Shim Seung-Hyuk, Lee Sun Joo, Dong Meari, Suh Jung Hwa, Kim Seo Yeon, Lee Ji Hye, Kim Soo-Nyung, Kang Soon-Beom, Kim Jayoun
Department of Obstetrics and Gynecology, Konkuk University School of Medicine, 120 Neungdong-ro, Gwangjin-gu, Seoul, Korea.
Research Coordinating Center, Konkuk University Medical Center, 120 Neungdong-ro, Gwangjin-gu, Seoul, Korea.
PLoS One. 2017 Jun 1;12(6):e0178610. doi: 10.1371/journal.pone.0178610. eCollection 2017.
The potential risk of postoperative morbidity is important for gynecologic cancer patients because it leads to delays in adjunctive therapy and additional costs. We aimed to develop a preoperative nomogram to predict 30-day morbidity after gynecological cancer surgery.
Between 2005 and 2015, 533 consecutive patients with elective gynecological cancer surgery in our center were reviewed. Of those patients, 373 and 160 patients were assigned to the model development or validation cohort, respectively. To investigate independent predictors of 30-day morbidity, a multivariate Cox regression model with backward stepwise elimination was utilized. A nomogram based on this Cox model was developed and externally validated. Its performance was assessed using the concordance index and a calibration curve.
Ninety-seven (18.2%) patients had at least one postoperative complication within 30 days after surgery. After bootstrap resampling, the final model indicated age, operating time, and serum albumin level as statistically significant predictors of postoperative morbidity. The bootstrap-corrected concordance index of the nomogram incorporating these three predictors was 0.656 (95% CI, 0.608-0.723). In the validation cohort, the nomogram showed fair discrimination [concordance index: 0.674 (95% CI = 0.619-0.732] and good calibration (P = 0.614; Hosmer-Lemeshow test).
The 30-day morbidity after gynecologic cancer surgery could be predicted according to age, operation time, and serum albumin level. After further validation using an independent dataset, the constructed nomogram could be valuable for predicting operative risk in individual patients.
术后发病的潜在风险对妇科癌症患者很重要，因为它会导致辅助治疗延迟和额外费用增加。我们旨在开发一种术前列线图，以预测妇科癌症手术后30天的发病率。
回顾了2005年至2015年间在我们中心连续进行择期妇科癌症手术的533例患者。其中，373例和160例患者分别被分配到模型开发或验证队列。为了研究30天发病率的独立预测因素，使用了具有向后逐步消除的多变量Cox回归模型。基于该Cox模型开发了列线图并进行了外部验证。使用一致性指数和校准曲线评估其性能。
97例（18.2%）患者在术后30天内至少发生了一种术后并发症。经过自助重抽样后，最终模型显示年龄、手术时间和血清白蛋白水平是术后发病的统计学显著预测因素。纳入这三个预测因素的列线图经自助校正后的一致性指数为0.656（95%CI，0.608-0.723）。在验证队列中，列线图显示出较好的区分度[一致性指数：0.674（95%CI = 0.619-0.732）]和良好的校准（P = 0.614；Hosmer-Lemeshow检验）。
根据年龄、手术时间和血清白蛋白水平可以预测妇科癌症手术后30天的发病率。使用独立数据集进一步验证后，构建的列线图对于预测个体患者的手术风险可能具有重要价值。
