did not have a complication. The patient registry was randomly divided. A logistic regression model developed and validated from the Collaborative database was externally validated with hysterectomy cases from the National Surgical Quality Improvement Program and a nomogram was developed to create a clinical risk assessment tool.
Of the 41,147 included women, the overall postoperative complication rate was 3.98% (n=1,638). Preoperative factors associated with postoperative complications were sepsis (OR 7.98, CI 1.98–32.20); abdominal approach (OR 2.27, CI 1.70–3.05); dependent functional status (OR 2.20, CI 1.34–3.62); bleeding disorder (OR 2.10, CI 1.37–3.21); diabetes mellitus with HbA1c ≥9% (OR 1.93, CI 1.16–3.24); gynecologic cancer (OR 1.86, CI 1.49–2.31); blood transfusion (OR 1.84, CI 1.15–2.96); American Society of Anesthesiologists Physical Status Classification System class ≥3 (OR 1.46, CI 1.24–1.73); government insurance (OR 1.3, CI 1.40–1.90); and Body Mass Index ≥40 (OR 1.25, CI 1.04–1.50). Model discrimination was consistent in the derivation, internal validation, and external validation cohorts (C-statistics 0.68, 0.69, 0.68, respectively).
Conclusion:
We validated a preoperative clinical risk assessment tool to predict postoperative complications within 30 days of hysterectomy. Modifiable risk factors identified were preoperative blood transfusion, poor glycemic control, and open abdominal surgery.
Keywords: gynecologic surgery, postoperative complications, risk factors, risk assessment tool, tool validation
Précis
A preoperative clinical risk assessment tool that quantifies the risk of postoperative complications 30 days after hysterectomy for benign or malignant indications was developed and validated.
Introduction
More than 400,000 women undergo hysterectomy each year in the United States and up to 16,000 will suffer a postoperative complication.[1–6] The risk of a complication is dependent on patient characteristics, comorbidities, and operative factors. For instance, minimally invasive surgical routes (vaginal, laparoscopic) for hysterectomy are associated with lower rates of surgical site infection, decreased blood loss, shorter hospital stays, and quicker recovery when compared to open abdominal surgery.[2,3]
Risk assessment models have the potential to make risk assessment more explicit and to quantify an individual’s risk for surgical complications. They have been developed and used to predict postoperative morbidity and mortality in multiple surgical specialties including bariatric surgery,[7, 8] general surgery,[9, 10] and gynecology.[6, 11] They facilitate identifying patients at risk for a poor outcome and direct attention to risk factors which, if modified, might reduce the potential for complications. They also aid in complex decision-making and help to communicate realistic expectations. Erekson et al developed a clinical prediction tool for postoperative complications after benign gynecologic surgery.[6] Using hysterectomies performed for benign and malignant gynecologic disease, we sought to develop a risk assessment model that quantifies the risk of postoperative complications within 30 days of surgery.
Materials and Methods
We performed a retrospective analysis from the Michigan Surgical Quality Collaborative (MSQC) database of women who underwent hysterectomy. Hysterectomies performed for gynecologic indications between June 2012 and October 2017 were included. Cases were excluded if hysterectomy was performed for a non-gynecologic malignancy or obstetric hemorrhage. This study was deemed “not regulated” by the University of Michigan Institutional Review Board because the MSQC is a limited dataset.
Statewide Surgical Collaborative
The MSQC is a clinical quality initiative in which 70 Michigan hospitals voluntarily participate. The goal of the collaborative is to improve the quality of hysterectomy care. The clinical registry consists of data collected via chart review by a specialized clinical quality reviewer employed by each respective hospital. Data are routinely validated through scheduled site visits, conference calls, and internal audits. The sampling strategies utilized by MSQC to select cases have been previously described [2]; to briefly describe, the standardized data collection methodology used collects the first 25 cases of an 8-day cycle (which starts on different days of the week for each cycle).
