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.
- Risk prediction
- Gynecologic oncology
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The authors declare no conflicts of interest.
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