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Multicenter Study
. 2025 Apr 29;18(1):88.
doi: 10.1186/s13048-025-01624-3.

The impact of preoperative immunonutritional status on postoperative complications in ovarian cancer

Xingyu Liu #  1   2 , Ming Li #  3 , Yingjun Zhao  3 , Xiaofei Jiao  1   2 , Yang Yu  1   2 , Ruyuan Li  4 , Shaoqing Zeng  1   2 , Jianhua Chi  1   2 , Guanchen Ma  1   2 , Yabing Huo  1   2 , Zikun Peng  1   2 , Jiahao Liu  1   2 , Qi Zhou  5   6   7 , Dongling Zou  4   5   6 , Li Wang  8 , Qingshui Li  9 , Jing Wang  10   11 , Shuzhong Yao  12 , Youguo Chen  13 , Ding Ma  1   2 , Ting Hu  14   15 , Qinglei Gao  16   17
Affiliations
Multicenter Study

The impact of preoperative immunonutritional status on postoperative complications in ovarian cancer

Xingyu Liu et al. J Ovarian Res. .

Abstract

Background: Preoperative immunonutritional status can influence postoperative complications. Malnutrition in ovarian cancer patients diminishes the body's resilience to abdominal surgery, resulting in inferior surgical outcomes and increased postoperative complications. We aim to investigate the effect of preoperative immunonutritional status, including NLR, PLR, LMR, TCLR, FAR, FLR, SII, PNI and CONUT on postoperative complications in epithelial ovarian cancer (EOC) in a large population.

Methods: A multicenter real-world study included 922 patients with histologically confirmed EOC who received comprehensive staged surgery or debulking surgery at seven tertiary hospitals in China between 2012 and 2023. Logistic regression and Lasso regression analyses were employed to identify variables associated with postoperative complications. A predictive nomogram model was developed based on multivariate modeling.

Results: The study included a total of 922 patients diagnosed with epithelial ovarian cancer across seven medical centers with 565 (61.3%) patients experiencing postoperative complications. Significant differences were found in the distribution of inflammatory and nutritional risk indicators, including NLR, PLR, LMR, TCLR, FAR, FLR, SII, PNI and CONUT between the two groups (all P < 0.01). A multivariable model identified several predictive factors for postoperative complications: PNI > 46.73 (odds ratio [OR] = 0.49, P < 0.001), FAR > 10.77 (OR = 1.60, P = 0.019), LMR > 3.70 (OR = 0.68, P = 0.044), hydrothorax (OR = 2.60, P = 0.005), laparoscopy (OR = 0.59, P = 0.010 vs. laparotomy), enterectomy (OR = 2.50, P = 0.001).

Conclusion: Poor immunonutritional status can increase the risk of postoperative complications. These findings suggest that prompt nutritional interventions may reduce the incidence of postoperative complications and improve surgical outcomes. The risk prediction model, including PNI, FAR, LMR, hydrothorax, laparoscopy vs. laparotomy, and enterectomy, might facilitate patient-centered decision-making and risk stratification.

Clinical trial registration: The study was registered in the Clinical trial registry: NCT06483399. ( https://clinicaltrials.gov/study/NCT06483399 ).

Keywords: Ovarian cancer; Postoperative complications; Preoperative immunonutritional status.

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Conflict of interest statement

Declarations. Ethics approval and consent to participate: This study was approved by the Research Ethics Commission of Tongji Medical College of Huazhong University of Science and Technology (TJ-IRB202401053) and the consent was waived. Consent for publication: Not applicable. Competing interests: The authors declare no competing interests.

Figures

Fig. 1
Fig. 1
Flow chart of patient inclusion and exclusion
Fig. 2
Fig. 2
Lasso regression analysis of variables associated with postoperative complications. (A) Dynamic variation of Lasso regression coefficients, (B) Tenfold cross-validation for LASSO model parameter adjustment using Lambda.1se criteria. Lasso, Least absolute shrinkage and seletion operator; MSE, Mean-squared error
Fig. 3
Fig. 3
The nomogram for predicting the risk of postoperative complications. FAR, Fibrinogen to albumin ratio; LMR, Lymphocyte to monocyte ratio; PNI, Prognostic nutritional index
Fig. 4
Fig. 4
Evaluation of the prediction model in the training and validation sets. (A) Receiver operating characteristic (ROC) curve for the training set, (B) Calibration curve for the training set, (C) Decision curve analysis (DCA) for the training set, (D) ROC curve for the internal validation set, (E) Calibration curve for the internal validation set, (F) DCA for the internal validation set
See this image and copyright information in PMC

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