Data Analysis
The primary outcome, or dependent variable in all analyses, was a major postoperative complication within 30 days of surgery, which included any of the following: cardiac arrest, myocardial infarction, cerebrovascular accident, cardiac dysfunction, death, deep venous thrombosis (DVT), pulmonary embolism, deep or organ space surgical site infection (deep SSI), wound dehiscence, unplanned intubation, pneumonia, sepsis, severe sepsis, septic shock, acute kidney injury, fistula, central-line associated bacterial infection (CLABI), C. Difficile, bowel injury, ileus, bowel obstruction, postoperative bleeding complications (defined as including at least one of the following: hemorrhage (intraperitoneal or vaginal), hematoma, vaginal cuff bleeding, placement of >= 2 sutures to address bleeding, return to OR to evacuate or remove hematoma, or control bleeding), bladder injury, or ureteral injury. Urinary tract infection and superficial surgical site infection were not included, as these were considered minor complications that can be easily managed as an outpatient. Reoperation, readmission, and postoperative blood transfusion were considered utilization measures and not part of the primary outcome, as they are often the result of a specific postoperative complication (captured in the composite outcome described above) and based on clinical judgement rather than specific, consistent criteria.
After bivariate analysis, demographic and clinical variables were selected for inclusion in the multivariable model. Variables were categorized for analysis based either on clinical relevance or prior research. Age was categorized by decile as <40, 40–49, 50–59, and >60. Body Mass Index (BMI) was dichotomized as <40 and ≥40 based on previous work that established this threshold.[6] Race was categorized as Caucasian, African-American, and all others (which included if race was not indicated). A woman was considered to have “dependent functional status” if she required assistance with all activities of daily living (ADLs), and not dependent if able to perform some ADLs. Diabetes was categorized by a diagnosis of diabetes mellitus established at least 2 weeks prior to surgery and a preoperative hemoglobin A1c into 4 groups[12]: (1) no diagnosis of diabetes mellitus and no A1c documented; (2) no diagnosis of diabetes mellitus and A1c of 4–6.5%; (3) a diagnosis of diabetes mellitus with A1c <9% or no documented A1c; or (4) a diagnosis of diabetes mellitus and A1c ≥9.0%. American Society of Anesthesiologists (ASA) classification was dichotomized into those women whose status was <3 versus ≥3.[6] Bleeding disorder, tobacco use disorder (tobacco use within 1 year of surgery), preoperative hematocrit <36%,[6] preoperative sepsis (meeting either criteria for severe sepsis or septic shock within 72 hours before surgery), gynecologic cancer diagnosis (based on whether the primary surgical indication included an appropriate International Classification of Diseases version 9 or 10 code [see Appendix]), as well as if they received a preoperative transfusion (up to 72 hours prior to surgery) or had a history of prior pelvic surgery were dichotomized variables and considered either present or absent. A patient’s insurance was considered “Private” if a member of a preferred provider organization or health maintenance organization (HMO); “Government” if covered by Medicare, Medicaid, Veterans Administration, or TriCare; and “Other” if uninsured, self-pay, or means of payment unknown. Medicaid and Medicare HMO products offered by a commercial carrier were considered “Private.” Surgical approach categorized as abdominal, laparoscopic, robotic-assisted laparoscopic, vaginal, or laparoscopic-assisted vaginal; uterine weight; and weight loss were abstracted from the operative note or the pathology report and the medical record, respectively.
Internal Derivation and Validation
The MSQC database was randomly split into 2 groups—one for derivation of the model and the other for internal validation. Bivariate analysis was performed to determine if there were differences in the 2 cohorts.
External Validation
The logistic regression model developed and validated from the MSQC database was subsequently externally validated with an analysis of hysterectomies from the National Surgical Quality Improvement Program (NSQIP). Similar to MSQC, NSQIP is a clinical surgical registry built with medical record review by certified reviewers and automated medical record extraction when feasible. The dataset includes selected cases from a systematic sampling process used to capture a representative portion of surgical cases performed at each site. The dataset, available to co-author EE, consisted of cases collected between January 2005 and December 2014. Hysterectomies were identified from the gynecology cases in NSQIP with Current Procedural Terminology codes (see Appendix).
