卵巢癌术后患者肠内营养不耐受的影响因素及早期预测模型

何珍,徐洋,吴贝,张园园,刘柳,杨思思,韩慧敏

肿瘤代谢与营养电子杂志 ›› 2023, Vol. 10 ›› Issue (4) : 517-522.

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肿瘤代谢与营养电子杂志 ›› 2023, Vol. 10 ›› Issue (4) : 517-522.
论著

卵巢癌术后患者肠内营养不耐受的影响因素及早期预测模型

  • 何珍,徐洋,吴贝,张园园,刘柳,杨思思,韩慧敏
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Influencing factors and early prediction model of enteral nutritional intolerance in postoperative ovarian cancer patients

  • He Zhen, Xu Yang, Wu Bei, Zhang Yuanyuan, Liu Liu, Yang Sisi, Han Minhui
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摘要

目的 探讨卵巢癌术后患者肠内营养不耐受的影响因素并构建早期预测模型,以识别高危人群并为其术后营养支持 提供参考。 方法 选取 2021 年 3 月至 2022 年 6 月武汉市第三医院收治的卵巢癌术后患者 170 例作为研究对象,根据术后早期肠 内营养耐受情况分为耐受组和不耐受组。 比较两组的一般资料、实验室指标等资料,采用多因素 Logistic 回归模型分析影响卵巢 癌患者术后早期营养不耐受的相关因素并构建列线图预测模型,绘制校正曲线对模型效能进行验证。 结果 42 例术后早期出现 肠内营养不耐受患者归为不耐受组,其余 128 例患者为耐受组。 不耐受组血清白蛋白、使用镇痛镇静药占比较耐受组低,营养风 险筛查 2002(NRS 2002)评分、空腹血糖水平较耐受组高(P< 0. 05)。 多因素 Logistic 回归分析显示:高白蛋白水平(OR = 0. 836,95%CI = 0. 754~0. 927)为卵巢癌患者术后肠内营养不耐受的保护因素,使用镇痛镇静药(OR = 3. 306,95%CI = 1. 169 ~ 9. 349)、高空腹血糖水平(OR= 1. 631,95%CI = 1. 168~2. 277)、高 NRS 2002 评分(OR = 2. 558,95%CI = 1. 650~ 3. 965)为卵巢癌患 者术后早期肠内营养不耐受的危险因素(均 P<0. 05),上述因素预测卵巢癌患者一致性指数(C-index)为 0. 801(95%CI = 0. 722~ 0. 880),且校正曲线显示实测值与观察值基本相符。 结论 低血清白蛋白及使用镇痛镇静药、高 NRS 2002 评分和空腹血糖水平是卵 巢癌患者术后早期肠内营养不耐受的影响因素,由此构建的预测模型可为临床中卵巢癌患者术后的营养支持提供一定的参考价值。

Abstract

Objective To explore the influencing factors of enteral nutritional intolerance in postoperative patients with ovarian cancer and to construct an early prediction model identifying high-risk groups and providing reference for postoperative nutritional support. Method A total of 170 postoperative patients with ovarian cancer admitted to the Third Hospital of Wuhan from March 2021 to June 2022 were selected as research subjects and were divided into tolerance group and intolerance group according to their tolerance towards early postoperative enteral nutrition. The demographical data laboratory indicators and other data of the two groups were compared. The multivariate Logistic regression model was used to analyze the related factors affecting the early postoperative nutritional intolerance of ovarian cancer patients and the prediction model was constructed with a column graph. The calibration curve was drawn to verify the effectiveness of the model. Result Two patients with enteral nutrition intolerance early after surgery were classified as intolerance group and the other 128 patients were classified as tolerance group. The levels of serum albumin and analgesic sedatives in intolerance group were lower than those in tolerance group and NRS 2002 scores and fasting blood glucose levels were higher than those in tolerance group P < 0. 05 . Multivariate Logistic analysis showed that High albumin level OR = 0. 836 95% CI = 0. 754-0. 927 was a protective factor for postoperative enteral nutritional intolerance in ovarian cancer patients. The use of analgesic sedatives OR= 3. 306 95%CI = 1. 169-9. 349 high fasting blood glucose level OR = 1. 631 95%CI = 1. 168-2. 277 and high NRS 2002 score OR= 2. 558 95%CI = 1. 650-3. 965 were risk factors for early postoperative enteral nutrition intolerance in ovarian cancer patients all P<0. 05 . The above factors predicted the consistency index C-index of ovarian cancer patients to be 0. 801 95%CI = 0. 722 - 0. 880 and the calibration curve showed that the measured values were basically consistent with the observed values. Conclusion The influencing factors of early postoperative enteral nutritional intolerance in ovarian cancer patients were low serum albumin use of analgesic sedative high NRS 2002 score and fasting blood glucose level. The prediction model constructed by this method can provide certain reference value for nutritional support in clinical ovarian cancer patients after surgery.

关键词

卵巢癌 / 手术 / 肠内营养不耐受 / 影响因素 / 预测模型

Key words

Ovarian cancer / Operation / Enteral nutritional intolerance / Influencing factor / Prediction model

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何珍,徐洋,吴贝,张园园,刘柳,杨思思,韩慧敏. 卵巢癌术后患者肠内营养不耐受的影响因素及早期预测模型[J]. 肿瘤代谢与营养电子杂志. 2023, 10(4): 517-522
He Zhen, Xu Yang, Wu Bei, Zhang Yuanyuan, Liu Liu, Yang Sisi, Han Minhui. Influencing factors and early prediction model of enteral nutritional intolerance in postoperative ovarian cancer patients[J]. Electronic Journal of Metabolism and Nutrition of Cancer. 2023, 10(4): 517-522

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