Abstract
Objectives:
The American College of Surgeons National Surgical Quality Improvement Program (ACS NSQIP) surgical risk calculator calculates risk of postoperative complications utilizing clinically apparent preoperative variables. If validated for patients with gynecologic , this can be an effective tool in to use for shared decision-making, especially in the older (70+ years of age) patient population for whom surgical risks and potential loss of independence is increased. The primary objective of this study was to evaluate the ability of the ACS NSQIP surgical risk calculator to predict discharge to a post-acute care among older (age 70+ years) gynecologic oncology patients undergoing laparotomy. The secondary objectives were to assess its ability to predict postoperative complications and death.
Methods:
This was a retrospective cohort study of gynecologic oncology patients 70+ years of age undergoing laparotomy. Surgical procedures, 21 preoperative variables, postoperative complications, and patient disposition were abstracted from the medical record. Risk scores for seven postoperative complications and discharge to post-acute care were calculated. The association between risk scores and outcomes were assessed using logistic regression and predictive ability was evaluated using the c-statistic and Brier score.
Results:
204 surgeries were performed on 200 patients between January 1, 2009 and December 31, 2013. The mean age was 76.3±5.1 years; 87% were independent at baseline. A total of 79 (41%) were discharged to post-acute care. The calculator’s ability to predict discharge to post-acute care was reasonable (c- statistic =0.708, Brier=0.205). Although the calculator did not accurately predict all postoperative complications, the calculator’s ability to predict death was strong (c-statistic=0.811, Brier=0.015).
Conclusion:
For older patients with an elevated calculated risk of discharge to post acute care the possibility of discharge to post-acute care should be discussed preoperatively. For patients with a higher risk of death, non-surgical management options should be considered when available.
Keywords: Gynecologic Oncology, laparotomy, NSQIP, older patient, post-acute care, surgical risk calculator, discharge planning
INTRODUCTION
The mean age at diagnosis of ovarian cancer, which commonly requires an extensive laparotomy surgery, is 63 years, but 45% of patients diagnosed with ovarian cancer will be older than age 65 years, and 24% will be older than age 74 years1. Population-based studies have shown higher rates of perioperative morbidity and 30-day postoperative mortality in older patients undergoing surgery with any indication2,3. Additionally, 14% of patients age 70–79 years and 33% of patients older than 80 years will require discharge to post-acute care3. However, individual patient risk for discharge to post-acute care will vary by performance status and other medical comorbidities. Previous studies have shown that fulfillment of preoperative patient expectations is associated with improved patient satisfaction, postoperative quality of life, and decreased disability4. If individual patient risk is determined, personalized preoperative counseling and planning can occur.
The American College of Surgeons National Surgical Quality Improvement Program (ACS NSQIP) surgical risk calculator is designed to predict the risk of any complication, serious complication (defined as death, cardiac arrest, myocardial infarction, pneumonia, progressive renal insufficiency, acute renal failure, pulmonary embolus, deep venous thrombosis, return to the operating room, deep incisional surgical space infection (SSI), organ space SSI, systemic sepsis, unplanned intubation, urinary tract infection (UTI), wound disruption), and seven specific postoperative complications, length of stay, and discharge to post-acute care5,6. The calculator was developed using a regression model to determine the strength of association between preoperative variables and postoperative outcomes using data from 1.4 million patients at 393 NSQIP hospitals. The variables within the calculator were weighted based on the regression coefficient5,6. Data from all surgical specialties except trauma and transplant were included in the development of the calculator, but gynecologic surgery patients comprised only 5.3% of the original cohort, and only 1.1% of the population that was used to develop the discharge prediction tool5,6. Other retrospective studies of the predictive ability of the calculator in gynecologic oncology patients have shown poorer performance compared to its performance in colorectal surgery patients who served as the original validation cohort for the calculator5,7,8. The higher postoperative morbidity and mortality rate in older patients may improve the predictive ability of the calculator. Additionally, particularly relevant to this older cohort, a risk percentage for discharge to post-acute care has been added to the calculator, which was not available at the time of previous analyses6. The primary objective of this study was to evaluate the ACS NSQIP surgical risk calculator’s ability to predict discharge to post-acute care in older (70 years of age or older) gynecologic oncology patients undergoing laparotomy. Secondary objectives were to evaluate the ability of the calculator to predict postoperative complications and death in this population.
