Morbidity and Mortality Risk Assessment in Gynecologic Oncology Surgery Using the American College of Surgeons National Surgical Quality Improvement Program Database. Issue 4 (May 2018)

• Record Type: Journal Article
• Authors: Kohut, Adrian; Orfanelli, Theofano; Poggio, Juan Lucas; Gibbon, Darlene; Buckley De Meritens, Alexandre; Richard, Scott
• Is Part Of: International journal of gynecological cancer. Volume 28:Issue 4(2018)
• Publication Date: 2018-05
• Subjects: Risk prediction -- Mortality -- Morbidity -- NSQIP -- Gynecologic oncology; Generative organs, Female -- Cancer -- Periodicals; 616.99465
• DOI: 10.1097/IGC.0000000000001234
• Languages: English
• ISSNs: 1048-891X

Abstract

Abstract : Introduction: Gynecologic oncology patients represent a distinct patient population with a variety of surgical risks. The American College of Surgeons National Surgical Quality Improvement Program (ACS NSQIP) database provides an opportunity to analyze large cohorts of patients over extended periods with high accuracy. Our goal was to develop a postoperative risk assessment calculator capable of providing a standardized, objective means of preoperatively identifying high-risk patients in the gynecologic oncology population. Methods: We queried the ACS NSQIP database for gynecologic oncology patients from 2005 to 2013. Multivariate logistic regression was performed to generate predictive models specific for 30-day postoperative mortality and major morbidity. Results: There were 12, 831 patients with a primary gynecologic malignancy identified: 7847 uterine, 3366 adnexal, 1051 cervical, and 567 perineum cancers. In this cohort, 125 (0.97%) patients died, and 784 (6.11%) major morbidity events were recorded within 30 days of their surgery. For 30-day mortality, the mean calculated predictive probability was 0.128 (SD, 0.219) compared with 0.009 (SD, 0.027) in patients alive 30 days postoperatively ( P < 0.0001). The mean predictive probability of major morbidity was 0.097 (SD, 0.095) compared with 0.059 (SD, 0.043) in patients who did not experience major morbidity 30 days postoperatively ( P < 0.0001). Conclusions: Using NSQIP data, these predictive models will help to determine patients at risk for 30-day mortality and major morbidity. Further clinical validation of these models is required.