The Predictive Value of PCT and Other Infection Indicators in Postoperative Infection of Epithelial Ovarian Cancer

Infect Drug Resist. 2023 Mar 17;16:1521-1536. doi: 10.2147/IDR.S399666. eCollection 2023.


Purpose: To study the early predictive value of WBC, CRP and PCT on infectious complications after epithelial ovarian cancer surgery, draw ROC curves, and construct a nomogram prediction model.

Patients and methods: The clinical data of patients with epithelial ovarian cancer in Shengjing Hospital from August 2019 to August 2022 were included. The levels of WBC, CRP and PCT were statistically analyzed on the first, third and fifth days after surgery, and the ROC was plotted. Multivariate logistic regression analysis determined independent influencing factors, individualized nomogram model for predicting the occurrence of postoperative infectious complications was constructed, and the correction curve was used for verification.

Results: A total of 116 patients were enrolled. The postoperative test levels of WBC, CRP and PCT were compared between two groups, and the differences on POD3 and POD5 were statistically significant. The ROC area on POD5 was 0.739, 0.838 and 0.804, respectively, better than that on POD3. Among them, CRP has the greatest value; The predicted value of the combined test of WBC, CRP and PCT on POD5 was greater than that of a single index on POD5. The nomogram model on POD5 was constructed, and the ROC analysis showed that it had a good degree of differentiation.

Conclusion: WBC, CRP and PCT can effectively predict the occurrence of postoperative infectious complications, among which CRP alone has the greatest diagnostic value on POD5, and the combined test value of the three indicators is higher than that of a single index. The nomogram model constructed by the combined indicators on POD5 can assess the risk individually.

Keywords: epithelial ovarian cancer; postoperative infectious complications; prediction; surgery.

Grant support

This research received the support from Scientific research funding project of Liaoning Provincial Department of Science and Technology (No.2020JH2/10300050).