METHODS
This retrospective cohort study was reviewed by the University of Minnesota Institutional Review Board and meets the requirements for protection of human subjects. The gynecologic oncology surgical database was queried to identify patients who underwent a laparotomy procedure with the gynecologic oncology service at the University of Minnesota Medical Center from January 1, 2009 through December 30, 2013. All patients 70 years of age or older at the time of their laparotomy procedure were included in this study. Data coding and entry of surgical procedures into the ACS NSQIP surgical risk calculator have been described previously7. In brief, surgical procedures were categorized as detailed in Table 2. “Less than Hysterectomy” included exploratory laparotomies with limited biopsies and/or adnexal surgery; “staging” referred to any procedure performed to assess the extent of malignancy, including but not limited to omentectomy, lymphadenectomy, peritoneal biopsies; “debulking” included any procedures to remove gross tumor excluding bowel resection which had its own category due to the unique risks associated with this procedure (e.g. delayed bowel function; anastomotic leak). Common Procedural Terminology (CPT) codes for the procedures performed were entered into the surgical risk calculator. For surgeries with more than one CPT code, a different iteration was run under each CPT code and the CPT code resulting in the highest estimated surgical risk was used in the analysis.
Table 2.
Patient baseline characteristics (N=204)
| Variable | n | (%) |
|---|---|---|
| Age, years, mean ± SD | 76.3±5.1 | |
| Functional Status | ||
| Independent | 177 | (86.8) |
| Partially dependent | 22 | (10.8) |
| Totally dependent | 5 | (2.5) |
| Emergency Case | ||
| No | 202 | (99.0) |
| Yes | 2 | (1.0) |
| ASA Class | ||
| Healthy patient | 12 | (5.9) |
| Mild systemic disease | 98 | (48.0) |
| Severe systemic disease | 93 | (45.6) |
| Severe systemic disease/threat to life | 1 | (0.5) |
| Wound Class | ||
| Clean | 43 | (21.2) |
| Clean/contaminated | 153 | (75.4) |
| Contaminated | 7 | (3.5) |
| Steroid use of chronic condition | ||
| No | 196 | (96.0) |
| Yes | 8 | (4.0) |
| Ascites within 30 days prior to surgery | ||
| No | 174 | (85.3) |
| Yes | 30 | (14.7) |
| Systemic sepsis within 48 hours before surgery | ||
| No | 204 | (100) |
| Ventilator dependent | ||
| No | 204 | (100) |
| Disseminated cancer | ||
| No | 108 | (52.9) |
| Yes | 96 | (47.1) |
| Diabetes | ||
| None | 161 | (78.9) |
| Oral | 32 | (15.7) |
| Insulin | 11 | (5.4) |
| Hypertension requiring medication | ||
| No | 70 | (34.3) |
| Yes | 134 | (65.7) |
| Previous cardiac event | ||
| No | 171 | (83.8) |
| Yes | 33 | (16.2) |
| Congestive heart failure in 30 days prior to surgery | ||
| No | 195 | (95.6) |
| Yes | 9 | (4.4) |
| Dyspnea | ||
| None | 176 | (86.3) |
| Moderate exertion | 21 | (10.3) |
| At rest | 7 | (3.4) |
| Smoker | ||
| No | 190 | (93.1) |
| Yes | 14 | (6.9) |
| History of severe COPD | ||
| No | 190 | (93.1) |
| Yes | 14 | (6.9) |
| Dialysis | ||
| No | 204 | (100) |
| Acute renal failure | ||
| No | 200 | (98.0) |
| Yes | 4 | (2.0) |
| BMI Category | ||
| Underweight (<18.5 kg/m2) | 3 | (1.5) |
| Normal (18.5–24.9 kg/m2) | 46 | (22.8) |
| Overweight (25–29.9 kg/m2) | 68 | (33.7) |
| Obese (≥30 kg/m2) | 85 | (42.1) |
| Missing | 2 | |
| Surgery | ||
| Less than Hysterectomy | 28 | (13.7) |
| Hysterectomy with or without bilateral salpingo-oophorectomy | 31 | (15.2) |
| Staging | 72 | (35.3) |
| Debulking | 66 | (32.4) |
| Exenteration | 7 | (3.4) |
The 21 preoperative variables required for the ACS NSQIP surgical risk calculator were abstracted from the electronic health record and are detailed in Table 1. These data along with the CPT code, as described above, were entered into the calculator and prediction scores for discharge to post-acute care, length of stay and risk of any postoperative complication, serious complication, seven specific complications (pneumonia, cardiac complications defined as cardiac arrest or myocardial infarction, superficial, deep or organ space SSI, UTI, VTE, and renal failure, death) were calculated and recorded.