The composite postoperative complication outcome definition for NSQIP was as follows: cardiac arrest, myocardial infarction, cerebrovascular accident, cardiac dysfunction, death, DVT, pulmonary embolism, deep SSI, wound dehiscence, unplanned intubation, pneumonia, sepsis, severe sepsis, septic shock, and acute kidney injury. Clinical events that were not available in NSQIP included fistula, CLABI, C. Difficile, postoperative bowel and bladder injury, ileus, bowel obstruction, and postoperative bleeding complications. Some covariates used in the MSQC analysis were either not available or were identified differently for the NSQIP external validation. Insurance status, postoperative visceral organ injury, and classification of robotic assisted laparoscopy for surgical approach were not available in NSQIP. Gynecologic cancer was identified when there was radical dissection or lymphadenectomy, current cancer treatment (chemotherapy or radiation), presence of disseminated cancer, or presence of CNS tumor.
Statistical Analyses
Bivariate analyses were performed using student t-tests for continuous variables and Chi-square tests for categorical variables. In the derivation cohort, variables that had a plausible clinical association or a reported association with postoperative complications were included in the build of a multivariable logistic regression model. Variables not associated with postoperative complications were removed. Correlation matrices were used to examine collinearity among candidate variables to be included in the models. Spearman rank correlation coefficients were calculated. When the model, which controlled for age, was determined in the derivation cohort, the same factors were tested in the validation cohorts. All variables included in the model had <1% missing data. Documented weight loss >10% and preoperative hematocrit all had >5% missing values and were therefore excluded. Model performance in the derivation and validation cohorts was evaluated with a receiver operating characteristic analysis to determine the concordance index or “C-statistic.” Observed and predicted probabilities were plotted to determine the calibration across model deciles. Using the entire MSQC sample (both the derivation and validation cohorts), a nomogram was created from the factors independently associated with a postoperative complication. Statistical analyses were performed using SAS (version 9.4; SAS, Cary, NC) and STATA (version 16.1, College Station, TX) for the nomogram. The TRIPOD checklist was used for reporting on our development and validation of the risk assessment model.[13]
Results
In the MSQC registry, among the 41,147 women who met the inclusion criteria, 1,638 (3.98%) had a postoperative complication within 30 days of surgery. The most common postoperative infectious complications were deep SSI (n=424, 1.03%) and postoperative sepsis (n=349, 0.85%). The most common medical complications included acute renal failure (n=134, 0.33%); pulmonary embolism (n=111, 0.27%); DVT (n=84, 0.20%); and cardiac complications (n=100, 0.24%).
Preoperative characteristics of women with and without a major postoperative complication are compared in Table 1. Rates of complication were highest among women >60 years of age and those of African-American race. Women who had an ASA class ≥3, diabetes, a bleeding disorder, a preoperative hematocrit <36, gynecologic cancer, uterine weight >500 g, or a history of pelvic surgery also had higher rates of complications. Women with Government (versus Private) insurance, those who used tobacco within one year, those who received a preoperative transfusion, and those who underwent an abdominal surgery (compared to vaginal or laparoscopic) also had higher rates of complications.
Table 1.
