Prognostic Nutritional Index as a Predictive Biomarker of Post-Operative Infectious Morbidity in Gynecological Cancer Patients: A Prospective Cohort Study.

IF 2 4区 医学 Q3 NUTRITION & DIETETICS
Vasilios Pergialiotis, Nikolaos Thomakos, Theodoros Papalios, Vasilios Lygizos, Dimitrios Efthimios Vlachos, Alexandros Rodolakis, Dimitrios Haidopoulos
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Abstract

Malnutrition significantly impacts the post-operative process of gynecological cancer patients. A prominent variable for determining perioperative morbidity is the Prognostic Nutritional Index (PNI). To investigate PNI's predictive value on the risk of post-operative infections, we conducted a prospective cohort study involving women who underwent surgery for gynecological malignancies. Out of the 208 patients enrolled, 28 (13.5%) were malnourished and post-operative infections occurred in 43 patients. Notably, there was a significant difference in PNI between patients who developed infections and those who did not (p = 0.027), as well as between malnourished patients and those with normal nutritional status (p = 0.043). Univariate analysis showed that preoperative PNI predicts the risk of post-operative infections better than post-operative white blood cell count (AUC of 0.562 vs 0.375). However, the most accurate diagnostic results in the multivariate analysis were obtained from random forest and classification tree models (AUC of 0.987 and 0.977, respectively). Essentially, PNI and post-operative white blood cell count provided the best information gain according to rank probabilities. In conclusion, PNI appears to be a critical parameter that merits further investigation during the preoperative evaluation of gynecological malignancies.

作为妇科癌症患者术后感染发病率预测生物标志物的预后营养指数:一项前瞻性队列研究
营养不良严重影响妇科癌症患者的术后进程。预后营养指数(PNI)是确定围手术期发病率的一个重要变量。为了研究 PNI 对术后感染风险的预测价值,我们对接受妇科恶性肿瘤手术的妇女进行了一项前瞻性队列研究。在入组的 208 名患者中,有 28 人(13.5%)营养不良,43 人发生了术后感染。值得注意的是,发生感染的患者与未发生感染的患者之间的 PNI 有显著差异(P = 0.027),营养不良患者与营养状况正常的患者之间的 PNI 也有显著差异(P = 0.043)。单变量分析显示,术前 PNI 比术后白细胞计数更能预测术后感染的风险(AUC 为 0.562 对 0.375)。不过,在多变量分析中,随机森林模型和分类树模型的诊断结果最为准确(AUC 分别为 0.987 和 0.977)。从根本上说,根据等级概率,PNI 和术后白细胞计数提供了最佳的信息增益。总之,PNI 似乎是妇科恶性肿瘤术前评估中值得进一步研究的关键参数。
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来源期刊
CiteScore
5.80
自引率
3.40%
发文量
172
审稿时长
3 months
期刊介绍: This timely publication reports and reviews current findings on the effects of nutrition on the etiology, therapy, and prevention of cancer. Etiological issues include clinical and experimental research in nutrition, carcinogenesis, epidemiology, biochemistry, and molecular biology. Coverage of therapy focuses on research in clinical nutrition and oncology, dietetics, and bioengineering. Prevention approaches include public health recommendations, preventative medicine, behavior modification, education, functional foods, and agricultural and food production policies.
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