Table 1.
Preoperative variables and predicted postoperative outcomes of the ACS NSQIP surgical risk calculator
| Preoperative Variables | Postoperative Outcomes |
|---|---|
| Age | Death |
| Sex | Any serious complication |
| Functional Status | Cardiac complication |
| Emergency Case | Pneumonia |
| ASA* Class | Progressive renal insufficiency |
| Wound Class | Acute renal failure |
| Steroid use for chronic condition | VTE* |
| Ascites within 30 days of surgery | Return to the operating room |
| Systemic sepsis within 48 hours before surgery | Deep incisional or organ SSI* |
| Ventilator dependent | Systemic sepsis |
| Disseminated cancer | Unplanned re-intubation |
| Diabetes | UTI* |
| Hypertension requiring medication | Wound disruption |
| Previous cardiac event | Any complication |
| CHF* in 30 days prior to surgery | Pneumonia |
| Dyspnea | Cardiac Complication |
| Current Smoker | SSI* |
| History of severe COPD* | UTI* |
| Dialysis | VTE* |
| Acute renal failure | Renal failure |
| BMI* Category | Readmission |
| Return to OR | |
| Death | |
| Discharge to Acute Rehab |
ASA, American Society of Anesthesiologists; CHF congestive heart failure; COPD, chronic obstructive pulmonary disease; BMI, body mass index; VTE, venous thromboembolic event; SSI, surgical site infection; UTI, urinary tract infection
The primary outcome of interest was discharge to post-acute care; secondary outcomes included postoperative complications and length of hospital stay. Information on each patient’s discharge location and postoperative complications within 30 days of surgery were abstracted from the medical record. Patients who were discharged to hospice or a long-term care facility were also categorized as discharged to post-acute care.
Baseline demographic and clinical characteristics were summarized and descriptive statistics are presented. For each outcome, an aggregate median risk score for each event was calculated for those who did and did not experience an event. The association between the median calculated risk score of discharge to post-acute care and actual disposition was calculated using logistic regression. The ability of the calculator to accurately predict those who would and would not need post-acute care was assessed using the c- statistic and Brier score. The c-statistic is the area under a receiving operating characteristic (ROC) curve. A c-statistic (range 0.5–1.0) of 1.0 indicates the model perfectly predicts the outcome, and a c-statistic of 0.5 indicates that the prediction model is no better than chance. Models are considered “reasonable” when the c-statistic is higher than 0.7 and “strong” when it is greater than 0.89. The Brier score describes the mean squared differences between the predicted risk and the actual outcome. A model that perfectly predicts the outcomes of all individuals has a Brier score of 0. Data were analyzed using SAS 9.4 (Cary, NC) and p-values <0.05 were considered statistically significant.
RESULTS
Between January 1, 2009 and December 31, 2013, 200 individuals underwent a total of 204 surgeries; four individuals underwent two separate eligible surgeries at least 30 days apart and risk scores from both surgeries were included in the study. The demographic data are presented in Table 2. The mean age of patients was 76.3±5.1 years. Most were independent (defined by NSQIP as having the ability to perform activities of daily living, without the aid of another person, although prosthetics, devices or other equipment may be used) prior to surgery (87%), and only one patient resided in a nursing home prior to surgery. Almost half (45%) had an ASA class III and IV. Three-quarters of patients were overweight or obese (76%). A majority of patients (86%) had a final diagnosis of malignancy (Table 3), and 47% of all patients had disseminated cancer diagnosed on imaging preoperatively. A total of 72 patients (35%) underwent staging surgeries, and an additional 66 (32%) underwent debulking procedures.
Table 3.
Malignancy origin (N=200)
| Malignancy | n | (%) |
|---|---|---|
| Cervix | 4 | 2.0 |
| Fallopian tube | 7 | 3.5 |
| Ovary | 72 | 36.0 |
| Primary Peritoneal | 13 | 6.5 |
| Uterine | 55 | 27.5 |
| Vagina/Vulva | 6 | 3.0 |
| Non-Gynecologic | 14 | 7.0 |
| Benign | 29 | 14.5 |
Two patients died prior to discharge from the hospital and disposition location was not documented for seven cases. Of the remaining 195 cases for which disposition data were available, 79 patients (41%) received a recommendation for postoperative discharge to post-acute care, including the one patient who lived in a nursing home prior to surgery. Two patients were discharged to home despite a recommendation for discharge to an acute rehabilitation facility, and these patients were analyzed as discharged to post-acute care. The median calculated risk for patients who were discharged to post-acute care was 8.6% compared to 4.0% for those discharged to home (OR=1.14, 95% CI 1.08–1.20; p<0.0001) (Table 4). The surgical risk calculator reasonably predicted discharge to post-acute care (c-statistic=0.708, Brier=0.205) (Table 5).