Preoperative patient- and hospital-level factors in women with and without complications
| Patient Factors | Overall (n=41,147) | No Complications (n=39,494) | Complications (n=1638) | p-value |
|---|---|---|---|---|
|
| ||||
| Age, years | <.001 | |||
| <40 | 9431 (22.92) | 9081 (22.99) | 350 (21.37) | |
| 40–49 | 16,975 (41.26) | 16,396 (41.50) | 579 (35.35) | |
| 50–59 | 7910 (19.22) | 7593 (19.22) | 317 (19.35) | |
| ≥60 | 6830 (16.60) | 6438 (16.30) | 392 (22.93) | |
| BMI ≥40 | 5475 (13.34) | 5110 (12.97) | 365 (22.34) | <.0001 |
| Race | <.0001 | |||
| Caucasian | 31,797 (81.23) | 30,612 (81.50) | 1185 (74.91) | |
| African-American | 6819 (17.42) | 6444 (17.16) | 375 (23.70) | |
| All others | 2532 (6.15) | 220 (0.56) | 43 (2.63) | |
| Dependent functional status | 263 (0.64) | 220 (0.56) | 43 (2.63) | <.0001 |
| Diabetes | <.0001 | |||
| No diabetes mellitus, no A1c | 34,228 (83.18) | 33,046 (83.64) | 1182 (72.16) | |
| No diabetes mellitus, A1c 4–6.5% | 2238 (5.44) | 2125 (5.38) | 113 (6.90) | |
| Diabetes mellitus, A1c<9 or no A1C | 4243 (10.31) | 3939 (9.97) | 304 (18.56) | |
| Diabetes mellitus, A1c≥9 | 438 (1.06) | 399 (1.01) | 39 (2.38) | |
| ASA class ≥3 | 947 (23.02) | 8796 (22.26) | 675 (41.21) | <.0001 |
| Bleeding disorder | 481 (1.17) | 424 (1.07) | 57 (3.48) | <.0001 |
| Tobacco use | 10,051 (24.43) | 9612 (24.33) | 439 (26.80) | .02 |
| Insurance Status | <.0001 | |||
| Private | 28,139 (68.38) | 27234 (68.93) | 905 (55.25) | |
| Government | 11,114 (27.01) | 10,461 (26.48) | 653 (39.87) | |
| Othera | 1894 (4.60) | 1814 (4.59) | 80 (4.88) | |
| Uterine size, g | <.0001 | |||
| <100 | 12,752 (35.34) | 12,285 (35.45) | 467 (32.48) | |
| 100–250 | 15,896 (44.05) | 15,283 (44.11) | 613 (42.63) | |
| 250–500 | 4100 (11.36) | 3934 (11.35) | 166 (11.54) | |
| >500 | 3340 (9.26) | 3148 (9.09) | 192 (13.35) | |
| Preoperative Hct <36 | 7768 (19.81) | 7343 (19.50) | 425 (27.33) | <.0001 |
| Preoperative Transfusion | 339 (0.82) | 290 (0.73) | 49 (3.00) | <.001 |
| Preoperative Sepsis | 29 (0.05) | 14 (0.04) | 6 (0.37) | <.001 |
| Gynecologic Cancer | 3038 (7.38) | 2762 (6.99) | 276 (16.85) | <.0001 |
| Prior Pelvic Surgery | 5200 (12.64) | 4920 (12.45) | 280 (17.09) | <.0001 |
| Surgical Approach | <.0001 | |||
| Abdominal | 9958 (24.23) | 9256 (23.46) | 702 (42.91) | |
| Laparoscopic | 5344 (13.00) | 5176 (13.12) | 168 (10.27) | |
| Robotic-assisted laparoscopic | 17,589 (42.80) | 17,047 (43.20) | 542 (33.13) | |
| Vaginal | 4404 (10.72) | 4291 (10.87) | 113 (6.91) | |
| Laparoscopic-assisted vaginal | 3799 (9.24) | 3688 (9.35) | 111 (6.78) | |
Data presented as n (%)
Uninsured, self-pay, or means of payment unknown
ASA=American Society of Anesthesiologists Physical Status Classification System; BMI=body mass index
Complication rates of the derivation cohort (n=20,573) and the internal validation cohort (n=20,574) were 4.11% and 3.85%, respectively, and did not differ significantly (p=0.17). The MSQC cohorts did not differ significantly in demographics and preoperative characteristics. Factors independently associated with postoperative complications were consistent in the derivation and validation cohorts (Table 2), with the exception of preoperative sepsis. Women who had preoperative transfusion, ASA class ≥3, dependent functional status, a bleeding disorder, diabetes, gynecologic cancer, and Government insurance had higher rates of complications in the derivation and internal cohorts. Age was not independently associated with complications and was not kept in the model. Model discrimination was consistent across the MSQC cohorts, with C-statistics for the derivation and validation cohorts of 0.68 and 0.69, respectively. A calibration plot (Figure 1), which examined predicted and observed rates of postoperative complications, had a slope of 1.06, demonstrating calibration across groups considered low- and high-risk. A nomogram was then created using these independent risk factors to estimate the risk of a major postoperative complication (Figure 2).