Table 4.
Median calculated risk scores for those who did and did not have an event (N=204)
| Outcome | n | Median (Min, Max) | n | Median (Min, Max) |
|---|---|---|---|---|
| Death | 201 | 1.0 (0.5–37.0) | 3 | 6.0 (1.0–22.0) |
| Any serious complication | 144 | 9.0 (4.0–34.0) | 60 | 11.0 (5.0–35.0) |
| Any complication | 105 | 11.0 (3.0–46.0) | 99 | 15.0 (5.0–48.0) |
| Pneumonia | 193 | 1.0 (0.5–7.0) | 11 | 1.0 (0.5–2.0) |
| Cardiac complication | 197 | 0.5 (0.5–4.0) | 7 | 0.5 (0.5–1.0) |
| SSI | 167 | 4.0 (1.0–36.0) | 37 | 5.0 (1.0–17.0) |
| UTI | 173 | 4.0 (1.0–29.0) | 31 | 5.0 (3.0–19.0) |
| VTE | 199 | 1.0 (0.5–6.0) | 5 | 2.0 (0.5–2.0) |
| Renal failure | 198 | 0.5 (0.5–4.0) | 3 | 1.0 (0.5–1.0) |
| Discharge to post-acute care* | 116 | 4.0 (1.3–20.3) | 79 | 8.6 (1.4–53.5) |
Excludes 2 patients who died prior to discharge and 7 without discharge information
Table 5.
Complication rates and model statistics for the ACS NSQIP surgical risk calculator in older gynecologic oncology patients (N=204)
| Outcome | Events, n (%) | Odds Ratio (95% CI), p-value | c-statistic | Brier score |
|---|---|---|---|---|
| Death | 3 (1.5) | 1.12 (1.01–1.25), p=0.03 | 0.811 | 0.015 |
| Any serious comp | 60 (29.4) | 1.08 (1.03–1.14), p=0.003 | 0.629 | 0.198 |
| Any complication | 99 (48.6) | 1.06 (1.02–1.09), p=0.003 | 0.652 | 0.237 |
| Pneumonia | 11 (5.4) | 0.79 (0.39–1.63), p=0.53 | 0.486 | 0.051 |
| Cardiac | 7 (3.4) | 0.77 (0.12–4.90), p=0.78 | 0.480 | 0.033 |
| SSI | 37 (18.1) | 1.10 (0.99–1.22), p=0.07 | 0.637 | 0.146 |
| UT1 | 31 (15.2) | 1.12 (1.02–1.22), p=0.01 | 0.661 | 0.125 |
| VTE | 5 (2.5) | 0.94 (0.43–2.07), p=0.88 | 0.468 | 0.024 |
| Renal failure | 3 (1.5) | 1.23 (0.21–7.36), p=0.82 | 0.646 | 0.015 |
| Post-acute care* | 79 (40.5) | 1.14 (1.08–1.20), p<0.0001 | 0.708 | 0.205 |
Excludes 2 patients who died prior to discharge and 7 without discharge information
Higher calculated aggregate median risk scores were associated with increased rate of any complication (OR 1.06, 95% CI 1.02–1.09; p=0.003), any serious complications (OR 1.08; 95% CI 1.03–1.14; p=0.003), and UTI (OR 1.12, 95% CI 1.02–1.22; p=0.01). Despite these associations, the calculator was not a good predictor of these complications with c-statistics of less than 0.7 and high Brier scores (Table 5). The calculator performed best for predicting death (c-statistic=0.811, Brier=0.015), although this statistic was based on only three death events. Median risk score for those who died was 6.0% compared to 1.0% for those who did not have the event (OR=1.12, 95% CI 1.01–1.25; p=0.03).
DISCUSSION
Independent of postoperative complications, a substantial proportion of previously independent older women are not be able to return immediately home after surgery due to need for additional physical, occupational therapy, or other nursing care2,3. While results are not uniform, a systematic review of 60 studies including 13 surgical subspecialties showed realistic postoperative expectations to be positively associated with patient reported outcomes in 40% of studies, including satisfaction, quality of life and disability4. For example a study of patients undergoing colorectal surgery found that patients with inflated postoperative expectations experienced worse postoperative disability and increased postoperative fatigue10. Therefore, Center for Medicare and Medicaid services requires that discussion of disposition to post-acute care be included in the informed consent process when indicated11. Our study showed that the ACS NSQIP calculator was a reasonable predictor of discharge to post-acute care for older gynecologic oncology patients undergoing laparotomy. This is supported by previous validation of the ACS NSQIP surgical risk calculator, which showed that the calculator was a strong predictor of discharge to post-acute care among all surgical patients6. It is not entirely clear why our study showed decreased predictive ability compared to the initial validation study, but this may be due to differences in the study design or study population. In the initial validation cohort, only 35% of the patients were older than 65 years of age, while in our study all patients were age 70 years or older. We hypothesized the older patient population would enhance the predictive ability of the calculator since the initial study found age older than 85 years to be a significant predictor of discharge to post-acute care, however this was not confirmed by our study’s results. It is notable that the calculator does not consider postoperative complications in its discharge prediction; it is possible that our patients who are often older and sicker but who still undergo aggressive surgical procedures in the setting of cancer had a higher likelihood of post-acute care discharge due to postoperative complications. Our findings in patients with gynecologic cancers are supported by another retrospective cohort study showing reasonable but decreased predictive ability of the ACS NSQIP calculator in patients with bladder cancer undergoing radical cystectomy with urinary diversion. Also similar to our study, a discrete cut-off value for prediction of discharge to post-acute care and death could not be determined12. Although the original validation paper suggests that the discharge risk score should be used to inform shared decision-making when deciding whether or not to proceed with surgery, the results of our study and other oncology studies suggest that this should be part of the discussion but should not be the only factor in the decision making process for patients with cancer due to the large overlap in risk scores among patients who did and did not require post-acute care.
Other efforts to preoperatively predict postoperative risks include the addition of a frailty score (calculated using unintentional weight loss, grip strength, exhaustion, level of physical activity, and walking speed) to other predictors of postoperative outcomes such as ASA class. A study of 595 patients undergoing elective surgery found that the addition of the frailty score strengthened the predictive ability of these other indices to a predictive ability of approximately 80%13. A retrospective cohort study utilized a modified frailty index which used diabetes, impaired functional status, chronic obstructive pulmonary disease, impaired sensorium, transient ischemic attack, and cerebral vascular accident to assess baseline frailty in patients with endometrial cancer undergoing hysterectomy, and its association with discharge to post-acute care. With frailty defined as having two of the eleven indices present, the study showed frailty had an odds ratio of 1.95 (95% CI 1.91–5.01) and disseminated cancer had an odds ratio of 10 (95% CI 2.28–44.1) for discharge to post-acute care14. The strong association between the presence of disseminated cancer and discharge to post-acute care may partially explain why our study, in which almost half the patients had disseminated cancer, found the ACS NSQIP calculator tended to overestimate risk of discharge to post-acute care and thus decreased predictive ability compared to studies in the general surgery population.
Age 70 years or older has been shown to be an independent risk factor for postoperative cardiac and non-cardiac major morbidity2,3. Additionally, for patients with advanced malignancies, even less severe surgical complications may cause a delay in adjuvant therapies such as chemotherapy or radiation, which may be detrimental to the patient’s overall disease outcome15. The Society of Gynecologic Oncology (SGO) and American Society of Clinical Oncology (ASCO) have published joint practice guidelines recommending that patients with a high-risk of perioperative morbidity receive neoadjuvant chemotherapy rather than primary cytoreductive surgery for treatment of clinically advanced ovarian cancer16 based on randomized controlled trials showing lower postoperative morbidity with neoadjuvant chemotherapy and similar overall and progression-free survival17,18. Our study showed that though the ACS NSQIP calculator was a poor predictor of postoperative complications, it was able to predict death in our older gynecologic oncology population. Although our results need to be interpreted with caution given that the statistics are based on only three death events, neoadjuvant chemotherapy should be considered in those patients with advanced-stage ovarian cancer who have a higher-than-average risk of death per the surgical risk calculator. This is supported by previous research showing that the ACS NSQIP surgical risk calculator reasonably predicted death among gynecologic oncology patients undergoing a surgery with the gynecologic oncology service regardless of age7,8, as well as other studies in non-gynecologic patients with cancer19. Additionally, a prospective study in patients without cancer showed the postoperative mortality rate can be significantly decreased with preoperative optimization of medical comorbidities and palliative care as indicated20.