Table 2.
Preoperative factors independently associated with postoperative complications after hysterectomy in derivation, internal validation, and external validation cohorts
| Factor | Internal Validation (MSQC) | External Validation (NSQIP) | ||||
|---|---|---|---|---|---|---|
| Derivation Cohort | Validation Cohort | |||||
| Odds Ratio | 95% CI | Odds Ratio | 95% CI | Odds Ratio | 95% CI | |
|
| ||||||
| Preoperative Sepsis | 7.90 | 1.97–32.20 | 2.09 | 0.42–1.76 | 8.6 | 6.25–11.83 |
| Surgical Approach | ||||||
| Vaginal | Referent | Referent | Referent | |||
| Abdominal | 2.27 | 1.70–3.05 | 2.45 | 1.81–1.75 | 1.95 | 1.74–2.18 |
| Laparoscopic | 1.26 | 0.89–1.77 | 1.20 | 0.84–1.73 | 0.89 | 0.80–1.01 |
| Robotic-assisted laparoscopic | 1.11 | 0.83–1.49 | 1.07 | 0.79–1.46 | N/A | |
| Laparoscopic-assisted vaginal | 1.08 | 0.74–1.58 | 1.26 | 0.86–1.85 | 1.06 | 0.92–1.21 |
| Dependent Functional Status | 2.21 | 1.34–3.63 | 2.00 | 1.20–3.34 | 2.15 | 1.69–2.73 |
| Bleeding Disorder | 2.10 | 1.37–3.21 | 2.06 | 1.35–3.11 | 1.91 | 1.57–2.33 |
| Diabetes | ||||||
| No diabetes mellitus, no A1c | Referent | Referent | Referent | |||
| No diabetes mellitus, A1c 4–6.5% | 1.29 | 0.98–1.70 | 1.29 | 0.96–1.74 | N/A | |
| Diabetes mellitus, A1c<9 or no A1C | 1.30 | 1.07–1.60 | 1.42 | 1.16–1.54 | 1.04 | 0.92–1.17 |
| Diabetes mellitus, A1c≥9 | 1.92 | 1.15–3.22 | 2.46 | 1.55–3.91 | 1.73 | 1.49–2.00 |
| Gynecologic Cancer | 1.85 | 1.49–2.31 | 1.97 | 1.58–2.46 | 1.89 | 1.72–2.08 |
| Preoperative Transfusion | 1.84 | 1.15–2.96 | 2.34 | 1.35–3.76 | 1.51 | 1.18–1.93 |
| ASA class ≥ 3 | 1.46 | 1.24–1.73 | 1.63 | 1.38–1.94 | 1.81 | 1.68–1.95 |
| Insurance | ||||||
| Private | Referent | Referent | N/A | |||
| Government | 1.63 | 1.40–1.90 | 1.30 | 1.11–1.54 | N/A | |
| Othera | 1.34 | 0.96–1.86 | 0.97 | 0.68–1.40 | N/A | |
| BMI ≥40 | 1.25 | 1.04–1.50 | 1.46 | 1.22–1.77 | 1.35 | 1.24–1.48 |
Uninsured, self-pay, or means of payment unknown
MSQC= Michigan Surgical Quality Collaborative; NSQIP=National Surgical Quality Improvement Program; ASA=American Society of Anesthesiologists Physical Status Classification System; BMI=body mass index
Figure 1.