The strengths of our study include the relatively large number of older patients who underwent laparotomy procedures, which are standard for a gynecologic oncology practice. We had complete preoperative data for all patients, allowing for accurate risk score calculations. All surgeries were performed in a university setting by fellowship-trained gynecologic oncologists, which may limit generalizability of our results. However, the university is a large referral center for patients from varying geographic, racial, and socioeconomic backgrounds, and accepts patients with and without health insurance, thus increasing generalizability to a number of practice settings. Additionally, the statistical tests used in the original calculator validation study were applied in this study. The limitations of our study are largely due to a retrospective assessment of a tool that is designed for prospective use. The calculator has a “surgeon risk adjustment” tool that allows a surgeon to increase or decrease the risk based on the patient’s overall status, and this tool could not be applied retrospectively. Although we have complete preoperative data on all patients, we were missing disposition data on seven patients (3%), and due to our large referral base which includes rural Minnesota, North and South Dakota, it is possible that information on postoperative complications between the two-week outpatient postoperative evaluation and postoperative day 30 could be missing due to treatment at outside facilities; however, a previous study conducted by our group showed that we had complete postoperative data on 95% of patients through phone notes and other communication from patients, their family members, or outside healthcare providers21. Since the calculator tends to over-estimate risk, under-reporting of postoperative complications may falsely decrease the predictive ability of the calculator. Additionally, only patients who underwent laparotomy were included in this study, and we do not have data on patients for whom the surgeon recommended against surgery due to co-morbidities or on patients who elected not to proceed with surgery. Lastly, although the statistical results of our study suggest that the ACS NSQIP surgical risk calculator is a reasonable predictor of discharge to post-acute care and a strong predictor of death, there is a large overlap in risk scores between those who did and did not experience an event. For example, for those who died, the median risk score was 6.0 compared to 1.0 for those who did not die, but 95% confidence intervals were wide at 1.0–22.0 and 0.5–37.0, respectively, making it difficult to determine a specific risk score above which surgery should not be recommended. In general the calculator tends to overestimate the risk of adverse events, thus achieving a high sensitivity but a low specificity.
In conclusion, the ACS NSQIP surgical risk calculator may be used to identify older gynecologic oncology patients who are at higher risk of discharge to post-acute care, and should be incorporated into preoperative planning and discussion with patients. However, since the calculator over-predicted the number of patients who would require discharge to post-acute care, we caution the use of the risk score as a determinate of whether or not to proceed with surgery. Additionally, for those patients with an increased risk score for death, non-surgical treatment options should be considered when available. Despite these strengths of the ACS NSQIP surgical risk calculator, a better tool is needed to predict postoperative complications in the older gynecologic oncology population undergoing laparotomy.
FUNDING:
1. Research reported in this publication was supported by NIH grant P30 CA77598 utilizing the Biostatistics and Bioinformatics Core shared resource of the Masonic Cancer Center, University of Minnesota and by the National Center for Advancing Translational Sciences of the National Institutes of Health Award Number UL1TR000114.
2. Research reported in this publication was supported by The Masonic Cancer Center Women’s Health Scholarship sponsored by the University of Minnesota Masonic Cancer Center, a comprehensive cancer center designated by the National Cancer Institute, and administrated by the University of Minnesota Deborah E. Powell Center for Women’s Health.
3. Research is supported by the Building Interdisciplinary Research Careers in Women’s Health Grant (# K12HD055887) and administered by the University Of Minnesota Deborah E. Powell Center for Women’s Health. This award is co-funded by the Eunice Kennedy Shriver National Institutes of Child Health (NICHD) and the Office of Research on Women’s Health (ORWH). This award is also funded by the Office of the Director, National Institutes of Health (OD), National Institute of Mental Health (NIMH), and the National Cancer Institute.
Dr. Teoh reports grants from National Institute of Health, and Dr. Isaksson Vogel reports grants from National Institute of Health, during the conduct of the study.
The content is solely the responsibility of the authors and does not necessarily represent the office views of the co-funders. The co –funders were not involved in the writing or decision to submit the manuscript.
Footnotes
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Portions of this research have been presented at:
Poster at the International Gynecologic Cancer Society biannual meeting October 29–31, 2016, Lisbon, Portugal
Oral Presentation Minnesota Surgical Society 2016 Fall Conference September 30-October 1, 2016, Brainerd , MN
CONFLICTS OF INTEREST AND DISCLOSURES
Conflicts of interest: None
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