Calibration plot showing predictive performance of risk assessment model
Figure 2. Risk assessment nomogram.
TVH=total vaginal hysterectomy
R=robotic-assisted laparoscopic hysterectomy
LVH=laparoscopic-assisted vaginal hysterectomy
TLH=total laparoscopic hysterectomy
TAH=total abdominal hysterectomy
DM=diabetes mellitus
ASA=American Society of Anesthesiologists Physical Status Classification System
The NSQIP external validation cohort included 136,621 women who underwent hysterectomy. Major postoperative complications occurred in 2.79% of hysterectomies in the NSQIP dataset. Logistic regression modeling using the prior model is shown in Table 2, with all prior identified factors being associated with a higher estimated risk of postoperative complications. Model discrimination assessed by a C-statistic was 0.68.
Discussion:
Using a statewide surgical registry with a postoperative complication risk of 4%, we developed and validated a preoperative model to assess the risk of a postoperative complication within 30 days of hysterectomy. We developed a tool that clinicians could use to quantify risk, enhance shared decision-making, and when possible, shape efforts to reduce risk of complications. This analysis specifically identified potentially modifiable risk factors associated with increased risk for postoperative complication, including use of preoperative allogeneic blood transfusion, poor glycemic control, and open abdominal surgery.
This analysis expands upon other risk assessment models,[6, 11, 14] with three important distinctions. First, our analysis focuses on women who underwent hysterectomy for benign or malignant gynecologic conditions, compared to Erekson et al,[6] who studied complications after hysterectomy and other benign gynecologic surgery. By adding malignant disease, we were able to account for the variation in complication rates across all types of hysterectomy. Second, we included women who underwent hysterectomy using any surgical approach, while others have limited their analysis to a specific approach.[11] Third, we used our multivariable logistic regression model to create a nomogram rather than a risk classification. This tool allows a user to calculate a score that provides an individualized percent risk for a postoperative complication, rather than an estimated risk range. Using such a tool can also be interactive, allowing physicians and patients to see how adjusting modifiable risk factors can affect the risk of complications. For example, if a patient with poorly controlled diabetes mellitus with a hemoglobin A1c of 10% were to undergo an abdominal hysterectomy, their estimated risk based on the nomogram would be almost 8%. However, if that same person were to delay their hysterectomy until their A1c was <9% and they underwent a laparoscopic approach, their estimated risk would be <5%.
We identified multiple modifiable risk factors for clinicians to consider. First, we found that abdominal surgical approach confers increased odds of postoperative complications—consistent with American College of Obstetricians and Gynecologists recommendations that a vaginal or laparoscopic hysterectomy, rather than abdominal, be performed when feasible.[15] We found increased odds of complications with elevations of HbA1c, particularly when HbA1c was ≥9%—consistent with prior studies showing glycemic control in patients with diabetes mellitus is associated with postoperative complications.[5, 6, 16, 17] Third, we found that preoperative blood transfusion is independently associated with postoperative complications consistent with prior studies showing that preoperative anemia and allogeneic blood transfusion are associated with surgical complications and poorer outcomes.[18–22] Our final model included only preoperative blood transfusion due to collinearity between hematocrit <36% and preoperative blood transfusion and excessive missing data for hematocrit. While this has yet to be proven, these findings suggest that efforts to use a minimally invasive hysterectomy route, improve glycemic control, and avoid preoperative blood transfusion may reduce the risk of a postoperative complication.
This study has important strengths. This analysis reflects a wide variability in clinical practice because it is from a large database registry with cases from academic and community hospitals and includes patients with any gynecologic indication and surgical approach for hysterectomy. Second, we developed a model that performed well, by validating internally with MSQC data and externally with NSQIP data. Calibration analysis demonstrated that the model estimated risk well across a spectrum of different populations (low versus high risk for a postoperative complication) (Figure 1). Finally, our nomogram provides a tool clinicians can use in counseling patients on their overall risk for postoperative complications and potentially highlights the importance of optimizing modifiable factors preoperatively. Future studies should focus on the prospective use of this risk assessment tool for further validation of our model, as well as on implementing the optimization of these modifiable risk factors to decrease postoperative complications.
Limitations of our study are important to consider. With observational data, there is potential for confounding. We identified differences in bivariate analysis and adjusted for these factors when there was an independent association in a multivariable model. We ultimately decided not to include age and other common factors that had no perceptible effect on the model and the predictive ability of the nomogram. The only preoperative variable not statistically significant in all cohorts was preoperative sepsis. Preoperative sepsis is a relatively rare clinical scenario before elective hysterectomy. We suspect that the lack of consistency in significance of sepsis across all cohorts is related to its low prevalence. Another limitation is that our database consists mostly of Caucasian or African-American persons or those with a race that was unknown, while persons of other races made up less than 1% of the database, which limits the generalizability of our study. Finally, although differences in the MSQC and NSQIP data regarding postoperative complications prevented us from developing an identical composite postoperative outcome, model discrimination was remarkably similar.
In this study, we developed and validated a risk assessment tool using preoperative factors to predict postoperative complications within 30 days of hysterectomy. We identified multiple risk factors that may potentially alter the 4% average risk of postoperative complications, including poorly controlled diabetes, receiving a blood transfusion within 72 hours before surgery, and undergoing an open (abdominal) hysterectomy. Our tool could be useful in counseling patients on their overall risk of a complication and identifying, and potentially modifying, risks prior to undergoing hysterectomy.
Financial Support:
Salary support for N.S.K and D.M.M. is provided by Blue Cross Blue Shield of Michigan (BCBSM) for the Obstetrics Initiative. Investigator support for C.W.S. was provided by the National Institute of Child Health and Human Development (NICHD) WRHR Career Development Award #K12 HD065257. BCBSM and NICHD played no role in the study design; in the collection, analysis and interpretation of data; in the writing of the report; or in the decision to submit the article for publication.
Appendix. ICD-9, ICD-10, and CPT-4 codes used to identify gynecologic cancer and hysterectomies
| Gynecologic Cancer: ICD-9 and ICD-10 Codes | |
| ICD-9 | 179, 180, 1800, 1801, 1808, 1809, 182, 1820, 1821, 1828, 183, 1830, 1832,1834,1838,1839,1840, 1841, 1842, 1843, 1844, 233, 2330, 2331,2332, 2333, 23330, 23331, 23332, 23339, 1986, 19881, 19882 |
| ICD-10 | C530, C531, C538, C539, C549, C540, C548, C569, C5700, C573, C574, C574, C52, C510, C511, C512, C519, D0590, D069, D070, D0739, D072, D071, D0739, C7981, C7981, C7982, C541, C518, D060, D0730, C7960, C542, D061, C543, D067 |
| Route of Hysterectomy: CPT-4 Codes | |
| Abdominal Hysterectomy | 51925, 58150, 58152, 58200, 58951, 58953, 58954, 58956, 58210, 58285, 58548, 58180 |
| Vaginal Hysterectomy | 58260, 58262, 58263, 58267, 58270, 58275, 58280, 58290, 58291, 58292, 58293, 58294 |
| Laparoscopic-Assisted Hysterectomy | 58550, 58552, 58553, 58554 |
| Laparoscopic Hysterectomy | 58570, 58571, 58572, 58573, 58541, 58542, 58543, 58544 |
Footnotes
Disclosure Statement: The authors declare that they have no conflicts of interest.
IRB: Not regulated by the University of Michigan Institutional Review Board (HUM00073978).
Publisher's Disclaimer: This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.
Data Availability:
Data available upon reasonable request from the lead author.
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Data Availability Statement
Data available upon reasonable request from the lead author.